Studies in Surface Science and Catalysis 104 EQUILIBRIA AND DYNAMICS OF GAS ADSORPTION ON HETEROGENEOUS SOLID SURFACES
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Studies in Surface Science and Catalysis A d v i s o r y Editors: B. D e l m o n a n d J.T. Yates Vol. 104
EQUILIBRIA AND DYNAMICS OF GAS ADSORPTION ON HETEROGENEOUS SOLID SURFACES Editors
W. Rudzir~ski Department of Theoretical Chemistry, Maria Curie-Sklodowska University, Lub/in, Poland W. A. Steele Department of Chemistry, Pennsylvania State University, University Park, PA 16802, USA G. Zgrablich Department of Physics, University of San Luis, San Luis, Argentina
1997 ELSEVIER A m s t e r d a m m L a u s a n n e - - N e w York m O x f o r d m S h a n n o n m T o k y o
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ISBN 0-444-82243-7 91997 Elsevier Science B.V. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the publisher, Elsevier Science B.V., Copyright & Permissions Department, P.O. Box 521, 1000 AM Amsterdam, The Netherlands. Special regulations for readers in the U.S.A.- This publication has been registered with the Copyright Clearance Center Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the U.S.A. All other copyright questions, including photocopying outside of the U.S.A., should be referred to the copyright owner, Elsevier Science B.V., unless otherwise specified. No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. This book is printed on acid-free paper. Printed in The Netherlands
PREFACEThere was a period in the development of adsorption science when the theoretical descriptions of gas adsorption on solid surfaces were based on highly idealized models of solid surfaces. Most commonly, models of localized adsorption were used in which the gas molecule approaching a solid surface "saw" a regular array of the local minima in the gas-solid potential function - the adsorption sites. In the first introductory chapter of this book by Cerofolini and Rudzinski, that period is called the "pioneering age" of adsorption science. It began in 1917 when Langmuir published the theory of his famous isotherm equation. It was based on the model of one-site/one molecule - occupancy adsorption with no interactions between the adsorbed molecules. Until the Second World War, the main stream of the theoretical work was oriented toward generalizing that model to account for possible interactions between the adsorbed molecules. It started with the application of the mean-field approach to develop the Bragg-Williams isotherm. Next various forms of the Quasi-Chemical Approximation were introduced. That trend reached its peak by the publication of the exact solution for the isotherms of molecules adsorbed on two-dimensional regular arrays of adsorption sites in 1944 by Onsager. We could reasonably set this year to be the end of this "pioneering age" of adsorption science. Although most of the theoretical work was based on models of localized adsorption, mobile adsorption also received interest during that period. Here the most spectacular achievement was the application of the two-dimensional analogue of the Van der Waals model to develop isotherm equation. However, the most famous achievement of that time was probably the generalization of the Langmuir model by Brunauer, Emmet and Teller (BET) to account for the possibility of a secondary adsorption on the already adsorbed molecules. The BET equation became a standard in the determination of the surface area of adsorbents but was also criticized for various thermodynamic inconsistencies and shortcomings. An alternative well-known approach to multilayer adsorption was then proposed by Frankel, Halsey, and Hill, usually called the FHH slab theory. However, in spite of the advanced theoretical treatments seen in many papers published before the Second World War, adsorption experiments showed more and more discrepancies between theory and experiment. Attempts to fit experimental isotherms by various theoretical equations always showed negative deviations from experiment at low adsorbate pressures~ and positive deviations at higher adsorbate pressures. Deviations between theory and experiment were probably most dramatically demonstrated in measurements of the enthalpy changes upon adsorption. The theoretically predicted isosteric heats of adsorption should be increasing functions of the surface coverage. The reason is as follows: Although the solid phase must perturb to some extent the interactions between adsorbed molecules, the interactions should basically preserve their Lennard - Jones character. This means that there exists only a narrow range of distances at which adsorbed molecules interact via moderate repulsive forces. At still smaller separations they will not be adsorbed at all, and at all other distances they should interact via attractive potentials. The probability of finding a physical situation where adsorption sites are located at distances such
vi that admolecules interact via moderate repulsive forces is therefore small compared to the probability of finding the situation where adsorbed molecules interact via attractive forces. It means that, in general, the experimentally observed isosteric heat of adsorption should increase with increasing adsorbed amount due to the increase in attractive lateral interaction energy at high density. Meanwhile, almost all the reported experimental heats of adsorption showed an opposite trend. As the theoretical adsorption isotherms could not be successfully used to fit experimental data, various empirical equations were used for that purpose. At small adsorbate pressures, the Freundlich empirical isotherm was commonly applied as superior to Langmuir equation. (It is interesting to note that this isotherm equation was published in 1909 and was the first ever used.) In 1927 Bradley proposed a hybrid which preserved the superiority of Freundlich isotherm at low adsorbate pressures, but predicted also that the surface coverage will tend to unity at high pressures. Bradley's name was later forgotten and the hybrid isotherm is now generally called the Langmuir-Freundlich isotherm. Other empirical isotherm equations include Temkin's and Tdth's isotherms. The more and more frequent discrepancies between theory and experiment showed that there must exist another yet important physical factor that had not been taken into account in the theories. It was Roginsky in the former Soviet Union who, at the end of the thirties, launched the idea that the missing factor is the energetic heterogeneity of the actual solid surfaces which is crucial in governing the behaviour of actual adsorption systems. Roginsky's works marked the end of the "pioneering age" of adsorption science in which theoretical works were based on highly idealized models of solid surfaces. However, it should be emphasized that it was Langmuir himself who first introduced the idea of adsorption on heterogeneous solid surfaces. In his second theoretical paper, he emphasized that in the case of adsorption on real solid surfaces, one will have to deal with a number of distinct kinds of adsorption sites. His equation, with suitably chosen constants, should be used to describe adsorption on a certain kind of sites, and the experimentally observed isotherm will be a sum of these Langmuir terms, each multiplied by the fraction of a given kind of site on the solid surface. Roginski assumed that in the case of the real adsorption systems, the spectrum of adsorption energies characterizing various adsorption sites will be very dense so that the differential distribution of the number of sites over the values of the adsorption energy e can be represented by a continuous function X(e). That function is nowadays called commonly the adsorption energy distribution. Roginski also introduced another idea that appeared to be extremely fruitful in further theoretical studies. He postulated that, as the adsorbate pressure increases in the case of real heterogeneous solid surfaces, adsorption proceeds gradually on adsorption sites in the sequence of decreasing adsorption energies. At a given temperature T and pressure p, the adsorption sites having energy larger than a certain critical adsorption energy e~(p, T) are completely filled, whereas the others are totally empty. After the Second World War, the idea of adsorption on energetically heterogeneous solid surfaces received strong interest by Americans like Hill, Adamson, Halsey, Sips and Zettlemoyer who, in the forties and the fifties, made an enormous contribution to this area of research. The feeling that a certain era of adsorption science was closed and a new period was starting encouraged some authors to write monographs reporting on the current state of
vii the art. Thus, Young and Crowell published Physical Adsorption of Gases in 1962 and two years later Ross and Olivier published their book: On Physical Adsorption. In the latter case, adsorption theories based on idealized models of solid surfaces were reviewed, but some elementary principles of adsorption on energetically heterogeneous surfaces were also introduced. Ten years later Steele published his book The Interaction of Gases with Solid Surfaces, oriented already toward new trends in the theoretical studies of adsorption. It was the time when the nature of the gas/solid interactions was studied in more detail, as was the combined effects of admolecule interactions and of surface energetic heterogeneity. The so-called virial formalism was extensively used to study adsorption at low surface coverages. Adsorption at higher surface coverages was studied commonly by applying the so-called integral equalion for adsorption isotherm. The experimentally observed isotherm was assumed to be an average (integral) of the local adsorption isotherm, describing adsorption on adsorption sites characterized by an adsorption energy e, with the adsorption energy distribution X(c). As mentioned already, the idea of an integral isotherm equation was introduced by Roginski at the end of the thirties and was developed further by American scientists in the fifties, based almost exclusively on use of the Langmuir equation as the local adsorption isotherm. An explosive development of that research took place in the seventies and the eighties, and was marked by local isotherm equations that took account of the interactions between the adsorbed molecules. Simultaneous consideration of the effects arising from the interactions between adsorbed molecules and the effects of the energetic heterogeneity of adsorption sites involves another important factor yet to be taken into account. This is the way in which the various adsorption sites are distributed on an energetically heterogeneous surface. In 1949 Hill published the first fundamental paper concerning that problem. In his paper he studied the model of the solid surface in which various adsorption sites are randomly distributed on the solid surface, now commonly called - the random model. Some ten years later Ross and Olivier introduced another extreme model to represent the topological (topographical) distribution of different adsorption sites on partially graphitized carbons by assuming that identical adsorption sites are grouped into large patches. That model is now commonly called the patchwise model. Both these models were studied extensively by the group working in the Department of Theoretical Chemistry of Maria Curie-Sklodowska University in Lublin. Very advanced theoretical works were published by Tovbin in Russian journals. The explosive development in the seventies and in the eighties of the research based on the integral equation was accompanied by another vigorous trend of solving the reverse problem. That necessitated solving the integral equation to calculate the adsorption energy distribution from an experimental adsorption isotherm. This trend was initiated by the paper published by Sips in 1948, who used the inverse Stieltjes transform for that purpose, but it was not until the beginning of the seventies that this research started to develop vigorously. A variety of methods were proposed to solve the integral equation. The most stable and convenient of these seem to be the methods which were advanced versions of the Condensation Approximation approach introduced by Roginski. Here, the method proposed by Adamson and co-workers in 1966 was the first attempt of that kind. Later developments were based on the work of Cerofolini, Rudzinski and Jagiello.
VIII
Much attention was also devoted to the effects of surface energetic heterogeneity upon the adsorption of gas mixtures on real solid surfaces. In 1967 Hoory and Prausnitz published the first paper on the applicability of the integral equation approach to describe mixed-gas adsorption on solids. However, essential progress toward further development of this idea was due to the papers published by Jaroniec. At the beginning of the eighties, Myers and his co-workers showed how the other fundamental approach to mixed-gas adsorption- the Ideal Adsorbed Solution Approach- can be generalized further to take account of energetic surface heterogeneity. The new period of adsorption science which started after the Second World War reached its peak in the eighties. It can be characterized by: 9 consideration of the energetic heterogeneity of real solid surfaces, 9 looking for more and more advanced analytical solutions for the combined effect of surface energetic heterogeneity and of the interactions between adsorbed molecules. The beginning of the nineties seems to mark the beginning of a third era of development of adsorption science. In our opinion, the following two factors opened that new era: 9 the development of STM and AFM microscopies, which allow one to "see" the molecular structure of real solid surfaces, 9 the explosive development of computer simulations of adsorption processes, which take account of more detailed mechanistic models of solid surfaces. Again, the feeling that a certain era of adsorption science had closed encouraged some authors to review the state of art. Thus, Jaroniec and Madey published in 1989 their monograph: Physical Adsorption on Heterogeneous Solids, and 3 years later Rudzinski and Everett published their monograph: Adsorption of Gases on Heterogeneous Surfaces. There is a tendency to describe the beginning of every new era as "modern" so we will follow that tradition by calling the third era starting at the beginning of the nineties "the modern era of adsorption science". The judgment of every era at its beginning must always be imbalanced in many respects. Nevertheless, we believe that there has been a need for a book presenting the current trends in this new era of adsorption science. This was the idea behind our efforts to publish this monograph. So far we discussed adsorption on essentially fiat solid surfaces with no limits in either the direction parallel or perpendicular to the solid surface. Meanwhile, many of the adsorption systems of great technological importance are the systems with the so-called restricted geometry, where the dimensions of the adsorption system are limited. First of all, these are porous carbons and zeolites of various kinds. In the case of porous sorbents, one usually distinguishes between two adsorption mechanisms. One is the adsorption in micropores having dimensions less than 2 nm. This adsorption mechanism is frequently called - pore filling. The other adsorption mechanism is one occurring in mesopores having dimensions between 2 and 50 nm. Adsorption in macropores having dimensions bigger than 50 nm is essentially the same as on a fiat solid surface. Historically, the first theoretical works on adsorption in porous materials concerned adsorption in mesopores. A very special feature of this adsorption mechanism is the appearance of capillary phenomena, demonstrated by the hysteresis loops on the experimental adsorption isotherms.
They make it possible to calculate mesopore size distributions using Kelvin's equation and its further improvements and modifications. As to adsorption in micropores, the achievements of Dubinin's school of adsorption have for many years remained the most essential contribution in this area. The theory of micropore filling, developed by Dubinin and Radushkevich in the late 1940's, is a major contribution to adsorption science, alongside the Langmuir and BET isotherms. This approach was based on Polanyi's idea of adsorption potential, which was assumed to be different in different micropores. An implicit assumption was made that the adsorption proceeds in a stepwise fashion in micropores in the sequence of decreasing adsorption potential. This was the basis for the first attempts by Dubinin and co-workers to calculate the distribution of micropore sizes. The idea that the Dubinin-Raduskhevich (DR) equation is related to the geometric heterogeneity of porous solids has to be tested critically soon. First, it is a surprise that adsorption in hundreds of different carbon samples studied by various investigators obeyed the DR equation, i.e. had the same type of pore size distribution. Secondly, at the beginning of the sixties, Hobson in Canada published his surprising discovery that the DR equation developed for adsorption in porous carbons can be successfully used as a general isotherm equation to describe low-coverage adsorption on real flat solid surfaces. Hobson was first to consider the DR isotherm as a result of averaging the Langmuir isotherm with a certain adsorption energy distribution. He was also first to use the CA (Condensation Approzimation) approach to show that the energy distributions appeared to be gaussian-like, with a widened function of e on the high energy side. Later experimental studies showed that at higher surface coverages, adsorption can be correlated by Freundlich's equation, and at still higher surface coverages, by the Langmuir-Freundlich isotherm. This striking behaviour was studied theoretically by Cerofolini, who launched a well-documented hypothesis that this behaviour should be a universal feature of all adsorption systems, and is related to certain rules governing the formation of the real solid surfaces. As a result, the fact that the DR equation can describe low-coverage adsorption on flat solid surfaces must put into question the hypothesis that it is related to factors affecting the adsorption in micropores. Also, deviations between the DR equation and the experimentally observed behaviour of microporous adsorption systems were reported more and more frequently. The modification of the DR equation proposed by Astakhov and Dubinin did not solve the problem. Thus, at the beginning of the eighties Stoeckli and Dubinin launched the hypothesis that the DR equation should be used "locally" to describe adsorption in a certain class of micropores having the same dimensions. Their hypothesis received strong support from SAX experimental studies showing some relation between the size of micropores and the parameter in the DR equation. This experimental finding was the basis for developing a new method of determining the distribution of micropore sizes (Dubinin-Stoeckli, MacEnaney, Jaroniec). It seems, however, that an important conclusion that follows from these works has not received enough attention. Namely, if the DR equation describes adsorption in pores of the same geometric dimensions, it cannot be related to geometric surface heterogeneity. Two factors may be responsible for the successful application of the DR equation to systems (subclasses) of homogeneous micropores:
9 the energetic heterogeneity of micropore walls, 9 the mutual effects arising from energetic heterogeneity and those due to the restricted geometry of the system. Recent computer simulations seem to favour the first explanation. Simulated adsorption isotherms in micropores show behaviour far different from that predicted by the DR equation. These simulations were based on the assumption of a regular micropore shape with chemically homogeneous walls. Such idealized models of micropores were used to calculate the pore size distribution by applying certain analytical solutions (Everell-Powell, Horvath-Kawazoe), as well as by comparison with computer simulations. Recently, however, a certain feeling is spreading that more realistic models should be applied to represent the geometric - energetic features of real microporous solids. The "pioneering age" can be characterized by the attempts to apply Langmuir, BET, FHH and virial equations, along with certain specific approaches like t-plot, c~,-plot and others. They were accompanied by extensive studies of capillary phenomena in mesopores. That period was covered by the review by Gregg and Sing published in 1976 - The Adsorption of Gases on Porous Solids and by Dubinin in 1972 - The Adsorption and Porosity (in Russian). The book Adsorption, Surface Area and Porosity covering the period ending at the beginning of the nineties, was published by Gregg and Sing. In considering the time dependence of adsorption on heterogeneous solid surfaces, it can be seen that studies of the equilibrium and the time dependence of adsorption have proceeded on two somewhat separate paths. The lack of Langmuirian kinetics in real adsorption systems was primarily discussed during the "pioneering age" of adsorption science. As in adsorption equilibrium, various empirical equations were used to describe the kinetics of gas adsorption on the real solid surfaces. The most famous and commonly used is the Elovich equation proposed at the end of the thirties. Its appearance and common use marked more and more strongly the end of the "pioneering period" based on the use of idealized surface models to describe adsorption equilibria and kinetics. However, as in the equilibrium problem, these idealized models were the starting point for further generalizations that take account of surface energetic heterogeneity. Here, the most popular approach had been based on the application of Absolute Rate Theory, developed originally to describe the kinetics of chemical reactions in bulk systems. Soon after the first attempts were made to apply it to adsorption/desorption kinetics at the beginning of the seventies, serious discrepancies were reported between the theoretical predictions and experimental observations. This was especially seen in the coverage dependence of the kinetics. These reports led to the introduction of the concept of precursor states and of the sticking probability concept. One stream of research was to further improve the theoretical description of adsorption/desorption kinetics based on a model of the regular (homogeneous) solid surface. In 1986 Kreuzer and Gortel discussed this work in the exhaustive review Physisorption Kinetics. Another stream of work led to improving the Absolute Rate Theory approach by incorporating the idea of surface energetic heterogeneity. It was particularly popular among the scientists using TPD (Temperature Programmed Desorption) to study the thermal desorption of gases from solid surfaces. The principles of that method were published in 1963 by Amenomiya and Cvetanovic. Two years later papers began to appear suggesting that the activation energy for desorption should be considered to be a function of the surface coverage because of the existence
of various adsorption sites on a solid surface which should be characterized by different activation energies for desorption. The implicit assumption underlying that interpretation was that desorption proceeds in an ideally stepwise fashion in the sequence of increasing activation energies for desorption. As mentioned already, the inapplicability of the adsorption/desorption theories could be seen most clearly by comparing the predicted and observed coverage dependence of the kinetics. Very special, but also technologically extremely important, is the kinetics of adsorption in, and desorption from porous adsorbents. This is because the kinetics of molecula~r motion in restricted spaces may be predominantly governed by surface diffusion across pore walls. Diffusion of adsorbed particles is one of the most fascinating phenomena on solid surfaces and one of great importance in catalysis, metallurgy, microelectronics, material science, and many other scientific and technological applications. Beginning with Volmer and Eastermann's pioneering experiment in the early 20's, studies of surface diffusion have constantly expanded. From the early stages, the problem of surface heterogeneity was crucial. This can easily be understood in a figurative way since "the best way of feeling the landscape is to take a walk through the hills". For a long time, surface diffusion studies were mainly associated with the necessity of estimating the surface flux contribution to the total flux of a gas through a catalyst support (often, a random porous medium). This is a very complex problem and very limited tools were available at that time, such as the effective Arrhenius-type equations and the experimental technique of the diffusion cell. The development of field emission microscopy in the 40's and of field ion microscopy in the 60's opened a new horizon by enabling the observation of the migration of individual adatoms on more precisely characterized surfaces. With the help of lattice-gas theories, important advances were made in understanding surface self-diffusion for both physisorbed and chemisorbed species. The availability of many new surface spectroscopies and the utilization of computers starting in the late 70's allowed the possibility of studying new and exciting phenomena such as the behavior of surface diffusivity in the presence of two-dimensional phase transitions, the growth of thin films and clusters, and the effects of controlled defects (like steps) and other geometrical heterogeneities upon atomic motion on the surface. The invention of Scanning Tunneling Microscopy in the 80's and the continued growth of computer power produced a new revolution: detailed numerical simulations (either Monte Carlo or Molecular Dynamics) of diffusion of adsorbed species on heterogeneous surfaces and experiments performed on well-characterized surfaces became possible, fields which are now expanding rapidly. The old problem of diffusion on the surface of disordered porous media is still open and receiving new attention, while interesting problems such as the diffusion of chain molecules (k-mers) and polymers are continuously arising to make this field more exciting. Many adsorbents that are not porous in bulk are also not "flat". Their properties can frequently be described by using the concept of adsorption on fractal surfaces. The concept of the fractal nature of solid surfaces in relation to adsorption phenomena (Pfeifer, Avnir) is one of the new approaches marking the beginning of the modern era in the nineties. From this overview of the research on adsorption on heterogeneous solid surfaces, one can see that surface heterogeneity affects adsorption at the gas/solid interface in a variety of ways. For that reason, the papers treating various aspects of surface energetic hetero-
xii geneity were published in a variety of scientific journals addressed to various groups of scientists. No book has been published yet that would give an overview of all the effects of surface energetic heterogeneity of real solid surfaces. The present monograph is a first attempt of such a kind. It concerns both adsorption equilibria and the time dependence of adsorption phenomena. We have used the term dynamicsfor the aspects related to the time evolution of adsorption systems. This book was not aimed to provide the readers with the historical development of adsorption science. For that reason we decided to give a historical overview in this Preface. We have distinguished here basically three periods in the development of surface science. Our book was aimed to present the state of this art at the beginning of the third period which we call "modern", and which started at the beginning of the nineties. As mentioned already, this modern era is marked by the development of STM and AFM microscopies which provide the information about the mechanistic models to be accepted in computer simulations. The explosive development of the computer simulations seems to take the lead over the development of analytical approaches. The latter, of course, will follow that development as a necessary scientific synthesis of the information obtained from the true and simulated experiments. We are very aware that the panorama of the research presented here is incomplete. There are surely many excellent names to be mentioned and many adsorption problems to be discussed. Nevertheless, we believe, that this monograph is a substantial step toward presenting the present state of the art. We would like to express our warmest thanks to all the colleagues who contributed to this book.
Wladyslaw Rudzinski William A. Steele Giorgio Zgrablich
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AUTHOR INDEX
1. F. Bardot ~ Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 2. I. B e r e n d - Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 3. J . M . C a s e s - Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 4. G.F. Cerofolini ~ EniChem'- Instituto Guido Donegani, 28100 Novara, ITALY 5. YuDong C h e n - - The BOC Group, Inc., 100 Mountain Avenue, Murray Hill,
NJ 07974, U.S.A. 6. A.S.T. C h i a n g - Department of Chemical Engineering, National Central University, Chung-Li, Taiwan ROC 32054 7. J. C h o m a - Institute of Chemistry, Military Technical Academy, 01489 Warsaw, POLAND 8. D.D. D o - Department of Chemical Engineering, University of Queensland, Qld 4072, AUSTRALIA 9. J.A.W. Elliot o Thermodynamics and Kinetics Laboratory, Department of Mechanical Engineering, University of Toronto, CANADA M5S 1A4 10. M . F r a n c o i s - Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS, BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 11. S.P. Friedman w Department of Chemical Engineering, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, U.K. 12. G . G e r a r d - Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS, BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE
xiv 13. K.E. Gubbins ~ School of Chemical Engineering, Cornell University, Ithaca NY 14853, U.S.A. 14. M.Jaroniec ~ Separation and Surface Science Center, Department of Chemistry, Kent State University, Kent, Ohio 44242, U.S.A. 15. K . K a n e k o - Department of Chemistry, Faculty of Science, Chiba University Yayoi 1-33, Inage, Chiba 263, JAPAN 16. H.J. Kreuzer ~ Department of Physics, Dalhousie University, Halifax, N.S. B3H 3J5, CANADA 17. C . M . L a s t o s k i e - Department of Chemical Engineering, University of Michigan, Ann Arbor MI 48109, U.S.A. 18. C.K.Lee ~ Department of Environmental Engineering, Van-Nung Institute of Technology, Taiwan ROC 32054 19. Kuang-Yu Liu ~ Department of Physics and Astronomy, University of Missouri Columbia, MO 65211, U.S.A. 20. J.M.D. MacElroy m Department of Chemical Engineering, University College Dublin, Beldfield, Dublin 4, IRELAND 21. L.J. Michot - - Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS, BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 22. J.Narkiewicz-Michalek o Department of Theoretical Chemistry, Maria Curie-Sklodowska University, 20-031 Lublin, POLAND 23. S.H. Payne m Department of Physics, Dalhousie University, Halifax, N.S. B3H 3J5, CANADA 24. P. Pfeifer - - Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211, U.S.A.
XV
25. N.Quirke w Department of Chemistry, University of Wales at Bangor, Gwynedd LL57 2UW, U.K. 26. W. R u d z i n s k i - Department of Theoretical Chemistry, Maria Curie-Sklodowska University, 20-031 Lublin, POLAND 27. N.A.Seaton- Department of Chemical Engineering, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, U.K. 28. W. A. S t e e l e - Department of Chemistry, The Pennsylvania State University, 152 Davey Laboratory, University Park, PA 16802, U.S.A. 29. P. Szabelski ~ Department of Theoretical Chemistry, Maria Curie-Sklodowska University, 20-031 Lublin, POLAND 30. Yu.K. Tovbin - - Karpov Institute of Physical Chemistry, Vorontsovo Pole Str. 10, 103064 Moscow, RUSSIA 31. F.Villieras- Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS, BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 32. C.A. Ward m Thermodynamics and Kinetics Laboratory, Department of Mechanical Engineering, University of Toronto, CANADA M5S 1A4 33. T. Yang - - Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, U.S.A. 34. J . Y v o n - Laboratoire Environnement et Mineralurgie, ENSG et URA 235 du CNRS, BP 40, 54 501 Vandoeuvre les Nancy cedex, FRANCE 35. G. Zgrablich- Departamento de Fisica, Universidad Nacional de San Luis, Chacabuco y Pedernera, 5700 San Luis, ARGENTINA
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XVII
CONTENTS
Preface Author Index Chapter 1. Theoretical Principles of Single- and Mixed-Gas Adsorption Equilibria on Heterogeneous Solid Surfaces G.F. Cerofolini and W. Rudzinski Chapter 2. Application of Lattice-Gas Models to Describe Mixed-Gas Adsorption Equilibria on Heterogeneous Solid Surfaces Yu.K. Tovbin
105
Chapter 3. Theories of the Adsorption-Desorption Kinetics on Homogeneous Surfaces H.J. Kreuzer and S.H. Payne
153
Chapter 4. Theory of Adsorption-Desorption Kinetics on Flat Heterogeneous Surfaces Yu.K. Tovbin
201
Chapter 5. Statistical Rate Theory and the Material Properties Controlling Adsorption Kinetics on Well Defined Surfaces J.A.W. Elliot and C.A. Ward
285
Chapter 6. A New Theoretical Approach to Adsorption-Desorption Kinetics on Energetically Heterogeneous Flat Solid Surfaces Based on Statistical Rate Theory of Interracial Transport W. Rudzinski
335
Chapter 7. Surface Diffusion of Adsorbates on Heterogeneous Substrates G. Zgrablich
373
~
XVIII
Chapter 8. Computer Simulation of Surface Diffusion in Adsorbed Phases W. A. Steele
451
Chapter 9. Multicomponent Diffusion in Zeolites and Multicomponent Surface Diffusion YuDong Chen and Ralph T. Yang
487
Chapter 10. Energy and Structure Heterogeneities for the Adsorption in Zeolites A.S.T. Chiang, C.K.Lee, W.Rudzinski, J.Narkiewicz-Michalek and P. Szabelski
519
Chapter 11. Static and Dynamic Studies of the Energetic Surface Heterogeneity of Clay Minerals F.Villieras, L.J. Michot, J.M.Cases, I. Berend, F. Bardot, M.Francois, G.Gerard and J.Yvon
573
Chapter 12. Multilayer Adsorption as a Tool to Investigate the Fractal Nature of Porous Adsorbents P. Pfeifer and Kuang-Yu Liu
625
Chapter 13: Heterogeneous Surface Structures of Adsorbents K.Kaneko
679
Chapter 14. Characterization of Geometrical and Energetic Heterogeneities of Active Carbons by Using Sorption Measurements M.Jaroniec and J. Choma
715
Chapter 15. Structure of Porous Adsorbents: Analysis Using Denstity Functional Theory and Molecular Simulation C.M.Lastoskie, N.Quirke and K.E. Gubbins
745
Chapter 16. Dynamics of Adsorpion in Heterogeneous Solids D.D. Do
777
Chapter 17. Sorption Rate Processes in Carbon Molecular Sieves J.M.D. MacElroy, N.A.Seaton and S.P. Friedman
837
W. Rudzitiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
Theoretical Principles of Single- and Mixed-Gas Adsorption Equilibria on Heterogeneous Solid Surfaces G. F. Cerofolini ~ and W. Rudzifiski b EniChem- Istituto Guido Donegani, 28100 Novara, Italy b Department of Theoretical Chemistry, Maria Curie-Sklodowska University, 20031 Lublin, Poland
1. I N T R O D U C T I O N
1.1. History and logic of adsorption Logic and history often do not run parallel to each other. Therefore, when a treatise describes the status of a discipline, it has to avoid loops and misconceptions, and often to anticipate later results. Dirac's Principles of Quantum Mechanics does not furnish us with any information on the history of this discipline; conversely, Jammer's The Conceptual Development of Quantum Mechanics can hardly be considered a treatise on quantum mechanics. The theory of adsorption, possibly because of its non-foundational character, does not suffer from this difficulty, so that its logical development is almost the same as its historical development. This will allow us to sketch the theory of adsorption simply by following its evolution. Though periodization is always a risky activity, we have divided the (history of) adsorption into three phases, referred to as Pioneering Age, Middle Age and Modern Age. The Pioneering Age is characterized by experimental or theoretical discoveries of new adsorption isotherms; this period is marked by the 'equation of ...'. The Middle Age is characterized by the attempts of explaining the most frequently observed experimental isotherms in terms of surface properties like the adsorption-energy distribution; this period is marked by the 'method of ...'. The Modern Age is characterized by the attempts toward understanding of specific behaviours of complex adsorbents; it is characterized by the absence of general methods or equations and by the extended use of large computational methods. The Pioneering and Middle Ages are adequately covered by the literature, like Steele's compact treatise on adsorption [1], Jaroniec and Madey's and Rudzifiski and Everett's books on adsorption on heterogeneous surfaces [2, 3], and Gregg and Sing's treatise on adsorption on porous surfaces [4]. There is no book covering adequately the Modern Age and it would not be an easy job to write such a book at present. This is because of the explosive research in this area, and especially of computer simulations. It is to be expected that, after collecting a sufficiently large body of computer simulation data, they will lead to a theoretical synthesis having a form of compact analytical expressions.
Computer simulations will surely take the lead in explaining fundamental features of various adsorption systems. The application of the Density Functional Theory to adsorption in micropores shows, on the other hand, that analytical approaches will always be competitive as far as the computational time is considered. Some analytical expressions developed at the beginning of the century, (the Langmuir equation for instance), are still used at the end of this century in a more or less modified form. Adsorption technologies create a large market (demand) for compact analytical expressions that could be calculated fast to control technological processes. The fascination by the new possibilities created by computer simulations is also accompanied by a growing nostalgy for having simple expressions that could be applied by anyone in research and practice. As this chapter is supposed to provide one with "theoretical principles", we will try to review some analytical expressions and approaches that have most frequently been used, and perspectives of their further development and generalizations. Then, although the energetic surface heterogeneity, i.e. the dispersion of gas-solid interactions, and the admolecule-admolecule interactions affect the behaviour of adsorption systems in a cooperative way, we will focus here our atention on the role of the energetic surface heterogeneity. 1.2. Establishing the t h e o r y of adsorption ~ Gibbs e q u a t i o n Gas adsorption is a way through which the unsaturated forces at the surface of a given system in a condensed phase (adsorbent) are partially saturated by the interaction with gas-phase molecules (adsorbate). Being a way to approach equilibrium, adsorption is a spontaneous process, usually exothermic in nature. When the adsorption energy is higher than approximately 0.5 eV per an adsorbed molecule, a true chemical bond is formed between the adsorbate and the adsorbent; this case is referred to as chemisorption. Though chemisorption is a relatively highly exothermic process, it may be hindered by the activation energy required to destroy the molecular structure of the gas-phase molecule or the bonds at the reconstructed surface. When the adsorption energy is lower than about 0.5 eV per a molecule, adsorption involves secondary (electrostatic or Van der Waals) forces and this case is referred to as physisorption. The forces responsible for physisorption are essentially the same as those responsible for the condensation of a vapour to the liquid state. When a new chemical bond is not involved in the adsorption process, activation energy is not required for the formation of the adsorbed phase, so that equilibrium is attained in a short time, thus allowing a direct experimental characterization of this state. The founding father of the theory of adsorption equilibrium was Gibbs, who at the end of the 18th century established his celebrated equation, r
b2
Odln
p
(1)
where k is the Boltzmann constant, T is the absolute temperature, b2 is the area occupied by one molecule in the surface, p is the equilibrium pressure, pt is a reference pressure, 0 is the fraction of surface covered by the adsorbate, and r is the chemical potential of the system relative to that in the absence of adsorbate
Equation (1) stands on purely thermodynamic considerations and is therefore unable to specify a functional relationship r = r (actually, it holds true whatever is this relationship) so that it is unable to specify the adsorption isotherm 8 = 8(p). An isotherm can be specified only by assuming a microscopic model of the adsorbate, which eventually allows the function r to be determined.
2. T H E P I O N E E R I N G
A G E OF A D S O R P T I O N
2.1. A d s o r p t i o n of s t r u c t u r e l e s s gases on ideal surfaces
This Age of adsorption was characterized by the search of physically plausible and mathematically simple microscopic models for the adsorbate, and by the discovery of experimental isotherms which could hardly be understood in the frame of the theoretical models. The first models considered the very ideal case of the adsorption of structureless gases on ideal surfaces. With 'structureless' molecule we intend here a molecule characterized exclusively by its covolume b, and with 'ideal' surface we intend a surface with an energetically homogeneous distribution of adsorption sites. Moreover, the volume above each site is assumed to allow the accomodation of one and only one molecule, whatever is the value of b. In a first approximation, the potential energy of a gas-phase molecule near a surface will be the sum, extended to all the atoms of the solid, of the pair potentials acting between the molecule and each atom of the solid [5, 6, 7, 8]. Describing the interaction potential by the 6-12 Lennard-Jones pair potential [9], u(r) = uo [(r0/r) 12- 2 (r0/r) s] (where r is the nuclear separation of the pair, r0 the equilibrium distance and u0 the depth of the minimum), the adsorption potential acting on a gas-phase molecule in the point r, r = (Xl,X2, x3), in the vicinity of a solid is therefore given by U(r) = ~-~zu ( I r - RI[), where Rx is the position of the I-th atom in the solid, and the sum is extended to all these atoms. This potential depends both on the plane vector xll = ( x l , x 2 ) lying in the surface plane and on the distance x3 from the surface. A summary of key data concerning adsorption potentials over 250 gas-surface systems is given in ref. [10]. Some general features of the adsorption potential, almost independent of the specific crystal lattice, can be pointed out. Low-index faces are characterized by a two dimensional lattice of minima that reflect the periodic structure of the underlying surface. High-index faces are characterized by regular lattices with several distinct minima, of different depths, separated by barriers of different heights. The zones of atomic size centred on the minima constitute the adsorption sites. These sites are separated from one another by saddle points, so that an activation energy is required for surface migration. If the potential well at the adsorption site is sufficiently deep to contain one or more quantum states for the adsorbed molecule and is separated from the saddle point by a high energy compared to the thermal energy kT, the adsorbed molecule will be localized in one or another of these sites. Otherwise, the adsorbed molecules, although still held near the surface by the vertical part of the adsorption potential, will be free to move along the surface, in which case one speaks of mobile adsorption. The thermodynamic properties of the adsorbed molecules, mainly the adsorption isotherm, can be obtained from the knowledge of the adsorption potential. In the most
general and used scheme the partition function Zx,adof Af adsorbed molecules is evaluated from the adsorption potential U(r) and the gas interatomic pair potential u(r~j): Zx,ad . . . J .
J
exp
,,~ ( --~--~ 1 [~i U(ri)+~E'u(rij)])dr~...drx,
(2)
where: rij -- Iri - rj[, the indices runs over all the adsorbed atoms, and the prime means that the case i = j is excluded. With the possible exception of helium, spin of adsorbed molecules is irrelevant so that classical statistical mechanics can be used. Considering the adsorbed molecules as forming a distinct phase (the adphase) at temperature T, the chemical potential of the adphase can be computed,
~t~d=-kT (OlnZx'~d) 0Af
(3) V,T
(V denotes the system volume), and when equated to the chemical potential of the gas considered as perfect,
~tg~= kTln(p/po) (where p0 = kT(mkT/2rli) 3/2, m is the mass of the adsorbed molecule, and
(4)
h is the reduced Planck constant), the adsorption isotherm is obtained [11, 1]. However, the difficulties encountered in computing the partition function for realistic potentials make it necessary the use of drastic approximations, usually related to simplified models of the physical situation.
2.2. Submonolayer adsorption The models are particularly simple if one stipulates that adsorption is exhausted when one monolayer is filled. This situation is expected to occur when the adsorbent temperature is above the critical temperature of the adsorbate.
2.2.1. Localized adsorption Localized adsorption is characterized by the existence of an activation energy for surface diffusion and by a temperature so low as not allow an appreciable diffusion. In modern language, the models for submonolayer localized adsorption are variants of the two-dimensional Ising models, either with no interaction between nearest neighbours (Langmuir model) or with an interaction described in the mean field approximation (Fowler-Guggenheim model). L a n g m u i r i s o t h e r m . The first, and still the most used, adsorption model was established by Langmuir in 1918 in a paper with the reductive title The adsorption of gases on plane surfaces of glass, mica and platinum [12]. The original derivation of the Langmuir isotherm was kinetic in character, but a rigorous statistico-mechanical derivation was soon found. The statistico-mechanical derivation of the Langmuir isotherm is based on the following assumptions:
9 the gas is perfect; 9 adsorbed molecules are classical objects localized on their adsorption sites; 9 the surface is characterized by Afs identical sites; 9 adsorbed molecules do not interact laterally; and 9 adsorption is exhausted after the formation of the first layer; from which a straightforward application of eqs. (3) and (4) leads to the celebrated Langmuir isotherm [12, 11]: 0=
P
p+pLexp(-r
(5)
where 0 = A/'/A/'s, PL is a characteristic pressure given by PL = po/z, z is the partition function of an adsorbed molecule in the adsorption field, and e is the binding energy of this molecule to the surface. F o w l e r - G u g g e n h e i m i s o t h e r m . This equation is based on the same assumptions as the Langmuir model, however improved to include nearest-neighbour interactions in the Bragg-Williams approximation. In this approach lateral interactions are taken into account in a simple way by assuming that the total interaction energy is the same for all the possible configurations of Af molecules on A/'s sites. The resulting adsorption isotherm is [13] e
P=PLi
'0exp
kT
kT
'
(6)
where: PL and e maintain the same meaning as in the Langmuir equation, w is the nearest neighbour interaction energy, and c is the site coordination number. This isotherm exhibits phase transition loops at temperatures lower than a critical temperature Tr with Tr =
cw/4k. 2.2.2. Mobile adsorption Mobile adsorption is characterized by an activation energy for surface diffusion so low as not allow localization. In modern language, the models for submonolayer localized adsorption are two-dimensional (2D) variants of the Van der Waals model. V o l m e r i s o t h e r m . The hypothesis, that the adsorbed phase behaves as a 2D Van der Waals gas with only co-area effects, led Volmer [14] to propose the following isotherm: o
P=Pvi
'0exp
1--0
'
(7)
where Pv is a characteristic pressure,
pv = (mkT/27rh2)l/2kT/b2z•
(8)
z• is the partition function for the motion of the admolecule in the direction perpendicular to the surface, r maintains the meaning of adsorption energy, and b2 is the co-area. The co-area is the 2D analogue of the covolume in the Van der Waals equation of state. Though co-area effects are expected to be very important in an adsorbed phase (because this phase may have a density of the same order as the bulk phase) the Volmer model ignores ignores that lateral interaction between adsorbed molecules may be equally, or even more, important.
H i l l - d e B o e r isotherm. This difficulty was solved by Hill [15] who described the adsorbate as a 2D Van der Waals gas held at the surface by the adsorption field. The resulting adsorption isotherm is given by:
0 p=pv 1
{" 0 /~exPkl_O
a20 kT
kT
'
(0)
where a2 takes into account the interaction energy among the adsorbed molecules. The qualitative behaviour of this isotherm is roughly similar to that of the Fowler-Guggenheim one. Even this equation exhibits phase transition below a critical temperature To, with Tr = 8a~/27b2k. The first evidence for systems behaving as 2D Van der Waals gases was provided by de Boer [16] and since then the above equation is usually referred to as Hill-de Boer isotherm.
2.3. Multilayer adsorption In the previous part we have considered only submonolayer adsorption, i.e. the formation of only one layer of adsorbed molecules, held on the surface by gas-solid forces. However, since gas-solid interaction energies which are responsible for adsorption are not very different from vapour-phase interaction energies which are responsible for condensation to liquid state, it is not surprising that multilayer adsorption (i.e., adsorption on the top of already adsorbed molecules) occurs. Actually, when the temperature of a solid surface is lower than the critical temperature of the adsorbate, multilayer adsorption is a common phenomenon, resulting in a gradual increase with pressure of the adsorbed molecules up to bulk condensation as the adsorption pressure approaches the bulk vapour pressure. The possibility of multilayer formation complicates considerably the treatment of adsorption on homogeneous surfaces. In principle, the general equations (3) and (4) take into account the multilayer formation when the rigorous potential is included in the expression (2) for the classical partition function Z~c,~d: this is no more true for the various simplified models which lead to the local isotherms (Langmuir, Fowler-Guggenheim, Hill-de Boer, etc.) reported in the previous section. Simplified models for multilayer adsorption are formulated by explicitly allowing the possibility of multilayer formation. Among these models we shall consider only the ones due to Brunauer, Emmet and Teller (BET), and to Frenkel, Halsey and Hill (FHH). They can be considered as complementary, since the BET isotherm gives generally a satisfactory description of the real systems at coverages lower than 2 - 3 layers, while the FHH isotherm becomes adequate only at coverages higher than 3 layers. 2.3.1. The B E T isotherm and its extensions The original derivation of the BET isotherm was based on kinetic arguments [17], although statistical derivations are known [1]. The hypotheses upon which the BET theory is built are the following: 9 the gas is perfect; 9 adsorbed molecules are classical objects localized on their adsorption sites; 9 the surface is characterized by A/'s identical sites;
9 adsorption takes place either on surface sites or on the top of molecules already adsorbed but not in in-between positions; 9 the first layer only interacts with the surface; all other layers have interparticle interaction with the same energy as would apply in the liquid state, and involving only nearest neighbours in the vertical stack of adsorbed atoms in each site; and 9 adsorbed molecules do not interact laterally. Since the first three assumptions are the same as for the Langmuir isotherm, the BET model is essentially an extension of Langmuir model to multilayer adsorption. The above assumptions lead to the following expression for the surface coverage: 1 Cx e(x) = 1 - x 1 + ( C -
1)x"
(10)
where x is the relative pressure (x := p i p . , ratio of the equilibrium pressure p to the saturation pressure ps), and C = z-~ exp ( ~ -- 81iq) Zr,n
kT
(11)
"
In eq. (11) z and znq are the partition functions for a molecule in the first layer and liquid phase, respectively, while ~ and enq are the adsorption energies in the first layer and higher layers, respectively. If C >> 1 (BET isotherm of II type), higher layers are occupied only when the first layer has been filled almost completely. If C ~< 1 (BET isotherm of III type), adsorption in the first layer occurs in competition with adsorption in higher layers. The BET isotherm can formally be reduced to the Langmuir isotherm by a simple variable transformation, a fact that is useful to develop a unified treatment of mono- and multi-layer adsorption. In fact, defining an 'enhanced pressure' P [18], p=
1
P
(12)
and a modified surface coverage OM(P, ~), 0(P, ~) 8M(P,e) = 1 + P/p~'
(13)
for the BET isotherm one has 8M(P, 8)
=
p +
PL
P exp ( - e / k T ) '
(14)
where PL = PL e x p ( - e r , q / k T ) . The BET assumptions can be modified by imposing that piles with a maximum of n molecules can be accomodated on the surface. In this case one gets C z 1 - (n + 1)z '~ + n z T M 6,.,(x) = i - x I + ( C - l ) x Cx n+l' -
of eq. (10). Form lly a (x) for n pressure with above properties can be defined even for the BET equation with n layers [19].
(15)
A simple inspection of the BET assumptions clearly shows that the BET equation cannot give an adequate physical description of multilayer formation. However, in the neighborhood of the B point (i.e., the point in the x - 6 plot where the experimental isotherm changes its concavity) the BET equation has provided a rationalization of so many experimental data as to have become a standard for the quantitation of surface areas. Many treatments have been developed in order to improve the BET theory [20, 21]. Among them we mention the model by Hill [20], essentially based on the same assumptions of BET theory, but accounting for lateral interactions among adatoms in the same layer (within the Bragg-Williams approximation). The obtained improved isotherm is considerably more complicated without leading to better agreement with experimental results [22]; for these reasons it is not frequently utilized in p r a c t i c e - that occurs for other seemingly 'improved' isotherms. 2.3.2. T h e a d s o r b e d phase as a liquid - - F H H isotherm In the FHH theory [23, 24, 25] the adphase is considered as a liquid phase subjected to an external potential generated the adsorbing solid surface: these attractive gas-solid interactions are responsible for a stabilization of the adphase with respect to the bulk liquid. The FHH isotherm is derived by assuming the adsorbate as a uniform thin layer of liquid on a planar, homogeneous, solid surface and considering the effect of the replacement of the solid by the liquid: a molecule in the adsorbed layer will feel different potentials in these two situations. Equating such a potential-energy difference to the difference of chemical potentials between the adsorbed layer and the bulk liquid, one obtains the following implicit isotherm kT ln(p/p~) = up(t),
(16)
where t is the thickness of the adsorbed layer and up(r), known as perturbation energy, is the difference between the actual potential U(r) acting on a point r of the adsorbed layer and the hypothetical potential acting on the same point if the solid adsorbent were substituted with liquid adsorbate: up(r) = [U(r)-Unq(r)]. Equation (16)is interpreted by stating that a liquid condenses in a volume within a distance t from the surface when the perturbation potential in that volume is less than, or equal to, k T l n ( p / p s ) . The quantity (17)
c = -kTln(p/ps)
is usually referred to as Polanyi potential. See Steele's treatise for a compact discussion the the physical bases of the FHH theory [1]. An explicit form for the FHH isotherm (16) is obtained by putting t = 0d~ (where d~ is the thickness of each layer), and assuming that the perturbation energy is attractive and varies as an inverse power ; of the distance, U(x3) cx x3 r With this assumption the FHH isotherm becomes in
=
O~fsn
kTd~
(18)
'
where aFHH is a proportionality constant. Considering the long range part of the gas-solid interaction and the intermolecular interaction in the bulk liquid as due to dispersion interactions, the exponent s should be equal to 3.
Of course, the assumption t cx 0 provides a 'continuum' description of the adsorption process; this description is realistic only for 0 high enough, say 0 > 3. 2.4. Classic empirical i s o t h e r m s for s u b m o n o l a y e r a d s o r p t i o n None of the isotherms considered above is observed frequently: Clear-cut evidence for the Fowler-Guggenheim or Hill-de Boer isotherms has been provided only for adsorption on highly homogeneous lamellar surfaces (see [26] and references therein quoted). The BET isotherm describes poorly both the submonolayer region and the high-coverage region; it provides a satisfactory description of adsorption only for C >> 1 and in the vicinity of the B point. The FHH isotherm provides an adequate description of multilayer adsorption for 0 > 3, but most experimental data can be fitted with r = 2.1 - 2 . 8 [27] rather than with the theoretical value r = 3. These anomalies cannot surprise, because in most cases the surfaces of adsorbents of practical interest are highly non-ideal due to numerous physico-chemical factors, like: the presence of different compounds, phases or crystalline faces, the absence of short-range order, or the complex topographic structure. Rather, what is surprising is that in spite of the plethora of ways through which non-ideality can be manifested, only three adsorption isotherms and their combination are frequently observed in submonolayer adsorption: the Freundlich (F), Dubinin-Radushkevich (DR), and Temkin (W) isotherms. 2.4.1. Freundlich i s o t h e r m The F isotherm has both a historical importance, because it is the oldest reported rationalization of adsorption data (it was extensively used by Freundlich [28] well before Langmuir's derivation of his isotherm [12]), and a practical importance because it is still largely used in the description of real systems. The F isotherm is given by 0F(p) = (p/pF) ~,
(19)
(where PF and s are at the moment unspecified parameters characteristic of the adsorbent-adsorbate system, with s < 1) and is defined for 0 _< p _< PF- However dOF/dp ~ +oc for p ~ 0, so that eq. (19) is not reduced to the Henry isotherm in the low pressure limit. A variant of the Freundlich isotherm was proposed by Sips [29, 30]" 0F,S(p)=
P +PP F
(20)
Equation (20) is defined even for p _> p~, but does not behave asymptotically as the Henry isotherm in the low-pressure limit. Another isotherm which is reminescent of the F equation was proposed by Tbth [31]" OF,T(p) = [ 1Is
P
~PT -4- pUS
)s.
This isotherm is defined for all p and behaves as the Henry isotherm in the low pressure limit.
l0 2.4.2. D u b i n i n - R a d u s h k e v i c h i s o t h e r m
The isotherm, originally proposed by Dubinin and Radushkevich in 1947 for adsorption on microporous solids [32], reads lnA/" = CDR- B[kTln(p/p~)] 2,
(22)
where CDR and B are suitable constants, depending on the considered system. This equation is defined for 0 _ p < p~. Only later Kaganer observed that the parameter CDR coincided with lnA/'s, where Af~ is the monolayer coverage as determined by the BET technique [33], so that eq. (22) could be written In 0DR(P) = -B[kT ln(p/ps)] 2.
(23)
It was with astonishment that Hobson [34] found that eq. (23), an equation manifestly in the realm of the Polanyi potential theory, could be applied to adsorption on non-porous surfaces. Soon after Hobson's discovery the DR equation, eq. (23), was found to describe several adsorption systems, though in some cases better fits could be obtained by replacing psat by the saturated pressure of the solid phase [35, 36]. The theoretical analysis of the DR isotherm carried out by Cerofolini [37] and the subsequent numerical analysis by Rudzifiski et al. [38] then showed that for adsorption on non-porous surfaces the DR equation (23) should actually be replaced by the modified DR (mDR) equation: In 0mDR(P) = -B[kT ln(pm/p)] 2,
(24)
where Pm is a suitable pressure related to the minimum adsorption energy. A large number of systems is known to be described by eq. (24) in the deep submonolayer range (8 < 0.1); a nonexhaustive list of systems obeying this equation is given in ref. [2]. A short history of the DR isotherm is sketched in ref. [40]. Equation (24) is defined only for 0 < p _~ pro; however, as suggested by Misra [39], the equation in 0mDR,M(P) = -B[kT ln(1 + pm/p)]2
(25)
is very close to the mDR equation for p << Pm and extends the domain of p to the entire physical region (0, +oo). However, dO~DR/dp ~ 0 for p ---+0 and so does dOmDR,M/dp~ even in these cases the expected Henry behaviour is not reproduced. 2.4.3. T e m k i n i s o t h e r m
An adsorption isotherm where 0 increases logarithmically with p was first found by Slygin and Frumkin in 1935 [41]. However, the pressure-logarithm isotherm is usually associated with the name of Temkin, who had already proposed such a law on theoretical grounds in 1933 [42] and later applied it to the kinetics of ammonia synthesis and decomposition [43]. We shall write the T isotherm in the form 0T(p) = 1 + CT ln(p/pm),
(26)
where CT and pin are characteristic constants of the system. Equation (26) has a meaning for - 1 _ fiT ln(p/pm) < O, but is known to provide accurate description of physical systems only in a more restricted interval {p" 0.2 < 0T(p) < 0.8}.
ll 2.5. Blends of classic empirical isotherms The study of actual adsorption isotherms over extended pressure ranges has shown that pure F, DR or T behaviours are not observed in most cases; usually the actual isotherm exhibits a gradual transition from one classic empirical isotherm to another. 2.5.1. Merging the F and D R isotherms The FDR isotherm was proposed by Cerofolini [44] in the frame of the condensation approximation (see section 3.7.1) assuming that the adsorbing surface was obtained by quenching an equilibrium configuration and that lateral interactions are not negligible. The FDR isotherm is defined for 0 < p < pm and reads in 8FDR(P)
=
1 kTF
(kTln ( ~ ) [ l + - - l nkT ~o
(~-~)])
(27)
where TF is a 'frozen temperature' and ~0 is a suitable energy. For exp(-no/kT) <
In O = - Z
Bj[kT In(pro~p)]j for 0 _< p _< Pro,
(28)
j=0
where the temperature-independent coefficients Bj characterize the adsorbent-adsorbate system. Equation (28) follows from an idea of Heer, according to which the expansion of the logarithm of fugacity gives a better description of the adsorption isotherm than the traditional virial expansion [47].
12 2.5.2. M e r g i n g the T and F isotherms In an analysis of published experimental data, Gottwald and Haul [48] found that isotherms, previously referred to as obeying the DR equation, are better described for 0 < p ~ p~ by an equation of the type ( p / p m ) s -- 8TF
exp(STF/8O),
(29)
where 80 is a characteristic coverage. Equation (29) cannot be inverted in terms of elementary transcendental functions. However, it behaves as the F equation in the low coverage limit, 8TF << 80 =~ 8TF(P) (p/pm) s, while behaves as the T equation in the high coverage limit, 8TF >> 80 ~ 8TF(P) -- in 80 +8oS ln(p/pm), provided that 8oS is identified with ~T. Because of these behaviours eq. (29) will be referred to as TF equation. 2.5.3. M e r g i n g the T, F, and D R isotherms We have now the instruments for finding an expression behaving as the mDR equation at low coverage, as the T equation at high coverage, and as the F equation in the intermediate coverage region. Such an equation, which is defined for 0 < p
(~)]})=STFDRexp\
(STFDR 8o ) "
(30)
At last we note that the change of argument which allows the F and mDR equations (defined for 0 _ p < Pro) to be transformed into the generahzed Freundlich and DR equations (eqs. (20)and (25)), i.e. Pm/P ~ Pm/P + 1,
can also be used to extend the domain of the blended isotherms (27), (29) and (30) from (0,pro) to (0, + ~ ) . 2.6. A d s o r p t i o n on heterogeneous surfaces Most of adsorbents of practical interest in physical chemistry are not energetically homogeneous, so that they are expected not to obey the isotherms described in section 2. The problem of describing adsorption on energetically heterogeneous surfaces was considered by Langmuir himself [12], who described the coverage St(p) of the entire surface as a weighted average of the coverages 8(p, ej) of sites of adsorption energy ej:
0~(p) = Z 0(p,~)xj,
(31)
J where Xj is the fraction of sites with energy Q, and the sum is extended to all different sites forming the surface. By definition
Vj : Xj >- 0
(32)
and
Z ~ = 1. J
(33)
13 The function 0t(p) will be referred to as overall isotherm, while the function O(p,e) will be referred to as local isotherm. Langmuir could ignore surface topography because he assumed his equation as local isotherm. When other models are considered, however, surface topography is no longer mute in the description of adsorption. In this case, two extreme situations are usually considered: patchwise heterogeneity and random heterogeneity.
2.6.1. Adsorption on patchwise heterogeneous surfaces The description of adsorption is significantly simplified if the adsorption sites having the same adsorption energy are assumed to be grouped in homotattic patches, each large with respect to the atomic size, so that they can be considered as indefinitely extended and lateral interactions between molecules adsorbed on different patches can be neglected. This model, known as homotattic patch approximation (HPA) [49], allows the use for each patch of the theory developed for the homogeneous surface. If the family of different patches is so densely distributed as to allow the finite set {Xj} to be replaced by a continuous distribution function X(e), the overall isotherm is given by
~
M (34)
where ~m and ~M represent the minimum and maximum adsorption energy, respectively. For reasons of mathematical convenience the lower limit of integral (34) is often put to 0, while the upper limit is put to + ~ : (35)
j~O+~ This substitution does not produce difficulties, provided that X(r in (0, +oc) and identically null for r < Cm and for e > eMIt is stressed that in the realm of the HPA, eq. (35) holds true isotherm describing the adsorption on each homogeneous part of the Langmuir isotherm as well as for the Fowler-Guggenheim or
is considered as defined irrespective of the local the surface: it holds for the Hill-de Boer ones.
2.6.2. Adsorption on random heterogeneous surfaces In the second extreme model, known as random approximation, the adsorption sites having the same adsorption energy are assumed to be distributed completely at random on the heterogeneous surface. Although considered as early as in the forties by Hill [50] and Tompkins [51], this model is employed much less than the HPA, because of its greater computational difficulties and (perhaps) of its lower adherence to the physical reality. The Hill-Tompkins model extends the Fowler-Guggenheim theory of localized adsorption on homogeneous surfaces to adsorption on heterogeneous surfaces with a random distribution of sites. This model is based on a canonical ensemble approach, in which only interactions between adsorbed molecules are taken into account. On the basis of this assumption, Hill derived the following expression for the partition function, In ZX,~d -
z
Afs,i In
3'
+
7 +~1
nlw + k--T-- '
(36)
14
(
W (0rL 1 ~ 7 = !zi exp a - -s ' N'si' is the number of sites of adwhere: a = kT kT ~,-~,]' sorption energy ei, ~t is the chemical potential of the adsorbate, Af is the total number of adsorbed molecules, zi is the partition function of the adsorbed molecule on the i-th site, w is the nearest-neighbour interaction energy, the configuration factor n l is defined through the relationship exp
(TZlW E g(nll)exp(nlxw/kT) =
(37)
Eg(nlx )
g(n11) is the number of configurations of the adsorbed molecules with rill nearest-neighbour pairs, and the sum is extended to all possible configurations. Making use of eqs. (3) and (4), and replacing the summation over Af~,~by an integration in eq. (36), Hill derived the following overall isotherm
j[o~176 0t(p) =
(p/pL ) exp( r / kT)
exp [ - w / k T (Onl/ON')] + (P/PL) exp(e/kT)
(3S)
Using the Bragg-Williams approximation, Hill showed that Onl/OAf = cOt(p), so that the overall isotherm is given by Ot(p) =
/o=[
1 + P__L_L exp
p
(
r kT
cwOt(p) kT
)]
-1
X(e)de.
(39)
The comparison of this equation with the one which describes Fowler-Guggenheim adsorption on patchwise heterogenous surfaces, i.e. Ot(p) =
/=[
1 -4- PL exp
p
(
e
kT
cwO(p, r kT
)]
-1
Z(e)dr
(40)
shows that the HPA expression holds also for random heterogeneity provided that 0 t ( p ) is substituted for O(p,e) - - a rather intuitive conclusion.
3. T H E M I D D L E AGE: E M E R G I N G S T U D I E S OF S U R F A C E H E T E R O GENEITY EFFECTS 3.1. T h e inverse problem A coarse-grain look at the log p-O plot of all the considered theoretical isotherms describing submonolayer adsorption on energetically homogeneous surfaces shows that they remain close to 0 for p << Pe while become close to 1 for p >> pc, where p~ = 0(1) exp(-e/kT). The transition region from 0 to 1 is quite narrow and extends over less than one order of magnitude. The classic empirical isotherms, by contrast, are characterized by a transition region usually extending over several orders of magnitude. The most direct explanation for this behaviour is to ascribe it to a distribution of adsorption energy along the surface, so distributed as to allow for the observed transition region. This interpretation originates the problem of finding such a distribution.
15 3.2. S t a t e m e n t of the problem Adsorption on patchwise or random surfaces is described by eq. (35), provided that the possible dependendence of 8(p,~) on 0t or 8 are considered (as in eq. (39) or (40), respectively). Relationship (35) is the source of three problems [52]:
9 the problem of the unknown kernel, in which the overall isotherm 0t(p) is experimentally determined in correspondence to a given distribution function X(e), and eq. (35) is considered as an equation for 0(p,e); 9 the direct problem, in which 0(p,e) and X(r are assumed to be known and 0t(p) is calculated by a quadrature; and 9 the inverse problem, in which 0(p,e) is assumed as theoretically known,/~t(p) as experimentally determined, and eq. (35) is considered as a Fredholm integral equation of first kind for X(r The problem of the unknown kernel is often ignored, but is of fundamental importance since its solution would give information on the actual adsorption mechanism. This problem would be solved if one were able to prepare energetically homogeneous adsorbents. Actually, the preparation of energetically homogeneous sorbents with surface area so high as to allow rehable adsorption measurements is a conceptual problem which has practically been solved in few cases only [26]. Only few works have been devoted to the problem of the unknown kernel; its mathematical nature is discussed in ref. [52]. The direct problem is not simply a quadrature problem, but rather aims at calculating the adsorption isotherm for an assigned surface; the specification of X(r is a part of this problem, and its solution requires the formulation of a model of the adsorbing surface. This problem will be considered in section 2.4 for equilibrium surfaces. The inverse problem is the one more frequently encountered in practice, because (i) the overall isotherm is directly accessible to experiments, and (ii) there are good reasons for choosing one or the other local isotherms. 3.3. Choice of the functional space In mathematical terms, the inverse problem is the problem of solving eq. (35) for X(e) when 0t(p)and O(p,c)are assigned functions. The functional space in which the distribution function is searched plays an important role in the discussion of the problem. Indeed, as discussed in ref. [53], the choice of the functional space in a physical problem is not straightforward and may affect the solution of the problem; because of this fact this choice must be decided on physical bases. Consider, for instance, the case 0t(p) -- 8(p, ~). It is easy to show that in this case there is no continuous function X(e) satisfying eq. (35). However, in the space of distributions eq. (35) admits the trivial solution X(e) = 5 ( c - ~), where ~(-) is the Dirac 5 distribution. Ignoring this trivial case (and its natural extension, for which /gt(p) is given by eq. (31) with few coefficients Xj, each of the order of unity), one may reasonably assume that X(r is a smooth (however not necessarily continuous) function. Because of the physical conditions of non-negativity,
> 0,
(41)
and of normalization,
f0 ~176
= 1,
(42)
16 the function X(r belongs therefore to the space LI(0, +oo) of Lebesgue-summable functions. Equations (41) and (42)transcribe conditions (32) and (33), respectively, from the discrete case to the continuous one. The overall isotherm can in turn be considered a smooth function (so are the classic empirical isotherms considered in section 2). Since 0t(p) is monotonically increasing with p, from a famous theorem due to Lebesgue its derivative Ott(p), O~(p):-- dOt(p)/dp, exists almost everywhere [54] and is non-negative: o
Vp" O~(p) >_ 0,
(43) o
where the symbol ' > ' means '>_ almost everywhere'. Assuming, quite realistically, that the monolayer is completed when the pressure is increased to +oc (i.e., p ~ +oo =~ 0t ---* 1), one has
fo ~176 O't (p)dp = 1.
(44)
The combination of conditions (43) and (44) guarantees that O~(p) too belongs to LI(0, +oc). The function O~(p) plays a special role in the theory and will be referred to with a new n a m e - disotherm. Since a natural functional space for O't has been identified, rather than eq. (35) it is convenient to consider the equivalent equation
O't (p) =
fo
O'(p, e)X(~)de,
(45)
O0(p,e)/Op. where O'(p, e) is the local disotherm O'(p,r Considered as an equation for X(r eq. (45) is linear only if the local disotherm does not depend on 0~(p). If we want to remain in the frame of linear integral equations, we have therefore to limit ourselves to the HPA. Unless otherwise explicitly mentioned, all the forthcoming considerations are limited to this case. Moreover, the attention will mainly be concentrated on O(p, ~) given by the Langmuir isotherm. The 'good reasons' for choosing 0(p,e) are reasons of simplicity, since other choices of local isotherms do not allow us even to express analytically O(p, r in terms of elementary transcendental functions. When O(p, ~) is given by eq. (5), the local disotherm is given by O'(p, e) =
p2 exp
[
(
(46)
1 + PL e p exp -~-~
This expression takes a more compact form on defining er
~(~r
kTln(pL/p), := 0(p(~),~), :=
(47) (48)
17 for which the local disotherm is given by 6"c
6'(r162r --
1
exp
-- ~I
kT
Cc
where O'(r162
-- C
(49) 2~
i)0(p(r162162
3.4. Ill-posedeness of t h e problem Putting
(O'x)(P)
O'(p,e)X(e)dr
:=
(50)
eq. (45) can be written in operator form as 0it ---~Or,)(""
(51)
The corresponding inverse problem reads: given 0~ E L 1, find the element X E L 1 satisfying eq. (51). According to Hadamard, a physico-mathematical problem is well posed when: (a) it admits at least one solution; (b) it is unique; and (c) it is stable. None of these conditions is trivially satisfied. To demonstrate that existence and uniqueness are not trivial, we shall construct specific counterexamples; for instability we shall use the property that if the kernel @'(p, c) is sufficiently regular, then the operator | is compact. 3.4.1. E x i s t e n c e Equation (51) admits solution only if 0't belongs to the image of O'. This condition is not trivial. For, let {r be a complete orthonormal system of L 2 belonging to L 1 too. An example is given by Laguerre functions which form a complete orthonormal system in L2(0, +or and belong to LI(0, +cr If the local disotherm is given by l
o'(;, ~) = ~ o'~jC~(p)~j(~),
(52)
i,j=l
then
O~(p)must
be of the form
l
0'~(p) = ~ b,r
(53)
i=l
with
b~ = ~ I % j--1
jfO§
~j(~)~(~)d~.
(54)
18 with j > l or 0't E LI\L2), eq. If O[(p) has not this form (for instance, if O~(p) = r (51) does not admit solution. However, we shall later see that for O(p, e) given by Langmuir isotherm, it is possible to modify slightly each of the classic empirical isotherms to such a form which allows a solution to be found.
3.4.2. Uniqueness Let ker O' := { f : O ' f = 0}.
(55)
The set ker O' is not void since the null element 0 of L 1 belongs to ker O'. If X0 is a solution of eq. (51), then any other element X0 + f, with f E ker t9', is a solution of this equation because of the linearity of 19'. Necessary and sufficient condition for a solution X0 of (51) be unique is that ker O ' = {0}.
(56)
Condition (56) is not trivially satisfied by all operators on L 1. Consider, for instance, a finite-rank kernel O'(p,e) of the form (52). Then any element (i with j > 1 of {~,} belongs to ker O'. The analysis of the next section will however show that the solution of eq. (51) is unique when O'(p, ~) is the Langmuir disotherm.
3.4.3. Stability Rather than the 'true' overall disotherm 0~(p), one knows an 'experimental' overall disotherm O~,~(p), i.e. a disotherm known in a certain pressure domain (pl, p~) (usually, a proper subset of (0, + ~ ) ) and differing from the true isotherm by less than a given quantity a (the experimental error). The best one can do is to perform a set of measurements, in which the pressure domain (pl, p2) is gradually extended and the error a is reduced. In so doing one determines a sequence {0't,~} of disotherms converging to the true isotherm for n ---, + ~ . Assuming that for each 0~,~ a solution exists and is unique, one can thus construct a sequence of functions {X~}. The solution is stable if 0~,n ---, 0't implies Xn --* X. This property, however, does not hold true for the Langmuir disotherm. In fact, since the kernel O'(p,e) of equation (45) is continuous and bounded, the associated operator O' is compact [55]. That means that for each bounded sequence {X~ }, one can extract from the the corresponding sequence {0't,~} a subsequence {0't,m} converging to the true disotherm 0't. Assume now that the measurements have actually produced just the sequence {0't,,~}. In this case the solution of the inverse problem generates a bounded, however non-convergent, sequence {X-~}The above difficulties is more fundamental than practical. In fact, though we cannot be sure that from a convergent sequence {0~,m} we can always calculate a convergent sequence {X-~}, in practice a convergent sequence may result (and often does). Even in this case, however, we run in difficulties. Indeed, the experimental and true overall disotherms satisfy the condition
I o',,.(p) 1
O,(p)
@
<
(57)
19 Taking the limit for an ~ 0 means that 0't,n tends to O't with the metric of L 2. Even assuming that xn converges to X, this tendency is however in the mean. A uniform convergence is not guaranteed, and it may happen that even small errors are responsible for large local deviations of X,(e) from X(e). This difficulty is made more dramatic by the fact that the n~ighbourhoods of p = 0 and p = +oo are physically inaccessible. That the convergence in the mean of X,(e) to X(e) does not guarantee the uniform convergence is immediately realized by observing that a piecewise continuous function X(e) having a finite number n of simple jump discontinuity points (say e l , ' " ,e,), may be approximated by a continuous function Xcont(e) obtained by replacing X(e) in ( e ~ /Xe, e~ + Ae), by means of the straight line joining the points ( ~ - / X e , X(e~- Ae)) and (c~ +/X~, X(r + Ar for all i = 1 , . . . , n. Indeed if M = sup [ X(e)I, one has ! X ( e ) - Xr
de < 8M2nAr
(58)
which can be made vanishing taking/Xe ~ 0 [56]. 3.5. B e y o n d l i n e a r i t y The analysis of section 3.4 is essentially based on the properties of linear integral operators, so that it cannot be applied to eq. (39). However, as shown by Cerofolini and Re [52], it is possible to reduce the problem of solving the integral equation (39) to the problem of solving (35) with the Langmuir local isotherm (5). This reduction is possible only when 0t(p) is a monotonically increasing continuous function of p. In this case the functions f ( p ) = exp(cwOt(p)/kT) and pf(p) are continuous and monotonically increasing. Hence, Vp E (0, +cxz) the equation p = pf(p) has a unique solution p = p(p). Defining v~(p) = 0t(p(p)), eq. (39) becomes ~(P) =
P X(e)de, P + PL e x p ( - e / k T ) jr0+~176
(59)
which is exactly eq. (35) with the Langmuir local isotherm as kernel. Equation (59) also allows the direct problem for the random approximation to be solved. In fact, if 0L(p) is the solution for the direct problem in the patchwise approximation with the Langmuir local isotherm corresponding to a given distribution function X(e), then /~t(P) = t~L(pexp(cwt?t(p)/kT))
(60)
is the solution of the direct problem in the random approximation for the same distribution function. The above relationship is an implicit equation for 0t(p) and its solution, supposedly existing, may be calculated iteratively, starting from 0~~ = 0L(p) and proceeding with the recursive relationship
3.6. Exact m e t h o d s Three exact methods have been proposed to solve the integral equation (35) with Langmuir kernel: The first method, essentially an application of Stieltjes transform theory,
20 was proposed by Sips in 1948 [29] and improved two years later [30]; the second method is an application of Wiener-Hopf technique and was proposed by Landman and Montroll in 1976 [57]; and the third method is a kind of infinite-order approximate method and was developed by Jagietto et al. [58, 59] in the period 1989- 1991 following early ideas of Rudzifiski et al. [60, 61]. 3.6.1. Sips method The method considered in this section is based on the theory of Stieltjes transform [62]. Putting
x := --PL/P-- 1
(--oo < x < --1)
y := e x p ( e / k T ) - 1
(0 <_y < +oo)
(62) (63)
Ot(z) := (1/kT)Ot(-PL/(1 + x))
(64)
2(v) := ~(kTl.(1 + v))
(65)
(the symbol x has not the same meaning as in section 2.3.1), eq. (35) becomes
~(~) = f0 ~176 y_~ 2(Y)du.
(66)
Consider now the analytic extension 0t(() of 0t(x) in the whole complex plane; for 0t(() the Cauchy theorem holds 0~(r
c
-
where (0 is any internal point of any simply connected set C, and 0C is the boundary of C. If (a) [([ ~ +oo =~ 0t(()---* 0, and (b) the singular points of 0t(() lie only on the positive semiaxis, eq. (67) gives 1 [+oo 0t(y + ir - 0t(y - ir dy, v-~
0t(~) = ~i ~o
(68)
where r is an arbitrarily small positive number. Comparing (66)with (68)one eventually gets 1 :~(y) - ~i[0t(y + i~b)- 0t(y - i r
(69)
The uniqueness of this solution is ensured by the uniqueness of the analytic extension. This solution was applied to physically interesting cases by Sips himself [29, 30] (generalized Freundlich isotherm), Misra [39, 63] and Sokotowski [64] (generalized DR isotherm). Here, rather than to apply eq. (69) to real cases, we are interested in discussing the difficulties it hinders.
21 First of all, the conditions (a) and (b) are suggested (but not ensured) by the behaviour of St(p) for p ---, 0 and by expression (66), respectively. Moreover, ~Jt(()is experimentally known only in a portion of the segment P = ( - o o , - 1 ) (the 'physical region'), while eq. (69) gives ;~(y) when ~Jt(() is known in the small strip E = {( 9Re ( E (0,+oo) A Im ( E ( - r 1 6 2
(70)
Hence the evaluation of ;~(y) requires the knoledge of the analytic extension of ~Jt(x). But (a) and (b), is known only in an approximate form ~Jt~(x), whose analytical expression is chosen in order to satisfy (a) and (b). The maximum one may assume about the knowledge of 0t(x) is that
~t(Z), supposedly satisfying conditions
w
--oo
<
9<
-1 ~ 16.(~)-
#t(~).
I< o-,
(71)
where (r is the precision of the measurement and a physical meaning can be given only to ~t~(x) for a ~ 0 and no to 0t (x) itself. This means that we can extract from {St~(x)} a sequence of functions converging uniformly in the physical region to the true function as O" --'4 0 .
No difficulties would occur if the uniform convergence of {/t~(z)} to^~t(x) for x < - 1 ensured the uniform convergence of the analytic extension {8t~ (() } to 8t (r in the whole complex plane or, at least, in the small strip across the positive serniaxis, but this fact usually does not hold true [65]. 3.6.2. L a n d m a n - M o n t r o l l
method Through the change of variables
r162
:=
~(cr
kTln(pL/p)
:= O(p(~r
g,(~r
=
1 + exp
:= 0,(p(~r
( "kT )]_1 g'c --
5"
(72) (73) (74)
eq. (35) is transformed into an integral equation whose kernel depends only the difference e r ~, rather than on r162and e separately. This occurrence makes the corresponding integral a convolution integral: t~t(~r =
K(er ~0+~176
-
r
(75)
given by eq. (73). with K(er ~ ) = 0(er To solve this equation assume X(r be analytic, consider its analytic extension to (-oc, +oc), and define f+(er :=
K(c,: ~0+~176
1_(~r :=
F (x)
~)X(e)de,
K(~r - ~)x(~)a~.
(76)
(77)
22 Summing these equations member to member,
f+(r
+ f+(r162= ~.oo g(r162- e)X(~)de,
(78)
taking the Fourier transform of both members, 9v[f+ + f _ ] ( x ) = 3:[K](x).T'[X](x )
(79)
solving for .7-[X](x), and calculating the inverse Fourier transform, one eventually gets X(e) = $--1[$-[/+ + f_]/J:[g]](e).
(80)
Since f+(er experimentally known (f+(er = 0t(ec)), the problem of finding X(e) would be reduced to the calculation of Fourier transforms if one knew f_(x). In the absence of this knowledge, however, the previous calculations are only a formal development. The Wiener-Hopf technique is a method which allows the calculation of X(e) even without knowledge of f_(x). This technique is based on suitable decomposition of K(ec - e) and requires that each decomposition is analytically continued [66]. Its practical use is difficult, but Landman and Montroll succeeded in this job and applied the Wiener-Hopf technique to the generalized Freundlich and DR equations, and to any other overall isotherm admitting a virial expansion [57]. 3.6.3. dagietto m e t h o d Though both Sips's and Landman-Montroll's methods work well for equations which strictly resemble the F and DR equations, they however suffer from a fundamental diff i c u l t y - both methods require indeed that both 0t(p) and X(~) are analytic functions. In general this is false: the support of X(~) is compact (that excludes the possibility that this function is analytic) and the overall isotherm is known only in an interval (pl, p2) and is raccorded at the boundaries in a piecewise manner (see, for instance, the high pressure behaviour of the classic overall isotherms, or Hobson-and-Armstrong's low-pressure extension of the DR isotherm [67]) rather than by analytic continuation. A solution is said to be local when a one-to-one correspondence ~ ~ p exists such that the unknown function X(~) is completely determined by the behaviour of the given function 0t(p) in the neighborhood of p. While naive considerations suggest that the adsorption behaviour at pressure p is related to the energy distribution function X(~) in the neighborhood of ~ = ~c(p) (thus suggesting the possibility of local solutions), this fact is recognized neither in the Sips method nor in Landman-Montroll one. In other words, the solution is expected to be local, but the described methods provide global solutions [65]. A local method was first proposed by Jagietto in different collaborations [58, 59] and will be referred to as Jagietto method. A similar method was independently proposed by Chen for the mathematically equivalent problem of finding the density of states of Fermi systems [68]. It is however noted that this method is based on a formula already known to Widder in 1938 [69] and that a similar result was also obtained by Brs et al. in 1985
[7o]. The derivation is obtained from the following representation of the Dirac ~ distribution: +o~ )~7r2~ 02~+1 t~(x- y ) = ~ (-1 ~ ,=0 (2n + 1)! 0y 2n+1 1 + e~-y"
(81)
23 The proof of this relationship is obtained in two steps. First, the cases y < x and x < y, are considered separately, (1 + e=-Y) -1 is expanded in powers of e -Ix-yl, and the resulting series are summed and found to be null. Second, the integral of the rhs of eq. (81) is calculated and found equal to 1, that completes the proof. To apply this formula, we first define the local and overall nisotherms:
~,(=c,~) := i - O(~c, ~)
(82)
i(er
(83)
:= 1 - 0 t ( c r
for which
i(~o) =
(84)
A(s~,s)x(~)&
because of the normalization condition of X(e). Then we observe that for Langmuir adsorption A(sc, e ) =
(
l+exp
e-er kT
(85)
so that we can apply eq. (81): +~
(-1)n(,rkr) 2n o2n+l
~ ( ~ - ~ c ) = n=o
(2n + I)!
O~cn+,A(~c,e).
(86)
Inserting this relationship into the identity
x(~c) =
f0
(8;)
~(~ - ~c)X(~)&
one has x(er
(2n + r~=O
n=o +c~
L n=o
(2n + 1)1
1)!
n+--------~(ec'e)X(e)de Oe~
Oe~n+l
fo
A(ec, e)X(e)de
(-1)n(~kT) ~n O~n+' ~(~o) (2= + 1)!
b-~c~:;~
(88)
Equation (88) gives X(er as a function series; its first two terms are
X(~c) =
o~(~c) &c
(89)
and
oh(~c) X(~c)=
&c
(~kT) = O~h(=c) 6 &~ "
(90)
24 Since the series (88) involves (odd) derivatives of the experimental nisotherm in ~ = ~r it gives a local solution, thus satisfying the initial need which motivated Rudzifiski and Jagietto. It is however noted that the_ Jagietto method allows an exact solution to be calculated only if all the derivatives of )~(~r are known. On another side, if all derivatives of an analytic function are known, the function is known everywhere by analytic extension, so that the described method is equivalent to any other non-local methods. One serious advantage of this method, however, is that it does not assume that X(r is analytic (though the first derivation was carried out under this restrictive condition [58, 59, 60]) so that the method seems suitable for application to practical systems where X(r is not analytic, the support of X(r being compact.
3.7. Approximate methods The methods described in this section are mely, the condensation approximation and the considered as special cases of eq. (88) obtained second term, respectively, we shall nevertheless aiming at clarifying their physical bases.
local methods. Though two of them (naRudzifiski-Jagietto approximation) can be by truncation of the series to the first and propose for them independent derivations,
3.7.1. Condensation approximation The condensation approximation (CA) was first proposed by Roginsky [71] and later better formalized by Harris [72] and Cerofolini [73]. This method is based on the replacement of the the true local isotherm by a step function, 0r
J" 0 for 0 < p < pr l ] for pr < p,
~)
(91)
where the pressure pc(~) can be interpreted as a condensation pressure, i.e. the pressure at which the gas suddenly condenses on the patch with adsorption energy ~. A convenient criterion for choosing the condensation pressure pc(~) is the minimization of the distance between O(p,e) and Or [73]. Using whether the Lagrangian distance,
dc..[O,Or =
sup I O(p, ~) - Or pe(0,+oo)
~) I,
(92)
or the L2(0, +oo) distance, dL2[O,/~c] =
{/0
I /~(p,r
Oc(p,e)! 2 dp
,
(93)
one obtains O(pr
= 89
(94)
which for the Langmuir isotherm gives
pc(c) = PL e x p ( - c / k T ) .
(95)
The same result is obtained with Harris's criterion, which states that pc(c) must be chosen as the pressure at which the local isotherm changes concavity in a log p - / 9 plot.
25 Operating as in eq. (48), i.e. using er = - k T l n ( p / p L ) , as independent variable (obtained by inverting eq. (95)), expressing p as a function of er and putting 0t(er Ot(p(er the integral equation (35) becomes t~t(ec) =
Xc(e)de,
(96)
where Xr is the distribution function corresponding to the condensation approximation. Equation (96) can easily be solved by a differentiation with respect to ~r (97)
Xc(ec) "-- --l~#t(ec)/l~ec.
Interesting consequences of eq. (97) are: (a) since er is defined on the real axis (-c~, +co) while only positive values of adsorption energy have physical meaning, X:r162 must be 0 for er < 0, i.e. for p > PL; and (b) Xr is temperature independent, as physically required for hard adsorbents (see section 4.2), only if 0t(er does not depend on T, i.e. if 0t(p) does not depend on p and T separately, but only on kT ln(pL/p). The errors occurring in the CA may be estimated by defining 0(ec, e) as in (48) and writing the integral equation (35) in the form 0t(er =
f0+= 0(er
(98)
e)X(e)de.
Differentiating eq. (98) with respect to er and taking eq. (97) into account, one has Xr162 = -
f
+~ 0~(~r e) O-e-cf
X(e)&-
Equation (99) shows that the more -0t}(er162 resembles 6 ( e - er similar to :~(er For the Langmuir local isotherm one has 0#(So, e) _ i Oer kT
exp
-
1 + exp
(
kT ec - e kT
(99) the more Xr162
(i00)
This function has a bell-shaped form, with a maximum centred on er = e, and becomes sharper and sharper as T decreases, tending to a Dirac distribution when T --, 0. It may hence be assumed that the CA gives reliable results when applied to surfaces whose distributions have a spread much greater than kT. In the opposite case, i.e. for surfaces whose distribution functions present peaks with width smaller than kT, the CA gives distributions which are smoother than the true ones.
3.7.1.1. Application to classic overall isotherms The application of CA to the classic overall isotherms and their blends specifies the meaning of p~ - - the characteristic pressure pm is given by p~ = PL e x p ( - e ~ / k T ) , where r is the minimum binding energy. This physical meaning specifies how pm depends on T (at least when Cm is temperature-independent, as it happens for hard adsorbents) and states that p~ < PL.
26 The application of CA to the classic empirical isotherms is straightforward: the F isotherm is associated with an exponential energy distribution function; the mDR isotherm with a Rayleigh distribution, and the T isotherm with a uniform distribution. 3.7.1.2. E x t e n s i o n to o t h e r local isotherms
The condensation approximation can easily be extended to other local isotherm, like the Fowler-Guggenheim and the Hill-de Boer ones. While in the first case Harris's and Cerofolini's criteria predict the same condensation pressure [74], in the second case they lead to slightly different choices [75]. This small difference affects only marginally the resulting distribution function [75]. 3.7.2. Asymptotically correct approximation
The asymptotically correct approximation and modified to the present form by Cerofolini true local isotherm by an approximate kernel behaviours both at low pressure (Henry law)
(ACA) was first introduced by Hobson [76] [73, 78]; it is based on the replacement of the O~(p,s) which shows the correct asymptotic and at high pressure (saturation):
{ (p/pL)exp(s/kT)l forf~ Pa(~)0 _ p _~
(101)
where p~(e) is a suitable pressure. If this pressure is so chosen as to minimize the distance
bCtw~r 0(p,~) ~.d 0~(p, c), o,r h~s p.(~)= p~ ~xp(-~/kT). Since p~(e) - pc(e), the arguments of the previous subsection can be repeated here. In particular, expressing p as a function of ~c and putting 0t(~r = Ot(p(ec)), the integral equation (35) becomes #t(sc) = exp ( - ~ T )
/o
(k-~)X,(s)ds +
Xa(s)ds,
(102)
where Xa(e) is the distribution function corresponding to the condensation approximation. Equation (102) can be solved by two differentiations with respect to sr and gives 00t(er x~(~r
=
0~r
kTO20t(er 0~r~
(103) '
to which all the general considerations made in the previous subsection for the CA distribution Xr apply again. In order to evaluate the errors occurring in the ACA consider eq. (98) with its first and second derivatives: 0~t(er
= rl +or a~(~r ~)x(~)d~
a~r -_ a~0~(~o)
o~ x(~)d~. f0+~176 a:0(~o,~)
Multiplying both members of eq. (105) by eq. (103) into account, one has
(104) (lo5)
kT, adding the result to eq. (104) and taking
0_ kT0~r ) 9(~r ~)x(~)d6
(~06)
27 Equation (106) shows that the more its kernel resembles 6 ( r r the more Xa(Cc) is similar to X(r For example, for the Langmuir local isotherm, one has 1-exp( 0 0ec
0(~r162
kT-~r
ec-e)kT
1+
kT
l+expk,
kT
(107) l+exp
kT
This function is bell-shaped and tends to a Dirac 6 distribution when T --, 0 K. Furthermore, a comparison may be performed between the CA and ACA approximations. In fact, it is easy to verify that the width at half maximum of the ACA kernel (107) is about 3 k T compared with 3.5 k T of the CA kernel (100). However, this advantage of the ACA is neutralized by the fact that the in this case the kernel is not centred on cc but on cr + k T In 2 [37].
3.7.2.1. Extension to other local isotherms Like the CA, the ACA can be extended to other local isotherms, namely the Hill-de Boer one (for which it was developed [76]) and the Fowler-Guggenheim one [78]. The mathematical machinery employed in these two cases is the same. We do not intend to describe here the details of the calculations; rather we call the attention on the following mathematical aspect. Any method (like the CA and ACA) in which the local isotherm is approximated by an isotherm which is identically equal to 1 for p greater than a suitable pressure p'(~) transforms the Fredholm integral equation of the first kind (35) into a Volterra integral equation. This process removes the instability. While solving the resulting Volterra equation for the CA is essentially reduced to the calculation of the derivative of the overall isotherm, in the ACA the procedure is somewhat more complex. While the solution of the Volterra equation for the Langmuir local isotherm is immediately reduced to the calculation of two derivatives, for the Hill-de Boer and Fowler-Guggenheim isotherms it requires the method of solution proper of the Volterra equation of the second kind [73, 78]. 3.7.3. Rudzifiski-Jagietto approximation This approximation was first developed by Hsu et al. [79], Rudzifiski et al. [61] and Jagietto et al. [58] without assuming any local isotherm approximation. Their derivation is based on equation (99) which can be considered as an alternative formulation of the integral equation (35); its kernel is the derivative of the local isotherm with respect to ~c which in the case of the local isotherm is, as already seen, a bell-shaped function. The true distribution function is then expanded in power series of ~ around r = ec, so that eq. (99) may be written as
0r
=
\
j=o
(lo81
) ~=~c
where Cj(~r = k T
~r
' O~c
kT
d
--~--]
"
(109)
28 When only the first term of the series in eq. (108) is retained, the resulting distribution coincides with that given by the CA, while when also the second term is retained eq. (108) becomes O~c
= -X(e,:) -
( kT) 6
(110)
0~
which is a second order linear differential equation for X(ec). If the second term in eq. (110) can be considered as a correction, this equation can be solved in an iterative way, giving the CA distribution function at the first step, and the RJ distribution function, )~RJ (gc) -----
~
0~c
t (~kT) ~ 0 ~ t (~r 6
(111)
0g 3
at the second step. Rudzifiski et al. [61] have shown that, when applied to real systems, eq. (111) gives results remarkably better than those obtained by the CA or ACA. 3.7.4. B e y o n d the R J a p p r o x i m a t i o n In view of what wassaid in section 3.6.3, the CA and RJ approximations can be seen as the first- and second-order aproximations of general formula (88). A somewhat different procedure, the logarithmic symmetrical local isotherm approximation (LOGA), was developed by Nederlof et al. [80]. Approximating the local isotherm with the kernel ON(p,e) =
-~ (p/pL) ~ e x p ( ~ e / k T ) 1 - ~1 (pL/p) ~ e x p ( - ~ e / k T )
for 0 _< p < pN(e), for PN(r _< P,
(112)
(with PN(r = PL exp(--e/kT)), changing the independent variable from p to r162= k T ln(pL/p), inserting the local isotherm (112) into eq. (48), taking three successive derivatives with respect to r162and noting that in the equation for the third derivative enter the same integrals which appear in the equation for the first derivative and which can therefore be eliminated, one obtains XN(gc) = (~0t(gc_____~)_[_ ( k T ) 2 030t(er 0ec ~2 (~ec3
(113) "
For ~ = x/~/~r = 0.780 this equation coincides with the RJ approximation, so that this procedure can be considered as a way to deduce the RJ formula in a scheme in which the local isotherm is suitably approximated. Nederlof et a/., however, optimized /3 by minimizing the distance between the Langmuir isotherm and its approximating form (112). They obtained fl = 0.7 wheter using the Lagrangian distance or using the L2(0, +oo) metric, and showed that with this choice eq. (113) works slightly better than the RJ approximation. Although both the RJ and LOGA approximations lead to good results in a wide experimental range of pressures, the previous methods present a common drawback - - they are not reduced to the Henry isotherm at low pressure. Consequently, both methods may lead to physically meaningless values of X(r at high adsorption energies. Accordingly, the
29 analysis of the adsorption isotherm in this regime is performed more conveniently within the ACA. A general method for the determination of the distribution function approximated to the n-th order, in which the local isotherm is approximated consistently with its asymptotic behaviours, has been proposed by Re [81]. 3.8. N u m e r i c a l m e t h o d s
The particular nature of Fredholm integral equation of the first kind poses severe difficulties to its solution and strict limits to the range of numerical methods that can be utilized for its solution. Instability in particular poses the most strict limitations from a computational standpoint. Several numerical algorithms have been developed in order to solve the Fredholm integral equation of the first kind (35) [82, 83] and many of them have been applied to the determination of the adsorption energy distribution function from experimental adsorption isotherm [84]. The most frequently employed codes are described in refs. I85, 2]; the methods upon which they are based are shortly discussed in ref. [52]. A list of them includes: 9 discretization methods, in which the solution of the integral equation is reduced to the inversion of a square matrix; 9 regularization methods, in which the ill-posed problem is replaced by a stable minimization problem in various ways; 9 iterative methods, which use various iterative algorithms in order to improve an initial guess for the distribution function; 9 ezpansion methods, which are based on the expansion of the functions A(p), X(~) and A(p,r in series of a complete orthonormal set (the concept of nisotherm is especially important for this class of methods); 9 integral transform methods, which are based on analytical methods in which the experimental isotherm is approximated by a suitable interpolating expression (e.g., a cubic spline) and the analytical inversion formula is expressed in an easily computable form; and 9 optimization methods, based on the choice of an analytical form for the distribution function containing some parameters which are subsequently determined by a best fit of the calculated global isotherm (through a simple numerical integration) to the experimental isotherm.
The last class is briefly considered here because these methods can be considered to solve the direct problem. Without pretending to be complete, among the numerous attempts we mention: 1. The description of heterogeneous adsorbents in terms of exponential energy distribution was probably considered first by Zeldovich [86] and later by Temkin and Levich [87]. Halsey used the exponential distribution for explaining the Freundlich behaviour [88], while Sparnaay used the discrete (rather than continuous) exponential distribution in eq. (31) with Fowler-Guggenheim local isotherm to explain the Dubinin-Radushkevich behaviour [89].
30 2. The possibility of using a Gaussian distribution function for X(e) and the Hill-de Boer isotherm for 0(p,r was explored by Ross and Olivier [90]. For a long time Ross and Olivier's work remained the unique systematic analysis of experimental submonolayer isotherms in terms of surface heterogenity, and created great interest on this topic. 3. Skew distributions with several maxima were described by van Dongen with exponentials of higher-degree polynomials X(r = exp j=0 cjr j , where Co, .-. , ct are adjustable parameters, l of them being independent and the remaining one being obtained with the normalization condition [91]. van Dongen's choice is a natural extension of the choice l = 2 of Ross and Olivier. 4. Wojciechowski and his coworkers described most of classic empirical adsorption isotherms in terms of Langmuir local isotherm and Maxwell-Boltzmann energy distribution function [92, 93].
)
In the absence of 'good reasons' for chosing one or the other energy distribution function and in view of the variety of possible choices, the above approaches must considered essentially as fitting schemes. 3.9. Direct p r o b l e m Ideally homogeneous surfaces can hardly be obtained. In general, they can be prepared either by exfoliating Van der Waals lamellar crystals (like mica or graphite) or by cleaving crystals along low-index planes (e.g., the (111) plane of diamond-cubic silicon). In the latter case, however, the cleavage of true chemical bonds results in appreciable relaxation of subsurface planes and reconstruction of the surface lattice. Once these really few cases are excluded, real surfaces are usually characterized by a complex situation: The surface of crystalline solids will contain several crystalline faces with different extension; each face will be relaxed and reconstructed, and will contain point defects (like vacancies or self-interstitials) and line defects (like emerging dislocations and grain borders). The surface of dispersed amorphous solids will be characterized by an even more complex distribution, since dispersion is often based on processes (like milling) responsible for phenomena (fracture, plastic deformation, etc.) where a macroscopic energy is imparted to few degrees of freedom. The concentration of a large energy on few degrees of freedom, with the consequent creation of highly defective sites, is unavoidable during the preparation of highly dispersed solids even when the process is formed by close-to-equilibrium steps (like supercritical extraction of embedded liquids in sol-gel preparation techniques). What is truly surprising is that in spite of the complexity and variety of formation modes of real heterogenous surfaces, they exhibit striking similarities in their adsorption behaviours, usually obeying one or the other classic empirical isotherm in various pressure range. 3.9.1. E q u i l i b r i u m surfaces The problem of explaining why so few experimental isotherms describe so many adsorbent-adsorbate systems was raised by Cerofolini [94], who suggested that the classic empirical isotherms are observed when the surface was formed in equilibrium conditions
31 (at a certain temperature Tf) and was quenched at room temperature preserving the previous equilibrium structure. Equilibrium surfaces are neither geometrically plane nor energetically homogeneous. The problem of determining the equilibrium shape of a body is a classic problem of surface science, solved within a continuum approximation. Considering first crystalline solids and assuming that the faces with (hkl) Miller indices can be described by a surface tension 7hkt, the equilibrium extension Ahkt of the face (hkl) of a body at assigned temperature T is determined by the condition E h k i AhklThkl
--
V =
min, const.
(114)
If Vhkl : 7hkt = 7 (this situation is characteristic of amorphous bodies) the above condition gives A = min for assigned V, so that condition (114) is strong enough to specify the equilibrium shape - - the sphere. Wulff showed that even when "~hkt is not constant (that occurs in general for crystalline bodies), condition (114) is sufficient to specify the equilibrium shape of the body m the polyhedron obtained via the Wulff's construction [95, 96]. Though very powerful and elegant, the continuum description of the surface of real bodies is inadequate to account for their adsorption properties. Indeed, while the surface of an isotropic amorphous adsorbent is described in a continuum approximation by a unique parameter (the surface tension), it is characterized by a distribution of surface configurations which in turn result in a distribution of adsorption energy. For the reasons discussed at the beginning of this section this conclusion is also true for crystalline adsorbents. The first elementary description of the energetic heterogeneity of equilibrium surfaces was proposed by Cerofolini, who postulated that the number N~ of surface atoms with excess energy/Xi relative to the ground state energy is given by a Boltzmann distribution N~/No = exp(-U~/kTf), where No is the number of atoms in the ground state (with null excess energy//). Assuming a dense distribution of excess energy, the following distribution function was then derived: 1
exp(-U/kTf)
(115)
where b/M is the maximum value of/4;//M is not greater than the energy of sublimation. Two limiting cases of eq. (115) are of interest: weak heterogeneity: strong heterogeneity:
> 1 =~ Xs(//)~- k-~exp
/~M 1 ~ << 1 =~ Xs(//) ~ ~MM"
(116)
3.9.2. Adsorption on equilibrium surfaces For a description of adsorption on equilibrium surfaces it is necessary to specify the adsorption energy e as a function of the excess energy//. Since sites w i t h / / = 0 are those for which surface atoms are most fully coordinated, they are expected to have the minimum tendency to attract gas-phase molecules; since
32 sites with higher excess energies are less strongly bonded to the substratel they are expected to manifest a higher tendency to attract gas-phase molecules. The combination of these occurrences strongly supports the idea that, in the absence of steric or electronic factors,/4 is an increasing function of 6. At the present stage it is not possible to derive on physical considerations the function / / = / / ( 6 ) . However, a Taylor expansion with positive coefficients truncated to the second power, ~I/ --- C~1(6
-- ~ m )
"IF C~2(6
(i17)
-- ~ m ) 2
(with ai, a= positive coefficients and null constant term, because b/ = 0 for e = ~m), is sufficient to explain why the FDR and Temkin are so frequently observed on real adsorbents: the FDR isotherm is associated with adsorption on weakly heterogeneous equilibrium surfaces [94], while the Temkin isotherm corresponds to adsorption on strongly heterogeneous equilibrium surfaces [97]. See ref. [98] for a discussion of this matter. For, it is necessary to calculate the adsorption-energy ditribution function X~q(e) of an equilibrium surface,
Xoq(e)
=
x~(U(e)) I OU/a~ I.
(118)
The first conclusion follows from a comparison of the energy distribution function calculated within the CA for the FDR equation (27) with the energy distribution X~q(e) : l:ul ted from er Conclusion follow from Comparison of the energy distribution function calculated within the CA for the T equation (26) with the energy distribution X~q(e) calculated from eqs. (116) and (117), and ignoring the quadratic term. It is also noted that the case blM/kTf ,~ 1 is expected to originate the TFDR blended iso;therm (30).
4. T H E C U R R E N T SOLIDS
TRENDS
IN T H E S T U D I E S OF A D S O R P T I O N
ON
4.1. A d s o r p t i o n of s t r u c t u r e l e s s molecules on rough surfaces As already observed, the formation of the first layer is particularly interesting when observed in log p - 0 plot. If the attention is limited to homogeneous surfaces, any adsorption isotherm is defined by a characteristic pressure p~ below which (say, for p/p~ << 1) 0 is negligible and above which (say, for p/p~ >> 1) 0 is close to unity. Since adsorption on different homogeneous surfaces is in general defined by different characteristic pressures, p~ is a free (external) parameter. Plotting these isotherms vs. log(p/p~), all of them have the same shape so that p~ is a scale parameter. When the constraint of surface homogenity is removed, the pressure region in which 0 varies from 0 to 1 extends over several orders of magnitude. The attempts to account for this situation have originated the theory of
adsorption on heterogeneous surfaces. The above remark seems to suggest that the simplest adsorption systems (homogeneous adsorbents) can be described by a unique scale parameter. This conclusion, however, ignores that experiments do not provide the isotherm 0t(p), but rather the isotherm JV'(p). The calculation of 0t(p) requires one additional (scale) parameter - - the number of sites
33 As. This would not be a great complication if Af~ were simply related to the number of sites Afs,~at of a geometrically flat surface (Af~,nat = 1014-1015cm-2). This however occurs in very special conditions only, and almost never for adsorbents of interest for chemistry. Even for systems for which a geometric surface area can be defined, the ratio Afs/Af~,flat may exceed 1 by several orders of magnitude and varies from one system to another. The attempts to account for this situation have originated the theory of adsorption on rough surfaces. Of course heterogeneous surfaces can hardly be flat and, conversely, rough surfaces can hardly be homogeneous. There is always a certain correlation between heterogeneity and roughness, so that any theory of adsorption on real surfaces should take both them simultaneously into account. Fortunately, the problems can however be decoupled since the adsorption isotherm is sensitive to heterogenity mainly in the submonolayer region, while is sensitive to roughness mainly in the multilayer region. The theory of adsorption on rough surfaces is complicated by the fact that the surfaces of chemical interest usually cannot be described by a smooth function z3 = x3(xl,x2), because they often exhibit very pathological behaviours. In most of the theoretical treatments of adsorption on rough surfaces, the adsorbent is assumed not to vary appreciably during adsorption itself. Adsorbents with this property will be referred to as 'hard'. In general, an adsorbent is expected to be hard when the energy which stabilizes its configuration is much higher than the adsorption energy. Typical examples of hard adsorbents are: ionic or covalent crystals with high melting point, and highly reticulate polymers like amorphous carbon or silica. Very rough surfaces are possible for hard adsorbents only. This conclusion is based on the fact that highly rough surfaces are non-equilibrium configurations, and they can be preserved only when an appreciable activation energy is required to bring the adsorbent to its equilibrium configuration. Hard adsorbents are usually characterized by X(e) independent of T. 4.2. P o r o u s solids Porous solids are known by a long time and are very familiar in chemical practice. In spite of this, modelling adsorption on porous surfaces is a very difficult task, because of the enormous number of possibilities in which porosity can be manifested. Assuming that the complex topography can be represented in terms of distribution of voids in an otherwise bulk adsorbent, these voids will be referred to as 'pores'. Pores can be defined by a characteristic shape and parametrized with one or more characteristic length (like radius for spherical pores, diameter and height for cylindrical pores, etc.). According to the International Union for Pure and Applied Chemistry (IUPAC), pores are classified as in Table 1.
34 Table 1: IUPAC classification of pores in relation to their size
type
width (nm)
micropores mesopores macropores
< 2.0 2 . 0 - 50 > 50
The adsorption properties of pores are very different in relation to their sizes. In fact, if the adsorbed layer is thinner than approximately 3 monolayers, its description in terms of properties of bulk phase (possibly perturbed by the vicinity of a surface) is manifestly meaningless. Assuming that adsorbed molecules have diameter around 3/~, the typical pore size below which the adsorbate cannot manifest the properties of the bulk phase is around 20/~. This might be the ultimate reason supporting IUPAC classification.
4.2.1. Adsorption in micropores The description of adsorption on adsorbents with pore size below 2 nm is expected to require both the volume available to adsorption and the special nature of the adsorption potential inside the pore (where the potentials generated by different walls can superimpose). Adsorption in micropores is described with two methods: In the first method one assumes that inside each pore adsorption is described by a BET behaviour, with the additional stipulation that only a finite number of site can be filled. In the second method (essentially due to Dubinin and Stoeckli) one postulates a special law for adsorption in micropores and stipulates a scaling law for adsorption in pores with different size T h e B E T equation with finite n. The adsorption in a restricted geometry can be described in terms of BET model by expicitly assuming that a finite number of layers can be formed at the surface. This assumption leads to eq. (15). By construction, On(x) remains finite even for x = 1, where attains a maximum coverage 0M given by OM=n+l 2
Cn
1 +Cn"
(119)
Of course, a real porous surface is expected to be characterized by pores with a given distribution w(n) of n, so that this scheme has the possibility to describe the complexity of real system. A method for the determination of w(n) is discussed in ref. [52]. This method is however unpractical, since requires the knowledge close to x = I of experimental isotherms at different temperatures. In most cases micropores are studied via the semiempirical Dubinin-Stoeckli method. T h e D R equation and Stoeckli extension. The description commonly employed for adsorption on structurally heterogeneous solids (i.e., solids possessing pores of different sizes) has been developed essentially by Dubinin and Stoeckli [99, 100] and recently extended by Jaroniec and Madey [101,102]. This description is semiphenomenological and
35 essentially based on the experimental obvervation that adsorption on homogeneous microporous solids (mainly activated carbons) may be described by the DR equation [103,104], usually written in the form (120)
O(e, B) = exp[-B(e/eo)2],
where: the Polanyi potential e, defined by eq. (17), is used as independent variable in place of the equilibrium pressure p; e0 is a similarity coefficient depending on the chemical nature of the adsorbate; and B is a structural parameter that depends on the adsorbent properties only. Various experimental studies on carbonaceous adsorbents with cylindrical pores showed that B is related to the micropore size d by the relationship
B = add 2,
(121)
where O~D is a proportionality constant [105]. Representing the structural heterogeneity with the distribution function F(B) of the parameter B, the characteristic adsorption curve 0t(e) for a structurally heterogeneous solid may be written Or(el =
= exp
-B
~
F(B)dB,
(122)
with the normalization condition .fsM F(B)dB = 1, where Bm and BM are the minimum and maximum values of B, respectively. Although eq. (122) has the same form as the fundamental equation (35) for adsorption on heterogeneous surfaces and poses essentially the same problems faced in sections from 3.1 to 3.9, only the direct problem is usually considered for it. A gGussian [99] or, better, a gamma type [101, 102] distribution for F(B) leads in fact to an adsorption isotherm 0t(e) which fits satisfactorily almost all experimental data; the use of free parameters in the distribution function and their optimization enables a quantitative characterization of F(B). Because of (121), the distribution function J(d) for the micropore size d is eventually obtained
J(d) = 2aDd F(B(d))
(123)
This treatment does not explicitely consider the energy heterogeneity, which is implicitly contained in the kernel (120) of eq. (122). Jaroniec and Madey have developed a simple model for evaluating the adsorption energy distribution function X(e) for porous solids with a given structural parameter distribution function F(B) [101].
4.2.2. Adsorption in mesopores Multilayer-type isotherms are usually exhibited by adsorbates below their critical temperature. When the pore size is higher than 20/~ the adsorption at high relative pressure produces therefore the formation of films which can, with a good deal of approximation, be regarded as liquid. Liquid layers in a concave region can manifest a lot phenomena usually referred to as capillary condensation. As these phenomena involve the formation of menisci, they are usually analyzed in terms Laplace equation (describing the mechanical stability of the vapour-fluid interface) or Kelvin equation (thermodynamic stability).
36 Adsorption in mesopores is always associated with the existence of hysteresis phenomena in adsorption-desorption cycles. Hysteresis can be explained in terms of capillary condensation. Hysteresis is a complex phenomenon and several explanations have been proposed. For instance, it can be understood by assuming that (a) during adsorption the vapour is held at the surface with adsorptive filling until capillary condensation occurs; (b) once this has occurred the liquid-vapour interface area is drastically reduced so that the adsorption branch of the isotherm has a plateau; and (c) in the desorption branch vapour is gradually desorbed from filled pores by preserving a bulk filling [1]. For any assumed geometrical model of pores and for any mechanism supposedly responsible for hysteresis, one can calculate from the isotherms describing a complete adsorption-desorption cycle a pore size distribution. This distribution is however strongly model-dependent and should be substantiated by more structural analysis, like x-ray diffraction, electron microscopy, etc. Porous adsorbents either are queer systems or have fractal surfaces.
4.3. Queer s y s t e m s The area A of bodies with regular shape (such as the cube, sphere, regular polyhedra, etc.) increases with volume as V 2/3. For bodies with a regular shape the effect of the surface on their intensive thermodynamic properties (such as specific heat, magnetic susceptibility, etc.) disappears in the thermodynamic limit (infinite volume, constant density). A body whose area increases with V faster than V 2/3 is said to have a queer shape. The area of a queer system is a well defined quantity; its effect on intensive thermodynamic properties, however, can persist even for V ~ ~ . The effect surely persists in the thermodynamic limit for bodies with A cx V. Though it may appear that the concept of queer system is an extravagant concept, Nature however abounds in bodies with queer shape (the first systematic analysis for queer systems was given in ref. [106]). Among the most interesting queer systems we mention: zeolites (in which queerness is due to a lattice of void cages connected by tubes, regularly arranged in the system), biological systems (because reproduction is a way which allows the overall area to increase in proportion to the volume), and films obtained by low temperature physical vapour deposition (where the condensed film grows in such a way that the with an exposed sites increase in proportion to the average thickness [107]). 4.4. Fractal surfaces When the geometric irregularities in the surface have spatial extensions which are comparable with the size of the adsobed molecules, new phenomena are expected to occur. In particular, several surfaces observed at a length scale 3 - 15 ~ give evidence for fractality. For the observation of geometric irregularities at a certain length scale, one needs probes with the same length. Molecules with diameter betwen 3 and 15 ~ have areain the range 7-180/~2. Assuming a local density of site given by Afs,~at (Afs,~at ~ 1015 cm -2 = 0.1/~-2), according to its size a molecule can cover from just one site to approximately 18 sites. In this case an adsorption measurement can provide the monolayer coverage A/'m (number of adsorbed molecules per unit area at 0 = 1) rather than the number of sites Afs. The surface area of a solid can be determined by choosing a probe gas, with effective cross-section b~ (known, for instance, from the molar volume of liquid adsorbate), de-
37 termining with adsorption techniques the number N ~ of molecules forming a monolayer (e.g., from a BET plot), and taking the product A ~ = A/'~ The area determined by the BET plot of the N2 adsorption isotherm in the BET confidence range has become a widely accepted standard. This procedure would not produce ambiguities if such a product for a different gas, A = A/'mb2, were independent of the gas itself. Actually, the relationship
is found to be satisfied only for certain adsorbents [108]. Adsorbents which are not described by eq. (124) are usually found to obey the equation
Arm = Nm(b /b )
(125)
with 2 < D < 3 [108]. Surfaces whose surface areas vary with the size of the probe as in eq. (125) are said fractal, and D is their fractal dimension [109]. For fractal surfaces the concept of surface area loses its original absolute meaning and becomes relative to the probe through which the area is determined. Defining the characteristic length ~ of the probe as ~0= v~2, the surface area varies with ~0as A cx ~2-D,
(126)
which diverges for D > 2. From a mathematical point-of-view, a fractal set exhibits the property that the 'whole' can be represented as the collection of several parts, each one obtainable from the 'whole' by a contracting similitude [110]. A typical fractal object is self-similar, i.e. a magnified portion of it appears identical to the entire object observed under lower resolution: from this point of view it is said to be invariant under scale trasformation. A fractal object can usually be defined through an iteration process in which an initiator is contracted with a similarity ratio 1/~ and put v(~) times in a given arrangement called generator, the same operation being then repeated without end. The area of a fractal set varies with the probe yardstick ~ as in (126), the fractal dimension D of the set being given by D = - / slim I ln t,(~)/ln(1/~) i-
(127)
The identification of an irregular surface with a fractal set poses strict constraints on the surface characteristics: it implies the recurrence of the same irregularity details when the surface is magnified successively. Although this seems to be a very limitative condition, evidence for the fractal behaviour of a few adsorbing surfaces has been provided [108, 111, 112, 113]. The fractal nature of an adsorbing surface has important consequences on its adsorption behaviour [114, 115, 116, 117, 118, 119]. Indeed, any adsorbed molecule has a number of available adsorption sites between that for two and three dimensions, and this fact affects their statistico-mechanical behaviour. Such an effect is even more pronounced when lateral interactions between adsorbed molecules are taken into account. Several attempts have recently been performed in order to understand the influence of fractal geometry of the adsorbent on the adsorption isotherm. We can distinguish two general approaches: a statistical mechanical one which is based on the general relations (3) and (4) with the fractal nature of the surface accounted for in the partition function Z~r
38 through different geometrical considerations [114]; and a kinetic approach which considers how the fractal nature of the surface affects the rates of adsorption and desorption processes [116]. An ultimate understanding of adsorption on fractal surfaces has not been reached yet and such studies constitute an active area of research. In particular, tittle is known as regards the adsorption on simultaneous geometrically (fractal) and energetically heterogeneous surfaces. A fractal surface is a typical non-equilibrium configuration, so that the occurrence of fractal surfaces may seem surprising. Fractal surfaces are typically obtained under strong non-equilibrium conditions, such as those typical of electrochemistry (anodic oxidation leading to dendritic corrosion) or sol-gel technology [120] (SiO2 gels, especially via acid catalysis, where filamentary, weakly branched, structures are produced [121]). The difference D - 2 can be seen as a generalized driving force toward equilibrium. Since adsorption is a way to restore equilibrium at unsatured surface bonds, adsorption is presumably a way to reduce dimensionality. Surface defractalization by adsorption was actually observed [115]. Particularly intriguing is the situation encountered when a surface displays a fractal behaviour in the size scale characteristic of microporosity, so that each pore could be described in terms of DR isotherm. Reminding that for cylindrical pores the pore size distribution function J(d) is related to the fractal dimension D by J(d) c( d 2-D [109], Avnir and Jaroniec [118, 119] have shown that inserting this distribution function in the integral (122) and taking into account eq. (123) one obtains a global isotherm which, in the limiting case of a very wide range of possible size values, may be approximated by an isotherm with the same functional form as FHH isotherm, i.e.
(i2s) Reminding the definition (17) of the Polanyi potential, the comparison of the Avnir-Jaroniec isotherm, eq. (128), with the FHH isotherm, eq. (18), shows that they have the same functional dependence of 0 on p, 0 oc (ln(psat/p)) -1/~. In the FHH theory, however, the exponent q is determined by the adsorption potential (r = 3 for the 6-12 Lennard-Jones potential), while in the Avnir-Jaroniec model 1/~ = D - 3. The currently known values of ~, in the interval 2.1 - 2.8 [27], interpreted in the realm of Avnir-Jaroniec model give D in the interval 2.5 - 2.7, which is expected for fractal surfaces. The question, whether or not the FHH behavior is due to a fractal nature of the surface combined with the Dubinin-Radushkevich isotherm, rather than to a special behaviour of the adsorption potential, remains unanswered. 4.5. A d s o r p t i o n of s t r u c t u r e l e s s molecules on soft a d s o r b e n t s An adsorbent which is not hard will be referred to as 'soft'. Not only do not hard adsorbents exhaust the class of adsorbents, but also soft adsorbents have practical and conceptual interest. Though adsorbent softness-hardness results from a comparison of the configuration energy with the adsorption energy, the weakness of the adsorption field suggests that soft adsorbents belong to the class of soft matter (e.g., polymers with low reticulation degree, elastomers, Langmuir-Blodgett films, etc.), whose interest has dramatically increased in
39 last years [122] and which plays a fundamental role in biological phenomena. Other examples of soft adsobents are solids with non-directional bonds (like metals) and low melting point. In general, soft matter is characterized by an energy landscape with several minima, separated by low barriers. Because of either thermal excitation or external stimuli, the system can therefore undergo even large structural transformations. Roughness is a non-equilibrium property, and in most cases rough-to-smooth transitions are exothermic. If the energy landscape of the adsorbent is as described above, any rough configuration of the system will tend toward a smoother configuration. An example, showing how the rougness is progressively lost with time, was presented by Endow and Pasternak, who determined the monolayer volume of molybdenum films deposited and kept at 77 K. They showed that the monolayer volume decreased monotonically with time during the first half day, at which time it was reduced by a factor of 2 with repect to the value measured 2 h after sample preparation [123]. Since different adsorbent conformations are characterized by different energy distribution functions or topographic configurations, adsorption will consequently feel these structural changes. Conversely, the adsorption isotherm can be used as a probe to test the softness of any adsorbate. Since an adsorbent is softer the higher is the adsorption energy or the adsorption temperature, it is not surprising that softness is particularly evident for adsorption of highly polar molecules (like H20) at room temperature. 4.6. R e c o n s t r u c t a b l e surfaces Surface reconstruction has ever been well known to occur in chemisorption, in situations where the adsorption energy is of the same order as the binding energy of surface atoms. In chemisorption (and especially in oxidation) the surface reconstruction is expected to affect the process rate. Landsberg's explanation [124] of the Elovich equation (a time-logarithm law which plays in kinetics a role as important as the ones of the the classic empirical isotherms in equilibrium [125, 126]) is an example showing how surface reconstruction can affect kinetics. While surface reconstruction of hard adsorbents can occur only after chemisorption, an appreciable surface reconstruction may occur during physisorption on soft adsorbents. Large substrate modifications are indeed known to occur during the uptake of small polar molecules like H~O or CH3OH by biological macromolecules like proteins or cellulose. Adsorption in polymers, originally described either as adsorption on rigid porous surfaces or as absorption in adsorbent bulk with the formation of a solid solution, was based on a picture of hard adsorbent. Limited attempts to overcome these limits were based on a description of the polymer as a flexible linear chain [127], though interchain bridges could be taken into account [128]. These methods were however based on a specific model of adsorbent, rather than on the details of the adsorption process. A model for surface reconstruction resulting after adsorption has recently been proposed by Cerofolini [129] along the line indicated by Landsberg in kinetics. For adsorption on unreconstructable surfaces one has
d N / d p = A/'~dO/dp,
(129)
40 with N's = constant. Even though this constraint is no longer valid for adsorption on reconstructable surfaces, one can reasonably admit that eq. (129) continues to hold, provided that one considers the actual number of sites N's resulting from surface reconstruction. The following assumption is a way to consider a self-similar reconstruction: dp
=
77
A/'~
(130)
=
(131)
where 77 is the available area destroyed (7/> 0) or generated (7? < 0) after adsorption of one molecule. Equation (131) can be solved by separation of variables 1
N'~(p) = N'~(0) 1 +
rlAf~(O)O(p),
(132)
where Afs(0) is the number of exposed sites at p = 0. Inserting eq. (132) into eq. (129) one has the following differential equation dO dAf = Aft(0) 1 + r/Aft(0)0"
(133)
For r / = 0 eq. (133) gives Af(p) = Af~(0)0(p), so that the standard theory of equilibrium adsorption on unrecontructable surfaces is reproduced. Otherwise, i.e. for r/ 7~ 0, the solution of eq. (129) is 1 Af(p) = ~ In (1 + r/Af~(0)0(p)).
(134)
Equation (134) shows that, irrespective of 0(p), adsorption on reconstructable surfaces occurs in the low coverage limit with the same law as on unreconstructable surfaces, 0 << 1/[~IX.(0 ) ~ Zr(p)_~
~(O)O(p).
(135)
At higher coverage, the adsorption isotherm on the reconstructable surface is immediately calculated provided that the isotherm O(p) is otherwise known. For instance, if O(p) is the Langmuir isotherm, adsorption on the reconstructable surface is described by the isotherm
1
N(p)=~ln
(l+p/p'~) 1+p/p,
(136)
and p'~ := p~/[1 + bN~(0)]. Formally speaking, this isotherm is where p~ = PL exp(-r just the Temkin equation [3, sect. 5.3]. However, the appearance of the Temkin behaviour (i.e., Af increasing with In p) requires !
<< p', >> p~
(i.e.,
>> l) for > 0, 1 + r/A/'~(0) = o(1)) for 77 < 0.
(137)
41 4.7. Space-filling surfaces The above model applies to surface shrinkage (77 > 0) as well as to surface magnification (r/< 0). While no problem are met for 77 > 0, for r/< 0 a divergence of A/'(p) may occur for Af~(0)r/ < - 1 . When this condition is satisfied, as soon as O(p) approaches a value 0. = -1/Af~(0)b < 1, A/'~(p) goes to infinite. At a first glance, one is tempted to reject this conclusion and to advocate the existence of physical factors (like the amount of available matter) which limit the otherwise unlimited increase of Af~ with p. However, an infinite surface area is not absurd, provided that space-filling surfaces are allowed. Curves filling a space are quite common: linear polymers are good examples of space-filling curves. Any globular protein "is essentially a one dimensional system folded into a three dimensional structure" [130]. Less familiar are space-filling surfaces: an example might be given by dendromers (highly branched polymers like the 94-met Cl134Hl146 and 127-met C139sH1278 described in ref. [131]); this work suggests that space-filling surfaces can also be produced by physisorption on highly reconstructable (hence soft) adsorbents. Of course, a space-filling surface is a mathematical, rather than physical, concept because of the limitations imposed by the atomistic structure of matter. These limitations hold true in other situations too, like for fractal surfaces, whose scale invariance is usually valid in a quite restricted scale range. In view of the atomistic nature of matter the maximum allowed value of Afs is given by the condition (1 + u)A/', ~ ~bL,
(138)
where u is the number of atoms in each adsorbed molecule, ~b is a typical atomic density in bulk condensed matter (~b ,~ 1023 cm-3), and L is the thickness of the adsorption region. On another side, the complete exposure of the adsorbent, with atomic density to the gas atmosphere implies A/'s = ~L. Combining this equation with (138), one gets the density conditions for space-filling surfaces: ~ = ~b/(1 + u). Typically u = 1 - 3, so that space-filling surfaces cannot be obtained under extreme dispersion. Picturing the space-filling surface as a porous adsorbent, it should be microporous. It is worthwhile noting that, as follows from the analysis of carbon blacks [108], even fractality tends to appear only for relatively little dispersed adsorbents. 4.8. Allosteric surfaces An allosteric molecule E is characterized by n equivalent sites, which modify their structure (binding energy) when the sites are progressively filled. Allosteric transitions play an important role in biology: the transport of oxygen by haemoglobin is a classic and important example [132]. The theory of allostericity has been the matter of one of the seminal works in molecular biology [133]. The theory of allostericity has been developed for molecules formed by a small number of large subunits each containing an active site [133]. Usually the conformation of each subunit is ignored except for the change it brings to the adsorption energy on the active site. This approach has been found extremely interesting for the description of phenomena like 02 adsorption on hameoglobin, etc. Allostericity plays also a major role in phenomena like the adsorption of polar molecules on proteins, or protein denaturation in suitable solvents. The description in this case is complicated by the wide variety of adsorption sites and of protein configurations resulting after adsorption. Allostericity and heterogeneity combine together in a truly intriguing case in the adsorption of polar molecules on proteins [134]. In this case, the adsorption energy is comparable to the energy stabilizing the secondary structure of the proetein, so that a kind of surface reconstrunction takes place after the adsorption; accordingly, the adsorbent and
42 adsorbate cannot be considered as slightly coupled entities, but rather as a new molecular system. The adsorbing properties of the protein are due to its exposed polar sites: the back-bone groups of the peptide bonds, )CO and )NH, and the side-chain groups,-COOH, -NH2, -OH, - S H and - S - S - , in neutral or ionized forms. The complete distribution of the adsorbing sites is determined both by the primary structure of the protein, which specifies the site number and types, and from the secondary and tertiary structure, which specify the relative spatial arrangement of the sites. The heterogeneity of the protein surface is due both to the different chemical nature of the various explored polar sites and to the splitting of each adsorbing energy produced by the particular position of the site in the protein arrangement. In this context the protein surface could be classified as a random heterogeneous surface. However, the protein presents an additional particular adsorbing behaviour. The folding of protein chains, described by the secondary and tertiary structure, can mask several sites to the adsorbate. This masking is due both to steric constraints to the motion of adsorbate molecules and to interactions between polar sites: more polar sites have a higher probability to be unexposed. When small polar molecules like water are adsorbed on the dried protein, the long range interaction between polar sites is reduced by protein swelling and chain folding is consequently reduced. The result of such an unfolding is an increase in the number of the exposed sites. As the adsorption process continues, the straightening of protein chains increases: this straightening renders available new sites which, in turn, adsorb more water, thereby reducing further protein folding. The whole process is concerned with heterogeneity in the first stages and with allostericity later: both concur to make its description a complicated problem. 4.9. Mobile surfaces An extreme case of softness is represented by mobile adsorbents. A physically interesting situation of mobile adsorbent is represented by an ordered Langmuir-Blodgett film adsorbing a multisite molecule. The description of adsorption of structured molecules on solid surfaces requires complicated statistico-mechanical analysis and will be the matter of the next section. In the case of mobile adsorbents, however, the comparison of the following energies: 9 Ea, energy required to deform the adsorbate to a configuration which allows it to be accomodated on the adsorbent in a given configuration (Ea > 0); 9 E~os, energy gained in forming adsorptive bonds between all site of the adsorbate in its original configuration and the site of the deformed surface (E~os < 0); 9 E~0, energy gained in forming adsorptive bonds between all site of the adsorbate in its modified configuration and the site of the unperturbed surface (E~.,0 < 0); 9 E~ energy required to deform the surface to a configuration which allows the adsorbate in its original configuration to be accomodated on the adsorbent (E~ > 0); the following inequality chain is easily established: E~ > ]E~01 _'2 [E~0~I > E~. This comparison suggests that the configuration resulting after adsorption is an almost unchanged adsorbate, whose polar sites are bonded to molecules of the substrate; the enthalpy change resulting after adsorption is expected to be around E~o. + E~(< 0). Though the described situation may appear quite extravagant, a lot of cases of large biological relevance runs in it. Reminding that the surface of eukaryotic cells is essentially constituted by a phospholipidic Langmuir-Blodgett vescicle, phenomena like antigen attack or pinocytosis probably run in the considered situation. The adsorption of proteins on Langmuir-Blodgett films is a case of large practical importance and extensively considered in the literature (see, for instance, the reviews [135, 136]).
43 The description of adsorption of a hard adsorbate on a soft adsorbent is dramatically simplified if one assumes that the adsorbent is undeformable and the surface sites are mobile. In a statistico-mechanical approach, the hypothesis of site mobility allows the partition function to be specified. This produces a description of adsorption in terms of Langmuir isotherm with modified PL [137]. The statistico-mechanical description ignores however the complicated temporal pattern characterizing the kinetics of adsorption and desorption, probably associated with conformational changes of the adsorbate [135, 136]. 4.10. Multi-Site O c c u p a n c y A d s o r p t i o n on Rigid H e t e r o g e n e o u s Surfaces In many cases, the adsorbed molecule can be considered as consisting of a number of quite distinct chemical fragments (mers). The adsorption characteristics show then, to a good approximation, additivity of various features. In other words, one may assume, that the adsorption is affected in a more or less independent way, by the adsorption of these chemical fragments. It is, therefore, much more realistic to consider the whole adsorption process as an adsorption of interconnected fragments of the minima of the fragment (mer)-surface interaction potential. Because the various mers are connected in some way by chemical bonds, a simple analogy emerges to collective adsorption of molecules interacting via Van der Waals forces, for instance. So, there is no surprise, that even in the absence of interactions between the molecules composed of distinct mers, their adsorption will be influenced by surface topography, like in the case of collective adsorption of simple molecules. Until the surface has a patchwise topography, the problem is relatively simple. One can apply directly one of the already existing approaches to multi-site adsorption on homogeneous surfaces, which are now the patches. In the case of random topography, the problem becomes much more complicated, and it was only a decade ago, when a first solution of that problem was proposed by Nitta[138]. However, Nitta's original approach could be applied only to surfaces characterized by discrete distributions of adsorption energy. Some few years ago, Rudzinski and Everett have developed further Nitta's approach to apply also continuous adsorption energy distributions. Let )r be a continuous distribution of the possible occupancies of n sites on the surface by an adsorbed molecule, among the values of its total adsorption energy cn corresponding to a particular occupancy of sites. For a simple molecule occupying one adsorption site the term "surface" occupancy means occupancy a certain surface esite. To calculate the overall (total) adsorption isotherm St, one canthen use anyone of the methods discussed in the previous sections, based on employing the "condensation function", co. Rudzinski and Everett[3], have developed the form of the condensation function for an adsorbed molecule occupying n sites, cc~. For surfaces for patchwise topography, con takes the following form,
e~ = - k T l n
( nl+n/2(nl/2 +
1)n_l g'p
)
(139)
whereas for surfaces with random topography
e~ = - k T l n ( n g ' p ) - ( n - 1)kTln ~t
(140)
where K ~ is a temperature dependent Langmuir-like constant. Marczewski et. a1.[139] published a paper which is of a crucial importance for the application of Nitta's approach. Namely, they have shown how the number of mers in an adsorbed molecule affects the multi-site-adsorption energy distribution X~(e~). The multi-site-occupancy adsorption on heterogeneous solid surfaces is becoming rapidly a hot topic in the recent studies of adsorption at gas/solid interface.
44 5. M I X E D - G A S A D S O R P T I O N E Q U I L I B R I A : T H E C U R R E N T STATE OF THE RESEARCH In the fundamental studies of mixed-gas adsorption the knowledge of the mechanism of adsorption and a possibly best agreement between experiment and theory have the highest priority. The experimental problems and computational time are of a secondary importance. In the numerical programs developed for engineering purposes the priorities are different. At first the computation of mixed-gas adsorption isotherm must be fast, as it is only a part of a large computer program. Then the data necessary to predict mixed-gas adsorption should not require time-consuming experiments on single and mixed-gas adsorption. In the early stage of theories of gas adsorption on solids, theoretical considerations were based on the idealized models of a solid surface. This sometimes led to dramatic discrepancies between theory and experiment. Nowadays the role of the energetic heterogeneity of the actual solid surfaces is almost commonly taken into account by the authors proposing various theoretical approaches to the mixed-gas adsorption. They may be classified into three groups: 1. the molecular approachesemploying methods of statistical thermodynamics[140-210]; 2. the thermodynamic approaches based on the methods of phenomenological thermodynamics [211-260]. 3. the computer simulations of mixed-gas adsorption[304-318] The molecular approaches attracted many scientists working on fundamental problems of mixed-gas adsorption, but they were rarely used by the scientists working on gas-separation processes. The complexity of the accompanying theoretical considerations based on the methods of statistical thermodynamics seems to be the main reason for that. The second group of approaches was also very popular in the fundamental studies of mixed-gas adsorption, but was rarely applied in the theoretical description of gas separation by adsorption. The main reasons for that were the time-consuming computations necessary to predict mixed-gas adsorption from the pure gas isotherms. The exception was the so-called Potential Theory (PT) approach which required very simple computations and knowledge of a small number of data for the adsorption of single components. Recently, a new group of works based on computer simulations of mixed-gas adsorption has emerged rapidly. There are obviously two main factors affecting predominantly the mixed-gas adsorption on solids: 1. the gas-solid interactions, 2. the interactions between adsorbed molecules. As in the case of single-gas adsorption equilibria, we will focus our attention on how the dispersion of the gas-solid interactions across a solid surface, called surface energetic heterogeneity, affects mixed-gas adsorption on solids. We do it with the purpose in mind to present in a possibly simple and clear way the theoretical approaches which were used, or could be used to describe mixed-gas adsorption equilibria. The role of the interactions between the.adsorbed molecules in the mixed-gas adsorption was considered in the earlier
45 review by Rudzinski et. a1.[262] Fundamentals of Mixed-Gas Adsorption on Heterogeneous Solid Surfaces. At the same time the analysis of the energetic surface heterogeneity was limited there only to the Gaussian-like symmetrical adsorption energy dispersion, which in the case of single-gas adsorption leads to the Langmuir-Freundlich isotherm. Although we give here a pretty exhaustive list of references of the papers dealing with the theories of mixed-gas adsorption equilibria, we are not going to present a typical balanced review. We will focus our attention on the two approaches that have most commonly been used in the hithero theoretical studies of mixed-gas adsorption. One of them is the Integral Equation approach introduced into literature by Hoory and Prausnitz[143] at the beginning of the seventies. That approach was later developed by the adsorption group in the Department of Theoretical Chemistry, Maria Curie--Sklodowska University in Lublin. The other fundamental approach is the Ideal Adsorbed Solution approach, also introduced into literature at the beginning of the seventies by Myers and Prausnitz[211]. That approach was used and developed further in numerous papers on mixed-gas adsorption. In the next chapter of this book, Tovbin will review the achievements of the Russian school of adsorption, which are less known as most of them were published only in Russian, and in Russian journals. The newly emerging computer simulations of mixed-gas adsorption will be briefly reviewed at the end.
6. T H E I N T E G R A L E Q U A T I O N A P P R O A C H 6.1. T h e o r e t i c a l principles The first approach ever used to predict mixed-gas isotherms from the single component data is known as the integral-equation (IE) approach and is based on the integral representation for Oti [143,262]:
0ti(p,T ) -- [
1
..-~_ J~n
Oi(E,p,T)X(n)(lf.)d6l...ds
(141)
where: 8ti(p, T) is the total surface coverage by the component i at the set of the partial pressures p -- {p~,p2, ...,p,}, 0~(e, p, T) is the fractional coverage by the component i (i --- 1, 2, ...,n) of a certain class of adsorption sites, characterized by a set of the adsorpt ion energies e = { o , ~ 2 , . . . , ~,} for the single components; X(,)(e) is the n-dimensional normalized differential distribution of the number of the adsorption sites among various sets e,
9" ~ 1
X(n)(e)dQ"'den--1,
(142)
n
and f~i is the n-dimensional physical domain of ei. For the adsorption isotherms of single components, we have /gti(p, T) = f Oi(ei, p, T)xi(ei)dei, f2i
(143)
46 where
Xi(s163163163
(144)
The integral (143) can be easily evaluated using the Rudzifiski-Jagietto method, oo
Ot(p,T) = - E
(kT)l l~ Cl
1=0
where:
x(~) = f
(146)
x(e)de,
Ct-s are the temperature-dependent coefficients given by,
[
(~M-er Cz - J(~-~r
tte t (1 + e')2dt'
the function er
T) is found from the condition,
(147)
(0~0/0e~),_,~ = 0,
(148)
and 0 is the local isotherm O(e,p,T) under the integral in eq. (143) When T ~ 0, all the terms under the sum in eq. (145) vanish, except the leading one. It is also true when the variance of X(e) is much larger than that of the derivative O0/Oe. Then, Ot,(p,T) = -X,(er
(149)
The features of the adsorption model are coded in the function er is the Langmuir equation,
T). When 0(e, p, T) (150)
gpexp(e/kT) O(e,p,T) = 1 + K p e x p ( e / k T ) '
condition (148)is fulfilled when 0(e = er
x Then 7"
(151)
ec(p,T) = - k T l n K p .
If Xi(ei) is the following bell-shaped adsorption-energy distribution 1 exp x~(~,) =
c,
(152)
~,
l+exp(e/-e~
2'
then
-Xi(eei)= [l+ exp ( eci-
-I
(153)
47 so we arrive at the Langmuir-Freundlich (LF) isotherm for the single-component adsorption isotherm 0ti,
Oti(pi, T)=
[Kipiexp(e~ 1 + [Kipi exp(eoi/kT)] kT/c'
(154)
The experimentally measured adsorbed amount, ~ti(P, T), is equal to A/'~Oi(p,T), where N's~ is the number of the adsorption sites on a solid surface, expressed in the same units aS J~fti(P, T). A convenient way to analyze an experimental adsorption isotherm in terms of eq. (154) is to use the following linear regression
~,IN~,
_
In 1 - Xti/Afsi -
kT l n K + c--~-
eoi ) k-T
+ -kT -lnp. c,
(155)
The only adjustable parameter is the monolayer capacity Af~i, which is chosen in such a way as to make the 1.h.s. of eq. (155) a possibly best linear function of In p. The heterogeneity parameter kT/ci is then the tangent of that linear plot, and K exp (eo~/kT) is found from the intercept multiplied by c~/kT. The actual adsorption energy distributions are much more complicated functions, so Xi(e~) in eq. (152) is a 'smoothed' form of an actual energy distribution, the third central moment of which is equal to zero. In many cases, the actual energy distribution may be better represented by the following non-symmetrical function
X,(e,) = r , ( c - e~,) r'-I E~'
exp
-
e, - e~i
Ei
,
for e, > e~,
(156)
This is a right-hand-widened Gaussian-like function when r < 3, a pretty symmetrical function for r = 3, and a left-hand-widened one for r > 3. The parameter Ei is the variance of that function. Then --X(er
(--(er Eem) ),
(157)
or
Ot(p, T) -- exp (-[~-~--T In ( p ~ ) ] r )
,
( 58)
where ln pm = - l n K em/kT. Equation (158) is the well-known Dubinin-Astakhov (DA) equation. While analyzing an experimental isotherm in terms of this equation, it is convenient to make the following linear regression In Aft = lnAfs - (~--~T) r [ln ( ~ )
]~ 9
(159)
The adjustable parameters are pln and r which are now chosen so as to make lnAft a possibly best linear function of [ln(pm/p)] r. It is a frequent practice to choose p~ to be saturated vapour pressure, but it is not correct. The DA isotherm was first used to correlate the experimental isotherms of adsorption in porous carbons. Such a choice of Pm reflects the classical view on adsorption in porous materials as filling micropores by a bulk liquid-like adsorbate phase. In this picture of adsorption, e = kTln(p~/p) is the
48 value of the adsorption potential that causes the liquefaction of adsorbate molecules in an empty pore at the pressure p. The present molecular simulations show that the condensation pressure depends on the pore dimension, and may be several orders of magnitude lower than Pm- When r = 2, eq. (159) becomes the Dubinin-Radushkevich (DR) isotherm equation. In many theoretical works the following rectangular (constant) function is accepted to represent X(e): I
for
eM--em
x(e) =
0
em < e < --
eM,
(160)
elsewhere.
The corresponding isotherm equation Ot(p, T) takes then the form of Temkin's isotherm:
kTlnK
8t(p,T) = ~ s
kT
+
~
(161)
~lnp.
s
~M ~
s
The linear relationships between Aft(p, T) and In p were reported in literature for strongly heterogeneous solid surfaces. The theoretical background for that is following: On every heterogeneous solid surface there will exist minimum and maximum values of adsorption energy Cm and eM- The assumption that r varies from a minimum value s to plus infinity, like in eq. (156), is due to a mathematical convenience. The consequence of making this assumption is that the corresponding isotherm equations do not reduce to the Henry's isotherm when p ~ 0. Meanwhile, for some fundamental thermodynamic reasons such reduction must take place in every adsorption system, provided that the adsorbent structure is fully rigid, i.e., it is not affected by the presence of adsorbate. The latter assumption seems to be a good approximation for the majority of the adsorption systems. The assumption that e varies within infinite energy limits has no large impact on the behaviour of the calculated adsorption isotherm, except for the regions of small (St ~ 0) or high (8t --~ 1) surface coverages. For most of the industrial applications of mixed-gas adsorption, the mediate surface coverages are of interest. While using the IE approach to predict the mixed-gas adsorption, it is sufficient to know the behaviour of the single adsorption isotherms at mediate surface coverages. Therefore the problem of infinite energy limits, accepted for mathematical convenience, is not essential for that theoretical approach. On the contrary, it is essential when one uses the ideal adsorbed solution (IAS) approach to predict mixed-gas adsorption from pure component isotherms. Therefore we shift the discussion of that problem to the section on the application of IAS for predicting mixed-gas adsorption equilibria. Further, it is known that the rectangular function (160) applies only to strongly heterogeneous surfaces. For that purpose we consider that X(r is represented by the function (152) defined in the interval (era, eM), so, it has to be written as follows, 1
c
c "
where FN is a normalization factor, FN=
[
l+exp
(
era. --co c
[
-- l + e x p
. (2
(163)
49 For the purpose of illustration, we assume that s = 0 , G0 = 5kT, s = 10kT, and make the heterogeneity parameter c to accept higher and higher values, corresponding to stronger and stronger surface heterogeneity. The result of that model investigation is shown in Fig. 2 of the chapter 6 by Rudzinski. One can see in Fig. 2 that as the heterogeneity parameter c increases, X(e) defined in eq. (162) becomes more and more similar to a rectangular adsorption energy distribution. One fundamental problem of mixed-gas adsorption occurs, when the monolayer capacities estimated from single-gas adsorption isotherms are different for different components. One faces that problem even in the case of mixed-gas adsorption on a hypothetical homogeneous solid surface. In such a case even the generalization of Langmuir equation for mixed-gas adsorption is difficult. So far it has been done only for the case when Vj(j -# i): .N'sj = A/'~,[140]:
Ki pi exp Oi(e,p,T) =
-ff-f ,
n
(164)
1 + ZK p exp j=l
Provided that N'.~ and A/'sj(j # i) are not much different, the next problem is to define the multidimensional adsorption energy distribution X(,0(e), which would reduce to X~(e~) after n - 1 integration steps, as outlined in eq. (144). This, however, is not a trivial problem at all. Then, the multidimensional integrals would have, for sure, to beevaluated numerically. Although such computer calculations could be carried out, there is still a strong demand for relatively simple analytical expressions for 0t(p,T). Firstly, because carrying out necessary computer calculations would not be convenient in many cases. Secondly, these might be time-consuming too much for certain purposes. Finally, having analytical expressions for 0t(p,T) is convenient for carrying out further mathematical operations leading to other thermodynamic quantities of interest. The success of using the 'integral equation' (IE) approach depends much on making proper assumptions about the nature of an adsorption system under investigation. A general strategy is to reduce the nD integral (141) to a 1D integral by using various physical arguments. Most commonly, it is done by considering correlations between the adsorption energies e~ and ej(j # i), with i,j = 1, 2 , . . . , n. Two physical situations have been considered so far: 1. The adsorption energies ei and ej are not correlated at all for j -# i; 2. Functional relationships exist:
ei = fij(ej),
i r j = 1,2,.-.,n.
(165)
6.2. The case w h e n adsorption energies are strongly correlated, and the nature of the gas-solid interactions is similar for various components In such a case one can expect that the local minima of the gas-solid potential function for one of the components, will simultaneously be the local minima for other components. Then, until the sizes of the adsorbed molecules are not much different,we m a y assume that all the components are adsorbed on the same lattice of energetically different adsorption
50 sites. Next, it is to be expected that the higher will be the adsorption energy for a certain component on one of the adsorption sites, the higher will also be its adsorption energy for other components. It means, the function c~ = f~j(ej) in eq. (165) will be one-to-one function. Now, let us consider the extreme case of high correlations existing between the adsorption energies of various components, represented by the condition on every adsorption site:
Aji, j,i = 1 , 2 , . . . , n ,
ej = ci +
(166)
where Aji-s are the constants. Then, 0i(c~, p, T) can be rewritten to the following form:
Kipi
Oi(p, ei, T ) =
exp (~--~)
E Kjpj exp kT ] J
./
,
(167)
j
and the averaged function
0ti(P, T)
is given by the 1D integral
E Kjpjexp
9 1 + E Kjpj exp kT]exp ( ~ T ) J J The above integral can also be evaluated by using the RJ approach. The function ec is defined now as follows: ~r
T) =
-kT In
Kjpj exp kT ]
"
Equation (168) is again a kind of a master equation from which various isotherm equations can be developed corresponding to various adsorption energy distributions X(ei). For the Gaussian-like function (152) leading to the LF isotherm for single gas adsorption, 0ti takes the following form
( kT eo, ~ Kip~exp ~, ]
0ti(p, T) =
Kjpj exp k kT] J (170)
because (e0i + Aji) is the most probable value of ej. (The numerator and denominator before the integral in eq. (168) have been multiplied by exp (eoi/kT).) When the adsorption isotherms of single components are correlated well by the DA equation (158), the equations for mixed-gas adsorption take the following form ri
0ti(P, T) = ~
Pi/Pmi exp Pi/Pm,
j=l
~-ln
1
~ P.i/Pmj j=l
(171) "
51 Eq. (171) has not been tested in the literature yet, but both eq. (170) and (171) seem to be promising for correlating mixed-gas adsorption data for the components having a similar chemical character. Another obvious condition is that the molecules of the different components should have similar sizes. However, even in the case of such chemically similar molecules, the functional relationship (165) may have a form more complicated than that in eq. (166). It can be deduced from the low-temperature adsorption isotherms of single components. At low temperatures, the adsorption will proceed in a fairly stepwise fashion, and the experimentally measured isotherm Ot~(p,T) will be given by eq. (149). At the same coverage of surface by two components i and j, the following relation will hold:
-x,(~r
= -xj(~r
(172)
Now let us assume that the adsorption isotherms of both components obey the LF behaviour originating from the fully symmetrical, Gaussian-like energy distribution (152). Then, according to eq. (172) we have:
[
l+exp
(
er
)]' [ =
ci
l+exp
(
ecj-eoj cj
))'
.
(173)
From eq. (172) we obtain the following linear relation
cj
~j=--ei+ Ci
(r
cj ) ,
(174)
Ci
where we have already omitted the superscript 'c' in cr When the single-gas adsorption isotherms of all the components are described by the DA isotherm (157), then, we obtain from eqs. (157) and (172) the following interrelation ri/r 3
(175) As the function X(e) is called the 'cumulative adsorption-energy distribution', eq. (172) is called 'seeking for the adsorption energy interrelations through the cumulative distributions'. That idea has been proposed independently by Valenzuela et a/.[243] and by Jaroniec and coworkers[198], in the same year (1988). One difficult fundamental problem in the application of lattice formalism to describe mixed-gas adsorption is when the values of .Ms, (the total number of sites on a solid surface), estimated either from eq. (155) or eq. (159) is different for different components. Moreover, the value of A/'s is temperature dependent as a rule. There might be various reasons for that. One possible reason is that the linear regression (155) or (159) is made only for a certain region of pressures, i.e. surface coverages, corresponding roughly to a certain region of the adsorption energies. The functions (152) or (156) approximate the true function X(e), in that region of energies. Beyond that region, X(e) may not be described by the function (152) or (156), or at least not by the same sets of parameters {e0,, c~} or {em~,Ei, ri}. Thus, such local approximations may not give the true value of The commonly observed temperature dependence of A/'s could mean that some weakly adsorbing sites simply disappear. Of course, the number of the local minima in the
52 gas-solid potential function will not be affected by temperature. It will rather happen that a part of the adsorbed molecules is no longer in the localized states. Going into 2D or 3D mobile states means switching to another isotherm equation predicting weaker adsorption at the same pressure and temperature. If, in spite of that, one uses the same (Langmuir) isotherm, valid for localized adsorption, to correlate the experimental data, then the changing mobility of a part of the adsorbed molecules may simulate decreasing a number of adsorption sites. Assuming that the substantial part of the adsorbed molecules is still in the localized states, and remembering that this is the total number of adsorbed molecules essential for the seeking correlations through the cumulative distributions, eq. (172) should be considered rather in the following form: (176) For the same reason, while calculating the experimentally monitored quantities Nti(p, T) one is likely to use the relation Nt~(p, T) - Afu0u(p, T). It should, however, be remembered that this is a kind of approximate relation. Before we approach the other extreme case when no correlations exist between the adsorption energies of various components, we would like to mention the first paper on mixed-gas localized adsorption on a heterogeneous solid surface, published in 1975 by Jaroniec and Rudzinski[150]. As the local i s o t h e r m - the Langmuir-Markham-Benton[140] equation for mixed-gas adsorption was used, whereas X2(el,e2) for the (ethane + ethylene) coadsorption on an activated carbon was represented by a two-dimensional Gaussian function. That function was introduced in the adsorption literature by Hoory and Prausnitz [143] who used an extension of Hill-de Boer isotherm, to represent the local isotherm $(E,p,T)in eq. (141) along with patchwise model of surface topography. The 2D Gaussian function includes a parameter $ E [0,1], describing the degree of correlations between el and e2. When $ - 0, no correlation exists between s and e2, whereas for ~ - 1, perfect correlation exists. This kind of intermediate correlations will be discussed in more detail in the forthcoming section 8. 6.3. The case when the adsorption energies of various c o m p o n e n t s are not correlated at all This is the case of coadsorption of components exhibiting a much different character of interactions with the same solid surface. These differences are usually due to different chemical nature of the coadsorbed components. There is a certain tendency in literature to apply again eq. (141) to such cases, and to consider X(~)(E) to be the following product: n
X(=)(E) - 1-I X,(e,).
(177)
i=l
This seems to be an improper strategy. Even if the molecules of different components have similar sizes, their different nature of the gas-solid interactions will result in various depths and positions of the local minima in the gas-solid potential function. It means, we
53 have to consider various non-overlapping and even not correlated lattices of adsorption sites for each of the coadsorbing components. The case when ei and ej (with j ~t i) are not correlated at all was considered by Wojciechowski et al.[192]. They used the following argument: When the energies e~ and ej#~ are not correlated at all, there should also be no spatial correlations between the local minima (adsorption sites) of the components i and j. So, when the adsorption energies of components i and j, e~ and ej (with i,j = 1 , 2 . . . ,n,i # j), are not correlated at all, the presence of other components will affect the adsorption of i only by random blocking of adsorption sites, proportional to their total coverages 8tj-s. (The probability that the molecule j will be adsorbed on a certain site does not depend on ei.) Thus, 0,~(p,T) = -
1- ~
0,~(p,r)
X~(e~),
(17a)
where the functions e~i-s are found from the conditions
(028,/0e~)~,=~., = O,
i = 1,2,-.-,n,
(179)
When 8~-s are Langmuir isotherm equations, one arrives at the same definition of er as in eq. (178). er = - k T l n ( g , p , ) . Equation (178) is another kind of a master equation from which various expressions for the mixed-gas isotherm can be derived by assuming various adsorption energy distributions X(e). The equation system (178) is linear with respect to 8ti(p, T), so, it can be solved easily, to express 8t~(p,T) by X'~(er For practical purposes it is sufficient to consider the adsorption from a ternary gaseous mixture (1 + 2 + 3). Let r denote -X'~(er then, the solution takes the following form: - x ~ - x ~ x 2 x 3 + x~x2 + XlX3
t~tl(pl,p2,Pa, T) = 1 - 2X1X2X3 + XIX 2 -~- X I X 3 -4- X 2 X 3 ' - X2 - X1X2 X3 + X~ X2 + X2 X3
0t2(pl, p2, P3, T)
=
1 - 2,u
-[- X 1X2 + ,'~'1r
(~80)
(181)
-[- X 2 X 3 '
- Z 3 - Z~X2X3 + XlX3 + X2X3 8t3(p~,p2,p3,T) = 1 - 2X~X2X3 + X1X2 + X~X3 + X2X3"
(182)
Generally if all -k'i-s are the LF isotherms for single component adsorption, the solution of the equation system (180-182) yields the generalized LF mixed-gas adsorption isotherm
[Kipi exp ( e~ ) ] kT/c' 8t,(p,T) =
,
1--b ~
i = 1,2,-.-,n.
[Kjpjexp (-~T)] kT/~'
j-'l
m decade ago Ruthven wrote[263] (See page 108 of his monograph): "Although not thermodynamically consistent, these expressions, have been shown to provide a reasonably good empirical correlation of binary equilibrium
(183)
54 data for a number of simple gases on molecular sieve adsorbents, and are widely used for design purposes. However, because of the lack of a proper theoretical foundation this approach should be treated with caution". The theoretical origin of that isotherm had remained a mystery until Wojciechowski et al. published its derivation[192]. A convenient way to check the thermodynamic consistency of a system of equations for mixed-gas adsorption is to use the criterion proposed by Keller[264]" 1 (0Afti~
=1
(OAftj)
(lS4)
In the case of the generalized LF isotherm, eq. (183), that criterion is fulfilled when ci = cj even for j :/: i. The theoretical derivations based on the mechanistic models will always contain a number of simplifying assumptions so the lack of the thermodynamic consistency is to be expected here as a rule rather than an exception. The only problem is, how much serious impact on predicting mixed-gas adsorption that thermodynamic inconsistency may have in a particular case. The IAS approach always thermodynamically correct may lead in many cases to a less accurate prediction of mixed-gas adsorption equilibria than a thermodynamically inconsistent system of equations developed by using the IE approach. In view of the wide applicability of DR and DA isotherm equations, it should be interesting to investigate the behaviour of the corresponding equations for mixed-gas adsorption. Thus, we will consider the adsorption from a binary gaseous mixture, when the adsorption of single gases follows the DA isotherm (158). For adsorption from a binary gas mixture, the equation system (180-181) reduces to the following one 0.(p~,p~,T)
=
8t2(px,p2,T)
=
- & + x~& l+&& -&+&& I + X~X2
(lS5)
.
(186)
So, when A'i is given by equation, eq. (157), we arrive at the following explicit expressions:
O.(pl,p~,T)
=
1-exp (-- [~21n (Pm2~]"2)]exp P2 / (- [~~~ln \ Pi / kT (pm2 Pro1~ rl 1 ex (
T1) ,
(lST)
"~2 In x,-~2 /
and
(Pml"~"~) ] exp (-[~-T-T2In ( ~ )
1 - e x p (-[~---~Tlln k - ~ / ] 1 - exp
-
~ l In Pml \ Pl /
_
~
r2)
In Pro2 \ P2 /
This equation is to be compared with eq. (171) which is the extension of the DA isotherm for the case of mixed-gas adsorption, when the adsorption energies of various components are strongly correlated. Equations (171) and (188) should also be compared
55 with the semiempirical extension of the DR equation for mixed-gas adsorption proposed by Bering et. a1.[266,267], at the begining of the seventies. By using the master equations (180-182) or (185-186) one can generate a still larger variety of the theoretical expressions for mixed-gas adsorption, by putting for X/any of the expressions for single gas adsorption. Now, there still remains the problem of relating the theoretically calculated functions 0ti(P, T), to the experimentally monitored quantities Aft~(p, T). Applying the multicomponent LF equation, eq. (183), most of the authors assumed that Aft~ = N'si0ti. This statement is not obvious and deserves further theoretical studies. We summarize our consideration in this section as follows: For a mixture of components the single-gas adsorption isotherms of which are known, one may develop a variety of expressions for the adsorption from their mixture. Thus, there is no universal guideline how to apply the IE approach. For every adsorption system a suitable method is to be chosen on some rational basis. This, however, requires certain experience in the theoretical description of adsorption at all. The attractive side is the compact analytical form of the obtained expressions, which makes computer calculations of mixed-gas adsorption equilibria fast. The rapidly growing industry of gas separations by adsorption creates, however, a large market for such theoretical expressions. Having such expressions, one can also develop easily compact analytical expressions for the heat effects accompanying mixed-gas adsorption, and mixed-gas surface diffusion characteristics. This is because their calculation involves only calculation of derivatives from a certain system of isotherm equations.
7. T H E I D E A L A D S O R B E D S O L U T I O N A P P R O A C H
(IAS)
7.1. Theoretical principles The IAS approach was launched first in the work by Myers and Prausnitz[211] and reexamined recently by Rudisil and Le Van[251]. The IAS theory is based on the assumption that the adsorbed phase can be treated as an ideal solution of the adsorbed components. The reduced spreading pressures H~, p~
II; = ri,A = f ~dp,, RT pi
(189)
0
of the components i = 1, 2 , - . . , n, in their single-gas standard states, are equal to the reduced spreading pressure of the adsorbed mixture, H'" n~ = n~ . . . .
= n: = n'.
(190)
The function A/'~(p,,T) denotes the adsorbed amount, (moles per unit mass of adsorbent), at the pressure pi and temperature T, and A is the specific surface area. A/'i(pi, T) = A/'s~0~(p~,T), where 0~ is the fractional coverage by component i of the surface, and Afs~ is the maximum adsorbed amount when 8ti = 1. It is assumed in the present considerations that Af~ and Afu are expressed in moles (per unit area or unit weight).
56 The relation between the mole fraction in the gaseous phase Yi, and the mole fraction in the adsorbed phase Xi is described by Raoult's law for ideal solutions, (191)
PY~ = p~(II*)X,,
where p~ is the pressure of the single component i in its standard state which is fixed by the spreading pressure of the mixture according to eq. (190). In addition, the mole fraction constraints are ~] Xi = 1 and i----1
Y~ = 1. z'-I
The total amount of the adsorbed gases Af* depends on the amounts A/'i* adsorbed in the standard state:
1 _ ~-, X!, N'*~ z',"
(192)
"__
where A/'~ is the value A;~ for the pure component i, at p~ = p;(II*). The actual amount of each component adsorbed is Afi = A/'*X~. There is one fundamental problem accompanying the application of IAS to predict the mixed-gas adsorption equilibria on heterogeneous solid surfaces. That problem has never been explicitly discussed in the papers reporting on application of IAS, except for the recent paper by Rudzifiski et a/[262]. The term ideal solution refers to the systems of interacting molecules in which the 'interchange energy' W is equal to zero: W = Wi, + Wjj - 2Wj, = O,
i , j = 1,2,.-. ,n,
(193)
were Wij is the interaction energy between two molecules i and j adsorbed on two neighbouring adsorption sites. As discussed in Sect. 4, when the adsorption of interacting molecules (collective adsorption) on a heterogeneous surface is considered, one must take also into account the surface topography. So far two extreme models of surface topography have been commonly considered: The patchwise model assuming that the surface is composed of arge homogeneous surface domains - - 'patches', grouping identical adsorption sites. Thus, the adsorption systems have to be considered as composed of macroscopically different subsystems being only in thermal and material contacts. At a certain adsorbate pressure p and temperature T the adsorbed amount Afi and the corresponding spreading pressure II~ are to be referred to a certain patch and will be a function of the adsorption energy e on this patch. Thus, the correct use of IAS to predict the mixed-gas adsorption equifibria will require the following sequence of operations. First, IAS has to be applied to every homogeneous patch to calculate {X i}. They are, of course, a function of the energy set e. Next, the calculated Xi-s values have to be averaged over all possible sets e as in the IE approach. The random model assuming that adsorption sites characterized by different adsorption energies are distributed on a heterogeneous surface completely at random. As a result, the local 'microscopic' composition {Xi} of the surface phase is the same across the surface. The adsorbed phase is a thermodynamic entity, the spreading pressure of which is the same across the surface, and has to be calculated by replacing Afi by Afti, in eq. (189).
57 One course, one may ask the question what the condition (193) for the mixtures of molecules exhibiting quite different character of interactions with the solid surface means. In the model of localized adsorption they will be adsorbed on different incompatible lattices of adsorption sites. It is clearly stated that when considering interactions between adsorbed molecules, the attice is treated as a kind of an abstract theoretical tool as in the lattice theories of liquid mixtures. 7.2. A p p l i c a t i o n of IAS to surfaces with r a n d o m t o p o g r a p h y Myers who was the first to consider the extension of IAS for the case of heterogeneous solid surfaces did it for the surfaces with random topography first[229]. He accepted the rectangular adsorption energy distribution to represent Xi(ei). Then, the problem was considered more extensively in his second paper coauthored by Richter and Schfitz[248]. Three isotherm equations were considered in their work: Freundlich, Langmuir, and DR. For the first two, the integration in eq. (189) can be performed analytically, whereas in the case of DR equation it must be carried out numerically. Then, the single and mixed isotherms of methane and ethane adsorption on an activated carbon were used for illustration. Of all the equations the DR isotherm fitted the adsorption isotherms of single components best. The DR equation was also superior to predict mixed-gas adsorption equilibria. Since DR does not reduce to Henry's isotherm as p ---, 0, a certain error in calculating the spreading pressure is surely involved. Even then DR appeared, to be superior over the Langmuir isotherm which reduces correctly to Henry's Law. That problem was more extensively analyzed in the paper by Talu and Myers[242]. They took into consideration the following three equations used frequently to correlate the single adsorption data: 1. The Langmuir-Freundlich equation, eq. (154), 2. The Dubinin-Radushkevich equation, eq. (158), in which p~ was taken to be the saturation pressure ps, 3. The T6th equation (21),
p---,0
The limiting slope, lim dAft/dp, is equal to: +c~ for the LF equation, 0 for the DR equation, and a finite value for the T6th equation. Here, the behaviour of the function Af/p is of crucial importance. That study led Talu and Myers to the following conclusions. The limit +c~ for the LF isotherm makes it unsuitable for calculating spreading pressure under any conditions. The limit of the T6th equation is too large because the slope of the Aft/p curve goes to - ~ at the origin, and the limit of the DR equation is too small (zero), because the slope of the A/'t/p curve goes to +or at the origin. However, the error in the integral (189) introduced by the incorrect limits of the T6th and DR equations is in fact small unless the pressure is very low. For the DR equation, the maximum of .N't/p is given by
/J~t/ T
J~Sexp(/ E /2)
max = P--/
.
(194)
58 The error in the spreading pressure/kII*, introduced by this false maximum, is /xm=N~exp
-
2-~
"
and the pressure p ~ x at which p~x = psexp
(195)
3/'t/p attains its maximum value is given by,
-~
(196)
According to Talu and Myers, this error is usually smaller than the experimental error attains its maximum value and below which in II* and the pressure Pm~ at which the calculated ratio deviates significantly from the experiment, is usually very low. In view of that positive judgement concerning the applicability of the DR equation, the readers may appreciate using the analytical approximation for IF(p), developed by Myers and coworkers for the case when Aft(p) is the DR isotherm,
A/'t/p
II* = IIakT= 2Ns(II)1/2 (k__~~In (~)) (kT/E--------~erfc
.
(197)
As the error function erfc(-) is the standard in computers now, eq. (197) may therefore be considered as the analytical expression for II*. The conclusion by Talu and Myers that Pmax is small, is likely to be true in the case of adsorption by activated carbons. But in the case of adsorption in zeolites, for instance, the adsorption isotherms which quite frequently obey the DR or DA equation at mediate coverages, reduce clearly to Henry's law at measurable pressures. The attempts to modify the DR equation toward an expression having Henry's limit have a long history, and were described in the monograph by Rudzifiski and Everett[3]. Most recent attempts of that kind have been published by Sundaram[268], who used a modified version of the DR equation to calculate spreading pressure II*[261]. All these modifications were made on an empirical basis. Sundaram used the following modification of DR isotherm
po
k-T
i=a
i
(198)
where the term within the square bracket in eq. (198) is the truncated series expansion,
-ln0t = ~ i=1
(1 -at)' ,
0 < 0, < 2
(199)
z
and where D is an additional temperature independent parameter. The problem of the transition to Henry's Law of the equations used to calculate II" in IAS is of primary importance for a successful application of IAS, but a good fit of the experimental isotherms at mediate coverages is another essential condition too. In the mediate coverage region a good fit is usually obtained by using isotherm equations which do not reduce to Henry's Law (LF, DR., DA, ... ). This is a serious difficulty accompanying the use of the IAS approach to predict mixed-gas adsorption equilibria.
59 Recently Rudzifiski et a/.[262] have proposed a general solution for that problem, by using the RJ approach. As already discussed, the failure of the LF, DR, DA, and other isotherm equations to reduce correctly to Henry's Law, has its source in assuming the infinite energy limits (-oc, +cr or (0, + ~ ) while developing these isotherm equations. For obvious physical reasons, there must exist finite integration limits, (era, eM). When RJ expansion is used to represent Oti(pi, T), the function eci(pi, T ) i s defined in the interval (-oo, +oo), and x(e) is to be defined as follows[3]:
X(e) =
0 forO_<e<e=, X for em_<e< eM, 0 for eM <_ e.
(200)
Accordingly, the function X(e) is to be defined now as follows: ,u
forO<e<em,
X(s
for ~M ~< s
(201) and the normafization condition states thatX(eM) -- X(em) = 1. In the case of finite energy limits (era, eM), the expansion (145) takes the more general form
Ot(p,T) = [O(eM)X(ei) - O(~m)X(s
oo ( k r ) ' l! el ~'-~cl /
]- E l=O
(202) s163
While carrying out our considerations we take the function (162) for illustration. In the limit of a strongly heterogeneous surface, when k T / c ---, 0 (practically when k T / c < 0.9), eq. (202) reduces to:
Ot = O(eM)[X(eM) -- X(er
+ O(e=)[X(e~) - X(em)].
(203)
At very low adsorbate pressures such that er > s from eqs. (201) and (203) it follows that 0t = 0(era). It means that adsorption is occurring as on a homogeneous solid surface characterized by the adsorption energy e = era. That apparently strange result was predicted even earlier in earlier theoretical works. Let us suppose that half of the surface is covered, and that function (162) is still symmetrical. Then, IX(~M)- X(,r
1 = IX(~c) -- X(*m)l = 5,
(204)
and eq. (203) reduces to (205)
0t = 0(eM)[X(~M)- X(~c) ] because 0(eM) >> 0(era). When the surface is strongly heterogeneous, i.e. when and eM ~ +cr then FN ~ 1, 0(ei)"-* 1, and X ( e i ) ~
0,
~m ~
-cr
(206)
60 as can be deduced from eq. (162). Then eq. (205) reduces to eq. (153), i.e. to the LF isotherm. When eM is large, the reduction takes place even at very small surface coverages (adsorbate pressures). This is why linear log 0t-vs.-logp plots are commonly observed at low adsorbate pressures. (The Freundlich isotherm is the low-pressure limit of the function -k'(cc) when X(e)is given by (162).) This also means that there is a transition within a certain range of pressures, around p = PM = ( 1 / K ) e x p ( - c M / k T ) , from the LF isotherm to the Langmuir isotherm. As expected at low adsorbate pressures, Langmuir behaviour means simply Henry's Law, p < PM =*" 8t " p K e x p ( e M / k T ) . This means that there is a transition region, where the tangent of the plot In 8t vs In p increases from a value smaller than unity (typically between 0.5 and 0.9) to values close to unity when p ~ 0. In view of what was said above, the integral (189) should be written as follows PM
P*
f
n-=
p
0
- f
PM
p
where
(2o8)
PM = ( l / K ) e x p ( - c M / k T ) .
Equation (207) is quite general and any function k'(~c) i.e. any X(C) can be accepted. It is convenient to carry out the integration by writing the second integral on the r.h.s. of eq. (207) in the following form p"
ec(p*)
(209) PM
ec (PM)
For the particular case of the function (162), we have
/
X(cr
_
c...._~_~In
c 1 -I- exp c
1 + (Kp" exp (~TT)) ~
c
(2~o)
FNkT
Then, the first integral on the r.h.s, of eq. (207) takes the form ,~o(pM)
PM
r 0
1 + exp
e(,,,,) dp =
p
de~
+oo
= I n (1 + e x p ( e = ' - - ; T ( P M ) ) )
kT
(211)
61 Finally H * = HM +
FNkT In 1 +
(~-~
,
(212)
where
1 + exp (era-Co H M = .hfs In
s -- s
kT kT [l + exp (e~ -- eM)]
"
(213)
For the purpose of further considerations we will rewrite eq. (212) to the following form HA I_[M c kT = H" = - Z ' ~ In (1 - 0t).
(214)
After solving eq. (212) for p*, we have
p. 1
[
) lc/T
where K exp(co/kT) is found from the linear regression (155) for the experimental adsorption isotherm. Let us consider the simplest case of a binary adsorbed mixture. Then
pl = p~Xx and p2 = p~(1 - X~).
(216)
The quantity p* defined in eq. (215) is a function of H* and of the parameters K exp {eo/kT}, kT/c, (eo - em)/C, and (eM -- eo)/C. The first two parameters are easily found from linear regression (155) of the adsorption data measured at moderate surface coverages for a single adsorptive. The other two parameters: (e0 - em)/c and (eM -- e0)/c can be found by an appropriate numerical analysis of low-coverage data, based on more general equation (203). Such low-coverage data are not commonly available, so the parameters (e0 - em)c and (eM -- eo)/C are likely to be treated as best-fit ones. Another general solution of the problem of Henry's Law in IAS was proposed by Morbidelli and coworkers[258]. The main idea is somewhat similar to that described above. It does not involve introducing additional best-fit parameters, but creates strong criteria for the accuracy of adsorption experiments. Recently, Rudzinski et a/.[269] have shown, that the PT (Potential Theory) approach to mixed gas adsorption is nothing else, but a special edition of IAS approach which is applicable only to strongly heterogeneous surfaces, characterized by random surface topography. The potential theory of adsorption, introduced by Polanyi, has been widely accepted as the basis for correlating the effect of temperature on the adsorption isotherms of pure gases. A number of authors have modified Polanyi's original method of treating pure-gas adsorption isotherms. These modifications attempt both to improve the temperature correlation and to account for the effect of the nature of the adsorbate on the adsorption isotherm for a given adsorbent. The milestones of the use of the PT for estimating adsorption equilibrium of gas mixtures are: (a) proposal by Lewis et a1.[270];
62 (b) formalization based on the thermodynamic arguments by Grant and Manes[214]; (c) improvements by Reich et a/.[223] and Mehta and Danner[233]. The most recent theoretical achievements along these lines have been reviewed by Moon and Tien[257]. The PT method for predicting mixed-gas adsorption equilibria is based on the assumption that the adsorption data for all pure components fall on the same line when plotted on appropriate coordinates. A number of methods based on the PT have been advocated for correlating pure-gas adsorption isotherms. The most recent extensive experimental and theoretical study by Mehta and Danner[233] showed that the method of Lewis et a/.[270] appeared to be the most useful one. It consists in expressing the adsorption isotherm of a pure component i in the following functional form
Nt,'ViS=F
(kTw (f,i'~)fi] v-;:,.In
(217)
,
where F is the temperature independent function of (kT/V~s) ln(f~,/fi), V~s is the saturated liquid molar volume of the i-th pure adsorbate at a pressure equal to the adsorption pressure pi, fsi is the saturation fugacity of pure i at the adsorption temperature T, fi is the fugacity at the pressure pi and the temperature T. Moon and Tien have shown that in most cases one may replace the bulk fugacities by pressures[257]. For surfaces having random topography the integral for the spreading pressure H* takes the form, Pt
'r
ii. = f N,idp__j_= ~i / Pi
o
kT
X(Qi)d6.ci.
(218)
+oo
Assuming H~ = II~#i, we have,
*r
r
.Afsi f Xi(Qi)deei =.A['sj J +oo +oo
Xj(ecj)decj.
(219)
The above equation will be important for our further consideration. Meanwhile we use the following transformation of the variables:
ti = e~i+ kT in(Kip~i) = v:
kT
----7In vi
(Psi ~ -- .
(220)
Then, eq. (219) takes the form (kr/vt)
~(,,,,/pr )
f +oo
(kr/~,) ~ (,,,, #,.; )
Vi'Nti(ti)dti=
f
V/Nt,(t,)dtj.
(221)
+o0
Thusl if it happens that V~SNti plotted as a function of ti coalesces with V/Ntj plotted as a function of tj, then the upper integration limits must be equal, i.e.
63 Taking into account the Raoult Law (191), we can rewrite eq. (222) to the following form Vi---;In
~i
= ~
Vjs
in
\
Pj
,
(223)
Pi
pi
where means the partial pressure of the adsorbate i. Usually, well approximates fi, so, eq. (223) becomes essentially the Grant-Manes equation. Thus, it should be clearly stated that the PT approach is nothing else, but a special form of IAS theory, valid only for the case of heterogeneous solid surfaces. An extensive study of a large body of experimental data brought Mehta and Danner to the conclusion that the potential curves of different adsorbates have, however, similar shapes and can be coalesced by an appropriate modification of abscissa. So, they have modified the functional relationship (217) as follows:
(kT kov/sln k(f,i'~) ,Z]
NtiVis = F
(224)
'
where k~ is the coalescing factor for adsorbate i relative to a standard adsorbate. a/.[269] have shown that their theoretical development of the PT approach Rudzifiski suggests a possibility of two coalescing factors to be introduced. These authors also give certain recommendations as to the way in which one can use the PT approach to predict mixed-gas adsortpion equilibria better than by applying the traditional 'coalescence' operation.
et
7.3. Application of IAS to the surfaces with patchwise topography In their review, Rudzinski et a/.[262] first emphasized it clearly, that the authors proposing various extensions of IAS for energetically heterogeneous surfaces did not address explicitly their approaches to a certain topographical model of surface topography. To illustrate the problem of surface topography Rudzinski et. al. took extensions of the Langmuir-Freundlich equation into consideration. Their reasults are briefly reported belOW. We start with calculating the spreading pressure l'I~ on a homogeneous surface patch characterized by the adsorption energy e,
or, in another form, II~A kT = n~ = - a r ~ In (1 - 0,).
(226)
After solving it for p~, we have P~=E
exp
~
-1
exp -~-~
.
(227)
For two-component adsorption, from eqs. (191) and (227) we have, X-exp (H'/N',x) - 1, P'
=
' K,
tl
X ' exp
(22S)
'
- 1
54 After solving the above equation system with respect to Xl and X2, we obtain
[(en./~2_l)/(eri./~_l)]P_zK1 XI =
P2 ~ exp 1+ [(en'/Ar'2- 1 ) / ( e n'/~a - 1)]
(el - e2) . .kT.
(229)
p--LKl ~ p2exp (el e2)kT -
When Afsa = A/'s2, on a local patch characterized by the adsorption energies el and e2, the molar functions X1, and X2 are not functions of the independent variables el and e~, but rather of their difference (Ca - e2). Of course, it is difficult to know whether Aft1 = A/'~2 on a certain microscopic patch, but it seems reasonable to assume it until their values found for the whole heterogeneous surface are not much different. Therefore, in order to arrive at the average values Xtl , Xt2 for the whole heterogeneous surface, the following averaging is to be done /
Xlt
Xl(eX - e2)Xa2(ea - e2)d(ex - e2)
(230)
f2(q _,~)
where Xa2(ex- e2) is the differential distribution of the number of adsorption sites among corresponding values of ( c a - e2). Trying to relate Xzx2 to Xi(el) and Xx(e2), one has to consider again the correlations between el and e2. For the case of the highest correlations represented by the condition in eq. (166), Xa2 in eq. (230) is a Dirac delta function. This, for the LF single-gas isotherms, leads to the following expression for Xu:
[(r
(eo, - Co2) ~22/~2 exp
Xtl
"-
1 + [(e:~,'2- 1) / (e:~i,'~ --1)]
p--2.E1Klpexp 2 (r
kT
(231)
' -k Tr
which is reduces to
plK1 Xu =
(e01 - e02)
P2 K2 exp 1 + P-22K-~2exp
kT kT
(232)
when Afsl = A/'s2, One can see that the above expression fulfills the condition (Xu + Xt2) = 1. Coming to the practical side of calculating Xu, Xt2 the problem is as follows: From linear regression (155) applied to the experimental isotherms of single components Kiexp(eoi/kT)values are found. Next, for a certain pair of values of pl and p2 appearing in eq. (231), the corresponding value of H* is calculated from equation system (228) in which the K~exp(e,/kT)are the values K~exp(eo,/kT)found from linear regression (155). In a similar way, one can calculate X1, X2 for the systems where single-gas isotherms are correlated by DA isotherms. (In eqs. (231), (232), eo~ has to be replaced by emi).
65 Thus, we are facing an interesting physical situation. When the adsorption energies of components are highly correlated, and Afsi ~- Afsj for j # i, then for the patchwise surfaces the adsorption from their mixture proceeds as on a homogeneous solid surfaces, although this surface may be strongly heterogeneous for single-gas adsorption. When el and e2 are not correlated at all, the function Xa is found from the following relation 1 exp (e2 - Co2)
x,', (t) = /
J n, 2
[
1 + exp
(
C2
C2
t + e2 - eol
(233)
Cl
where t = (el - e2) and fl,2 is the domain of e2. Our numerical exercises showed, that for the infinite energy limits, e 1 E (--OO, +(X)) and e2 E (-oo, +oo), the function xa(t) is still Gaussian-like, and may be well approximated by the following expression, 1 exp
(/o) t
C
(234)
[l+exp(t-t~ where to = e l 0 - e20, and ca is given by
(CA) 2 = V/( ~C1 ) 2 + (C2) 2+ ~
0.05.
(235)
For the special case when A/'sl = AYe2,Xtl is now given by
Xt, =
Pl K1 ~2 K-'22exp
Col - s kT
Contrary to eq. (232) the above eq. (236) shows the influence of the dispersions of the adsorption energies of components on the adsorption of their mixture. It means, that the lack of correlations between the adsorption energies of components on various patches will increase the effect of surface heterogeneity of the adsorption from their mixture. That conclusion must be true also when Af~i # Af~j for j -# i. The IAS theory is just one of the possible approaches which must lead to at least generally similar conclusions. Thus, the conclusion which we have drawn above must be of a general validity. Valenzuela et al.[243] were first to combine IAS with the concept of seeking the correlations between ei and ej for j # i through their cumulative distributions. They wrote the integral equation for Nti in the following form :
...
1
p,
n
(237)
56 where Afi(e, p,T) is the 'local' isotherm describing the mixed-gas adsorption on a patch characterized by the set of the adsorption energies e = el, e2,..., en. That isotherm was calculated by applying IAS to every patch, so, for the jth patch,
PY~= p~jXij,
i = 1,2,-.., n,
*
nxj(;,) =
:~
.
(238) $
n j(p j)= ri;,
,
(239)
and Pb 1-I:.j = f Afi(eij, p, T). dpp
(240)
0
The total adsorbed amount Afi(e, p, T) is obtained from
1 _- ~ X!j.,
(241)
where Af~ = Afi(eij, p, T)v=v;i(n;)
(242)
and Aft(E, p, T ) =
X~Aft*(e,p,T).
(243)
The integral (237) is reduced to a 1D one by considering the correlations between ei and Qr They seek these interrelations through the cumulative adsorption energy distribution,
F(e) = / X(e)de.
(244)
o j s
Thus, they express the function X(n)(e) as follows: n
X(,)(e) = H Xkx (ek/el) Xl (~1),
(245)
k-2
where Xka is the density function for the conditional probablity that a particular adsorption site has the energy ek, provided that it is characterized by the adsorption energy el for the component number 1. If every site on the surface, irrespective of the component chosen as a reference, has the same higher and lower energy neighbours, then the conditional probablity can be written: Xkx (ek/ex) = ~lFk(ek) -- Fx(ex)l,
(246)
67 where 5 is the Dirac delta distribution. Physically, eq. (246) means that the ordering of the sites from low to high energy is the same for each adsorbate; it is called 'perfect positive correlation'. Insertion of eq. (244) and (246)into eq. (237) yields Xt~(p, T) = [ ~ ( p , T, el, c-2,e~,..., (.n)Xl(s163
(247)
t /
where ek is found from the relation
Fk(Ck) = Fl(el).
(248)
For the purpose of practical calculation they use the binomial distribution,
X(s
= (~)uJ(1-- u) k-j,
(O < j <_ k),
(249)
which gives the 'probablity' of energy ej for (k + 1) sites, u is a skewness parameter, (0 < u < 1), and for a symmetric distribution u = 0.5. Valenzuela et al. took k = 40 which they believe is large enough to represent any unimodal adsorption energy distribution, but small enough not to be a computational burden. For the discrete adsorption energy distribution, the integral (237) is replaced by a summation, k
Aft,(p, T) = ~ Afi(p, T, elk, e2k,'--, e~k)AFk, j=O
(250)
where AFk is the fraction of the distribution for which each adsorbate has a constant energy eik. Of course ~ AFk = 1. Valenzuela et. al. took for illustration the experimental data reported by Talu and Zwiebel on the adsorption of the binary and ternary mixtures of H2S, CO2, and C3Hs on H-modernite at 303.15 K[237]. While predicting mixed-gas adsorption equilibria, they carried out their calculations twice. First they used the single-gas adsorption isotherms to calculate mixed-gas equilibria in terms of the ordinary IAS theory. Next, they calculated the mixed-gas isotherms by using the theoretical approach described above, and called by them the heterogeneous ideal adsorbed solution (HIAS) theory. The ideal-adsorbed-solution calculations show that (CO2+C3Hs) and (H2 +C3Hs) form highly nonideal mixtures, as can also be inferred from the azeotropic behaviour exhibited by these systems. For (CO2 + C3Hs), HIAS correctly predicts an azeotrope but does not predict correctly azeotropic composition. For (H2 + C3Hs), HIAS does not predict the azeotrope but the prediction of HIAS is better than IAS. The mixture H2S + C02 forms a nearly ideal solution; therefore IAS agrees fairly well with experiment. HIAS calculation is almost in the same agreement. Generally, the improvement in agreement obtained by adopting HIAS is only moderate when compared to that achieved by IAS, so, Valenzuela et al. concluded that still "something was missing in the theory". Just at the same time (1987) the concept of the 'site matching' was elaborated independently by Moon and Tien[244]. They made, however, one step forward by generalizing it through the assumption that instead of eq. (248) we will have in general
68
Fk((k) = F I ( s Skl, and called that approach the regular site-matching (RSM) model. When Ski = 0, the above model reduces to what Valenzuela et al. c a l l e d - the perfect positive correlation. In fact, these authors realized that it may not be true for some binary mixtures of molecules exhibiting much different interactions to a solid surface, so, in their work referred above they wrote that "heterogeneity is an important factor, for correlation of adsorption energies". That obvious physical fact was taken into consideration by Moon and Tien, who assumed another model when, Fk(e-k) = 1--FI(s S~: 1, which they called the complementary site-matching (CSM) model. For the purpose of mathematical convenience they accepted the rectangular function (160) to represent the adsorption energy distribution X1(q)- For this particular function ek -- emk ,
Fk(ck) = Ak = s
Cmk _< ~k _< ~Mk-
(251 )
- - ~mk
For regular site matching Ai(ei)
-
A/(ej)+ Sij,
[Sij[ < 1" i 7~ j,
(252)
whereas for complementary site matching A,(ei) = 1 - Aj(ej)+ S~i,
ISSsl _< x, i # j.
(253)
Then, it can be shown that the following identity requirements for Sii and S~ and the combination rules must be obeyed. The identity requirements are: Sij
=
--Sji,
S~j = S~s - S~k = s~j - s ~ =
Sji , Sks, s~,
s~k - s ~
sis.
=
(254)
The combination rules are: 1. If the match of the i - j pair and that of the i - k pair are of the same type (either regular or complementary), then the match of the j - k pair is regular. 2. If the matching of the i - j pair and that of the i - k pair are dissimilar (namely, one is regular and the other complementary), then the match of the j - k pair is complementary. The physical significance of the two types of matching conditions differs as follows. The RSM condition implies that if all the adsorption sites are arranged in an ascending order of their adsorption energy with respect to the i-th adsorbate, e-i, then these sites also fall into the ascending order of the jth adsorbate's adsorption energy. On the other hand, the CSM condition means that an arrangement in the ascending order of ei is an arrangement in the descending order of ej. As the high-energy sites permit a greater degree of adsorption, matching according to RSM, means that the sites which favour the adsorption of the ith adsorbate also favour that of the j-th adsorbate. The opposite is true with the CSM condition. Since the overall adsorption is the sum of results occurring at the individual
69 sites, the presence of two different ways to describe the competitive effect among the adsorbates makes it possible to provide a better or more accurate description of adsorption equilibrium. Determining which of the conditions will be used and the evaluations of S~j (or S~j) are done by comparing binary gas mixture data with predictions. Furthermore, the values of S~j (or S~j) determined from binary gas mixture data must obey the identity requirements. The satisfaction of the identity requirements ensures that the proposed site-matching criteria are self-consistent. With the identity requirements, the criteria used for an n-components system, in the limiting situation when one of the n-components vanishes, reduce automatically to those of the corresponding (n - 1)-component system. It should be stated clearly, that the HIAS approach used by Valenzuela et al. and its more general version - - the site matching (SM) approach used by Moon and Tien are nothing else but the application of IAS theory accompanied by the assumption that the solid surface has patchwise topography. It is important to emphasize it, because all the authors do not express this assumption explicitly, and use sometimes such confusing expressions as 'the solution at some point', instead of the fully rigorous expression - - 'the solution on some patch'. Like Valenzuela et al. Moon and Tien took for analysis the adsorption data for H~S, COs, and C3Hs adsorption on H-modernite reported by Talu and Zwiebel. When Sij (or S~j) becomes zero the Moon-Tien SM approach reduces to the Valenzuela et al. 'perfect positive correlation' HIAS approach. Then, both these groups of authors conclude that the improvement over the (classical) IAS approach is only modest or none. The assumption S~j ~ 0 (or S'zj -fi 0) does not improve it much. A much better agreement was, later on, achieved by Karavias and Myers[252] who additionally considered the non-ideality of the adsorbed phase due to the interactions between the adsorbed molecules. Their theoretical treatment, however, involves introducing not one but two additional best-fit parameters. Eiden and Schlfinder (ES)[222] have proposed another extension of the IAS approach to surfaces characterized by patchwise topography. As their theory was taylored to describe mixed-gas adsorption by activated carbons, the DA equation was assumed to represent the adsorption of single components. They prefer to consider it in terms of 'volume filling' rather than 'site filling', so, they write the DA equation in the following form:
(255)
V - Vs exp ( - (e~ EeSm)~ ) where
es=-kTln(p/p~),
e~m = - k T l n
(P~-~)
(256)
and esm is the lowest value of es below which the DA isotherm does not apply any longer. The esm value is that corresponding to the beginning of the hysteresis loop, which means the end of the filling of micropores, and the start of the adsorption in mesopores. The adsorption in mesopores is described by other than DA equation. This adsorption results into capillary condensation at the end. The value of V~ in eq. (256) is taken as equal to V(es = esm), i.e., is lower than that estimated as the maximum adsorbed amount, found in experiment at p---, p~.
70 Eiden and Schliinder studied experimentally the adsorption of three species: benzene, DM (dichloromethane), and HMD (hexamethyldisiloxane), on two kinds of activated carbons: Supersorbon and Kontisorbon. While considering the spreading pressure integral (189) ES introduced a new integration variable es, through the relation (256). They operated also with the spreading pressure r = HA. Then, in the case of adsorption of ith component, they wrote: oo
if
(257)
where In (P~ /Ps,) ,
c:~ = - k T
(258)
and ~ is the molar volume of the ith adsorbed species. (~ defined by ES is a constant, and should be close to the volume Vi~ defined in the Potential Theory approach). For the temperatures below the boiling point of adsorbate, ES take Q to be the molar volume of the bulk liquid. The Raoult's Law (191) takes then the following form pi = X i p s i e x p (_e~,(r
= r
....
= r
(259)
kT
Next ES divide the whole micropore volume into z subvolumes. The fiUing of these subvolumes is assumed to proceed in a perfectly gradual way in the sequence of decreasing values of e~ characterizing these subvolumes. After filling the j th subvolume, the adsorption is running in the micropores characterized by es = esj, so that e~ij = ~mi ~- Ei [ I n ( z / j ) ] 1/'' ,
i=
1,2,...
, n,
j = 1, 2, . . . , z.
(260)
The spreading pressure is defined as a local property for every subvolume, so r
= r
= ....
r
= Cj,
(261)
where (262)
=
cs*O and = - k T In
.
(263)
The above statements do not leave any doubt that ES introduce the concept of patchwise topography. The so-called 'subvolumes' are simply classes of identical micropores. The essential concept of patchwise topography is not necessarily related to adsorption on planar surfaces. We have to deal with patchwise topography every time when the adsorption system can be considered as a collection of independent subsystems. Then the adsorption properties must be the same in every part of a subsystem under consideration. And now some confusing statements appear in the theoretical considerations by ES, who
71 do not relate DA isotherm with the concept of adsorption on a heterogeneous surface. This immediately creates the fundamental difficulty in their consideration. Namely, how one should define the function V~j(e~) under the integral in eq. (262). Valenzuela et al. and Tien and Moon assumed V~j(e~/Q) to be simply the Langmuir isotherm. In order to overcome that difficulty ES launched the following tricky statement: "Because adsorption in phase j is only possible for the adsorption potentials
qi > esij and the adsorbed volume in phase j is always A V = ~z ' integration in eq. (257) is possible, and one obtains AV
= ~
r
(esij - el*j)
(")
pi=Xi.ip~iexp
-
e~iJ(r kT
(264)
, i=l,
2,
..
9 n j = l , ,
,
2,
..
9 z ,
(265)
o.ol,
While analyzing that tricky statement it is difficult to understand why the function V/j(~si) is replaced by the constant A V. However, all that can be true by adopting the following assumption: The Langmuir isotherm, or any other describing the adsorption in a sub-system of identical micropores is replaced by a step function, V/j(esi) =
0
AV
for esi < ~ j , for esi _> e~ij.
(266)
In other words the condensation approximation is used implicitly to describe the local adsorption in the sub-system of identical micropores. What should also be clearly said is, that ES introduce implicitly the same concept of the "perfect positive correlation" which was first applied by Valenzuela et al. ES combine equations (264), (265) and the condition ~lj = r . . . . . ~j. For the particular case of a binary gas mixture, they arrive then at the following equation:
(
(Xlj)~/V1 -- ~(Prl)V2/V1 e x p 1 - X~j pr2
(Y2/Yl)s
kT
- s
)
(267)
'
where pri is the relative pressure Pi/P~. As the mixed phase composition on jth patch (in the jth subvolume) is the function of qlj and q2j values, it means, that after filling the volume ( j - 1)V~/z, the adsorption of both 1 and 2 occurs on the same patch (subvolume). To calculate the amounts adsorbed in phase j, ES apply the equation, AV J~fJ -- X l j Yl -t- X2j ~r2
(268)
which is the analogue of the IAS equation (192). Then, A/'lj = X~jAfj. For the whole surface (all micropores) Vesij(> ei*j)" N" = Y'~Afj J Ve~ij(> e2ij) : .Af/= ~ A f / j ./
,
(269)
, i = 1,2
(270)
72 The overall (average) composition in the adsorbed phase is calculated as X1 = Af~/Af. ES have done interesting model calculations based on their approach. They showed that different exponents rl and r2 can lead to a strong 'real' azeotropic behaviour, even if there are no interactions between adsorbed molecules, i.e. the adsorbed mixture can be treated as an ideal solution. The above described approach developed by ES turned out to be very successful in correlating mixed-gas adsorption in activated carbons. ES argue that the above procedure for calculating mixed-gas adsorption can be used only when V~ has the same value for both components. If not, the sieving effects must take place, and they also proposed a special procedure for such cases.
8. T H E A P P R O A C H E S B A S E D ON T H E M O D E L S OF M O B I L E ADSORPTION 8.1. E x t e n s i o n s of Hill-de B o e r approach. A first treatment of mixed-gas adsorption, based on a model of mobile adsorbed phase was proposed in 1948 by Kemball et. a1.[142], who extended Volmer's equation for that purpose. This isotherm equation takes into account, and in a crude way, only repulsive interactions between adsorbed molecules, so, this equation seldom provides a good representation of single-gas isotherms. This approach is, therefore, of a limited value for predicting mixed-gas adsorption. Twenty years later Hoory and Prausnitz[143] extended the Hill-de Boer isotherm equation for a two-dimensional mobile monolayer of a gas mixture on a solid surface. To obtain the equation of equilibrium between the two phases, they introduced the concept of surface fugacity; for a component designated by subscript 1, the two-dimensional fugacity in the adsorbed layer is designated by f~'
d#~ = R T d In f~
(271)
and lim f~
l-I--.o X----'~ -
(272)
1"
where H is the spreading pressure,/z~ is the chemical potential and X1 is the mole fraction of 1, all in the adsorbed layer. The surface fugacity of component 1 in the adsorbed mixture is then calculated from a two-dimensional equation of state by the thermodynamic relation,
R T In f~ =
0II A
-
A--A-dA - R T In A/'IRT
(eTa)
T,A,A/'2 ....
where A is the total surface area available for adsorption and A/'I is the number of moles of 1 in the adsorbed layer. Let fl stand for the fugacity of 1 in the gas phase. It can then be shown that the equation of equilibrium is
f~=
KH1RT NA--.---..~.f~
(274)
73 where NA is the Avogadro's number, KH1 is the Henry's constant for pure component 1 and fll is the surface occupied by one molecule of component 1 when 01, the fraction of a monolayer covered by 1, is equal to unity. The parameters KH1 and fll are obtained from a suitable analysis of pure component data, and K m fulfills the following condition, 9
K m - l : m~ \ f ~ ]
(275)
A/'2 . . . .
(275)
0
Next, the two-dimensional analogue of van der Waals' equation of state was accepted by Hoory and Prausnitz to represent H II =
kT O" ~
a /~
(276)
0 .2
where k is the Boltzmann's constant, a and ~ are the characteristic constants (analogous to van der Waals' a and b) and a is defined by A (277)
o" = N A./V'F_,
where A/'z is the total number of moles adsorbed. Hoory and Prausnitz assumed further, that for a binary mixture, the constants a and are related to those of the pure components by (278) and a -- a1X21 + 2 a 1 2 X i X 2 + a2X 2
(279)
where a12 is a constant characteristic for the 1-2 interaction. They assumed that a12 =
x/ala2. Substitution into Eq. (273) yields then for the surface fugacity of component 1 X1 k T In f~ = In cr " / 3 -+
~1 a-~
2 akT
(miX1 + a12X2)
(280)
Further substitution into the equation of phase equilibrium, and the assumption that the bulk gas phase is ideal yields X1~1 ~1 In (a - ~)KH1PY1 -~ a - - ~
2 o'kT ( a l X 1 + a12X2) = 0
(281)
A similar equation can be obtained for component 2. The two equations of equilibrium predict the adsorption isotherm for a binary mixture using only the data obtained from the pure-component adsorption isotherms. Given the temperature, the total pressure, the composition of the gas phase and the parameters Ks, a and 3 for each pure component, the two equations of equilibrium can be solved to yield
74 cr and X. For binary mixture, the number of moles of component 1 which are adsorbed is found from All = X1/~IAf~
(282)
o"
where Aft1 is the number of moles of 1 adsorbed at the full monolayer coverage of pure 1. One has a similar relation for component 2. Since the area of the solid is a constant, it follows that (/3Afs)l = (/3A/',)2
(283)
To illustrate their equations, Hoory and Prausnitz performed numerical calculations for the adsorption isotherms of several mixtures on homogeneous graphitized carbon. In these calculations we have used pure-component parameters reported by Ross and Olivier[271]. These parameters are given in Table 2. X 10 30
Gas
(ergcmZl molecule z)
/3 • 10 TM
(cm21 T molecule)(~
Nitrogen Argon Benzene
33.85 43.98 112.0
15.55 13.60 30.60
Chloroform
283.0
29.40
CFC13
297.0
31.20
90.1 90.1 273.2 322.6 286.2 322.6 273.2 286.2
KH 1
6.30 13.20 0.71 9.93 15.10 87.63 15.20 28.80
Table 2. Parameters for pure-gas adsorption on graphitized carbon P-33 (2700~
(From Ross and Olivier[271]).
Unfortunately, they could not make any comparison with experimental results because no data for adsorption of these gas mixtures on a nearly homogeneous graphitized carbon, were available at that time. The application of the two-dimensional analogue of Van der Waals' equation (276) also makes it possible to describe the adsorption systems showing considerable departures from ideality in the two-dimensional adsorbed phase. The activity coefficient of component 1 is then given by, f~(X1, n, T)
~/1 = X~fp% l (H, T)
(284)
75 where f;~(II, T) is the surface fugacity of pure component 1 in the adsorbed layer at the same spreading pressure II and temperature T as those of the mixture. One can calculate 3' from the two-dimensional equation of state. For component 1 in a binary mixture /~I In 71 = In 0"1 -- /~1 a - ~ I a /3 -
a~
/~1 -
~1
2
akT(a~Xl+X~2X2)+
2C~I alkT
(285)
and there is a corresponding equation for component 2. In Eq. (285), al is the area occupied by one molecule of component 1 when pure component 1 is adsorbed at the same temperature and spreading pressure as those of the mixture. In the limiting case, when al = a2 and fll = f12, one obtains a = o" 1 - - a 2 and hence 71 = ")'2 = 1. If a l and a2 are not zero, the necessary (and sufficient) condition for the mixture to be ideal is that al = a2 and ~1 =/32. An insight into Table 2 must bring one to the conclusion, that the two-dimensional surface mixture (CFC13 + CHC13) is nearly ideal. On the contrary, the surface mixture (CFCI3 + C6H6) should exhibit considerable departures from an ideal behaviour. Hoory and Prausnitz demonstrated next that this surface phase non-ideality does not affect much the ( X - Y) diagram which is so frequently used to compare theory with experiment. At the same time, however, that surface nonideality affects strongly the relative volatility 7"]21defined as, r/2~ = I/1/X~
(286)
As the relative volatility is the reciprocal of the selectivity coefficient which is an important characteristics for gas separation, one may conclude it as follows. Small departures of a theoretically predicted (X - Y) diagram from the experimentally observed one may suggest a satisfactory agreement between theory of experiment, but this may mean serious errors in predicting separation factors. The graphitized carbon P33 is nearly homogeneous, but not perfectly homogeneous. Thus, Ross and Olivier studied the adsorption on this material by assuming patchwise topography, and mobile adsorption on every homogeneous patch. The local adsorption on n-th patch was described by Hill-de Boer isotherm, P=
(1) ~
0 (0 J : - O exp 1 - 0
2aO'~ kT~]
(287)
where o= - , Vr
= --exp Pv
(288)
Eq. (287) is the particular form of eq. (281) when X = 1, and Y = 1. (Please compare eq. (287) with its slightly different form (9)). It was assumed next that the distribution of the adsorption energy e can be represented by the gaussian function, g(e), =
exp
76 where w 2 is the variance of that function. Table 7.2 collects the values of the parameters ~, w, pv, and Afs obtained by fitting eqs. (287) and (289) to experimental adsorption isotherms of nitrogen and argon on P33.
N2 -g kcal/mole w kcal/mole pv(90-l~ mmHg Z's cm3(STP)/g
Ar
2.150 0.289 6.79x105 3.30
2.066 0.242 11.32• 3.76
a and f~ as in Table 7.1
Table 3. Adsorption parameters for N2 and Ar on P-33 (1000~
(From Ross and Olivier[271]).
Hoory and Prausnitz have generalized that treatment for the case of adsorption of a binary gaseous mixture by assuming that the local adsorption on every patch is described by their generalized Hill-de Boer isotherm (281) and that the two-dimensional adsorption energy distribution X2(el, e2) is the generalized Gaussian function, 1 ( X2(el,e2) = 2Hwlw2V/1 _ ~2 exp -
~I/12-- 2~I/1 ~2 + ~1/22) ~
(290)
2: e2)
where ~1 -- el -- s el
~i/2
e2 -- e2 W2
(291)
and where ~ois the correlation coefficient = cov(e,, e2)
(292)
WlW2
This correlation coefficient may be positive or negative; its absolute value must lie some-
where in the interval zero to unity. Let the probability that for a patch ij the adsorption energies lie in the intervals (el + Ael), and (e2 + Ae2) be designated by 5ij,
~ij
-- X(s
s163163
E E ~ij = 1 {
(293)
(294)
j
The parameters ~'1, ~'2, Wl, W2 can be found from the pure--component adsorption data, but the correlation coefficient ~ can be found only by the analysis of mixed-gas isotherm. For the components which are chemically similar ~0should take positive values and close to unity.
77 The mixed-gas isotherm is obtained by applying eq. (281) to each homogeneous patch, and then summing over all patches with the weighting factor 6ij. Upon applying the equation for homogeneous surfaces to a patch ij, one calculates for 6~j and the mole fractions Xlij and X2~j. Let F1 and F2 be the overall number of moles of components 1 and 2 respectively, adsorbed per unit area of the heterogeneous surface. To find F1 and F2 the summations are used,
Xlij i
(295)
j
F~ = ~ ~ ~y X2~j i
j
(296)
NAaij
The overall mole fractions of the adsorbed film on the heterogeneous surface are then given by F1 X1 = F1 + F2
and
X2 =
F2 F1 + F~
(297)
Experimental data for the adsorption of the (N2+ Ar) mixture on P33 were not available at the time when Hoory and Prausnitz worked on their publication. For this reason they presented only some model calculations. To be able to confront their theoretical predictions with the experimental data, Hoory and Prausnitz took into consideration the experimental isotherms of adsorption of the (ethane+ethylene) mixture on the Nuxit-A1 active carbon, reported by Szepesy and I1les[272]. These authors also reported the experimental isotherms of the pure components which were fitted first by Hoory and Prausnitz to estimate the single-gas adsorption parameters; ~, w, p~, ~, a, and A/'s. The values of these parameters found by Hoory and Prausnitz are collected in Table 4.
C.H6 -~ kcal/mole w kcal/mole
4.144 1.430 pv(90.1~ mmHg 3.84x106 x 1016 cm2/molecule 24.1 123.9 a x 1030 erg cm2/molecule 2 Afs cm3(STP)/g 218.0
C2H4 3.923 1.430 4.05x106 22.2 88.7 237.0
Table 4. Adsorption parameters for C2H4 and C2tt6 on Nuxit-Al active carbon (From Hoory and Prausnitz[143]). The calculations of mixed-gas adsorption were made by applying these single-gas parameters, pv, fl and a found from single-gas adsorption of C2H4and C2H6 on P33 to (ethane + ethylene) mixture adsorbed on Nuxit-A1 active carbon. Two values of the correlation
78 coefficient; ~0= 0, and ~o= 1, were assumed in this calculation. Because of the chemical similarity of C2H4 and C2H6, the value g = 1 seems to be a more reasonable assumption. This was confirmed by the calculation of the corresponding mixed-gas isotherms. The results calculated with full correlation (g = 1) gave an excellent agreement with the experimental mixture data. The hypothesis that the adsorption energies of hydrocarbons are strongly correlated received further support in the theoretical work by Nakahara. Nakahara et a/.[226,227] studied the adsorption of hydrocarbons by the carbon molecular sieve MSC-5A and showed failure in fitting the mixed adsorption data for these systems by either the semiempirical approach by Cook and Basmadjian[212], or the classical edition of IAS theory, as well as by VST approach proposed by Suwanayuen and Danner[222]. After proving the failure of fitting these data by theoretical approaches based on the model of homogeneous surface, Nakahara[273] turned to the approach developed by Hoory and Prausnitz. However, the procedure of fitting single-gas isotherms used by Nakahara was somewhat different from the procedure of Hoory and Prausnitz, because no data for a reference homogeneous carbonaceous adsorbent were used. While fitting mixed-gas adsorption isotherms, Nakahara employed the generalized Hill-de Boer isotherm (281) developed by Hoory and Prausnitz, but again the fitting procedure was different. Nakahara did not employ the generalized gaussian distribution (290), but introduced in an implicit way the assumption about high correlations between the adsorption energies of the adsorbates. In the calculation of probability densities for a model isotherm he used a normalized Gaussian distribution whose variables were continuous. But in the calculation of adsorption of binary gaseous mixtures he treated the number of patches as countable and discrete variables. The number of patches which have the same adsorption potential was calculated by the Gaussian equation multiplying the normalizing factor by 1000 and counting the fractions of 0.5 and over as a whole number and disregarding the rest. The important point of the extension of pure isotherms to describe the adsorption of mixtures was how to combine the two isotherms. In the first step of this study Nakahara set the 1000 patches in a series with respect to the potential values, being numbered consecutively by integer i from 1 to 1000 in highest potential, and increasing i values corresponds to either the same or higher potential. While considering adsorption of binary mixtures, the patch which has the highest potential in the adsorption of pure component 1 was considered to be the same patch which has the highest potential in the adsorption of pure component 2. The second patch which has the potential next to the highest for component 1 is also the patch which has the potential next to the highest for component 2. The same rule was applied to the third, fourth, ..., and to the thousandth patch. Thus, although it was not stated explicitly by Nakahara, the above procedure was equivalent to the assumption of high correlations between the adsorption energies of the adsorbates. The average surface coverage of each component 8tj was calculated as follows,
Zj
1
1000
Otj = 1000 i=1 ai = 1000 ~
Oii
(298)
i--1
The amount adsorbed of each component, to be compared to the experimental data in
79 the unit of rag/g-adsorbent, Aftj is calculated as follows .Aftj = .M'~jOtj
(299)
All the figures presented in the Nakahara's work must be viewed as an impressive evidence for the success of Nakahara's theoretical method. Also, they must be viewed as an evidence of the importance of the surface energetic heterogeneity of MSC-5A in mixed-gas adsorption. Nakahara's method can be applied only for the systems in which high correlations exist between the adsorption energies of the components of the adsorbed mixture. This, for instance, should be the large class of the adsorption systems in which a mixture of hydrocarbons is adsorbed on a carbonaceous adsorbent. In the systems where the adsorption has a mobile character rather, but the adsorption energies of the various components are not strongly correlated, using the classical Hoory-Prausnitz approach allowing to consider existence of limited correlations may be necessary. Danner and coworkers[220,221] attempted to use the Hoory-Prausnitz eq. (281) to describe the adsorption of (ethane + ethylene) mixture in 13X molecular sieve, which was treated as creating a homogeneous gas-solid interface. This led them to a limited success, and it turned out, that even the simplest edition of IAS worked better. In the case of (ethane + ethylene) mixture adsorbed on the nearly homogeneous carbon adsorbent Sterling FTG-DS, Lee and Connel[274] used successfully a statistical description based on a model of partially localized and partially mobile adsorption. The Hill-de Boer isotherm is known for that it does not describe well the repulsive (excluded area) interactions. So, Findenegg and coworkers[275,276] used the more accurate treatment offered by the Scaled Particle Theory to develop an isotherm equation for the adsorption of a binary gas mixture. That equation turned out to provide a good description of hydrocarbon mixtures on graphitized carbons which are known for having relatively homogeneous surfaces. Nitta et. a1.[204] developed an isotherm equation for mobile mixed-gas adsorption, where repulsive interactions were described in terms of the Scaled Particle Theory. Next, patchwise topography was accepted, along with Gaussian energy distribution. However, it is also essential to account properly for attractive interactions between adsorbed molecules. In the case of an adsorbed mixture which may be highly non-ideal, due to attractive interactions, the attractive terms in the Hoory-Prausnitz' extension of Hill-de Boer isotherm may not describe well the complicated nature of attractive interactions. The Hill-de Boer (Van der Waals) isotherm was developed statistically by using the simple mean field approximation to represent effects of attractive interactions. Thus, Patrykiejew et. al.[174] and next Konno et. a1.[193,194] used more accurate 2D equations of state to develop isotherm equations for adsorption of binary gas mixtures. In their second paper Konno et. a1.[193] used their mixed-gas isotherm to describe adsorption on patchwise heterogeneous surfaces. While describing the adsorption of (methanol + n-hexane), (methanol + acetone), and (acetone + n-hexane) mixtures on the sieving carbon MSC-5A, they assumed that its surface could be modelled as composed of two kinds of patches. Very recently, Zhou et al.[208] have developed a variety of mixed-gas isotherms corresponding to five 2D equations of state.
80 8.2. Low p r e s s u r e formalism.
adsorption
and
the
application
of virial
description
When the total bulk pressure of the adsorbed gas mixture is relatively low, the virial description formalism may become a very convenient tool to deal with mixed-gas adsorption. It has been used extensively to describe single-gas adsorption on both homogeneous and heterogeneous solid surfaces. A detailed exhaustive description of the use of the virial formalism to describe single-gas adsorption has been given by Rudzinski and Everett in Chapter (7) of their monograph[3]. Attempts to use the virial formalism in the theoretical description of mixed-gas adsorption were started in 1955 by Kwan, Freeman and Halsey[277], but these concerned adsorption on a homogeneous solid surface. Jaroniec and Sokolowski [278] were the first to use the virial formalism to describe mixed-gas adsorption on heterogeneous surfaces. For that reason we refer to their paper in a more detailed way. Let us consider a two-component gaseous mixture of volume V and temperature T in contact with a solid surface. The activities of gaseous molecules are aA and aB. The solid is assumed to be completely inpenetrable for gaseous molecules. The quantity adsorbed is defined as the difference between the average number of gaseous molecules in the system and the average number of molecules in a hypothetical system, so-called "calibration system" of equal volume but with no gas-solid interactions. The grand canonical function for the real adsorption system is
_NA _NB
-"
E
uA uB Z(NA, NB) NA~NB! NA,NB>O
(300)
whereas for the calibration system is
--*-":
aNAA NB E NA!NB!aB Z*(NA,NB) NA,NB~O
where Z(NA, Ns)and calibration systems:
(301)
Z'(NA, Ns)are the configurational integrals for the adsorption and
Z(NA, NB) = / drNAdrNB exp ( U(NA,NB)
(302)
V
Z'(NA, NB) =
drNAdrNB exp
--
kT
(303)
V In eqs. (302-303) U(NA,Ns) and U'(NA, Ns) denote the potential energies of the indicated set of molecules in the adsorption and calibration systems, respectively. The adsorption isotherm is obtained from the relation -
Nr = 01n(~.) + l n ( ~ . ) = -NA + -Ns cOIn aA In as -
(304)
81 where Nz, NA, NB are the average numbers of the molecules adsorbed. Using the expansion of In (~.), it is possible to express Nr. as an infinite series in powers of aA and aB. While retaining only the first power and quadratic terms in this expansion, we have
N--a= aABAs ~- aBBBs -+-aAaBCABs -F a~CAAs q- a~CBBs
(305)
where BAS and Bss are the second gas-solid virial coefficients, which in the case of a non-reactive, non-swelling solid considered here are given by,
B~s=/(g,-1)dri=/[exp(~'(ri)) v
- 11 dr i
kT
'
i = A, B
(306)
v
Cijs (i,j = A, B) are the gas-solid coefficients,
(307)
Cqs = j j(gigjfij - f~)dridrj v
where
f,j=exp(
wq~rj))_l
*
( uij(ri' rj) ) - i kT
f~j = exp
(308)
The function wij is an "effective pair potential" of interaction between two adsorbate molecules, one of species i, one of j, in the presence of solid surface, and is a quantity averaged over all allowable configurations on the adsorbent. The uij is the interaction potential between two gaseous adsorbate molecules under calibration conditions. The potential 0i(ri) is the average energy of interactions of i-th type of adsorbate with the adsorbent, averaged over all configurations on the adsorbent. As the adsorption on heterogeneous solid surfaces is usually considered in terms of localized adsorption, Jaroniec and Sokolowski accepted that model to arrive at a more detailed form of their gas-solid virial coefficients. They introduced next the simplifying assumption that adsorption sites are labelled only by their values of ei (i = A,B), i.e. by the local minimum of the gas-solid potential 0i(ri). While accepting that simplifying assumption, w
-ff~.
de, ,
i = A, B
(309)
12i
and
B,s = f B~
,
i = A,B
(310)
fti
where B~ is the expression for the second gas-solid virial coefficient for a homogeneous solid surface, on which the adsorption sites have adsorption energy equal to c,.
82 While B~s depends only on the energy distribution of the sites, the presence of lateral interactions introduces an additional dependence on the spatial distribution of sites, X(2) O (%i, e~,, Rni~~), giving a number of pairs of sites, having energies %, and e~j which are separated by a distance P~i~j. The function XI~) has the following features"
/
X~)(%,,e,,,,t?..,..,)dR,7,,r, = Xi(%,)X,(e,,,)
(311)
R,Ti ,,i >0
(312) where 6 is the Kronecker's symbol, the (77,x) label the sites, and species, On the other hand, we have
(i,j = A,B).
(i, j) label the adsorbed (313)
As usual, one may distinguish the following cases of site pair distributions: (i) patchwise distribution, distribution with finite correlation length, random distribution. In the case of single-gas adsorption the problem of surface topography was elaborated in the theoretical works by Steele[279], and by Pierotti and Thomas[280]. The case of the finite correlation length was elaborated by Ripa and Zgrablich[281]. In their study of mixed-gas adsorption, Jaroniec and Sokolowski limited their interest to only patchwise and random site distribution. For patchwise surface topography, (p), the third gas-solid virial coefficients take the following form,
(ii)
(iii)
exp
(2tgi(rl;ei)) kT dei] . fii- f~} -
drzdr2
(314)
and
X(2)(eA'eS)exp(--VqA(rA;eA)--~B(rB;eB))deAdeB] " k T
BS-V-V*
A XI2B
fAS 9 -- f~S } drAdrB
(315)
whereas in the case of random site distribution, (r), =
exp
kT
" (f~i +
f Xi(e')exp (-2tg'(rz; ei)) de' - f~]} t2i
1)-
(3 6)
83 and CA(r) BS
/ ~V-V*
[{j (
}
" {a/BgS(eB)exp(--tgS(rB;eB))deB}
/
X(2)(eA,eB)exp (--OA(rA;
(317)
eA)kT~S(rB; - eB)) deAdeB -- f~B] drAdrB
~A X 12B
The above considerations by Jaroniec and Sokolowski were kept on a purely theoretical level. The case of partial correlations has, more recently, been considered in the paper published by Zgrablich and coworkers[187]. They applied, however, another kind of virial expansion based on the assumption that the adsorbed phase can be considered as a strictly 2-dimensional one. The 2-dimensional compressibility factor Z of that adsorbed phase was represented in their work by the following expansion:
Z = A/'r~RT =
1 + B,(T)
-~
where A/'r~is the total number of moles in the adsorbed phase, a,~ = ~A) , and Bn is the 2-dimensional nth virial coefficient, which is the function of temperature. It can be shown, that the second virial coefficient of an adsorbed mixture is a quadratic function of the molar fractions Xi's of the components in the adsorbed phase composed of i and j.
B2 = x, 2 ii + 2X XjB ' + x,2Bjj
(319)
and the third coefficient B3m is the following cubic function, = Xi B3 +
~3 +
+ Xj B 3
(320)
The coefficients/3~i and -3 fqiii have the following form,
B~i= / / d r l d r 2 f 1 2 exp (e~(rl) ei(r2))kT +
(321)
A
Biii f f/drldr2drsf12f23f31exp (ei(rl) + ei(r2) + ei(rs)) a = kT A
(322)
84 The interaction potential uij(r), between two adsorbed molecules being at the distance r is approximated by the "square well" function, oc
uij(r) =
for r < too
-kTgg for roo < r < rgg 0
(323)
for r > rgg
The crossed 2D coefficients can easily be written by expressing properly f12 functions. In the case of B~~, for instance, u12 in fij is the potential function between two molecules i and j. Zgrablich and coworkers still used the square--well function (323) to describe the interaction potential between molecule "i" and molecule "j", along with the crossed parameters, . .
=
1 [(roo)i-t-(rcc)j]
(324)
[(T,,),.
(325)
The expression (318) was used in the relation (273) to carry out the integration leading to the isotherm equation for mixed-gas adsorption. For that purpose Zgrablich and coworkers wrote the Gibbs' relation in the following form,
In
RTKH, Y~P = / fli
[0(AfZ) -10am X, RT + In L (gAfi T,A,JV, am am
(326)
O'rn
where P is the total bulk pressure of the adsorbed mixture. Eqs. (318-325) yield the following expressions for the mixed-gas adsorption,
2 (X.B" 3 \.~,,i~.~3 (y2Riii In XifliO k ' 2 + XjB~j) 0 + + I~KHiYiP ~ In
~iij 2~ 2
- } - 2 X i X j ~'3
"Jl- X.Bijj ' 3 )
02
= 0
Xj~jO 2 (XjB~ j + X,B~j) 0 3 (X~B~ j~ + 2XiXjB~ jj + X}B~ 'j) 02 + + = 0 ~KHjYjP ~ 21~~
(327)
(328)
The virial coefficients were calculated by means of the Ripa-Zgrablich generalized Gaussian model[281]. While calculating the 2D coefficients for a heterogeneous solid surface, Zgrablich and coworkers replaced exp ( ~ ~ , e(ri)) under the integral sign by these functions averaged with the following multivariate Gaussian distribution X~(e) Xn(E, T1 -- T2,..., Tn--I -- Tn) =
[(2r) ~ det HI-' exp
-~ ~ i,j=l
(e(Ti) -- ~) (H~ 1) (e(rj) - ~)
)
(329)
where
Hii = w2C(ri- Tj)
(330)
85 is the covariance matrix, and C(Ti - rj) is a certain correlation function. In the simplest case n = 1 function (329) reduces to the ordinary Gaussian function (289). Thus, for a heterogeneous solid surface, the coefficients B~i and ~-3~" take the following form,
drldr2f12exp (~--~)C(rl-r2)
B~i=--~
(33~)
A 3 = -'~
drldr2drsfx2f23fm
exp
I w 2
(~-~)[C(rl-r2)+
A
\
+ C(r~ - ~ ) + c ( ~ - ~,)])
(332)
As for the correlation function C(r), it was expressed by the formula C(r) = exp
-~
,
rc e [0,1]
(333)
where rc was called the "correlation distance" between two points on the surface. When r~ = 1 the points are totally correlated. Then, when rc = 0 they are completely independent. The case rc = 1 means patchwise topography. For two different molecules / and j, the crossed correlation parameter (%)ij was approximated by the formula
(r~),j = [(rr189
(334)
Thus,
B2= --~
where ~ = exp
--~
- 1
(337)
The second virial coefficient can be evaluated analytically in terms of the integral exponential function E~(x), (Abramowitz and Stegun[2821),
(33S) rc
w
2
1
roo
86
[
w
2
{
1 (rgg~ 2
l(roo)2}]}
(339)
;:0 The i}3) integrals were calculated by using Simpson's tridimensional integration subroutine with 1% accuracy. The calculated values were next expressed in terms of the dimensionless coefficients; B~,, B],
B2
B; = (H~L/2)
'
B~ =
Ba
(n~L/2)
(340)
After solving the equation system (327-328) one obtains the values of the individual adsorption isotherms. The adsorption isotherms calculated in that way by Zgrablich and coworkers were tested against a variety of experimental mixed-gas adsorption isotherms already reported in literature, (Szepesy and Illes[272], Lewis at. a1.[270], Markham and Benton[140]), and mixed-gas isotherms measured in Zgrablich's laboratory in San Luis.
9. T H E STATE OF ART A N D T R E N D S IN A N A L Y T I C A L A P P R O A C H E S The term 'analytical' approaches is used here for all the works which are not purely computer simulations. They include, therefore, both the 'thermodynamic' and 'molecular' approaches. Of course, the analytical approaches also involve carrying out more or less complicated and time consuming computer calculations. Most of the papers using either molecular or thermodynamic approaches were based on the models of localized adsorption. Therefore, the papers based on the models of mobile adsorption have been discussed separately in the previous section. It seems reasonable to assume that in the real adsorption systems the mixed adsorbed phase is neither perfectly mobile or localized[144,173]. The choice of localized adsorption models in some cases may be viewed as adopting a convenient t o o l - the lattice description formalism, in a similar sense as it is made in the theories of liquid state. The works in which the thermodynamic approaches were used created in literature a certain tendency of verifying various approaches and corresponding mixed-gas adsorption isotherms, through the thermodynamic consistency test, which may be expressed in a number of ways. It should, however, be realized that this test is related to the sense of macroscopic thermodynamic phase, defined in the phenomenological thermodynamics. So, while applying that test one has to consider to which extent, the adsorbed molecules in a certain adsorption system can still be considered in that way. Various approaches involve accepting various simplifying assumptions, so, the most essential test would be that stressed by the simple question - how does the approach work. In other words, how effective a particular approach is, i.e. corresponding isotherm equations, in predicting mixed-gas adsorption equilibria. The VST ( Vacancy Solution Theory) which was so popular for some time, has recently been abandoned as it does not predict correctly selectivity in adsorption of a binary mixture at very low surface coverages[283]. At the same time using VST to predict mixed-gas adsorption equilibria is easier than using other thermodynamic approaches m IAS, NIAS and HAS, for instance. The VST approach does not involve necessity of studying carefully
87 low-coverage behaviour of single gas isotherms, what may be difficult in many cases. As it was emphasized by Talu and Knabel, VST "has a built-in flexibility" due to adopting powerful though semiempirical expressions for the surface activity coefficients. These are expressions which have been used in the theories of highly non-ideal bulk solutions (Wilson, Flory-Huggins). In VST bulk concentrations were replaced by surface concentrations (coverages). Danner an coworkers used their VST approach also to adsorption systems with strongly heterogeneous surfaces. It is, therefore, obvious that the parameters in the surface activity coefficients, found by computer, reflected not only effects due to interactions between adsorbed molecules, but also simulated effects arising from surface energetic heterogeneity. As far as the behaviour of adsaorption isotherms is concerned, these two effects can simulate each other to some extent. This has been discussed recently by Koopal et. a1.[207]. That mutual simulation, however, becomes much worse in the case of enthalpic effects accompanying single and mixed-gas adsorption. Thus, Talu and Knabel showed[240] that using VST equations to fit single-gas isotherms in various adsorption systems leads to wrong predictions of accompanying heats of adsorption. Rudzinski et. a1.[262] have shown that the VST approach can still be modified further to take separately into account the surface energetic heterogeneity, and the interactions between the adsorbed molecules. Their considerations were kept on a purely theoretical level, and the applicability of that modified VST approach has not yet been prooved. As far as adsorption at one temperature is concerned the test "how does it work" may justify using some usefull analytical expressions. In addition to the classical VST approach, another interesting example of that kind may be the NICA equation proposed by Koopal et. a1.[207]. Instead of Markham-Benton (Langmuir) equation (164) these authors put the generalized Langmuir-Freundlich isotherm 0~=
(/s
"~'
(341)
1 + E ([fiP') m' under the integral in eq. (141). The values m~ < 1 may arise from either repulsive interactions between adsorbed molecules or from energetic surface heterogeneity. The values mi < 1 may also account for both of them. On the contrary, the values mi > 1 may simulate attractive interactions between adsorbed molecules. In other words, one parameter mi is used to describe the combined effect of surface energetic heterogeneity and of the interactions between the adsorbed molecules. Koopal et. al. argue that this combined effect will be different on different adsorption sites so eq. (341) should, again, be averaged with the function describing the dispersion of the parameter (/~)m~. Provided that this parameter can be expressed a s / ~ 0 e x p \-~--/, where/s is the same for all adsorption sites, and the new variable e~~ changes from one site to another, one 9may assume a function X~(e~~ e~om,---, C~om)to exist, and next carry out the averaging in a similar way as it is outlined in eq. (141). Koopal et. al. assume, that c~~ are highly correlated like e~'s in eq. (~66), and that X~(~~~ is the Gaussian-like distribution function (152). Then, the result of averaging 0~ in eq. (341) with X(er176 takes
88 the following form,
8ti(p,T)=
([ffipi)m' j
(K'pi)s ~j (Rjpj) ms
1+
(342)
Koopal et al. called the above equation NICA (Nonideal Competitive Adsorption). There was a time, (the late seventies), when the Jovanovic isotherm equation was extensively used to describe mixed-gas adsorption on heterogeneous surfaces. For single-gas adsorption on homogeneous solid surface the Javanovic equation reads,
{
O(e, p, T) = 1 - exp - K p e x p
(343)
~-~
It was kineticaUy developed by Jovanovic[284], who also took into account the collisions between the adsorbed and bulk molecules while considering localized monolayer adsorption. (The kinetic derivation of Langmuir isotherm ignores these collisions). Jaroniec et. a1.[147] proposed then the following generalization of the Jovanovic equation for mixed-gas adsorption:
n
i) 8({e}, {p}, T) = 1 - e x p { - Z KiPi exp ( e~~
}
(344)
i'-1
Next, they applied the IE approach to extend it for adsorption on heterogeneous surfaces. A good correlation was obtained in that way for a variety of adsorption systems, by accepting some additional assumptions making analytical integration in eq. (8) possi-
b1~[151,175]. There are, however, certain problems related to the statistical derivation of the Jovanovic equation for which it was later abandoned. Still, some other approaches were used to describe mixed-gas adsorption on heterogeneous solid surfaces. Thus, Nikitas et. a1.[188] applied the whole theory of liquid state to describe localized and non-localized adsorption on heterogeneous surfaces. They developed appropriate theoretical expressions for both patchwise and random topography, but their considerations were kept on a purely theoretical level. Dubinin et. a1.[182] showed that the osmotic theory of adsorption might be useful in the studies of mixed-gas adsorption on heterogeneous surfaces. Jaroniec[173] and Patrykiejew et. a1.[174] attempted to extend the Kiselev's approach for collective adsorption based on the picture of associating molecules[285-289], to describe mixed-gas adsorption equilibria. Of course, considering the "associates" is only a theoretical tool to take into account interactions between the adsorbed molecules. The idea is similar to the graphs techniques used to describe nonideality effects in real bulk gaseous phases. Like in the case of single-gas adsorption, one may expect tendencies to exist toward multilayer adsorption in mixed adsorbed phases. Attempts to describe mixed-gas multilayer adsorption were started by Hill's extension of BET model in 1946. Gonzales and Holland[290] introduced in 1977 a number of simplifying assumptions to that model. A
89 year later Jaroniec and T6th[177] proposed certain generalizations of Freundlich and T6th equations for the case of mixed-gas adsorption. Jaroniec et. a1.[168] and Jaroniec[173] also attempted to use the Kiselev's approach, based on the picture of "associating molecules" to describe mixed-gas multilayer adsorption. Nowadays, there is widely spreading view that the differences in the dimensions of adsorbed molecules are an important factor affecting mixed-gas adsorption equilibria. When thermodynamic approaches are used, this factor is a source of strong deviations from ideality of the "adsorbed solution". The success of Danner's VST approach in some cases comes from using Wilson's type activity coefficients which have turned out to be very powerful in describing non-ideality effects in bulk liquid mixtures composed of molecules of much different sizes[291]. Effects arising from different sizes of adsorbed molecules are becoming now a hot topic in the works employing molecular approaches. Here not only the size, but also the structure of adsorbed molecules is very important. For molecules having a linear chain-like structure, the application of the ideas underlying the Guggenheim's theory of polymer solutions seems to be strightforward. Nitta et. a1.[190] derived an adsorption isotherm for a multicomponent gas mixture in which each molecule can occupy any number of adsorption sites on a uniform lattice of sites. Interactions among the homogeneous molecules were treated by using the Bragg-Williams approximation. Nitta et. al. also derived equations[138] for adsorption of molecules composed of different segments. Most recent attempts along these lines have been published by Russel and LeVan[292], and by Rudzifiski et. a1.[293]. Russel and LeVan have adopted Guggenheim's lattice theory of solutions[294], along with the Bragg-Williams approximation to describe the interactions between adsorbed molecules. The Guggenheim's lattice model was reduced to a 2D lattice of (adsorption) sites, but it was assumed, that adsorbed molecule may be composed of chemically different segments. One segment was assumed to occupy one lattice site, and the vacant sites were treated in the same way as monomer-solvent molecules in the original Guggenheim's theory[294]. Russel and LeVan assumed further, that different chemical segments may have their own adsorption energy distributions. If a certain segment is common for two or more different molecules in an adsorbed mixture, that fragment has still the same adsorption energy distribution. The integral equation (141) takes then the following form 0t~(p)
/ ' ' " / Oi(p,T, ~1,s ..., s163163 fh
(345)
~m
where n means as before the number of components in the mixture, ei is the adsorption energy of ith segment, and m is the number of different segments in the adsorbed mixture. Like in the case of eq. (141) Russel and LeVan considered the possibilities of reducing the m-th dimensional integral (345) to one-dimensional, by considering functional relationships between the adsorption energies ei and ejr of various segments, ei = ei(e i). For the purpose of illustration Russel and LeVan analyzed the Nakahara's experimental data for (ethene + propene) mixture on the carbon molecular sieve MSC-5A[292]. Two kinds of segments, -CH3 and = CH~ were considered in this case by Russel and LeVan. As the segments are similar with respect to the gas-solid interaction, a perfect positive correlation between e-OH3 and e=CH~ was assumed. So, the relation (172) was used to reduce the
90 integral (345) to one dimensional. Good agreement was obtained between the theoretical predictions and the experimental data. The estimated adsorption energy distributions X(eCH3), and X(e=CH,) turned out to be Gaussian-like but had different variances. There was found a broader distribution of adsorption energies for the = CH, segments. One limitation of this very interesting approach by Russel and LeVan is that it can be applied only to surfaces with patchwise topography. Multi-site-occupancy adsorption on heterogeneous surfaces having random topography has been considered very recently by Rudzinski et. a1.[292]. The starting point in their consideration was the paper by Nitta et. a1.[138] on adsorption of molecules composed of different mers, on heterogeneous solid surfaces. Rudzinski and Everett modified further Nitta's approach toward using continuous adsorption energy distributions. Then, the essential point in Rudzinski-Everett approach was to apply the theoretical considerations by Marczewski et. a1.[139] concerning the relations between one mer adsorption energy distribution Xj(ej), j = 1,2,...,m and Xm(el,e2,...,em). In the simplest case of a homogeneous molecule composed of m identical mers, the variance ~,~ of X,~(em) is related to the variance 01 of Xl(e) in a way which depends on surface topography. For patchwise topography, ~,~ = m01
(346)
whereas for random topography 0m = v/mOt
(347)
The variance 0 is related to second central moment #2 by, #2 = ~2
(348)
Marczewski et. a1.[139] also developed relations between 01 and 0,.,., for the cases when the adsorbed molecule is composed of chemically different mers. Marczewski et. al. showed that surface topography also affects the relations between higher central moments of Xm(em) and Xx(e). However, surface energetic heterogeneity manifests itself in adsorption through the second central moment (variance) first. It is related to the width of the adsorption energy distribution, which is the most crucial factor. Higher central moments describe the shape of that function, which is of a secondary importance. Thus, while considering the multi-site-occupancy adsorption on heterogeneous solid surfaces, Rudzinski et. al. took into account only the effect of the number of mers rn on the variance of Xm(em). While considering single-gas adsorption, Rudzinski et. a1.[3,294,295] took the following functions X~ (em) into consideration, . l e x p (~=-~=o) X,~ (em) =
~"
\
~"
(349)
where 0.~ = ~-Z-c~ v~ ' and (Era) ~
r) exp
-
E,~
(35o)
91 where zgm = Era. The effect of surface topography was, thus, coded in the relation 0m = zgm(m), and in the form of the condensation function ecru. Now m denotes the number of identical mers in the adsorbed molecule, and em,m is the minimum value of era. For patchwise surface topography em is given by eq. (139), whereas for random topography ec,~ is given by eq. (140). So, let us consider, for example, isotherm equations corresponding to the quasi-Gaussian adsorption energy distribution (348). Then, from eqs. (139), (149) and (348)we arrive at the following equation for single-gas adsorption on surfaces characterized by patchwise topography, (p), kT
O~P)(p,T)=
K(~')P~11 1 + K(p)p~
(351) kT
K(P) =
(m i]5 7
-~),.,.,-xK'
exp
Cl ]
(352)
where cx is the value of c for one mer. For random surface topography, (r), we have, kT
=
K(")[o~rn-1)p] K<")-(rnK')~r~lexp(e~
(353)
1+
\r
(354)
]
Depending on the correlations between the adsorpion energies emi and emj of the components i and j, one can have a variety of equations for mixed-gas adsorption, being generalizations of eqs. (351) and (353). In their first paper on that problem, Rudzinski et. a1.[291] have considered the Gaussian-like function (349). Then, for patchwise surface topography, we have, kT
e~ ) (p , T) =
K~P)P~"=~'~
(355)
1 + ~ Kj(p).~,j-,jca~ j----1
whereas for random topography, one arrives at the following equation, kT
up) C' "t,A(")(P,T) 9 =
1+
'~ r , , ( ~ - l )
i <'<j
1
kr
(356)
PJ
Eq. (356) turned out to describe well the adsorption of (oxygen + carbon monoxide) mixture in the molecular sieve 10X, studied experimentally by Nolan et. a1.[298]. Nitta's approach[138] which was the starting point for Rudzinski et. al.[3, 293], follows essentially the ideas underlying Guggenheim's theory of solutions. Thus, as in the case of the paper by Russel and LeVan[292], all the equations developed by Rudzinski et al.
92 should apply to chain-like molecules rather, composed of well-defined chemical fragments - - mers. The developed equations may not, therefore, be a good tool to describe adsorption of large molecules having a compact structure. For such molecules Scaled Particle Theory (SPT) should be a better tool to describe the effects arising from different sizes of adsorbed molecules. The STP approach is essentially related to mobile adsorption model, but it seems that surface phase mobility may be of a secondary importance, compared to the effects arising from different sizes of large adsorbate molecules. Interesting remarks concerning this problem can be found in the recent paper by Frances et. a1.[210]. There is one intriguing problem related to the Langmuir-Freundlich isotherm equations for single and mixed-gas adsorption. In all the considerations presented so far, the exponent k__T < 1 was related to the dic spersion of adsorption energies on a solid surface. Keller and coworkers[265, 299, 300] have shown, that Langmuir-Freundlich equations also may be deduced by considering fractal properties of the actual solid surfaces. Theoretical studies along these lines are being continued by Giona and Giustiniani[301-303]. This would suggest that certain relations exist between fractal nature, and surface energetic heterogeneity of the real solid surfaces.
10. C O M P U T E R S I M U L A T I O N S OF M I X E D - G A S A D S O R P T I O N As in the case of single-gas adsorption, computer simulations of mixed-gas adsorption have been used primarily to study adsorption in the systems having a limited geometry. These were mainly activated carbons and zeolites of various kinds. Computer simulations of mixed-gas adsorption were actually started in 1990, by Sokolowski and Fisher[304] using the methods of molecular dynamics, and by Karavias and Myers[305] using Monte Carlo calculations. The latter method has, almost exclusively, been used in the studies of mixed-gas adsorption. Karavias and Myers studied the adsorption of (C02 + C2H4), (C02 -4-CH4), and ( C4H10 q- C2H4) mixtures in zeolite 13X. Their attention was focused on the heat effects accompanying the mixed-gas adsorption. The theoretical predictions were compared with those given by the simplified IAS theory, but no comparison with the experimental data was presented. Rasmus and Hall[306] used MC (Monte Carlo) simulations in the grand cannonical ensemble to study the adsorption of the (N2 + 02) gas mixture in the zeolite 5A. Their studies of mixed-gas adsorption were proceeded by the studies of the single-gas adsorption. Good agreement was found between the experimental and the theoretical heats of 02 adsorption, and a dramatic discrepancy between the simulated and the experimental heats of N2 adsorption reported for this system by Miller et a/.[307]. At the same time the experimental heat of nitrogen adsorption showed a behaviour predicted by the Langmuir-Freundlich isotherm. The results for the multicomponent adsorption isotherms were quantitatively correct; however, their simulation was not able to predict the mixture data quantitatively. The failure of simulation was ascribed by these authors to inaccuracies in the adsorbate-zeolite potential used in the simulation. Such an explanation can also be found in the later studies of mixed-gas adsorption.
93 Maddox and Rowlinson[308] used the grand-cannonical MC simulations to study the adsorption of (N2 q- CH4) mixture in zeolite Y. In a first step, the adsorption of the single gases was simulated[309]. A dramatic discrepancy was observed between the simulated heat of N2 adsorption, and the experimental heat of adsorption measured for this system by Hampson and Rees (personal communication). Maddox and Rowlinson ascribed it to "the omission of the quadrupolar interaction of the nitrogen molecules with the zeolite". However, an inspection into Figure 2 of their paper also shows a disagreement between the simulated and experimentally observed heats of methane adsorption. The latter heats plotted versus the adsorbed amount show a behaviour typical for that predicted by Langmuir-Freundlich isotherm. The aim of the paper by Maddox and Rowlinson was to test the predictions of IAS theory versus the computer simulations of mixed-gas adsorption. Thus, it is interesting to note that for this particular adsorption systems both these theoretical methods led to similar results. Cracknell and Nicholson[310,311] used the grand cannonical MC simulations to study the adsorption of fluid mixtures in the slit-shaped micropores which are often assumed in modelling the adsorption in active carbons. The pore walls were assumed to have a graphitic structure, and the adsorbed fluid was assumed to be a (methane § ethane) mixture. The results of computer simulations were compared with the IAS predictions considering the selectivity isotherms. In a first step the simulated single-gas isotherms were approximated by either the Langmuir-Freundlich equation, or by the Temkin isotherm equation obtained by averaging the Langmuir isotherm with the rectangular adsorption energy distribution. These isotherm equations were next used to calculate the spreading - pressure integrals. The selectivity isotherms calculated by the subsequent application of IAS were practically the same for both the Langmuir-Freundlich and Temkin equations used to approximate the simulated single-gas isotherms. These selectivity isotherms were also close to those given by the direct computer simulations. All the calculations (simulations) were done only for the temperature 296.3 K. For that reason a certain inconsistency did not affect that comparison. Namely, the heat of adsorption curves calculated for both the Langmuir-Freundlich and Temkin equations will be decreasing functions when plotted versus the adsorbed amount. Meanwhile, the heats of adsorption of the single gases predicted by the molecular simulations were increasing functions of the adsorbed amount (Figure 3 in the paper by Cracknell and Nicholson[311]). It has been known for a long time[3], that finite geometry of an adsorption system affects adsorption isotherms (pressure dependence of adsorption) in a similar way to that of the surface energetic heterogeneity. So, this is why the Langmuir-Freundlich and Temkin equations could be successfully used by Cracknell and Nicholson to fit their simulated isotherms of adsorption in the slit micropores. At the same time, however, the finite system geometry must affect the temperature dependence of adsorption isotherms, (heats of adsorption), in a much different way from that the surface energetic heterogeneity does. As has already been mentioned already, the picture of slit pores is frequently used in modelling the adsorption in the carbonaceous adsorbents. Recently a new research has emerged rigorously. This is the determination of the micropore size distribution based on computer simulations of adsorption in pores having different dimensions. Most frequently
94 the slit pore model is accepted for that purpose. The experimentally observed adsorption isotherm is assumed to be the function obtained by the following averaging HM
Ot(p, T) = / O(p,T, H)~(H)dH
(357)
Hm
where O(p,T, H) is the simulated isotherm of adsorption in a micropore the dimensions of which (the width of slit pore, for instance), are given by the parameter H. Hm and HM are the minimum and maximum values of H found in the investigated adsorption system. Next, numerical methods are used to solve the integral equation (357) to recover the pore size distribution ~r The literature concerning that problem will be reviewed in detail in the forthcoming chapters by Lastoskie, Quirke and Gubbins, and by MacElroy, Seaton and Friedman. In most of the computer simulations reported so far, it was assumed that pore walls have a regular molecular structure. This picture may be too much idealized in many cases. Recently Chmiel et a/.[312] have used MC simulations to study the single--gas adsorption in the slit micropores having energetically heterogeneous walls. Gubbins [313] has started simulations of binary gas mixtures in micropores of such a type. Bojan and Steele[314] have performed simulations of single-gas adsorption in the cylindrical micropores with an irregular molecuIar structure of pore wails. That irregular structure created energetic surface heterogeneity. MacElroy and Raghavan[315] have used another model of heterogeneous porous structures while modelling gas adsorption in the silica xerogels. They assumed that the structure of silica gel may be taken to a first approximation as uniform spheres of silica arranged to correspond to an equilibrium configuration of hard spheres. That model has been elaborated further by Kaminsky and Monson[316], who simulated also the adsorption of (CH4 + Ar) mixture in such model systems. It is frequently emphasized in the papers on computer simulations, that a proper choice of gas-solid interaction potentials is of a fundamental importance. The failure of simulation of gas adsorption in zeolites in some cases was almost exclusively ascribed to inaccuracies in the definition of gas-solid potential function and its parameters. This has also been emphasized in the works by Van Tassel et a/.[317] who studied (simulated) the adsorption of Xe, Ar, CH4 and their binary mixtures in zeolite NaA. It seems, however, that one important factor has not been yet sufficiently recognized in the computer simulations of adsorption in zeolites. Namely, zeolites are probably less regular structures than it is commonly assumed. This has, recently, been emphasized by Golden and Sircar[318]. They draw attention to the fact that many common zeolites such as A, X, Y, modernite, etc., may exhibit a substantial degree of physicochemical energetic heterogeneity for gas adsorption caused by (a) structural defects, (b) presence of trace water in the cages, (c) nonuniform distribution of silica-alumina in the framework, (d) location and distribution of one or more cations of different charge densities in the framework, (e) presence of bare and hydrolized cations etc. These factors, which can drastically influence the polarity of the adsorbent and, subsequently, the adsorption characteristics of a gas in the zeolite, may not be quantitatively known or easily modelled. Consequently, gas adsorption isotherms generated for these zeolites by simplified models of gas-solid molecular interactions can be ambiguous.
95 While finishing our consideration in this chapter, we would like to mention that as in the case of single gas adsorption, Density Functional Theory appears to be a powerful tool to study the mixed-gas adsorption in porous systems[304,319-322]. It may be competitive to computer simulations in some cases, because of its advantage of being computationally faster than full molecular simulations.
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(~993) [297] W. Rudzinski, K. Nieszporek, J.M. Cases, L.I. Michot, and F. Villieras, Langmuir 12, 170 (1996) [298] J. W. Nolan, T.W. McKeehan, and R. P. Danner, J. Chem. Eng. Data 26, 112 (1981) [299] J.V. Keller, Ber. Buuserges. Phys. Chem., 1510, 92 (1988) [300] J.U. Keller, Physica, A166 (1990). [301] M. Giona, M. Giustiniani, Ind. Eng. Chem. Res. (in press) (1994) [302] M. Giona, M. Giustiniani, D. K. Ludlow, L. M. Sperle, Lecture delivered at the AIChE Annual Meeting, San Francisco, November 13-18 (1994) [303] M. Giustiniani, M. Giona, L. Marelli, A. Viola, "Fractal Adsorption Isotherms: A Critical Comparison of Energetic and Geometrical Descriptions of Heterogeneity in Adsorption", published in Chaos and Fractals in Chemical Engineering, First Italian Conference, G. Biaroli, M. Giona, A. R. Giona Eds; World Scientific: Singapore (1995), pp. 49-65 [304] S. Sokolowski and J. Fisher, Mol. Phys. 71,393 (1990) [305] F. Karavias and A. L. Myers, Mol. Sim. 8, 23 (1990), 8, 51 (1991) [306] D. M. Razmus and C.K. Hall, AIChE J. 37, 5 (1991) [307] G. W. Miller, K.S. Knaebel and K.G. Ikels, AIChE J. 33, ~94 (1987) [308] M.M. Maddox ~ a J.S. Rowli~so~, J.Chem. Soc. Faraday Trans. 89, 3619 (1993) [309] G.B. Woods, and J.S. Rowlinson, J. Chem. Soc. Faraday Trans. 85, 765 (1989) [310] R. F. Cracknell, D. Nicholson, and N. Quirke, Mol. Phys. 90, 885 (1993) [311] R.F. Cracknell and D. Nicholson, J. Chem. Soc. Faraday Trans. 90, 1487 (1994) [312] G. Chmiel, L. Lajtar, and S., Sokolowski, J. Chem. Soc. Faraday Trans. 90, 1153 (1994) [313] K. Gubbins, Lecture delivered at the NATO Summer School on Physical Adsorption held in Nice (France), May 20-31 (1996) [314] W.A. Steele, Lecture delievered at the Fundamentals of Adsorption conference, in Asilomar Center, California, May (1995) [315] J. M. D. MacElroy and K. Raghavan, J. Chem. Phys. 93, 2068 (1990) [316] R.D. Kaminsky and P.A. Monson, Langmuir 10, 530 (1994), see also J. Chem. Phys. 95, 2936 (1991) [317] P.R. Van Tassel, H. T. Davis and n. V. McCormick, Langmuir 10, 1257 (1994), see also Mol. Phys. 76, 411 (1992) and J. Chem. Phys. 98, 8919 (1993) [318] T.C. Golden and s. Sircar, J. Coll Interface Sci. 162, 182 (1994) [319] E. Kierlik, M. Rosinberg, J. E. Finn and P.A. Monson, Mol. Phys 75, 1435 (1992) [320] E. Kierlik and M.L. Rosinberg, Plays Rev. A 44, 5025 (1991) [321] Z. Tan, U.M.B. Marconi, F. Van Swoll and K.E. Gubbins, J. Chem. Phys. 90, 3704 (1989) [322] Z. Tan and K.E. Gubbins, J.Phys. Chem. 96,845 (1992)
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W. Rudziriski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces
Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
Application of Lattice-Gas Models to Describe Mixed-Gas Equilibria on Heterogeneous Solid Surfaces
105
Adsorption
Yu. K. Tovbin Karpov Institute of Physical Chemistry Vorontsovo Pole Str. 10, 103064 Moscow, RUSSIA
In order to describe the separation and purification of gases and liquids by adsorption [1,2], heterogeneous catalysis [3] and many similar processes, it is indispensable to describe the adsorption equilibria of mixtures of components on real solid surfaces. Early experimental data on the adsorption of mixtures were reported in papers [4,5]. Recent papers discuss a large number of binary adsorption systems [6,7]. Most of those data relate to the adsorption of hydrocarbon mixtures by activated carbons, silica gels and zeolites. A generalization of the Langmuir model for the adsorption of mixtures on homogeneous surfaces was given by Markham and Benton [8] (see also [9-11]). The energetic heterogeneity of real surfaces and the lateral interactions between adsorbed molecules can result in considerable deviations from strictly ideal behaviour of adsorbed phases on homogeneous surfaces. Calculations of adsorption equilibria are connected with a search for the optimum conditions for carrying out separation processes over a wide range of temperatures, total pressures and partial pressures of various components. In a similar way as for one-component systems, an increase in surface coverage results in a corresponding increase of the role of the cooperative effects and of the related processes of local clustering and macroscopic ordering of the adsorbed molecules. To describe real adsorption systems it is necessary to simultaneously calculate the cooperative effects and the heterogeneity of the adsorbent surface. At present, the theory of adsorption equilibrium for gas mixtures based on the lattice-gas model is elaborated at the same level as for one component systems. The equations obtained for adsorption equilibria can serve as a basis for the verification of the applicability of the simplified equations which are widely used in the description of experimental data [12,13]. Below, we will discuss the basic results obtained for the case of approximately equal sizes of the components of the mixture. This is the most commonly used assumption (explicitly or otherwise) in most papers on the adsorption of mixtures. Rejection of this assumption highly complicates the description of the adsorption equilibria. This problem is briefly considered in Section 6. Special emphasis is laid on the molecular basis of the theories and on the physical meaning of the simplifications used. Before considering the influence of surface heterogeneity and lateral interactions between adsorbed particles (Section 3), peculiarities of the adsorption of mixtures on heterogeneous surfaces are considered in the absence of lateral interactions (Section 1) and on homogeneous surfaces but including lateral interactions (Section 2). Multilayer adsorption of mixtures on non-porous adsorbents, and in
106 porous materials is also considered in Section 4. The equations for the partial adsorption isotherms are used to calculate the mixture separation coefficient (Section 5).
1. T H E O R Y OF M I X E D G A S A D S O R P T I O N O N H E T E R O G E N E O U S FACES IN A B S E N C E OF L A T E R A L I N T E R A C T I O N S
SUR-
1.1. T h e lattice gas m o d e l for adsorption of a m i x t u r e While describing surface processes, their equilibrium and kinetic characteristics usually are defined for unit surface . It means that we are interested in an average of various physical quantities which are obtained by averaging over all the local fragments of the surface. In the lattice model, the local fragments are surface centers (sites). In the monolayer lattice model, the surface of the adsorbent A can be divided into sites M ah, where h is the site number of the surface. For a homogeneous of area ah: A = ~--,h=l surface, ah = A / M , where M is the number of sites on unit surface. For a heterogeneous surface the area of a site can change from one to another. Let the site's volume be Vh = o'hd where d is the linear size of a molecule. Also, let the number of components of the mixture be s. We will limit our considerations to the case of localized adsorption. Every adsorption site can be occupied by one molecule of the mixture or it can be empty; i.e., the number of occupation states of every site is equal to s' = s + 1. In the case of adsorption of component i on a homogeneous surface, the sites of the lattice structure represent a periodically repeating area for localization of molecules, in their minima of the potential energy. On a heterogeneous solid surface, the dependence of the potential energies Ui(r) on the coordinates r (r = (x, y , z ) ) is more complicated. A wide spectrum of areas of localization will exist for adsorbate molecules having different sizes and different binding energies. The positions of these potential energy minima are, generally speaking, nonperiodic. The situation gets still more complicated when the adsorption of mixtures is considered. Even for a homogeneous surface, the potential Ui(r) for various kinds of molecules i differ, and can have different positions of the individual points: minima, maxima and saddles. Those particularities are connected with differences in the interactions of molecules with the adsorbent. On a homogeneous surface, however, the periodic character of Ui(r) is kept and the partition of the surface into sites (when the sizes of the mixture's components are equal), can be done for molecules of any kind. Then, at the center of every such point there is an energy minimum for a molecule i (for example), and other molecules j there may have other coordinates for their minima. In any case, the positions of the local minima shall not differ more than by half a lattice constant. Every site h can be characterized with a set of local Langmuir constants a~, 1 < i < s, 1 < h < M. For a homogeneous surface, Aij = a~ - a ~ , does not depend on the number of the site h. On a heterogeneous surface, the potential Ui(r) can be different for various molecules not only by the current meaning of energy and by the position of the particular points, but also by the number of the local minima N i. As a consequence, if the partitioning of the surface into sites is made according to such a rule as for a single component (the center of a lattice site ~ the minimum U~(r)), then, for various molecules i, various kinds of fragmentation can be done which may result in treating various functions by various kinds of lattice sites. In particular, the sites of the lattice for the kind i can be without
107
the local minima of energy for molecules j. In order to use the advantages of the lattice model - those connected with a discrete change of coordinates - it is necessary to formulate some more formal rules of partitioning the surface into sites. Let us remark that all the sites have more or less similar sizes though some differences cannot be excluded. This limits their differences in such a way that the main condition for the admittivity of the model could be assured, i.e. every site can be occupied or free, and every molecule can occupies one and only one site. The procedure of partitioning the surface into sites turns out to be common for all the molecules of a mixture. It requires the presence of one value of M, such that N i < M, 1 < i < s. Of course, as the support for a chosen kind of molecules g, such a kind of molecules should be used for which the condition N t = M can be fulfilled. If there are several such kinds, any of them can be used as the supporting one. To the supporting kind g corresponds a partitioning of the surface into sites with area a~ (as in the case of adsorption of one component g), as well as the local constants of the Langmuir equation. For molecules of any other kind, formally, local Langmuir constants are introduced which correspond to their volumes v~. Let us denote those constant values as a~ (g). In consequence, s meanings of the local constants of Langmuir correspond to every center h"
(i)
4(e) = f f exp [-#,d drda/F~
where ei is the binding energy of molecule i to the surface. The integrals are over the coordinate of the molecule's mass centre r, inside the site volume v~, and over the orientations of molecules ft. The formula (1) can be presented in the form [6,11]
a~(g) = F~(g)/3exp [/3Qh(g)]/F i ~= s
i [flQh(g)]
(2)
where Q~(g) is the energy of binding of molecule i to the site h, F;,(g) is a statistical sum for the molecule i on the site h, F ~ is the same quantity for the molecule i in the gas phase, and a ~ ( { ) = F;,(g)~/F ~ is the preexponential factor, l~ = (ksT) -1. On a heterogeneous surface, adsorption sites are characterized by a different adsorption capability and their coverage depends on the value of that capability. We can introduce the concept of a local coverage of the site h characterized by a~(g)" e~(g) --
a~(g)pi
,
1 + k a~(g)pi
0h(g)= ~ e~(g)
(3)
i=1
i=l
where 0},(g) is the local coverage of site h by molecules of i, Oh(g) is the local coverage of the site h by all the molecules of the adsorption mixture, and pi is the partial pressure of component i. The macroscopic coverage of the surface can be calculated from the local coverages (3), by taking the sum for all the sites M
MO~=~ O~(e), h='
M or
M
o~= M-1 E O~(e)-- M-1 E h=l
h=l 1 +
9
ai,(e)p~ fi a~(e) i=1
(4)
108 M
and similarly, 0 = ~] Oh(g)/M. h=l
Equations (4) describe isotherms of adsorption (partial ones for molecules i, as well as the full one) on a heterogeneous surface in absence of lateral interactions between adsorbed molecules. 1.2. D i s t r i b u t i o n f u n c t i o n s for various kinds of a d s o r p t i o n sites For large M, the calculation of macroscopic values by eq. (4) is not suitable. To simplify the calculations of averages, one introduces distribution functions for sites of various kinds. For the one component system, such a procedure is simply the partition of all the sites of the surface into t groups of sites having similar adsorption features. This allows us to apply a simpler procedure of calculating averages: instead of averaging over all the centers, we average over the groups of identical centers (t << M). Such a procedure means that one does not average over the space coordinates, but energetic coordinates because in the first approximation, the adsorption capability depends on the energetic binding between the adsorbent and the adsorbate. In the case of mixture adsorption, to every surface site h there corresponds s values of the Langmuir constants, and for every kind of molecule, such a partition results in its own distribution function. If one wants to keep the analogy with one-component adsorption, it is recommended that one carry out the partition with the help of molecules of supporting kind g defined in the preceding subsection. Thus, we introduce the discrete distribution function for sites of various kinds according to their adsorption capability fq(g) defined by the following relations: fa(e) = Na(g ) M '
t(O E fq(g) = 1, q=l
aq(g)in _< a~(g) _< aq(g)r~ aeq(g)in ~ atq(g)~ ,
for
1 <_ i(# g) < s,
(5)
for i = g,
where Nq(g) and t(g) are the number of sites of kind q and the number of types of surface sites with respect to molecules g. In the relations (5), it is assumed, that in the group of sites q are gathered all the sites h for which we have ath(l) = atq(1). The constants a~(1) are defined in eq. (2) where the index h is replaced by the index q for the constants depending on the molecular properties of the sites, and not on the assigned number. On the other hand, in a general mixture, the classification of sites according to identical values of the constants ath(g) leads to an absence of relations between the local Langmuir constants for other kinds of molecules aih(g). These differences depend on the nature of the interactions between the adsorbent and the adsorbate as well as on the lattice structure. The differences between a~ (g) become more important as the differences of potentials of interaction between the adsorbent atoms i and the adsorbed molecules i and g grow. In consequence, the values ah(g ) can change i(g)lin, where i(g)fin and i(g)in and the smallest in a certain range from a~(g)in to aq aq aq values of a~(g) correspond to a given kind of sites q. The last relation (5) results from the condition that the sites are partitioned into groups.
109 If the condition analogous to the second condition of (5) is fulfilled for molecule i -# g and
aiq(e)in ~ aq(e)fm ,
(6)
we can talk about a definite correspondence of the kinds of sites and the adsorption i capabilities of the molecules of various kinds. In such a case, A~,(h) = ah(ig ) - a~(g) does not depend on the number of sites h relative to the sites of kind q. The last case is usually assumed in the majority of the papers dealing with the theory of mixture adsorption . The expression for the adsorption isotherm can then be written as: t(s
Oi = ~
t(s
fq(/)Oq(/) = ~
q=l
q=l
fq(gl aq(/)pi 1 + s 4(g)pj j=l
t{l)
e = ~_. q(e)Oq(g)
,
oq(e) =
q=l
o~(e),
(7)
i=1
where 8~(g) is the coverage of the sites of type q with molecules i for supporting kind of molecules g and ~q(g) is the full coverage of the same sites. In the general case of eq. (5), it is necessary to use the multidimensional energy distribution functions f(qz,-.., q~) = N(ql, ..., q~) M '
t(1) ~""
ql=l
t(~~ cb=l
f(q~, ..., qs) = 1 ,
(8)
where N(ql, ..., qs) is the number of surface sites possessing the adsorption capabilities aq1(1) for molecules of the first kind, aq2(2) is for molecules of the second kind, etc, up to a~(s) for molecules of type s (the dimension of the function be defined by the number of components of the mixture s); 1 < qk < t(k), 1 < k < s; qk is the type of site for the adsorption of molecules of kind k, and t(k) is the number of types of surface sites for adsorption of molecules of kind k. The second equation (8) defines the normalized distribution for this distribution function. Using the distribution function (8), we can express the partial adsorption isotherms as"
0i
=
t(1)
t(s)
f(ql,---,; qslaq(g)pi qs=l 1% E aJq(g)PJ j=l
~---~ ... ~ q1=l
(9)
Eq. (9) is a symmetrical picture (contrary to eq. (7)) that takes account of the adsorption capabilities of all kinds of molecules on various surface sites, but with the initial partition of the surface into sites done with respect to the molecules of the selected kind g. The use of another kind of molecule k for the partition of the surface into sites is given by some 9 ) and corresponding aq~(k) . other distribution function f(ql, ...,q,) and local constant a'h(k On the other hand, the calculation of 0i should not depend on the kind of the selected
II0 molecules at their identical sizes if they do not bind the position of the local minimum Ui(r) with the center of the site, and if one uses the formal partition of the space into areas having the sizes characteristic of the diameter of the molecules. Let us observe that the application of the idea of grouping lattice sites q causes the disapearance of the information about the spatial distribution of the lattice sites, belonging to this group. This can be done accurately only in absence of lateral interactions. When one takes into account the lateral interactions (and especially in kinetic considerations) the spatial distribution of the lattice sites of various types plays an important part and it is necessary to consider it. 1.3. T h e integral f o r m of the a d s o r p t i o n i s o t h e r m If the number of types of lattice sites t is large (1 < < t < < M), then we can integrate instead of summing in eq. (7):
#i = / f l ( x ' ) O i ( x ' ) d x ' , t xi n
~i(Xl)=
O = f f~(xe) i xitn
ai(xt)pi i+ aj(x')pj j=l
(I0)
0i(x')dx'
i=l
n
/ f (x )dx = 1 x.tx n
where x e defines the adsorption capability of molecule ~, zi,t ~ and x~i~ are the initial and the final values of x t, f t ( x l) is the continuous density distribution function of sites according to their adsorption capability, defined for molecules of kind ~. It characterizes the lattice sites existing on the unit area of the surface and having the values x t in the interval from x e to x l + d x l. The differential distribution function f t ( x t) usually normalized to unity. The a i ( x l) is usually the local Langmuir's constant for molecule i that corresponds to lattice sites h which have a given adsorption capability x e for molecules ~. Strictly speaking, instead of x t in the expression f t ( x t) one should use a t ( x t ) . Usually, however, ^ -- aq ^e = 5t ,~ c o n s t , and the basic contribution to the change of the one assumes that a~h local Langmuir constants comes from the energy of binding of a molecule to the surface Qtq = Q~h. In this case, x corresponds to this binding energy, and the upper and the lower intervals of integration are the smallest and the largest energy of binding of the molecule to the adsorbent surface. In the case of a large number of types of sites, eq. (9) can be written in the form: xL
x~o
-/.../,/xi,..., xs ax , /--'/f(xl,...,xs)dxi---dxs=l
(11)
,
Oi(x = j--1
111 where f(xl, ...,xs) is the multidimensional continuous distribution function of the lattice sites of various types having the adsorption capability from x~ to xi + dxi, 1 < i < s; the function f(zl, ...,xs) is a continuous analogue of the discrete function of eq. (8). Formally, when ones chooses the integral form of equation (10), the description of the adsorption isotherm becomes simpler. It involves a considerably smaller number of molecular parameters than in eq. (7). If we use eq. (7) we must know the t 9s Langmuir constants, where t = t(/), and ( t - 1)is the number of h(g). Even for a simple where case s = 2 and t = 10, we need 20 Langmuir constants and 9 values of fq(g). When we use eq. (10) we must know x~,~, z~i ~, two parameters of the Gaussian distribution function fe(x ~) (for example), and the correlation between the values of ae(x l) and ai(xe). A considerable simplification can be achieved when the following conditions are fulfilled: 1) the definition of fq(g), 1 < q < t for a given adsorbent surface composition, can be approximated to a good accuracy by the distribution function fe(J) and, 2) there exist some simple relations between ae(x ~) and ai(xe). To fulfill the first condition is not obviously a difficult task but the realization of the second one is more problematic. If we assume that a simple relation between he(Z.e) and a~(x ~) does not exist, passing over to the integral form of the equation does not give any advantage in comparison to eq. (7). The reason is that in both cases (for any concrete q in (7) or for any current meaning x t in (10)), a full list of all the local Langmuir constants aiq(g) or a,(xe), 1 _< i <_ s is necessary. That is why looking for simple relations between ae(z ~) and a~(x e) is a central question in the attempt to simplify the description of the adsorption of mixtures on heterogeneous surfaces. In order to find the relation between ae(z t) and a~(x e) one should consider the molecular interpretation of the binding between molecules and the adsorbent surface. 1.4. Physics of the adsorption of mixtures on h e t e r o g e n e o u s surfaces The fundamentals of the theory of adsorption of mixtures on heterogeneous surfaces were presented by Roginski and Todes [14-17]. Those fundamentals have remained practically unchanged till the present. As a measure of the adsorption capability of the lattice sites of various kinds, one needs the energy of binding for various kinds of molecules with a certain adsorption site. The preexponential factors of the local Langmuir constants ^~(g) = 5~(x~) ~ hi are considered to be independent of the kind of lattice site. In ~h(g) = aq this case, the binary relations between the local Langmuir constants for molecules i and j on various sites x ~ were found as a functional dependence between the binding energies of those molecules:
Qj(x') =
[Qi(x')]
(12)
In fact, in most cases, the shape of the function ~2ij depends on the molecular nature of the system. Roginski and Todes separated three kinds of molecules that differ by their potentials of adsorbate-adsorbent interaction. The dominant factors are: 1) nonspecific dispersive forces, 2) electrostatic forces of interaction between the charged particles of the surface and the permanent or induced dipoles of the adsorbate, and, 3) chemical adsorbent-adsorbate interactions. 1. In the case of non-specific interactions between adsorbate molecules and the surface, their potential energy of attraction can be represented as: V = -CasoZaa/d 3, where C is a constant, depending on the ionization potential of the adsorbate molecules and of
112 the atoms of the adsorbent, aa is the polarizability of the adsorbate molecule, asol is the polarizability of the surface atoms, and d is the molecular diameter. It allows one to "separate" the contributions of the surface and the adsorbate. We may consider in the first approximation, that if we equate the adsorption heats with the potentials of interaction Qi "~ Vi "~ asotgi, gi = C o l i / d i 3, then, when passing from one part of the surface to another, the adsorption heats should also change proportionally to the local polarizabilities between the studied parts ol~ot(xl). In such a case, the ratio of the adsorption heats of two different gases should be constant to a first approximation: Qj(x e) = flijQi(x ~) ,
~ij = gi , gj
(13)
for all points of the surface, even though the heats can change according to that part. Changes in Qi and Qj for various x l, take place in the same direction (likeness, see Fig.l). If we neglect the changes of the polarizability of the molecules of adsorbate ai and aj for various x ~, the proportionality constant can be estimated from the relation ~ij = aiaj ~ T~,r ~ ~ , where Ti,c is the gas-liquid critical temperature of gas i, and bi is the Van der Waals constant for gas i.
Q2
1
3
~ ~2
Q1 Figure 1. The relation between adsorption heats Q1 and Q2 of the adsorbed mixed species: 1 - changes in the same direction; 2 - changes in the opposite direction; 3 - noncorrelated dependence (black dots)[16]. 2. In the first approximation, the electrostatic forces of interaction also result in the relationship (13) where the proportionality constant depends on the nature of the potential. In the case of adsorption of polar molecules on metallic surfaces, the mirror image potential is proportional to #2, where #i is the dipole moment of molecule i. That is why when the mirror image forces prevail in adsorption, ~ij '~
~
. In the case of adsorption
of slightly polarizable molecules on ionic lattices, the basic role (as in case of dispersive
113 interactions) is played by the polarizability of the molecules and therefore/3~j ~ ~. In aS the case of the adsorption of permanent molecular dipoles on an ionic lattice,/3ij ~ ttj u~. Thus, in the case of the simultaneous adsorption of various molecules characterized by identical kinds of interactions (dispersive or electrostatic), one should expect roughly likeness relations between all the adsorption heats. 3. Chemical adsorption. Chemical interactions are characterized by a strong specificity of binding and the character of the relations between the adsorption heats Qi and Qj for various parts of the surface can be quite different. 3-a. If one considers the adsorption of adsorbates which of a similar chemical nature, e.g., various halogens or various olefins, then one can expect likeness changes of adsorption heats while passing from one part of the surface to another. At the same time one can expect that the relationship (12) will be linear, Qj = AijQi + Cij ,
Aij, Cij -
const.,
Aij > 0
(14)
or more complicated. 3-b. When one considers the adsorption of substances of opposite functions, e.g., acids and bases, then it can be expected an antilikeness will exist in the heats. In such cases the condition (14) is fulfilled, but with Aij < O. 3-c. Finally, some situations are possible when, for two chemisorbing molecules, correlations don't exist between the adsorption heats Qi and Qj. In Fig.l, a set of points corresponds to such a case, (Qi, Qj) for various parts of the surface. Cases 3-b and 3-c should be more probable when examining gas mixtures characterized by potentials of interactions of various types. These will also depend on the peculiarities of the surface topography. When the adsorption heats of the components of a mixture are related to each other by the function (12), then if we know them and if we know the distribution function for one of the kinds of molecule, e.g. i, we can obtain the expression for the distribution function for the other kind of molecules. It follows from the fact that a group of lattice sites of surface dM can be characterized by the adsorption heats of various molecules [17] dM = fi(Qi)dQi = fj(Qj)dQj ,
or
fj(Qj) = fi(Qi)
dQi
(15)
where dQ,/dQj follows from the relation (12). Let eq mean an inverse function to ~,j(Q,). Then dQ~/dQj = e~j(Qj). The sign of the modulus concerns to the antihkeness run of the curves. The sign of eij is positive for the surface fragments dM. In a particular case, for the likeness relation (14), eq. (16) takes the form: fJ(QJ)=~~-jfi( Qj-CijAij )
(16)
When A~j = 1, the functions f~ and fj are identical. A change from Qi to Qj produces a displacement of the whole distribution by a constant amount toward increasing Q (at the positive value of Cij) or toward the opposite direction in the case of negative Cij (Fig.2a). When Aij > 0, the function fjcan be calculated from f~ by a simultaneous extension of the scales along the axis of the abscissa in Aij, one time, and diminishing of the scale
114 along the axis of the ordinates by the same amount of times. In other words, the curves
fj(Qj) and f~(Q~) can be considered as similar to each another. The displacement of the whole curve along the abscissa by the value ~AO corresponds in this way to Cij # O. (see the curves on Fig.2b).
a
b
f, I
I
I
C12
2
,
I
I I
~-~ C12
Q
C12
I
C fl
I
I
I
I
f2
I I I
Q
I I I
I I l
I I i
Q
C12/2
Figure 2. Distributions of the adsorption energies of the components of a binary mixture: (a) identical distribution functions, A12 = 1; (b) similar distribution functions, A12 > 0; (c) opposite dependence, A12 < 0 [17]. For Aij < 0 there is an antilikeness change of the adsorption heats. According to (15), eq. (16) can be rewritten in the form: fj(Qj) = f~
(Co-Qj) IA,~l /IA,ji. In such a case,
the distribution fj(Qj) can be obtained from the distribution f~(Q~), with additional reflection around the vertical line Qj = -q-~, as shown in Fig.2c at A~j = - 1 . It should be noted that these conceptions about a likeness and an antilikeness of the changes in the adsorption heats of the gas mixture mean that, generally speaking, a whole range of functional relationships exist, instead the linear relation (14). These equations are partially expressible: Qj = AijQ'~ and Qj = C~j exp [-AqQi], where Aij, Cij and n are constants. In Fig.3, relationships are shown between f(Q2) and the uniform distribution function f(Q~) = const, for n > 1 (a) and between f(Q2) and the exponential function
a
f
I I I
', I I ,
b
"
fl --i
fl I I I
I I I I
"-4.. ! I !
I I I I I
!
Q
I
I I I I / ~
I I
I i
I
I
Q
Figure 3. Examples of the relations between the distributions of adsorption energy for the components of a binary mixture: (a) power-law relation, Q2 = A12Q~2. For the first component, a constant distribution is shown; (b) exponential relation between adsorption energies: Q2 = C12exp(-A12Q1), C12, A12 > 0; for the first component, an exponential distribution function is used, f(Q1) = con,st, exp(gQ1), g > 0 [17].
ll5
f(Q1) = const, exp[TQl] when 7 > 0 (b). In other words, monotonic relationships are admissible: OQj/OQi > 0 in the first case and OQj/OQi < 0 in the second case, for any z I. The case 3-c corresponds to the absence of correlations between the adsorption heats Qi and Qj on various parts of the surface. That means the presence of more complicated relationships (12) than monotonic, as well as fullfilling the conditions (5). In the last case, the distribution function turns out to be multidimensional and the expression (15) is no longer true. If one uses the multidimensional functions then the calculation of the thermodynamic characteristics of the adsorption mixture becomes complicated. In order to do it, some additional suppositions must be introduced. One of the simpler ones is the statistical independence of each of the functions, x~
s
f(x,,...,xs)= Hf(Xm)
,
/f(xm)dxm=l
,
(17)
d x.m
m=l
in
This case was considered to be fundamental by Roginski and Todes when considering the adsorption of a binary mixture of gases, one of which is chemisorbed and the other physically adsorbed. In Fig.4a one can see the section of a two dimensional distribution function in the absence of correlations between the distribution functions of the individual components. To any value of the adsorption energy of the second component from Q~ to Q fin 2 , there may correspond any value of the adsorption energy of the first component Q~.
~
f--r-f2 ~,
'Q1 ~,
~ -9-
"
I |
f
S ' Q1
J ' Q1 I
Q
Figure 4. Range and constant energy contours of a two-dimensional distribution function for binary mixed adsorption: (a) with no correlation in adsorption energies; (b) with correlation; (c) with an unambiguous correlation between adsorption energies. In the case of a partial correlation between the adsorption energies, a limited range of [')fin , values from Qi2"(QT) to ~2 (QT) corresponds to O~(Fig.4b) (i.e. not the whole interval (-}fin from Q~ to , ~ corresponds to a particular energy Q~). This situation corresponds
116 to condition (5). This case of course, corresponds best to the real physical nature of adsorption of mixtures but it is the most complicated. This is because in equations (9), (11) the order of integration (summing) is very important, according to the "internal" variables from the dependence of their initial and final meanings on the current fixed meanings of the other "exterior" variables. Functional relationships between the adsorption heats (12) mean strong correlations: to every value of Q~ there corresponds a certain single value of Q2(Q~). Such relations correspond to the conditions (6) (see Fig.4c). They are fundamental for practical calculations and they are going to be considered in more detail further on. General expressions for the partial adsorption isotherms with multidimentional energy distribution (for any number of components mixture) were given first in a paper by Balandin [18]. The author used, however, only the case of the complete similarity of the individual distribution functions. Jaroniec and Rudzinski [19-21] gave a deeper consideration of the case of multidimensional energy distributions. A practical application of that idea meets many difficulties and cumbersome calculations are necessary. In the paper [20] the isotherms of two binary adsorption systems were described: ethane-ethylene/carbon (Nuxit AL) at 333~ (experimental data by Syepesy and Illes [22]) and CO-N2/CsI at 83.56~ (experimental dat~ by Tompkins and Young [23]). Assumption (17) was widely used in the first papers by Roginski and Todes. It somewhat simplifies the calculation which still remains complicated. The unavoidability of calculating the effect of competition for various kinds of molecules does not allow one to simplify the expressions for the local adsorption isotherms, even for the case described by eq. (9). That is why a calculation of the isotherms is indispensable. This assumption was used in paper [20] to describe the adsorption data for the mixture (CO + N2)/CsI [23]. Jaroniec[21] has pointed out that the use of the extended empirical equation of Jovanovic for gas mixtures on a homogeneous surface has the form: 0({p}) = 1 - exp [- ~i~__1Kipi] for the local isotherm. Then using the approximation (17), one obtains the following expression for a heterogeneous surface:
0({p}) = 1 - 12i f exp [-Kipi] f(xi)dxi = 1 - 11I (1 - 0i(pi)) i=l
9 xl.
(iSa)
i--1
where the 0i(pi) are adsorption isotherms of the individual components. When s = 2, a similar equation was obtained by Roginski and Todes [14,15] who postulated that it is valid for any kind of adsorption isotherm for the pure components. Misra [24] proved that the result of averaging a local isotherm of Jovanovic with an exponential distribution function can be effectively approximated by the classical Freundlich isotherm. Jaroniec then obtained the following general form of Freundlich's isotherm for gas mixtures [21] ..F ~2 -lxlr~2Pl T.F..F ~1P2~2 , 0 = K F p ~ l+ix2p2
(18b)
where K F and 0i are the parameters of the Freundlich equation for the pure gases. This equation was used for the description of experimental data for the binary systems (CO +
117 N2) and (At + 02)[23]. Other results of this approach have been discussed in a survey by Jaroniec and Madey [12]. 1.5. Simplified m e t h o d s of describing the a d s o r p t i o n of gas m i x t u r e s In the subsection, the partial adsorption isotherms for mixtures with functional relationships between the adsorption heats of various molecules are discussed for ideal adsorption.
1.5.1. Quasi-Homogeneous Heterogeneous surface The term "quasi-homogeneous surface" to describe the adsorption of mixtures on heterogeneous surfaces had been introduced and applied by Balandin in his theoretical paper on catalytic processes [18,25]. The expression means a full similarity of the adsorption heats of the components - a case where the change of adsorption heats of various components remains constant (see Fig.2a) when passing from one part of the surface to another. This is the simplest form of relationship (14): Aij = 1, and it leads to the following relationship between the Langmuir constants for various molecules: ai(X i) -- aj(Xi)~ij
,
~ij =
~ / ~ j exp{-~Cij}
,
(19)
(For simplicity, the index g is omitted below). In fact, the condition (19) removes the concentration differences of kinds of molecules in relation to sites of various kinds. In such a case, eq. (10) has the form: Xfin
0= / Xin
f(x)a,(x)p~f dx , 1 + a~(x)pef
0i = 0~ig p---Li Per
(20)
Per = ~ 6ijpj j=l
It can be seen from the above equations that the total coverage of the surface 0 with the molecules of a mixture appears as if it were concerned with one a kind of molecule g at the effective pressure p~f. Balandin applied in his papers a constant and an exponential distribution function. For the first distribution function, he arrived at a generalized form of the quasilogarithmic isotherm of Temkin for a gas mixture:
0i =
Pi . in (1 + a ~ A i ) Aifl(xfm - Xin) 1 + a~Ai
Ai=~ '
s j=a ai
(21) '
_fin and a iis are the Langmuir constants for the lattice site characterized by the where ui greatest and smallest adsorption heats, respectively. His equation was repeated many times in other papers (see e.g. [26-28]). Temkin has shown [27] that the condition (19) allows us to write a partial adsorption isotherm for the components of a binary mixture of A and B, provided that we know the adsorption isotherm of the pure substance 0A = F(0A), where F is a functional relation such as: OA = p A F (r)/~r where r = PA + 5ABPS. In particular, when the pure component
118
0 <_ 7 ~ 1, then the partial isotherm is described by Freundlich's isotherm 0A = I'(~pA, F of a mixture of A and B can be expressed as: OA = KFAPA/(PA + 8ASPS) 1-'Y. In the paper [26], the condition (19) had been used to obtain equations for partial isotherms by generalizing the quasilogarithmic (for dissociating molecules), negative power and Freundlich's isotherms for an arbitrary number of components , and also can be adopted to express configurational entropy and enthalpy. In particular, in the middle coverage range, for negative power distribution functions, the system of equations for the $ partial isotherms can be expressed as: a~ = D (1 - 8vDS), D = y~/~-~j=~ yj, where 0v is a portion of free surface (6v = 1 - 0). Here, yi is related to the sites characterized by the maximum adsorption heats with 1 > ~ > 0 by y~ = (aipi) 1/m~, where m is the degree of dissociation of the molecule i in the gas phase. The value rni is usually equal 1 or 2. Relations (19,20) make it possible to use the results obtained for one component systems. Thus, the isotherms of pure substances can be extended to mixtures. In this way, in paper [29] a total adsorption isotherm was obtained which turned out to be in the general form of the empirical four-parameter equations of Marczewski and Jaroniec [30] and of the equation by Dubinin and Astakhov [31]. In the first case, the total isotherm has the following form:
0=
( ( a ~ p e f ) k ) ~/k 1 + (a~pef)k
where a~ is the Langmuir constant of the species ~, for the sites having the maximum adsorption heats. From the above equation, other empirical equations follow. They are generalizations of the Langmuir-Freundlich isotherm when 0 = k (the Toth isotherm when = 1, and the generalized Freundlich isotherm when k = 1). In the second case, the total isotherm can be expressed as: 0 = exp { - B ~
j (~-~f) }
where B ~ is a parameter related to the surface heterogeneity, and pat is the energetic parameter for the l'th component. From this formula, at j = 1, one obtains the Freundlich isotherm, and with j = 2, the Dubinin-Radushkevich isotherm. From the partial isotherms (20), we can obtain the linear relationship:
Oi ~il Pi In ~j = In ~jl + in--pj
(22)
The slope of the expression is equal to 1 for both a homogeneous surface and for the quasihomogeneous heterogeneous surfaces. Its deviation from unity means that condition (19) was not fulfilled.
1.5.2. Other Relationships (14) Tompkins and Young [23] and Glueskauf [32] considered the adsorption of a binary mixture on a heterogeneous surface with an exponential site energy distribution function and with C~j = 0 (14). The authors found two characteristic cases: c~' < c~' + R T and c~' > d~ + RT, where c~ is the heat of adsorption of molecule i on a completely covered surface. In the first case, the adsorption isotherm at medium coverage of molecules of
119 the 1st kind, in the presence of molecules of the second kind, is similar to the Langmuir isotherm: 01 = Kip1/
_(<7'1<7') }~ x-21-,2 pl + ~,-
,
a = (e~ -
RT) 1~7,
r
e~ > RT
(23)
At small values of pl the relationship (23) is linear, and at large values of pl the relationship (23) saturates. In the second case, in the range of medium total coverage, the molecules of the first kind - even in presence of a large excess of the second component - follow the Freundlich isotherm" 01 = KI{pl/[p~ ~'/<7) + g2p2]}RT/(<7-~7). There is, however, a tendency toward saturation at larger coverage. If the surface is practically saturated and it contains an excess of the more strongly adsorbed component, then in both cases, the condition (22) is fulfilled. Some more convenient correlational relationships than formula (22) were suggested by Jaroniec [33] based on the following generalization of the Langmuir-Freundlich equation for gas mixtures: 0= ~
1 +
(a;Pi)~
'
oi =
O(a;pi)e
(24)
E(arpi) '~
i=l
i=l
where ~ is a parameter of heterogeneity which characterizes the adsorption of all the components on a given surface, a~ corresponds to the Langmuir constant for the component with the maximum adsorption heat. According to formula (24), there is a relationship between the partial isotherms:
Oi
a~'
Pi
&~'
pj
ln~ = ~ln-- +~ln--
(25)
A complete survey of the application of correlations was given in [12].
2. A D S O R P T I O N O F A M I X T U R E O N A H O M O G E N E O U S WITH LATERAL INTERACTIONS BETWEEN ADSORBATE LES.
SURFACE MOLECU-
The body of experimental data on the adsorption of gas mixtures on homogeneous surfaces is significantly smaller than for the adsorption of single components. Nevertheless, the available data suggest strong mutual interaction effects to exist between the components of an adsorbed mixture. For example, from the experiments on the diffraction of slow electrons by single crystals of platinum metal, data were obtained suggesting that in CO and 02 systems there are present either as stratified and ordered phases of individual components or mixed ordered phases, coexisting with an ordered stratified phase [34]. Mutual attractions between adsorbed K atoms and CO molecules on Ru (0001) faces accelerates the desorption process and modifies significantly the nature of the thermal desorption curves in comparison to analogous curves obtained for the individual components [35,36]. These data emphasize the importance of the lateral interactions which determine
120 the non-ideality of adsorption systems. For description of these interactions, various thermodynamic approaches were used. The first attempts were presented in [37,38] as well as by Bering and Serpinski [39,40,41]. Further development of this trend was reflected in the numerous papers published by different authors e.g. Myers and Prausnitz [42], Hoory and Prausnitz [43,44], Bering and Serpinski [45,46] (more detailed information is given in [47]; discussion of these trends is not a subject of interest here.) Lateral interactions in mixtures of adsorbed gases were considered using virial expansions[48]. Calculations made on such a basis are, hovever, time-consuming. At present semi-phenomenological models based on the concept of local composition are widely used [49-51]. Lattice-gas models allow one to check the competency and accuracy of different semi-phenomenological models. We shall discuss lattice-gas model with the pair potential interactions between adsorbate - adsorbate. At first the equations for partial isotherms adsorption of binary mixtures have been obtained by Temkin [52]. This equations take into account a lateral interaction in the mean field approximation between the nearest neighbors. Later such equations for multicomponent mixtures have been considered by Jaroniec et al [53] and by Tovbin [54]. For the adsorption of binary mixtures the quasichemical approximation was introduced first by Barter and Klinowski[55], whereas for multicomponent gas mixtures it appeared first in [54]. Vicinal neighbors make a major contribution to the energy of lateral interactions amounting to about 70-90% of the total energy. The remaining energy is connected with contribution of more and more distant neighbors. As in the analysis of single gases [56], these contributions significantly influence the equiliibrium and kinetic characteristics by changing either the numerical values of the estimated quantities or the conditions of realization of a condensed state (and therefore the transitions between different condensed states). A generalization of the theories of the adsorption equilibria of mixtures by considering contributions from the second and more distant neighbours was given by Tovbin [57,58]. Closed equations were obtained in the mean field approximation, in the quasichemical approximation, and in the superpositional approximation (the last one is more accurate approximation than quasichemical). Let us limit the discussion to the first two approximations. We measure the distances between molecules by the numbers of the coordination sphere; z(r) is the number of sites in the r-th coordinational sphere (z(1) = z). Let s be the interaction of molecules i and j at the distance r; the interaction of the molecules i with the free sites is equal to zero ei~(r) = 0. The potential of every pair of molecules ij is characterized by its radius of interaction Rij. Let us choose the largest Rij and denote it by R. The equations of partial adsorption isotherms for multicomponent mixtures have the following form [58] Oi =
aipiSi [1"~t]j--'l ~" "pj'~j
(26)
The structure of the equations for the partial adsorption isotherms (26) is maintained at any radius of interaction R. The expressions for the function Si (which is used to consider the non-ideality of the adsorption system) change for different approximations.
121 In the quasichemical approximation, the functions of non-ideality of an adsorption system have the form: R
Si "- H[Si(r)]-z(r)
,
Si(r) = 1 +
r=l
xij(r)tij(r)
(27)
j=l
xij(r) = exp {-/3eij(r)} -- 1, where tij(r) = Oij(r)/Oi, and 0ij(r)is the probability of finding molecules i and j at a distance r. (Thus the function tij(r) is a conditional probability). For the functions 0ij(r), we have the following system of equations
(2Sa)
0ii(r)0jj(r) = 0~(r)exp {/3 [en(r) + ejj(r) -- 2eij(r)]}
s+l
Z 0,j(r) =
(2Sb)
j=l
which has a well-known form for R = 1 resulting from the theory of solutions [59,60] (but here R > 1). The system of equations (28) can be solved with respect to the unknown values 0ij(r) at fixed values of the partial coverages 0i. The dimension of the system of equations (28a)is equal to Rs(s + 1)/2, and increases rapidly with an increase in the number of kinds of molecules s. But, as previously noted [61] (for R = 1), the system of equations (28) can be reduced into an identity 1 - 1, when new variables X~(r) are introduced with the help of the relationships:
O,j(r) =
ij(r)X (r)Xj(r),
aij(r) = exp {fleij(r)}
(29)
After that, the functions 8~ and S~ in equations (27) and (28b) must be re-written in terms of the new variables: s+l s+l Si(r) -- E X j ( r ) / E Xj(r)aij(r) ' j--1 j--1
s+l 0i -" xi(r) E Xj(r)t2qj(r) j--I
(30)
This sharply reduces the dimension of the system (28) [561. The solution of the system (28) when surface coverages 8i are small, can be written as: 8i~(r) = 8,8j exp[~e,j(r)] : for every distance, the average fraction of neighbouring pairs of molecules i and j depends only upon their direct interaction. The attraction increases and the repulsion diminishes the number of pairs ij. In the mean field approximation, we have the following expression for
[
Si = exp / 3 E r=l j=l
z(rl%eij(r)
I
= exp ;~z
]
eijSj j=l
,
122 R ~ij = Z Z(~)'~j(~)/Z, r=l
In the first equality, the contributions of the second and further neighbours enter additively to the index of the exponential function. They can be re-grouped and, as can be seen from the second equality, give the concentration dependence of the function Si upon 0i. Thus, the calculation of the long range contribution to the interaction results in a change of the short range eij(1) into the effective parameter ~ij. Its value can be determined by the shape of the cij(r) and by the structure of the surface (as shown by At small interactions both approximations give similar results. But for an increase of ~eij, the difference between them is related to the fact that Oij(r ) differs from the product 0~0j which defines the function 8~*j(r) = 0~0j in the mean field approximation.
0.5
~
01,02
0.3
3
7 / /
///4
01156 3 9 15 21 lnP1 Figure 5. Partial isotherms for the first (curves 1 - 4) and the second (curves 5 - 8) components of a binary mixture with z=4,/3ell = -4, and/3e22 = -8. ~ex2 = -1 for curves (1, 3, 5, 7); -4 for (2, 4, 6, 8). aa > a2 for curves (1, 2, 5, 6) and aa < a2 for (3, 4, 7, 8). Fig. 5 shows some typical binary chemisorption isotherms for a wide range of surface coverages. The initial and the final values of the partial pressure pl change by 5.104 at a fixed p2. Curves 4 and 8 qualitatively correspond to the behaviour of C O and 02 on the platinum metals [61-63]. At small pressures, two variants are possible, depending on the relation between Ya and Y2, yi = (aipi) 1/m~. When Yl > Y2, the coverage of the surface by the first component is larger than the coverage by the second component and vice versa, when Ya < y2. When the pressure Pa increases, the lateral interaction of the adsorbed particles starts to play its role. In such cases, the sign of the parameter e12 is very important. The curves 1 and 2 correspond to the case al > a2. The value of 81 for attraction between particles 1 and 2 is greater than in the case of their mutual repulsion. The fraction of the second component increases if there is attraction between particles 1 and 2 and decreases for repulsive interactions (the curves 5 and 6). The same shape of the curves, only in a more explicit form, is observed for al < a2 (the curves 7 and 8). It should be noted here, that 7 passes through the maximum and diminishes when pl continues to grow. This is connected with the substitution of particles of the second kind
123 for adsorption of the first component. A comparison of curves 1 and 3 shows, that with growing coverage by the second component, the surface coverage by the first component increases more quickly than their attraction. The mutual repulsion of molecules of various kinds causes a decrease in the adsorption of both components. A detailed comparison of the physical adsorption isotherms of a binary mixture with their individual isotherms was carried out by Barrer and Klinowski [55] for various values of the lateral interaction parameters and relations between the Langmuir constants. The calculations were carried out for an equimolecular gas mixture with increasing total pressure of the system P = pl + p2, and with Pl/P~ = const. Fig.6 shows the characteristic curves for the adsorption isotherms calculated in this paper. The full lines represent the partial isotherms, the dotted lines correspond to the pure components (the parameters wij of the paper [55] are connected with the parameters used here by % = -2w~j/z).
a
b
c
1.0 0"8 !0.6
B
0.4 tZD
< tZD
B ~
-
B.
0.2
0.8 -
,
0.6-
/
A
y a ~ """ -
B
0.4-
-
tt
,
-
i
I
0.2 00
A,' " ~
.~
40
80 120 160
d
0
40
- BtfB t A
r~
-
I~ tla I
-
t
80 120 160 0
e
40
/
80 120 160 200 pressure/Tort
f
Figure 6. The dashed lines show isotherms of pure A and B. The full lines denote partial isotherms of A and B in their 1:1 mixture. Here, z=4, ai = (p.)-I with i=A,B; P~, = 760 Torr, P~ = 380 Torr. Values of ~wAA: (a) through (c), 0; (d) through (f),-2.303. Values of ~WAB: 1.151 in (a), (e), and (f,);-2.303 in (b);-3.354 in (c);-1,151 in (d); ~WBB: 0 in (a) through (e);-2,303 in (f)
[~5].
Weak repulsion between molecules A and B results in a decrease of adsorption of both components, for zero parameters of the pure gas interactions (a). Their attraction increases the adsorption of both components (b). A strong attraction of various components (c) results in a stepped change of the adsorption of both components. This case can be considered as the formation of dimers AB, for the relation 0A : 0B .w. 1 : 1. In their shape,
124 such isotherms are similar to the experimental isotherms [64] for the equimolecular mixture of NH3 and HCI sorbed in zeolites. The calculations confirm the existence of a strong interaction between the sorbed pair N H 3 . . . HCl. At high temperatures (245-315~ each gas taken separately hardly adsorbs. 1.0
-
Oi
0.5
--
2 .,.
m
.
.
.
.
.
1 m..-.
.
.
.
.
.
.
.
s s
"
"
"
""
3
s
I
- 16
-
-
I
I
I
-9
-2
5
In P
Figure 7. The effects of second neighbors on the partial chemisorption isotherms of a binary mixture. The quasi-chemical approimation is used with z=4, XA=0.1, T=450 K, QA=30, QB=27 kcal/mol, a~4= a~=104 Torr, /3eAA(1) = --2 kcal/mol, eBB(r) = 1.2EAA(r), EAB(r) = (eAA + s r-'l a n d 2; e2 = 7eAA(1), with 7 = 0 (curve 1), 1/3 (curve 2),-1/3 (curve 3). As the total pressure P rises, a surface is fined faster when attraction of the second neighbors is included and slower with repulsion, relative to absence of the second neighbor contribution. The genera] influence of lateral interactions remains the same when one allows for the contributions of the second neighbors; however, as attractions with the second neighbors increases, the tendency to form regular adsorbed films becomes larger. Hence, one has to take account of these effects upon monolayer ordering. For the systems with a strong enough attraction between A and B, and an absence of interactions between molecules of B (d, e), the attraction or the repulsion between the molecules A and B changes their partial isotherms in various ways. In the first case, the attraction between A and B keeps them together and the divergence between the partial isotherms is considerably smaller than in the second case. In both cases, there can be observed an adsorption substitution of the component B simultaneous with an increase of
125 component A. When the molecules repel each other, the quantity of displaced molecules of B increases and the maximum on the curves 8B(P) is moved toward smaller pressures (e). In such a case, one can observe a loop on the curve 8A(P) which is a witness of an instability in the adsorption system. Depending on the correlation between the parameters of the intermolecular interaction and the partial coverages and temperature of the system, its one-phase disordered state can become energetically unfavorable and condensation can take place. The concentrations of the co-existing phases can be determined from the material balance as well as from the equality of the chemical potentials in the system [65]. The number of phases which originate in the many-component adsorption system is considerable and should be treated separately. This question has been investigated only slightly (see ref.[56,66,67]). Changing the lateral interactions between molecules B (f) at constant values of the other parameters produces a change in the kind of gas removed from the surface. In this way, the lateral interactions can essentially influence the equilibrium characteristic of an adsorption mixture. The effect of contributions of the second neighbours on partial adsorption isotherms are shown on Fig.7. While concluding this chapter, we shall compare the equations obtained using the quasichemical approximation with the concept of local composition introduced by Wilson [49]. The importance of this concept is contained in the fact, that the ratios of the molar concentrations of the components j and k in the vicinity of can be expressed with the help of the Boltzmann approximation xJ---Li= 0j exp [flCjk]/Ok,
Cjk = eji -- eva ,
(32)
Xki
(A positive sign of parameters corresponde to attraction of molecules - another sign is used in papers [49,50]). According to the definition of the above pair-functions t~j (here R = 1), they are identical to the xj~ [49]. At small coverage, it can be seen that the relationship (32) is fulfilled. But in the general case, equation (32) is not accurate and any further considerations based upon it result in half-phenomenological models. Their parameters have no clear molecular interpretation and they can be treated only as empirical. For example,the application of equation (32) does not reflect condensation in the solutions. In order to describe this effect, one should introduce additional parameters or modify the models [50,51].
3. D E S C R I P T I O N OF A G A S M I X T U R E ON A H E T E R O G E N E O U S FACE T A K I N G A C C O U N T O F L A T E R A L I N T E R A C T I O N S
SUR-
3.1. General remarks The most important problem in the theory of adsorption is the simultaneous description of the effects of the surface heterogeneity and lateral interaction upon thermodynamic properties. In this case, one must consider a joint realization of all the previously considered effects: adsorption substitution of the molecules of various kinds (competitive adsorption) and cooperative behaviour in the adsorption system, as well as redistribution of molecules on the various types of sites on a heterogeneous surface. The complex character of the
126 joint effects of heterogeneity and lateral interactions has stimulated the development of various thermodynamic approaches and semi-phenomenological models. The best known were presented in the papers by Hoover and Prausnitz [43,44], Myers [68], Nakahara [69], Bering et al. [70-72], Danner and co workers [73-75] and their modifications. For small coverages it was Jaroniec [76] who used a virial approach to describe these adsorption processes. Various empirical equations are applicable only for narrow ranges of concentrations and temperatures and their forecasts should be verified by more rigorous theories. They also can help to determine the ranges of concentrations and temperatures where empirical relationships can be considered as well-grounded scientifically. Theories considering both heterogeneity and lateral interactions, based on the model of a lattice gas started more than ten years ago [77-80] (see also [56,62,81]). In these theories, the surface of the adsorbent is considered to be practically unchanged during the adsorption of any component of the mixture and at any concentration. It allows one to consider as stable all the distribution functions used for the description of the composition and for the structure of the adsorbent surface. To describe the surface composition the function used is the distribution of sites according to their adsorption capability (see section 1). Surface structure can be depicted in various ways that differ only in the accuracy of their descriptions. The most exact is obtained by considering a given arrangement of a set of sites on a certain rather small fragment of the surface. If translational operations on the surface repeat such treatment, then the given fragment approximation turns out to be suitable. Otherwise, in more complicated situations, it may be necessary either to increase the size of the fragment or to simplify the description of the surface structure. In the latter case, a cluster approximation can be applied, or the even simpler pair approximation. In the cluster approximation, the surface structure is described in terms of a cluster site distribution function d(q{m}R). It characterizes the conditional probability of finding a cluster consisting of a central site of type q and of its zq(r) neighbours among which there are mqp(r) sites of type p and for which the following relationship is fulfilled: ~-]~p=~ t mqp(r) = zq(r), 1 < r _< R. Here R is a radius of interaction equal to the greatest value of all the radii of the pair potentials of interaction between molecules i and j, where 1 < i,j < s, and s is the number of components of the gas mixture. In the pair approximation, the structure of the surface is described in terms of the distribution of pairs of sites fqp(r), characterizing the probability of finding a pair of sites of type qp on the surface separated by distance r. Those functions obey the relations: ~-']~p=lfqp(r) = fq, l
127 3.2. A d s o r p t i o n i s o t h e r m s The equations which describe the distribution of adsorbed molecules on a heterogeneous surface can be considered from the viewpoint of the accuracy of calculating the correlation effects (the mean field and quasichemical approximations) as well as the accuracy of calculating the surface structure. The most detailed description of the interacting molecules can be obtained by a "site-by-site" consideration of a certain fragment of the surface (a fragment approximation). In such a case, one investigates the partial coverages of every site of the fragment. Such a detailed description allows one to analyze the distribution of the molecules within the fragment, at any displacement of the sites of various types, i.e. the description is carried out in the real coordinate space and not in an energy space as done when one uses surface composition distribution functions. Cluster and pair approximations are intermediate approaches. Let us illustrate this by an example of application of the average description of the surface structure by the pair distribution functions . The equations representing a more exact description of the surface structure were published in [56,80,81]. In all the approximations, the contributions from every coordination sphere can be calculated independently. Differences are connected with keeping or abandoning the correlation effects. This leads to various forms of the equations. The coverage of the surface by molecules of i (partial isotherm) is given by the expression: t(0
0i = E
t(g)
fq(g)0iq(g) ' E
q= 1
fq(g) = 1,
(33)
q= 1
where the local coverages of the sites of various types are calculated from the following equation system (0~ = 0~(g))" R i v = eqi H %pieq
[Siq(r)]zq(r) ,
~"~ eqi eqv = 1 - a..,
r= 1
(34)
i= 1
For the quasichemical approximation: siq(r) = [1 + ~
p=l
dqp(r) ~I~ tqp(r)Xqp(r) ij ij ] , j=l
xiJqp(r)= exp (--/3eiJqp)-- I,
E
t
p=l
t~(r) = O~(r) , Oiq
(35)
dqp(r)= 1,
where dqp(r) = fqp(r)/fq is the conditional probability of finding a site of type p at a distance r from site of type q, 1 < r < R, and the functions 0~J(r) give the probability of finding molecules i and j on the sites of type q and p on distance r. For every value of r the pair functions 0qp(r) ij can be calculated from the system of non-linear equations"
O~(r)t~J~(r)
"i ii e i ~ ( r ) - e"~ ( r ) e- i ~ ( r ) ] } = O~(r)t~qp(r) exp {~ [eqp(r)+
s+l
E0~(r) j=l
s+l i
=/gq,
E0~(r) i=l
= ~ ,
(36)
128
0.5-
/~2
b
Oi
1.0-
1.0- Oi
3/ 2
0.5-
1
3~ .... /
. . . .
. - _ -_ _ 2--_3
1
I
I
I
I
i
-1
2
5
8
11
Oi
l.O"
C
1
s-
i
1
lnP
~
1.0"
//I
2
-1
5
8
I
11
lnP
d
0A
/
2
1
0.5
0.5"
4
3 I
i
i
4
2
. . . . . . . . . . . :::::::-==-::1 i
7
i
!
!
!
!
2 5 8 11 lnP -1 Figure 8. Partial physical adsorption isotherms of a binary mixture in the quasi-chemical approximation, with similar dependence of adsorption energies and with heterogeneous surfaces consisting of two types of sites: Q1A = 3.0, Q~ = 2.5, QA = 2.0, Q~ = 1.5 (all in kcal/mol), with z=4, ~ E . A A - - 1, e B B "- 1.2eAA, ~-AB = (~-AA "Jr e B B ) ~ 2 . Here, T=78 K, a~ = a~ = 10 -2 tort. (a) The effect of random surface composition upon the partial adsorption isotherms at XA =0.2 with fl = 0.25 (curve 1), 0.50 (curve 2), 0.75 (curve 3). Lateral attraction decreases the total pressure for the nearly full surface, relative to the case of zero lateral interaction (curve 1, Fig. 8d). As the fraction of sites with stronger adsorption capability rises at constant pressure, the quantity of both adsorbed components increases. (b) The effect of bulk composition upon the partial isotherms of adsorption with XA = 0.2 (curve 1), 0.5 (curve 2), 0.8 (curve 3), all for s = 0.5 (a random surface). As the fraction of component A in the bulk increases, its adsorbed amount increases and the amount of the other component decreases. (c) The effect of heterogeneous surface structure on the partial adsorption isotherms at XA = 0.5 and s = 0.5 for a regular (curve 1), random (curve 2), and a patch-wise surface (curve 3). Curve 1 corresponds to an ideal adsorbed solution with all eij - 0 and is given for comparison. The structure of a heterogeneous surface plays an important role in determining surface composition. On the patch-wise surface, two steps are clearly shown because each patch is filled independently. On the regular surface, the curve is smooth (without steps) as on a homogeneous surface, but its slope is smaller. The joint influence of a regular structure and lateral attraction results in the same effects as lateral repulsion on a homogeneous surface. At medium coverages, curve 2c can be described by a straight line, i.e., by Tem-kin's isotherm. Curve 3 for the random structure is intermediate; and two slight steps are manifested. (d) The effect of regular (2, 4) and patch-wise (3, 5) structure at various fractions of sites of the first type is shown: fl = 0.25 (curves 2,3) and 0.50 (curves 4,5) at XA = 0.5. The partial isotherms of component A are presented but partial isotherms of component B are omitted due to their small differences on the given scale. Curve 1 is for an ideal adsorption system at fl = 0.25. As the fraction of the sites of either the first or the second type decreases in comparison with the case fl = 0.5, the effect of the structure becomes smaller for the sites that are more numerous. -1
2
5
8
11
lnP
129 A reduction of the dimension of the equation system (35), (36) by replacement of the changeable values can be done for every coordination sphere exactly in the same way as for a homogeneous surface (see above). For the mean field approximation [78]: i v aqPiOq
-
"
i Oq exp --/3
Zq(r) r=l
t i
-- 8q exp{-flz ~
ij " eqp(r)
dqp(r) p=l
=
(37)
j--1
s ~'j
dqp
p=l
"
~qp~p}, j=l
R
-_ij
ij
% =
4, = 4,(1)
r----1
The second equality can be obtained by replacing the sums and regrouping the items. It shows that for any radius of the interaction potential, the concentrational dependence of the index of the degree of the exponential can be reduced to the case R = 1. The "long range" contributions change the value of the effective parameters of the "short range" parameter eqv. "ij For physical adsorption, the energetic parameters hardly depend on the types of site and eqv -~j ~ ~j. For longrange potentials and chaotic surfaces, there takes place a sharp reduction of the dimension of the algebraic equations (37) where the effective constant values aqi are equal to: aq " The resulting dimension of -~ = aqi exp ~Zq E j = I s such equation system is equal to s. The joint effects of heterogeneity of the surfaces with sites of the two types, and the lateral interactions (R=I) are shown on Fig.8. 3.3. C o n t i n u o u s distribution functions The transition to a larger number of sites on the surface does not change the physical basis of the distribution functions, and the procedure is limited to a change of discrete variables into continuous ones. Let us limit our considerations to the use of the pair approximation to describe the surface structure. An increase in the number of the neighbouring sites increases the number of variables, so the transition to continuous functions corresponding to the cluster and fragment approximations can be done in an analogous way. In case when relations exist between the adsorption capabihties of various molecules, the continuous pair distribution function of types of sites f t ( z ~, ytlr) characterizes the probability of finding a pair of sites with adsorption capabilities within the intervals from z ~ to x l + dx l and from y~ to ye + dy l at a separation distance r. In absence of functional relationships between the adsorption capabilities of various molecules, the continuous pair distribution function of types of sites f(5,Y I r) characterizes the probability of finding a pair of sites with adsorptional capabilities which are given by the indexes varying from to ~ + d~ (5 = xl,...,x,) for the first and from ~ to ~ + d-ff (~ = yl,...,y,) for the second site occurring at a separation distance r. For both situations the differential pair
130 distribution functions are normalized to the single distribution functions (10) and (11)'. Y~n
Y-~n
/ fe(xe, S [ r)dy = fe(xe) ,
/ f(~, y [ r)dy = f(~),
#b
q]
Y~n
(38)
Y-in
where 5i,~, Yi,~ and ~f~n, Yfi~ are the vectors of the initial and final values of the adsorption capabilities of the sites on the heterogeneous surface for various molecules. The expressions describing the stucture of the heterogeneous surface (38) and the thermodynamic functions, are similar in form, and that is why below, in order not to double them, we use below only one vector descriptor 5. Aside from the functions (38) one can determine distributions d(5, ~[ r) characterizing the probability of finding some sites with adsorption capability ~ at distance r from the site for which the adsorption capability is 5:
/
Yfin
d(~, Y lr) =
f(~, y I r)/f(~),
(39)
d(2,ylr)dy = 1
Yin
Using the distribution functions describing composition and structure of a heterogeneous surface, we can write expressions for the partial adsorption isotherms which incorporate interactions between the adsorbate molecules,
0i(X)-" ai(x)piSi(x)/[1 -[-s aj(x)pjSj(x)] ,
(40)
j=l
The form of the functions Si(x) describing the non-ideality of an adsorption system depends upon the approximation used to calculate the effects of lateral interactions. For the mean field approximation: Si(x)
=
i
Yfin
R
exp fl E z(~l r) r=l
d(2, Y lr)
-
Yin
s ~, Y l rlOj(Yld
=
j=l
YfinM
exp
f a( ,y I Yin
(41) j=l
R
,-~j(~,y) = ~ z(~ I r)d(~,y) I r)~j(~,Y I r)/[z(~ I 1)d(~,Y I 1)1,
(42)
r=l
where z(5 [ r) - is the number of nearest neighbours in the r-th coordinational sphere around the site characterized by the adsorption capability 5, z(5) = z(S[ 1); eij(5,~[ r) is a parameter of interaction between the molecules i and j, occurring on the sites of type 5 and ~ at distance r in the second equality (41), a regrouping of the items took place that
131 allowed us to introduce the effective energetic parameters ~ij(x, y) The use of expressions (11), (40) is connected with the preliminary solution of the integral equation (40), (41). For the quasichemical approximation:
R Si(x) = I I [ s i ( ~ l r=l
r)]-z(xlr)
Xij(Y, Y lr) = exp [-/3eij(Y,y I r ) ] - 1 ,
s Si(~ I r) = 1 + / d(~, y [ r ) E Xij(~, y ! r)tij(x, y I r)dy, J j=l Yin Yfin
(43)
where tij(5,~ I r ) = 0,j(5,ff I r)/0i(5), 0~j(5,ff I r ) is a pair function, characterizing the probability of finding a molecule i on a site of type 5 and a molecule j on a site of type at a distance r. These functions are subject to equations which are analogoues of the equations (36)
0ii(x,y I r)0jj(x, ff I r ) -- 0ij(x,y [ r)0ji(x,y ! r)exp {fl [eii(~,y I r ) + ejj(~,y [ r) --s
s+l s+l if[r) -- eji(~,y [ r)]} , E 0ij(~' Y lr) = 0i(x) , E 0ij(x, Ylr) = Oj(y). j=l i=l
(44)
The dimension of the equation system (43), (44) can be lowered by changing the variable values as explained earlier. The above system of integral equations represents a correlated arrangement of sites of various types. In the general case, their solution is complicated. In order to make this easier, it is necessary to use admissible simplifications. As a rule, such simplifications are connected with certain kinds of heterogeneous surface. Traditionally, the greatest interest is connected with two kinds of surfaces" patchwise and chaotic. For such surfaces, the equation systems are simplified. 3.3.1. P a t c h w i s e surface This case is the simplest and it has frequently been used to describe the adsorption of pure components [82]. The structure of the surface is described by the function d(5,~ ] r) = 5 ( 5 - ~) for any value of r, where 5 is here a delta-function. That means we neglect the contributions from the borders between various surface fragments. As a result, the coverage of every fragment is described by the equations of section 2: For the quasichemical approximation
R Si(x) --
II[si(~ ] r)]-z(~l~) , r=l
Si(~ I r)
= 1 -1t- ~ Xij(x , ~ ] r)tij(~,~ I r) , j=l
(45)
where the function X,j(5,~ ! r) and t,j(5,~ ! r ) are given by eq. (43). For the mean field approximation
Si(x) = exp [flz(x)~ ~ij(x,x)Oi(x)dx] , j=l
(46)
132 where parameter ~d(5, 5) can be determined from eq. (42).
3.3.2. Chaotic surface The condition of a chaotic distribution of the sites of various types on the surface (and in the case of the patchwise surface) makes it possible to use the information about the surface composition of adsorbent which is given by the distribution functions f(g), since for these surfaces we have: d(~, ~ [ r ) = f(~) for any value of r. In this case, the functions which enable us to incorporate the non-ideality of the adsorption system ~5i(5), are the expressions (41) and (43). These can be written as: For the quasichemical approximation: Yfin
Si(K, [r) - 1 + f fly) ~
(47)
Xii(K, y [ r)tij(K, Ylr)dy,
j=l
Yin
where the interpretation Si(5 1r) and S/(5) is given by eq. (43). For the mean field approximation" Yfin
Si(K) = exp flz(~)
f(y) Y--in
(48)
~ij(g, y)Oj(y)dy j=l
Eqs. (47), (48) along with eq. (40) determine the partial adsorption isotherm. Their use is not much simpler than calculations made using equations (40), (41), (42). The evaluation of the integral equations involves integration along the values ~, appearing in eqs. (47), (48). It order to simplify the task, some additional assumption must be made. In case of physical adsorption c~j(5,~ ] r) = e~j(r). Such an assumption fundamentally simplifies the equations for the mean field approximation. Eq. (48) can be rewritten as
Si(K) = exp [flz(K) ~ c-ij/gjl
(49)
j=l
where the partial coverages of the surface, 0j, appear in the exponent. The form of eq. (49) is essentially that of eq. (27) for a homogeneous surface. Hence we have the system of equations having the dimension s, for the partial adsorption isotherms
0i = -~'in
Ai(x)pi/ 1
d~, j--1
Ai(~) = ai(~)exp [ / 3 z ( ~ ) ~ e"ij0j] (50) j=l
Thus, instead of looking for the local coverages 0,(5) (or 8~(s for the discrete distribution functions) it is necessary now to solve a nonlinear system of equations with respect to the 0i's. For such cardinal simplification in the quasi-chemical approximation, one must introduce an assumption about the additivity of the contributions from surface heterogeneity
133 and from the lateral interactions. Then, by analogy to eq. (49), we change the function of non-ideality of the adsorption system into the function for a homogeneous surface (28)
Si(g I r) = 1 + ~
tij(r)xij(r) ,
(51)
j=l
So the expressions for the partial coverages have the form as in eq. (50) where the coefficients Ai(5) are given by: Ai(~) = ai(~)l-I
1+
r=l
(52)
tij(r)xij(r) j--1
Equation (52) represents the generalization of the results obtained by Hill [83] for chaotic surfaces with mixtures of the adsorbed molecules. From equation (50) for the corresponding distribution functions f(5), one obtains the results of Jaroniec et al [55,84] which generalize the Langmuir-Freundlich equation for gas mixture on chaotic surfaces. This gives a possibility of molecular interpretation of the parameters in the models [55,84]. It should be noted that the hypothesis of additivity of the effects due to surface heterogeneity and to lateral interactions is in general in contradiction to the idea of calculating direct correlations between the neighbouring molecules in the quasi-chemical approximation for which the local coverages of the neighbouring sites are important and not just the surface coverage. Its applications should be controlled by calculations using more exact formulas such as (47). The hypothesis brings us to expressions for the partial adsorption isotherms of the components of a mixture which are analogous to those of section 1. In a series of situations, the integrals in those expressions can have simple analytical forms. This possibility is related to the correlations between the heats of adsorption of various components, and to the shape of the distribution function f(5). For an illustration we discuss the case of a uniformly heterogeneous surface for which expressions can be developed for the whole range of surface coverages. 3.3.3. U n i f o r m l y h e t e r o g e n e o u s surface We consider examples of the relations (14), corresponding to the likeness and antilikeness correlations of the energies of the components of the adsorbed mixtures. Let us write y(x) = (ai(x)p~) 1/m' as yi(x) = Ciexp(/~x), Ci = (fzip,) I/m'. Then, the expression (14) can be represented as yi(x) = [yj(x)]~ crij = Ci(Cj)-~'3. If aij > 0, we have likeness behaviour of the adsorption coefficients of molecules i and j. If on the contrary, aij < 0, the antilikeness; aij is the shift of the values of the adsorption coefficients for molecules i and j. To the assumption of a similar likeness behaviour of the adsorption coefficients for various kinds of molecules, there corresponds the expression yi(x) = yj(x)a~j. To the assumption of a strongly antilikeness behavior of the adsorption heats, there corresponds the expression yi(x) = C, Cj/yj(x). Using the tables [85] of the natural and rational values of aij, we can rewrite the system of equations by introducing elementary functions. With these limitations, these equations can be written as:
Oi
Xin) -1
- - f(x) Xin
exp [fl~ix]/
+
exp [tic,ix] j=l
dx,
(53)
134
1.0-
Oi
a
3
1.O-
4
Oi
b
0.5-
0.51
32
.--"-4-2
2222--f 3
-1
2
5
8
11 1.0
14 Oi
0.5
lnP
- '1
2
5
8
11
14
InP
C
3
4
3
Jd
_7, ~_-- .~ -_--- _ ~ ~ ' - = ~ " "
-'1
;1 ;4
ln e
Figure 9. Partial physical adsorption isotherms of a binary mixture in the quasi-chemical approximation', with similar dependence of adsorption energies and with heterogeneous surfaces having continuous distribution functions f(x): QB(X) = QA(X) = 0.5 kcal/mol, Q~A? = 1.2, Q~OH = 3.2 kcal/mol, z=4, t~E.AA "" 1, e B B - " 1.2eAA, i C . A B - " ( ~ . A A + eBB)~2, a~t = a~ = 10 -2 tort, T=78 K. (a) The case of a uniform distribution function: f(x) -" (QAoH -- Q HAA ? ) -1 , ZA = 0.2 (curves 1,4), 0.5 (curve 2), 0.8 (curve 3); for curve 4, eBB = 0.7EAA. As the fraction of one component in the bulk increases, its adsorbed fraction is also increased, as shown in curves 1 - 3. Decreasing the lateral attraction between molecules of B causes the quantity of the adsorbed molecules A to increase, and that of molecules B to decrease. (b) The gaussian distribution function f ( z ) = e x p [ - ( z - ~.)2/2a2]/n, where n is a normalization factor and a =1, ~' = ( Q A o H - Q~A?)/2 = 2.2 (curves 1,3) and 2.7 (curves 2,4) kcal/mol, ZA =0.5, for curves 1 and 2; all eij =0. When the lateral interaction is larger, more rapid filling A of the monolayer is present (compare curves 1 and 3 with 2 and 4). As the range ( QA li~ -Qi,~) increases, the region of monolayer filling becomes larger (compare curves 1 and 3 with 2 and 4). (c) Comparison of partial adsorption isotherms for various distribution functions f(x) at XA =0.2, QAoH --QHA? A = 2 kcal/mol. The uniform (curve 1), gaussian a = 1, ~, = 2.2 kcal/mol (curve 2), and exponential functions f ( z ) = exp(Tx)/n, 7 = 1 (curve 3) and 7 = -1 (curve 4). The uniform and symmetric gauss distribution functions are characterized by similar isotherms. As the fraction of the sites with strong adsorption capability increases, the surface is filled faster; a decrease in such sites results in the opposite effect.
135 where the coefficient ai characterizes the behaviour of the heat of adsorption of molecule i relative to the heat of adsorption of the selected type of molecule I (at = 1), for the sake of simplicity given the index I is omitted. For example, for a mixture of molecules with a similar likeness behavior for the adsorption coefficients of all components of the mixture, a~ = 1, we have Oi = CiSi In {(1 + C exp[~x~])/(1 + C exp[~xin])}/(~C(xf~ - Xin)) ,
c =
(54) i--1
In absence of lateral interactions, the functions S = 1 and eq. (54) reduces to the well-known expression [18]. In this way, one can arrive at a closed systems of equations which make it possible to account for the lateral interactions of the adsorbed molecules, as well as for the heterogeneity of the surface. Their dimension is equal to the number of the components in the mixture. These equations are nonlinear with respect to the values of 8~ in the functions Si. The simplifications which we have made while developing these equations are, however, sufficient that their applicability can be controlled with the help of some still more accurate equations. Curves on Fig.9. are illustrated the joint effects both the lateral interactions and the heterogeneity of the surfaces describing by continuous distribution functions. 4. M U L T I L A Y E R A D S O R P T I O N
OF MIXTURES
The description of the multilayer adsorption of gas mixtures appears in many practical calculations [1,2]. The first theoretical work by Hill [86] (see also studies by Bussey [87]) are a generalization of the BET model to gas mixtures. Hill's results were used by Arnold [88] for description of experimental data on the adsorption of a O~, N2 mixture on anatase for various compositions of the gas phase. Similar to the BET equation for the pure components, the equation of Hill predicts the quicker increase of the full coverage of the surface which was observed in the experiments. The principles of the BET model have been considered in ref. [89] for one component systems. In order to describe multilayer adsorption, phenomenological approaches have been used. Thus, Sircar and Myers [90] used a combination of the potential theory of adsorption and the theory of the ideal adsorbed solution, while Gonzalez and Holland [91] proposed a kinetic method to obtain the equation for bilayer adsorption.The main idea of their work reflects one of the postulates of the BET theory, that the influence of the surface does not concern the second and the further layers, so the isotherms for two component mixtures can be represented by: = g0(1) ,
A
B
g = 1 + a 2ph + % P s ,
(55)
where 0(1) is the total adsorption isotherm of a mixture in the first surface layer, and the quantity g expresses the coverage in the second layer. The form of the factor g was chosen by considering the two following limiting conditions: coverage should be equal to one at decreasing pressure of both components of the gas mixture, and it should increase without limit with increasing pressure in the adsorption system. Jaroniec [92] generalized that approach [91] for heterogeneous surfaces. Surface heterogeneity changes the function
136 in the expression for coverage in the first layer, although the general structure of equation (55) remains unchanged. As for the function g, any function can be chosen, provided that it fulfills the two limiting conditions. In the paper [93] dealing with many component S ai2pi) was proposed. mixtures , the function g = exp (~-~i=1 The description of the multilayer adsorption of a mixture is basically similar to that of calculating a concentration profile of non- electrolyte solutions in contact with a solid surface. That is why, for the description of mixture adsorption, it is necessary to use the same theories. With vacancies, the number of components s of the adsorbed mixture corresponds to the (s + 1)-component solution. The equations developed using the lattice model for surface regular solutions with an arbitrary number of components were proposed in the papers by Smirnova [94-96], as well as in the papers [97,98]. In all those works the quasichemical approximation was used to calculate the effects of the interactions between the nearest neighbours for specific interactions at various orientations of the adsorbed molecules [94,95]. It was assumed that only the molecules of the first layer interact with the surface. In paper [99] one considered more distant neighbour contributions as well as the possibility of changing the internal degrees of freedom of the molecules and their parameters of lateral interaction in various layers of the surface region. In paper [100], equations were obtained for the concentration profiles of molecules in the presence of the pair interaction, as well as for the three- and many-particle contributions to the short range potentials. These papers consider adsorption on homogeneous surfaces. The theory of adsorption of a mixture of molecules on a heterogeneous surfaces was elaborated by Tovbin [101,102]. He obtained equation systems for the cluster distribution functions by considering the pair interaction potential between molecules at any terminal distance, and obtained closed expressions for the quasichemical and for the mean field approximations.
4.1. A lattice model for mixture multilayer adsorption In adsorbed systems, in addition to the heterogeneity of the surface we also must deal with the nonuniformity of the distribution of molecules in the surface region. In the lattice model, this is reflected by the fact that the sites in various layers turn out to be sites of various types. The description of the surface region should reflect a strict sequence of the layers, starting from the first (surface) one up to the final layer of this region ~ where the density of molecules is practically the same as in the bulk phase. Here ~ = L / d , where L is the width of the surface region, d is an average diameter of the molecules of the mixture having more or less the same size. The layers are considered to be situated parallel to the gas-solid interface. The strict alternation of the layers can be calculated with the help of cluster distribution functions, which is why the structure of the whole surface region should be described by these functions. We shall assume that for the multilayer adsorption of a mixture, the same procedure can be used which was discussed in the subsection 1.1. This is based on the partitioning of the surface into separate sites of area 6~. The volume of the first monolayer is composed of the volumes of the separate sites v~. For a flat separation border, the second layer and all the following are divided into sites with respect to a molecule of the reference type I. For a rough interface, , there can occur atoms of adsorbent in various layers of the surface region. In such a case, the region which is not occupied by the adsorbent is also separated into sites v~ with the the molecules of reference type ~, when the partitioning of the surface of adsorbent is made into areas $~. For the surface region, the index h denotes
137 the sites of a volume structure and not those of the surface, just as in subsection 1.1. We shall also assume that the same way of grouping the sites according to their adsorption capability is kept as in subsection 1.2. The type of sites in the the surface region is determined by the layer number k and the value of the interaction of molecule i with the adsorbent Q~q(k) which is connected with a local Langmuir constant aq~(k) = aq Ai (k) is the preexponential ^~(k)exp[~Q~(k)], where aq factor of the local Langmuir constant. Its form is similar to that given by equation (2). Let tk be the number of types of sites in layer k. Then a general number of types of sites in the system is equal to t = ~]k=l tk. In a similar way, for every layer we can introduce its own distribution functions, characterizing the composition of a layer fq(k) and its structure d(q{m}R)k: tk
tk
Z fq(k) = 1 ,
Z
d(q{m}R)k = 1, Z
q=l
6q(k)
mqp(k Jr) = zq(k J r ) ,
(56)
p--1
where rnqp(k I r) is the number of the neighbours of type p at a distance r from the central site q in the layer k and zq(k I r) is the number of neighbours at a distance r around the site of type q in layer k ; 5q(k) is the number of a different clusters with a central site of type q in the layer k. Let the mole fraction of the coverage of the sites of type q with molecules i in layer k be expressed by O~(k). Then the partial coverages in layer k by these molecules and the partial coverage by these molecules throughout the surface region can be written
tk 0i(k) = ~
,` fq(k)0q(k) ,
s
0i = Z
q=l
0i(k)Lk/L,
k=l
L=
Lk,
(57)
k=l
where L is the relation of the sites in layer k to those in layer x (in the bulk gas phase) which can differ in their structural imperfection (for example as in their roughness) and in the structure of the adsorbed solution close to the surface relative to the bulk phase. The total coverages in layer k and throughout the surface region can be determined in the following way: O(k) =
•
Oi(k)
,
0
--"
i--1
i--1
The total numbers are given by Ni = LOIN,,,
•
Ni
Oi =
•
O(k)Lk/L,
(58)
k=l
of molecules i and their excess contents I'i in the surface region
ri = N,` s
- O~(,~)]L.,
(59)
k=l
where N,` is the number of sites in a homogeneous layer of bulk phase. For the calculation of the distribution of the values (58) and (59), it is necessary to know O~(k) which can be found from the solutions of the corresponding algebraic equations. As previously, we shall limit our considerations to interactions between neighboring molecules in the quasichemical and in the mean field approximations.
138 4.2. D i s t r i b u t i o n s of molecules in the surface region The distributions of molecules in the surface region can be described by the system of equations: aiq(k)piO;(k) = Oiq(k)Siq(k) ,
s Oiq(k) + Oq(k) = 1, i=l
(60)
where the function of non-ideality S~(k) help in the calculations of the contributions from various components of the adsorption mixture. In the quasichemical approximation, the system of algebraic equations consists of two groups. The first group of equations describes the local distribution of molecules of a mixture on various types of sites (60), where (for simplicity we assume the case R = 1) Sq(k) = E d(g{m}l) 6q(k) (61)
II H 1 + 0~(kn [1){exp (-fle~(kn I1)) - 1}/0iq(k) n=k-1 p=l j=l
The second group are equations for the local portion of the average amounts of pairs of various molecules occurring on sites of various types at distances r, where 1 < r < R. The equations of the second type resemble the analogous equations (36) and are omitted here. The difference consists in the fact that for the multilayer model, the type of site is given by two indexes: the number of the layer k and the number of the type of site q occurring in a given layer. In the mean field approximation, the function of non-ideality can be written as (R = 1):
[
sin(k) = exp
,,o,
s
--flZq(1) E E dqp(kn [ 1) n=k-1 p=l j=l
]
ij (kn [ 1)Oip(n) eqp
(62)
where dqp(kn [ r) is a conditional probability of finding the site of type p in the layer n at distance r from the site of type q in layer k. In the majority of tasks where the interest is focused on multilayer adsorption, it can be assumed that the parameters of interaction do not depend on the types of sites:
4.3. Simplified e q u a t i o n for the full i s o t h e r m For one component systems, the expression (55) has its motivation within the lattice gas model to a degree that makes it possible to formulate assumptions resulting in such formulas. Thus, the approximate isotherm of multilayer adsorption [103] results in a universal form of the factor g with arbitrary assumptions for the properties of the first monolayer in an adsorption system: the surface can either be homogeneous or heterogeneous, and lateral interactions can be present or not. In fact, there arises the possibility of obtaining some analogous approximate equations for the multilayer adsorption of mixtures. In this case, one should use the equations from section 4.2 and limit the consideration
139
300[ V
V
._-
240
2
.-'''
.-''- - "
180
00/160V 600
0
V
0
x
0.03
0.2
0.06
0.09
0.4
0.6
_.
0.8
x
Figure 10. Comparison of one component multilayer adsorption isotherms for oxygen (curve 1) and nitrogen (curve 2) on anatase. At low coverages, the approximate equation (63) was used to find Langmuir constants. The calculated (solid lines) and experimental data of Arnold [88] (dashed lines) are shown. Curve 1 is shifted by unity on the X axis. Curve 3 (50.2% 02 and 49.8% N~) was fitted by the parameters of the approximate equation with the help of the total adsorption isotherm. Thereupon the fitted parameters were used to calculate the total isotherms at other bulk composition without refitting. V is the volume of the adsorbed gases, x = P/P0. to the case where one considers the interactions between nearest neighbours on flat heterogeneous surfaces and a short range potential of adsorbent - adsorbate interaction. To arrive at the approximate equations one should eliminate the partial pressures (by dividing of all the equations of the system into the equation for a volume gas phase) and analyze the systems of equations describing the distribution of molecules of the mixture in the volume close to the surface according to the small parameter/3eij << 1. The details of these considerations result in a final expression that agrees with those for the approximate adsorption isotherm of a one component system [103]. Finally we obtain: t
0 = g0(1),
0(1) = E f q A ~ P / { 1
+ AXqP} ,
(63)
q=l
g = {(1 - 5P/Po)/(1 - P/Po)} ~/2
1 s
Aq =
i= 1
a;(1)xi
140
],'
V
Jf
2280
,'/:1
,'1:1 ,7,7
1710
1140 1
f 570
f 0
0.2
I
I
I
0.4
0.6
0.8
x
Figure 11. The predicted and experimental (dash-dotted lines [88]) total multilayer adsorption isotherms for the N2 - 02/mixture on anatase at 14.9 (curve 1) and 85.3 % 02 (curve 2). The same fair agreement was obtained at other bulk compositions. Curve 2 is shifted by unity on the Y axis. where 6 = ( z k k - 1)/z, x~ is the molar fraction of component i in the gas phase, P is the total pressure in the gas phase, and Pi = Pxi. Here 0(1) represents total isotherm for a heterogeneous surface in the absence of lateral interactions between molecules in the surface monolayer. At small pressures, equation (63) reduces to equation (7). If the number of the site types on the surface is large, then one can use continuous distribution functions and one arrives at equation (10) for 0(1). If one considers lateral interactions, then the factor 0(1) becomes expression (33)-(35)in the quasichemical approximation or (33),(34),(37) in mean field. Finally, for a homogeneous surface, the factor 0(1) is expressed by equations (26)-(28) and (26), (31), correspondingly. Thus, the lattice gas model gives universal expressions for the factor g and equation (55). This expression is essentially different from the empirical functions proposed previously. The expression for g (63) has the same form as that for one component systems. There we had P = p whereas P = pa + . . . p s for the many component systems. The saturated vapour pressure P0 depends on temperature and on the composition of the gas mixture. Figs. 10- 12 illustrate how the simplified equations apply for describing the multilayer adsorption isotherms for the system N2 + 02/anatase. In conclusion, while developing equation (63), we introduced some greatly simplified assumptions. Although (63) is considerably better for describing the full isotherm of multicomponent adsorption over a wider range of pressure than the various modifications based
141 on the BET model, its parameters are determined by the adjustment of the calculated curves to experimental data and have no obvious molecular interpretation. 5
........
-
3 2
0.5 1
~N2
1.0 -
""~-~--_
0.5-
- ~ --
"
1
""-~ 9 3 "
~
4
- 5 0
I
0.1
I
0.5
i
I
0.9
x
Figure 12. The values (i = Vi/V, ii=N2 and 02, found for various x with use of the experimental data [88], and their linear approximation obtained from the experimental points Xl =0.1 and X2 = 0.9: (i = [(i(X~)(X2- z ) + ( i ( z - X1)](X2- X1 ). This procedure was applied to extract the partial contributions from the total isotherm, since the distinction between mixed components vanishes upon constructing the approximate isotherm in this case (63). Therefore, one needs additional information to find the partial isotherms: 0~(x) = O(z)(~(z). As a result, the use of equation (63) together with any points X1 and X2 enables one to determine partial adsorption isotherms in the entire range of pressure.
4.4. Adsorption
of a mixture
in p o r e s
The adsorption of mixtures in pores can be formally described by the same equations used in the case of multilayer adsorption discussed above (subsections 4.1 and 4.2). The difference consists in the fact that the molecules can adsorb in the field of action of both walls of a pore. This can provoke an essential increase of the adsorption value. For wide pores one can separate such volumes in a pore where the molecule is not under any influence from the walls of the pore. In such a region, one can use equations (60), (61) or (62). The solution of the equations for all regions can be done together. Another peculiarity of adsorption of mixture of molecules in pores consists in the fact that the critical conditions of condensation inside the pore differ from the analogous conditions in the bulk gas phase. They may also depend on the relations between the
142 components of a mixture. It was deduced in paper [105] that the conditions of condensation of one component systems in pores with heterogeneous walls give critical temperatures and coverages that depend not only on the sizes of the pores, but also on the character of heterogeneity of the walls. Similar effects should also be observed for the adsorption of mixtures. Application of the lattice-gas model for describing adsorption Ar+Kr mixture in slit-like carbon micropore is given on Fig.13 as well as a comparison with the analogous molecular dynamic calculation [106].
16
l~J~
~
ff~\
Figure 13. Partial Ar adsorption isotherms for an Ar - Kr mixture in a slit-like graphite pore. (a) shows the results obtained in [106] by density functional theory and molecular dynamics simulation; and (b) shows the same curve obtained from the lattice model using molecular parameters from [106]. Both methods give the similar results.
5. T H E S E P A R A T I O N
COEFFICIENT
OF A M I X T U R E
The separation of mixtures by adsorption is characterized by the degree of purification of any particular gas from some other gases. For the quantitative evaluation of the degree of purification one can apply the double separation coefficients aij of components i and j for an adsorbent. The separation coefficient is determined [2,107] as follows: aij = 0iPj/0jPi 9
(64)
An important characteristic of adsorption separation is the separation coefficient concerning pairs of components, one of which occurs in a microquantitative amount and the other one is a "macrocomponent" as it determines the effectiveness of the purification of the special component by adsorption. It is a hard task to find experimentally such separation coefficients and therefore much effort is dedicated to its theoretical calculation. From the definition of the separation coefficients it follows that, in order to calculate them it is necessary to know the partial isotherms of adsorption which relate the partial coverages of the surface 0i with the pressure of the components in the gas phase. Let us limit our
143 considerations to binary mixtures only and let the microcomponent be denoted by A and the macrocomponent, by B. Let us study only the case when all the components of mixture occupy only one site of the surface. To calculate the separation coefficient a = aAB, one can use the previously considered partial isotherms of adsorption determined by the molecular features of the adsorption system. Let us study the relationship between the equilibrium coefficient of competitive adsorption in a given model with the separation coefficient. Competitive adsorption can be formally expressed as Agas + B~d, ~ A~,ds + Bg,~,. The thermodynamic equilibrium constant KAB for this process has the following form: /(AB = OAZ'/APBTB/(OBzysPATA) = KABZ'/A/ZYB, where 7/ and ~i are the activity coefficients of component i = A and B in the gas phase and in the adsorbed phase, and KAB is the equilibrium coefficient of the process. If the gas phase is ideal, then we have 7i = 1 and a = KAB. A calculation of the effects of nearest neighbor lateral interactions in the quasi-chemical approximation for heterogeneous surfaces described by the pair approximation leads to the partial isotherms (33)-(35). These equations give the separation coefficient as: t
t
=
fqqOq(SJ
/Z q= 1
,
q= 1
where the function S~ is given by eq. (35). For the binary systems microcomponent A macrocomponent B, this expression can be simplified, since 0A --+ 0. Then, the separation coefficient A depends on the local Langmuir constants of both components for various types of sites, on their parameters of lateral interactions, and on the composition and structure of the heterogeneous surface [108,109]. For many adsorption separations of mixtures, zeolites are used. Below we present the results of the calculations [110,111] of the separation coefficient of a microadmixture macrocomponent for the systems X e - K r and UH4 - N2 (the microcomponent is given first) using the lattice model. The calculations were carried out employing the information about the potential energy of molecule of every component in the zeolite cavity and about the potential of the intermolecular interactions. To calculate the potential energy, the data on the structure of zeolite were used (its model is discussed in papers [110,111]). The potential of interaction of molecules CH4 and N~ with the atoms of adsorbent and with the other molecules was first based on the approximation of effective spheres for these molecules. The zeolite cavity was broken into sites along the three coordinates according to the parallel planes to calculate the real spatial arrangement of the sites. The cluster approximation was also used. In this case, the functions of non-ideality of the adsorption system & in formula (65) have the form R
Siq = E
t
d ( q { m } R ) H I I [Si~(r)] mqp(~) '
6
r=l p=l
S~(r) = 1 + ~
8ijqp [exp {-/ge~(r)} - 1]/Siq,
(66)
j=l
To calculate the separation coefficient from equations (65) and (66), in addition to the parameters of adsorpion of the individual components (local Langmuir constants aiqai),
144
i~KrKr
SKrXe
SXeXe
SN2N2
8N2CH4
8CH4CH4
0.52 0.21 0.06 0.03
0.62 0.29
0.70 0.38 0.16
0.19 '0.10 0.05
0.25 0.13
0.33 0.18
0.02
0.06 0.02
0.09
0.04
O.11 0.03
0.03
Table i. Lateral interaction parameters (in kJ/mol)
t'~
~'1
0
9 ee
0.8
~
9
0.6
0.6
0.4
0.4
0.2
0.2 4
o ~
0.8
6
lg P
3-
2
4
61g p
In
b @Q I
I
I
I
0.2
0.4
0.6
0.8
0Kr
Figure 14. Theoretical adsorption isotherms for Kr and Xe and the separation coefficient a of their mixture. The parameters used are given in Table 1. (a) shows Kr isotherms at T=200 (1), 252 (2), and 303 (3) K; (b) shows Xe isotherms at 210 (1), 240 (2), 280 (3), and 303 (4) K; (c) shows the changes in In(a) for the Xe - g r mixture at T=200 (1), 252 (2), and 303 (3) K, [110, 111]. the interaction parameters of the molecules eqp(r), ii and the cluster functions d(q{m}R) describing the structure of the lattice) one also needs the parameters of the intermolecular interactions e~(r). In our calculations we assumed that e~p(r)= eij(r). The parameters c j ( r ) were calculated using the combination rules for the individual potentials u~(r) of the Lennard-Jones type. These were of the type e~j = (e~ejj) 1/2 and aij = (cr~q-ajj)/2. Finding the average value according to the volumes of the separate sites vq and vv occurring at
145
0
om 9
0.8
0.8 9
0.6
0.6
0.4
0.4
0.2
0.2 I ~
I
4
I
6
I
lg P
2
-'-
4
6 lg P
t.
0[ t" i,
I
I
I
0.2
0.4
0.6
I
0.8 0
Figure 15. Theoretical adsorption isotherms for nitrogen ((a), T=253 K), methane ((b), T=100 (1), 210 (2), 253 (3), and 303 (4) g), and the separation coefficient of their mixture ((c), T=253 K), [110, 111]. The values of the parameters used are given in Table 1. distance r from one another:
eli(r) = -~ uij(r) ~- = / /uij exp [-~uij(rij)] dridrj///exp [-~uii(rij] dridrj Vq Vp
,
(67)
Vq Vp
where rij is the distance between molecule i, on the site of volume vq of the type q, and j, occurring on the site of volume vp of type p. The results of the calculations for four coordination spheres are shown in Tab. 1. Figs. 14 and 15 show the comparison of the computed curves with the experimental data for the separation coefficients of the mixtures X e - Kr and C H 4 - N2. The small difference in the sizes of these pairs of molecules allows us to use the average values of the volumes of various molecules vi. In this case, the formula for the separation coefficient takes the form t t -zq / V i E fqaqOq(SJq) -z" , C~ij : Vi E fqaqOq(Sq) i v j v q=l q=l
(68)
One obtains satisfactory agreement between the theory and the experiments. Thus, the lattice model gives a possibility to calculate the separation coefficient of microcomponent macrocomponent mixtures, based on the data for the adsorption of individual components.
146 6. A D S O R P T I O N
OF A M I X T U R E
O F M O L E C U L E S O F V A R I O U S SIZES
All the theoretical considerations of the adsorption of a mixture of molecules presented in this chapter were based on the assumption that every molecule occupies one site of the lattice and that in every site there can occur not more than one molecule. This limitation allowed us to consider only mixtures of more or less similar molecular sizes. If the molecules can be greater than the dimension of the sites, we have to do with the "multisite adsorption" problem. The lattice model uses the concept of a site, the size of which is fixed. In confrontation of two molecules two problems arise: 1- the determination of the number of sites occupied by the molecules of each kind, and 2 - calculation of the deviation of the real sizes of molecules from the strictly discrete relations of their sizes given by the lattice structure. The first aspect has been very well elaborated in theory of the lattice models of solutions. As an example we should remember the Flory-Huggins theory [112-114] as well as its generalizations [59,60] such as that proposed by Nitta [115]. This kind of equation is well known in the theories of the surface tension of liquid solutions, as well as in the theories of adsorption of polymers on homogeneous solid surfaces. Such equations can also be found in papers [116-119], and their generalizations both for flexible chains and hard rods, in [94,98,120]. The second aspect of the problem concerns more precise calculations of the sizes of molecules than in a situation when as the unit distance of the calculation, there occurs a discrete site size. This aspect was discussed in papers by Prigogine and co-workers [121,122] on molecular solutions, and even earlier in paper by Wilson [123] and by Aptekar [124] for metallic alloys. The conclusion is that the lattice is considered to be "soft" and its states are determined from the condition of minimum free energy of the system. This assumption both allows to one to calculate some small deviations of molecules from the fixed site size, and to preserve all the convenience of the lattice models connected with (a) the simplicity of solving the algebraic equations for molecular distributions and (b) calculations of the peculiarities of the interaction potentials of the components of a mixture. In the papers [121-124], this approach was applied to homogeneous volume phases while calculating effects of lateral interactions in the mean field approximation. In paper [125], that approach was generalized and applied to heterogeneous lattices, with both monolayer and multilayer adsorption of components of a mixture, when calculating the effects of lateral interaction in the quasi-chemical approximation. So in principle, the formalism of the lattice model makes it possible to consider mixtures of components of molecules of various sizes and to calculate the thermodynamic characteristics of an adsorption system which are of interest for research.
Acknowledgement Athour wishes to thank Profs. W.Rudzinski, W.A.Steele and V.A.Bakaev for preparing the English version of this manuscript.
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152 122. I.Prigogine, A. Bellemans and A.Englert-Chowles, J. Chem. Phys., 24 (1956) 518 123. A.H.Wilson, Proc. Cambr. Phil. Soc., 34 (1939) 81 124. I.L.Aptekar, Zh. Eksp. Teor. Fiz., 21 (1951) 910 125. Yu.K.Tovbin, Zh. Fiz. Khim., 69 (1991) 104
W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.)
Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
153
Theories of the Adsorption-Desorption Kinetics on Homogeneous Surfaces H.J. Kreuzer and S.H. Payne Department of Physics, Dalhousie University Halifax, N.S. B3H 3J5, Canada 1. I N T R ODU C T I O N Whereas the equilibrium properties of a large system are controlled by the minimum of its free energy, the kinetics involve questions of energy transfer. To establish the relevant time scales for the adsorption and desorption processes, let us follow a gas particle approaching the surface of a solid. If it rids itself of enough energy within the attractive region of the surface potential, it will get trapped. However, even if it descends all the way to the bottom of the surface potential well, it will eventually evaporate again. For times to required for a particle to traverse the attractive potential well, the particle will remain close to the top of the well within an energy of kB T. In this time there is a fair chance that the particle acquires enough energy from the heat bath of the solid to escape again. If this escape, which we can identify with inelastic scattering, has not happened within a few round trips, the particle will begin its descent to the bottom of the potential well, which, in a quantum picture, corresponds to a cascade of transitions between the bound states of the surface potential, each downward transition accompanied by the emission of phonons into the solid and each upward transition with the absorption of phonons. This adsorption process, characterized by a time scale ta is, of course, more likely at low temperatures. After it has happened, the particle will try again and again to climb back out of the potential well through a sequence of phonon absorption and emission processes. It will eventually succeed in doing so after a desorption time ta. If ta is much shorter than td, then adsorption and desorption are statistically independent, and the processes of sticking, energy accommodation (i.e. thermalization) and desorption can be well separated. This is most likely the case if the thermal energy kB T is much less than the depth of the surface potential. The energy necessary to desorb a particle from the adsorbate can either come from the solid substrate or from some external source. As examples of the latter, lasers or other sources of electromagnetic radiation have been used in photodesorption and photon stimulated desorption [1,2]. Likewise, electron and ion beams are employed to cause electron and ion-stimulated des0rption, respectively ~]. Strong electric fields at field emission tips cause field desorption and evaporation, sometimes used in conjunction with lasers or electrons to produce photon- and electron-stimulated field desorption [4]. If the solid itself acts as the reservoir from which the desorption energy is taken, we speak of thermal desorption. Lennard-Jones and co-workers [5] argued that the thermal motion of the lattice should act as a time-dependent perturbation on the surface potential, with which the adsorbate is bound to the surface, and can hence supply the desorption energy. It has been shown in recent years that this picture, in modern parlance called phonon-mediated desorption is, by and large, correct, although coupling to
154 the electronic excitations in the case of metal substrates may also be important. Kinetic theories of adsorption, desorption and surface diffusion can be grouped into three categories: (i) At the macroscopic level one proceeds to write down kinetic equations for macroscopic variables, in particular rate equations for the coverage or for partial coverages. This can be done in a heuristic manner, much akin to procedures in gas phase kinetics or, in a rigorous approach, using the framework of nonequilibrium thermodynamics [7-10]. (ii) If it cannot be guaranteed that the adsorbate remains in local equilibrium throughout desorption, then a set of macroscopic variables is not sufficient and an approach based on nonequilibrium statistical mechanics involving time-dependent distribution functions must be invoked. The kinetic lattice gas model is an example of such a theory. It is derived from a markovian master equation, but is not totally microscopic in that it is based on a phenomenological hamiltonian and on postulated transition probabilities that are subject to the principle of detailed balance. For the kinetics in chemisorbed systems, this is as far as theory has progressed. (iii) Lastly, we realize that a proper theory of the time evolution of adsorption and desorption must start from a microscopic hamiltonian of the coupled gas-solid system. A master equation must then be derived from first principles with the benefit that transition probabilities are calculated explicitly involving microscopic parameters only. So far this programme has only been completed for phonon-mediated physisorption kinetics, as reviewed in a monograph by Kreuzer and Gortel [6]. This review is structured as follows: In the next section we briefly sketch experimental methods and procedures of data analysis to the extent they are needed in the ensuing discussions. Next, in section 3 we present the theory for adsorbates that remain in quasi-equilibrium throughout the desorption process in which case a single macroscopic variable, the coverage 0, and its rate equation are needed. We introduce the lattice gas model and discuss results ranging from non-interacting adsorbates to systems with multiple interactions treated essentially exactly with the transfer matrix method. Examples of the accuracy possible in the modelling of experimental data using this theory, from our own work, are presented for such diverse systems as rare gases and CO on metals and multilayers of metals on metals. Dissociative adsorption and the role of diffusion are also discussed. This is followed, in section 4, by a review of the macroscopic theory based on Onsager's approach to nonequilibrium thermodynamics, as adapted in recent years to surface kinetics by the authors. In section 5 we then p r o c e ~ to the semi-microscopic level by outlining recent advances in the theory of the kinetic lattice gas model. In section 6 we very briefly mention the few advances made in physisorption kinetics since 1985. The review concludes with a statement of policy, and the identification of some directions for progress. Recent reviews of adsorption-desorption kinetics since Menzel's articles in 1975 and 1982 [11,12] are by Rendulic [13], mostly on experimental advances, by Lombardo and Bell [14], with emphasis on Monte Carlo simulation, by Brivio and Grimley [15] on dynamics, and by the present authors [16]. 2. E X P E R I M E N T A L ANALYSIS
PRELIMINARIES, PHENOMENOLOGY
AND D A T A
Experimental studies of thermal desorption kinetics can be grouped into three approaches: (i) isothermal desorption, (ii) temperature jump desorption, and (iii) temperature programmed desorption. In an isothermal desorption experiment one removes the gas phase above the surface, either by reducing the initial pressure P~ to a final pres-
155 sure Pf <
(1)
as a balance between the adsorption rate, Ro, and the desorption rate, R~. Here the coverage, 0, is defined as the total number of particles in the adsorbate normalized either with the maximum number in a monolayer or with the number of adsorption sites; both conventions are used. The rate of adsorption is given by Ro = S(O, 7") Pa,/ ( 2 m n k B T ) 1/ 2
(2)
as the flux of particles of mass m hitting the area, as, of one adsorption site from the gas phase at pressure P and temperature T. The sticking coefficient, S(O, T ) , is a phenomenologieal transport coefficient that accounts for the efficiency of energy transfer between the substrate and the adsorbate. Two major assumptions have gone into writing down the rate of adsorption in the form (2): (i) we have assumed that the gas phase above the adsorbent surface remains homogeneous, i.e. that the process of adsorption does not deplete the gas phase. Depletion may occur in adsorption on small particles, or if fast reactions take place on the surface. An example for the latter is the oxidation of CO on P d ( 1 1 0 ) in the oscillatory mode. If depletion occurs we must couple the rate of adsorption to a gas phase diffu-
156 sion equation as done elsewhere in this volume. (ii) Adsorption can depend on the past history of the system. E.g. in dissociative adsorption at low temperature where surface diffusion is much slower than adsorption, single sites left over between occupied sites become ineffective such that the maximum coverage, for situations of negligible diffusion over the time scale of the experiment, is 0.907 for dimer dissociation [22]. Such situations can sometimes be accounted for through the temperature dependence of the sticking coefficient. Without further knowledge about the system, little can be said about the rate of desorption, R e , short of the fact that it describes an activated process. One frequently parametrizes the desorption rate Ra = rd (o, r)o ~
(3)
as a process with desorption order x. For atomic adsorption in the low coverage regime (where lateral interactions within the adsorbate are negligible) we have x = l , for dissociative adsorption in the low coverage regime we have x =2; but note that the coverage dependence of the rate constant in general precludes such simple interpretations. It is, indeed, the coverage dependence of the rate constant that contains all the interesting physics and is the reason why desorption experiments are done in the first place. We point out that the fact the rate of desorption as written in (3) is a function of temperature and coverage only is based on a major assumption, namely that the local configuration around a desorbing particle is completely determined by the overall coverage. This is clearly only the case if the adsorbate remains in quasi-equilibrium throughout the desorption process. Let us next implement the fact that desorption is a thermally activated process by writing the desorption rate constant in the Arrhenius parametrization, in surface science more frequently referred to as the Wigner-Polanyi equation, as ra = v exp(-E a / k B T )
(4)
in terms of an attempt frequency, J,, to escape the surface potential well, and an activation or desorption energy, E a . We will see below that both parameters are in general temperature- and coverage-dependent. To describe a TPD experiment we assume a linear temperature ramp, T(t) = T O + #t, which inserted in (1) with the adsorption term dropped, gives with (3), e.g. for x = l , dO dO dt v d--'T = - dt d T = # 0 exp(-E d / kBT)
(5)
If, and only if, g and E a are coverage and temperature independent, the temperature at the peak is given by the Redhead formula [23] Ed
-# = kB T exp( E,~/ kB T)
(6)
Conversely, determining the peak temperature experimentally, (6) provides one equation for two unknowns, i.e. p and Ed . To get a second equation, one can run TPD with a second heating rate differing by one or two orders of magnitude, and solve for p and Ea from the respective peak temperatures. We stress again that this analysis is only valid for systems in which v and Ed are c,overage independent, which is rarely the case (and uninteresting). Moreover, Redhead s analysis is invalid if two or more inequivalent adsorption sites are present on the surface, in which case a single variable 0 does
157 not suffice to characterize the adsorbate, and a rate equation like (1) must be written down for each partial coverage. In case particles can convert from one kind of adsorption site to another, additional diffusional terms must be added to the rate equations. This will be discussed below. To finish our discussion of phenomenology, we comment on methods of extraction of the kinetic parameters Ea and J,. First, we point out that inspection of TPD data can frequently lead to some (albeit semi-quantitative) insight into the structure and kinetics of the adsorbate. Let us assume that the TPD data show one peak. We can then extract a value for E a assuming that the prefactor r has a "standard" value of the order of 1013s -1 , characteristic of the vibrational frequency of an atom in a potential well with a spatial extent of an angstrom or so. The assumption of a "standard" frequency is rather dubious as r may vary anywhere from 10 ~0 to 1020s -~ from system to system and even over several orders of magnitude within one system as a function of coverage. Even so, with the assumption of a standard frequency and a heating rate of 5 Ks -1 one gets, approximately, from (6) that Ea = 31kBT p
(7)
If the TPD trace shows several (hopefully well-separated) peaks, this simple formula can be used to estimate different adsorption states, although great caution must be exercised and further analysis is required to substantiate such claims. Fortunately, this Redhead-type analysis, and variations on it [24], has been largely displaced by the "complete" analysis of a set of TPD curves, yet one still finds attempts at refinements [25], based on the coverage-independence of the parameters. A proper analysis of TPD data starts from the fact that in the desorption rate (5) the prefactor and the desorption energy are coverage (and particularly at low temperatures, also temperature) dependent quantities. The method proposed by several groups [26-28] consists of the construction of a series of isosteric (constant coverage) Arrhenius plots of enRd(O) (or en(ra) if the reaction order x is assumed) against 1/T, as interpolated from a family of TPD traces generated by varying initial coverages and/or heating rates. If the Arrhenius plots are straight lines or consist of sections of straight fines, the 0-dependence of E a and 1, can be deduced from the variations of slope and intercept, respectively, on these plots. Deviations from straight fines must be expected if (i) more than one adsorption state contributes to a TPD peak, or (ii) if the adsorbate does not remain in quasi-equilibrium throughout desorption, or (iii) if quantum effects produce a strong temperature dependence in the prefactor, e.g. in physisorbed systems. As for the interpretation of the parameters Ea(O) and r(0), we note that their zero coverage values refer to an isolated particle on the substrate, and their coverage dependence reflects lateral interactions between and different adsorption sites of adsorbed particles. A third method of determining such variations is based on the technique of threshold TPD [29,30]. Here the desorption rate is accurately measured at initial desorption for small changes in coverage and temperature, the initial coverage is then varied and the results fitted to (5). Compared to the second method, this extracts the kinetic parameters more directly, and is of advantage for systems away from equilibrium. Any temperature dependence of E d and J,, however, will not be as easily observed because the desorbing range is so short at each coverage value. We shall return to several points mentioned above in the following sections. Difficulties associated with the various methods of analyzing desorption data and remedies to overcome them are discussed at length elsewhere [31 ].
158
3. DESORPTION UNDER QUASI-EQUILIBRIUM CONDITIONS We consider desorption from an adsorbate where surface processes is so fast (on the time scale of desorption) that the adsorbate is maintained in equilibrium throughout the desorption process. That is to say that, at the remaining coverage 0(0 at temperature T(t), all correlation functions attain their equilibrium values. Thus the adsorbate can be characterized by its chemical potential,/z(0(t),T(t)). E.g., if we were to look at desorption of an adsorbate that shows coexistence of a dense and a dilute two-dimensional phase in a certain range of coverage and temperature, then the assumption of quasiequilibrium implies that at the remaining coverage, 0(t), the distribution of particles among the two phases is identical to that in equilibrium at coverage 0. In such situations a purely macroscopic description of the desorption process in terms of a single variable, the coverage 0(t), is sufficient. Its time rate of change has been written down in (1) in terms of an adsorption rate, R,, and a desorption rate, Ra. _We now look at a situation where the gas phase pressure, P, is different from its value, P, to maintain an adsorbate at coverage 0. There is then an excess flux to establish equilibrium between gas phase and adsorbate so that we can write
dO/at = S(O,T) (P-rJ)a,I (2mnkBT) ~/ 2
(8)
Next we express the equilibrium pressure in terms of the gas phase chemical potential, #s,
= kBT~-haexp(#gl kBT)Zi,I
(9)
where
X,h = hi (2~nkBT)l/ 2
(10)
is the thermal wavelength and Z~n, is the intramolecular partition function accounting for rotations and vibrations. However, in equilibrium, the chemical potential in the gas phase is equal to that in the adsorbate, #a, so that we can write the desorption rate in (1) as
R d = S(O,T)a, kBT/hXt2hZ~,,, exp(#o/kBT )
(11)
This is our principal result for the rate of desorption from an adsorbate that remains in quasi-equilibirum throughout desorption. Noteworthy is the clear separation into a kinetic factor, the sticking coefficient S(O, T), and a thermodynamic factor involving single particle partition functions and the chemical potential of the adsorbate. Thus a theory of desorption must simultaneously be a theory of adsorption, explaining not only kinetic data on desorption but also all thermodynamic data such as phase diagrams and heat and entropy of adsorption. The sticking coefficient is a measure for the efficiency of energy transfer in adsorption. Since energy supply from the substrate is required for desorption, the sticking coefficient, albeit usually at a higher temperature, must appear in the desorption rate by the detailed balance argument. The sticking coefficient cannot be obtained from thermodynamic arguments but must be calculated from a microscopic theory or be postulated in a phenomenological approach, based on experimental evidence for a particular system or some simple arguments.
159
3.1. Lattice gas model Most microscopic theories of adsorption and desorption are based on the lattice gas model. One assumes that the surface of a solid can be divided into two-dimensional cells, labelled i, for which one introduces microscopic variables n~ =1 or 0, depending on whether cell i is occupied by an adsorbed gas particle or not. The connection with magnetic systems is made by a transformation to spin variables a~ =2n~-1. In its simplest form a lattice gas model is restricted to the submonolayer regime and to gas-solid systems in which the surface structure and the adsorption sites do not change as a function of coverage. To introduce the dynamics of the system one writes down a model hamiltonian
H=E,
~ ni + ~ V2 E ninj + . . . i (ij)
(12)
Here Es is a single particle energy and V2 is the two-particle interaction between nearest neighbours (ij). Interactions between next-nearest neighbours etc. and many-particle interactions can be easily added to (12). As long as the number of particles in the adsorbate does not change, which is the case for systems in equilibrium, or for diffusion studies, the first term in (12) is constant and can be dropped from further consideration. However, if we want to study adsorption-desorption kinetics, the number of particles in the adsorbate changes as a function of time and a proper identification of E, is mandatory. Arguing that the lattice gas hamiltonian should give the same Helmholtz free energy as a microscopic hamiltonian (for noninteracting particles) one can show that the proper identification is given by
E, = - Vo - kBT en(q3qi,,, ) - kBT [en(X?hP/kBT ) - en(Z,,,,)]
(13)
where v 0 is the (positive) binding energy of an isolated particle on the surface. Moreover,
q3 = qzchy
(14)
is the vibrational partition function of an adsorbed particle with
qz = exp(hvz/ 2kBT)/ [exp(h~'z/ kBT)- 1]
(15)
its component for the motion perpendicular to the surface. Likewise, q~. is the partition function for the motion parallel to the surface. We have also made a'djustment for the fact that the internal partition function for rotations and vibrations of an adsorbed molecule might be changed from its free gas phase value Zi,,, to qi,,,, if some of the internal degrees of freedom get frozen out or frustrated.
3.2. Non-interacting adsorbates To elucidate our principal result (11) for the rate of desorption we first consider a noninteracting adsorbate in the submonolayer regime. Its chemical potential is given by
Izo = -V o + kBT[en(O/ ( 1 - 0 ) - en(q3qi,,,)]
(16)
If the adsorbate is not localized, i.e. if the corrugation of the surface potential parallel to the surface is negligible, we can treat it as a two-dimensional ideal gas and have
160
q~y = a~/ N2h
(17)
Thus (11) reads in the high temperature limit, i.e. for kBT>>h~,z, for a nonlocalized, noninteracting adsorbate R a = S(O,T) 0/(1-0) Z,.,/q~., ~ exp(-Vo/kBT)
(18)
In particular, if S(O,T)=So(1-O), then (18) is the familiar first order rate expression, with i, and E d identified. Let us next consider a noninteracting, localized adsorbate for which q~y has two factors like (15) with frequencies ~,~ and z,y. In this case we find in the high temperature limit Ra = S(O,T) 0/(1-0) Z~.,/q~., 2 m n a s / k B T g~PyPz exp(-Vo/kBT)
(19)
Since substrate vibrational frequencies ~,x, ~, ~z, are typically of order 1012-10~3s -1, the prefactor here can be larger than that in ~ 1~,j by a factor as great as 104, depending upon the system under study. Thus (zero coverage) prefactors up to 1017s-1 are to be expected even if Z~,,, = q~,,t [32]. Nevertheless, one sees statements in the literature that prefactors as large as 1020s- ~ are erroneous or unphysical. A word of caution about the high temperature limit: typical vibrational frequencies correspond to vibrational temperatures Tv =50-500K. Recall that desorption of chemisorbed species, with a heat of adsorption of a few eV, occurs at temperatures well above room temperature so that the high temperature approximation of the partition functions is justified. This is not the case for physisorbed systems with heats of adsorption of less, and frequently much less, than an electronvolt. In such cases the high temperature approximation is not justified. Indeed, for very weakly bound systems, such as rare gases on metals or even N2 on Ni(ll0), one might well be in the low temperature regime when desorption occurs. In such cases we write for (15) qz = exp(-ht'z / 2kB T)
(20)
and find, instead of (19) R a = S(O,T) O/(1-0) Zi.,/qi., a s kBT/hk,2hexp(-Ed/kB T)
(21)
Ea = Vo - h(vx +vy +Vz)/2
(22)
Thus the effective desorption energy is reduced from its high temperature value by the zero point energy in the surface potential, a reduction which is significant for some systems. Similarly, the prefactor in (21) is reduced (by orders of magnitude) from its value in (19) to a quantity independent of substrate vibrations. Expressions similar to (21) have been used frequently in the literature without reference to the fact that they are only valid in the low temperature regime [33]. While neither the classical nor the quantum limits of the vibrational (and internal) partition functions may apply to a system, they underscore the point that, across systems, there is no fixed relation between the desorption energies and prefactors, as determined by an Arrhenius analysis, and the microscopic binding energies and frequencies of the underlying dynamics. Whereas V0 and ~'i are parameters in a Hamiltonian, which gives the energy level structure of the substrate binding potential, Ed and v are strictly phenomenological (but physical) parameters. There is a considerable confusion in the literature on this point. It is useful at this stage to realize that for systems that remain in quasi-equilibrium throughout the desorption process, the desorption energy is more or less equal to the
161 isosteric heat of adsorption, as derived from the adsorption isotherms OenP aiso ( O, T) = k B T2 " ~
(23)
To see this we first define a differential desorption energy as
e,d (O, D = ksT2
Oen(Rd) Or
(24)
corresponding to the local slope of an isosteric Arrhenius plot. The desorption rate here can be identified from (8) in terms of an equivalent pressure that would be maintained if the adsorbate with the instantaneous coverage 0(t) were in equilibrium at temperature T. We can therefore write (24) equivalently as
Ea(O,T ) = Qiso(O,T) - ~ k B T + kBT2
OenS ( O, T) OT
(25)
Note that the second term is essentially irrelevant in the exponential. If the temperature dependence of the sticking coefficient is small (which is not the case for all systems), then the effective desorption energy is given by the heat of adsorption, provided that (i) surface processes are fast enough to keep the system in quasi-equilibrium throughout desorption, and (ii) that precursor states are absent. The generalised prefactor, v(O,T), is now defined via (3) and (24). In passing we note that a fit to the equilibrium data (isosteric heats, isobars) also provides a sensitive method of deducing V0 and {~'i}3.3. Nearest neighbour interactions in the quasi-chemical approximation Next we look at the effect of lateral interactions between adsorbed particles which are assumed localized so that, in the simplest model, we can think of the adsorbate as a lattice gas with a nearest neighbour interaction of strength V2. We illustrate some elementary but important results within the quasi-chemical approximation which gives [34] ].ta/ k n Z = - Vo/ kB Z + [1--~c] en[O/(1-O)] - en[q3qi,,, ]
+ cV2/2knT + ~ cen[(~ 1 +20)/(a+ 1.-20)]
(26)
where c is the site coordination number and a,2 = 1 - 4 0(1- O)(1- e x p ( - V2 / k B T) )
(27)
The desorption energy, via (11) and (24) can be expressed as Ea(O,T) = E a ( 0 , T ) + ~cV 2 [(1-20)/tx- 1]
(28)
For a large, repulsive (V2 >0) interaction, for example, Ed(O,T) exhibits two distinct
162 and essentially constant values for 0 ~: 1/2, implying two peaks in the TPD spectrum for initial coverages 00 _<1. The case of attractive interactions is more interesting, especially for temperatures below the critical value, To, for the phase transition. Here dilute and condensed phase regions coexist and (26) and (24) are replaced, within the coexistence region, by ~a = 89cV2- ksren(q3q~,)
(29)
z ~ ( o , r ) = E~(o,r~
(30)
- ~cv2
Inserting (29) into (11) we readily get zero order desorption kinetics within the coexistence region as a result of the coverage-independent chemical potential. As an example we look at the Ag/Mo(110) system for which the isosteric heat of adsorption at zero coverage, identified with Ea, has been measured to be 2.91eV [35]. A fit to the isothermal desorption data by choosing vz = 3.0x1012s -1, vx = p = 3.4x1012s-l and cVz = -0.41 eV produces the TPD traces of Figure 1, their p e ~ heights agreeing, within 10-20%, with those of Figure 4 of [35]. We note that these vibrational frequencies result in an effective prefactor in the Arrhenius parametrization of about 101Ss-1 as obtained in the evaluation of the experimental data, as discussed in [10]. The coverage dependence of the kinetic parameters is of particular note. At a fixed temperature, Ed(O,T) assumes a constant value, (30), if 0 lies within the coexistence region, but a quite different value, (28), otherwise. Figure 2 shows this dependence for three temperatures spanning Tc ( = 969K for V2 above), but as calculated from the local slopes of a series of isoteric Arrhenius plots, i.e. as if the data were generated experimentally. Note that, because we have imposed first-order kinetics on a zero-order process, the prefactor, u(0,13, is not constant in the coexistence region, but decreases like 0-1. This artifact is often contained in the evaluation of experimental data as well. The abrupt changes in E a and ~, mark the phase boundary at that temperature, and a complete temperature sequence will map out the phase diagram of the system. As an example we take the work of K olaczkiewicz and Bauer [36] on the desorption of metals from metals: having increased considerably the sensitivity of their measurements over earlier work [27], they noted that most Arrhenius plots did not yield one, but two, straight lines. We reproduce their resulting plots of Ed,(0) and ~,(0) for metals on ~(110), in Figure 3. For comparison purposes only, we show in Figure 4 the values of Ed and obtained by assuming two linear fits to the theoretical isosteres obtained from Figure 1. These curves are the temperature averages over the desorption range of a complete sequence of curves of Figure 2. The double-valuedness of Ed(O) and ~,(0) is clearly the indicator of desorption through the two-phase region. The results of Figure 3 must be considered as one of the examples of considerable progress in experiment [37]. There are many examples in the literature where similar features should have been seen, e.g. in [38]. Finally, we observe from Figures 3 and 4 the familiar compensation effect, namely an approximate proportionality between the single phase values of Ed and v, as a function of coverage. While this is observed experimentally for systems with attractive interactions (see e.g. ref.[39] for a further example), and is understood in this case by consideration of entropy change, the argument does not apply for repulsive interactions; nor is the proportionality well founded in lattice gas models. It has received much attention, however [40,41]. Any application of a compensation 'law" to the modelling of TPD spectra is suspect [42]. It is clear from the rate expression (11) that for systems that remain in quasi-equilibrium during desorption, one can extract the chemical potential as a function of cover-
163 [,~
o o
/'X
/ /
s"--
x 0
(.J (D 03
.
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.
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.
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.
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.
.
.
.
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.
1 0 0 0 90
Temperoture
.
.
.
I
1050. 0
(K)
Figure 1" Temperature programmed desorption calculated for A g / M o ( 1 1 0 ) with data from ref. 35. Solid lines: quasichemical approximation with Vo = 33760 K = 2.91 eV, cV 2 = - 4 7 1 5 = - 0 . 4 1 e V , c = 4 , Tc = 9 6 9 K , vz = 3 x 1 0 1 2 s -1,v x = v . = 3 . 4 x 1 0 1 2 s -1, a, = 5 A2, S~ = S2 = 1. Dashed curves: Bragg-Wilhams approximation for which Vo = 2.93 eV, cV 2 = -4725 K and Tc = 1074 K. Heating rate is 1.21 K s- 1, initial coverages (top to bottom): 00 = 0.75, 0.55, 0.35, 0.15. From ref.[10]. .
.
.
.
Y
age and temperature, i.e. exp(#~/kBT) = ra / [S(O, T)a.kBTZ,., / hX,2h]
(31)
provided the coverage dependence of the sticking coefficient is known. Thus isosteres can be constructed, isosteric heats of adsorption can be calculated, and phase diagrams can be deduced. This procedure to use thermal desorption data to extract equilibrium properties is complementary to direct equilibrium measurements because the latter can usually be performed at low temperatures whereas the former extend to much higher temperatures, e.g. in the case of metals on metals to temperatures above critical. If equilibrium data can be measured independently in addition to thermal desorption, then (31) can be used to extract the sticking coefficient, although nobody has attempted this so far.
164 o t.,o ',z" 0 0 0 v
k_ I13 t't.IJ
o
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f
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.
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t/
:J"
o I21
n r._.J
o w
mO.O
0.2
0.4
Coverage
0.6
I
08
(ML)
Figure 2" Upper triple of curves: differential desorption energies, Ed(O,T), as a function of coverage and for three temperatures, derived from the local slopes of isosteres as calculated from TPD spectra generated in the quasichemical approximation;temperatures are Tl = 1000 K (dash-dotted curve), T2 = 950 K (solid), T3 = 900 K (dashed). Lower triple of curves: (logarithm of) the corresponding effective prefactors. From ref.[10]. 3.4. 'Exact' results using transfer matrix methods The rate expression (11) allows one to set up as precise a kinetic theory as the equilibrium properties of the system can be modelled. One can extend the quasichemieal approximation to more general cluster methods [43]. To overcome the restriction to finite clusters one can employ transfer matrix methods [44,45] to calculate #a, with multiple lateral interactions included, on an M x oo lattice. The transfer matrix is constructed for interactions between particles in adjacent columns of M sites in the finite direction; its leading eigenvalue, X1, gives the grand partition function, ~ T , M, #) = XlM, and hence the coverage
165 o
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+
+
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o o
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9
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(ML)
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9
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38
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I 08
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=-
10
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1
z
02 04 06 COVERAGE 8 (ML)
i
~
08
10
Figure 3: Desorption energies Ed(O) and pre-exponential factors p(~ of Ni (a), Cu (b), Ag (c) and Au (d) on ~ 1 1 0 ) as a function of coverage O. The data are derived from the evaluation of 5,4,6 and 7 series of TPD spectra of Ni, Cu, Ag and Au, respectively, characterized by different symbols. O = 1 corresponds to 14.12x10 TM atoms/cm 2. The lines through the experimental points are only intended to guide the eye. From ref.[36].
n
kBT aen~ M a#
(32)
Exact results can be obtained by this method in the limit of large M. In practice, it suffices to consider only the totally symmetric subblock of the transfer matrix for this calculation; 0 is obtained from the corresponding eigenvector. One finds rapid conver-
166 0
',e"
o o o
f 0
!I3
J E
l.j o
.9 k.
\
0 m ~ Cl
\ \
o
.
0 u o ~ 12-
. m
E
f
f
..J
o
,....
mO.O
I
0.2
II
i
0.4
0.6
Coverage (ML)
i
0.8
Figure 4: ,Coverage dependence of desorption energies (upper pair of curves) and (logarithm of) prefactors in quasichemical approximation, obtained by fitting equ.(5) to generated isosteres in the one-phase and two-phase regions separately (solid and dashed curves, respectively). From ref.[10]. gence of the method; e.g. for a system with nearest neighbour interactions on a hexagonal lattice, M-9 is essentially exact (but M=3 is not good enough). The transfer matrix method has been used extensively to determine phase diagrams of adsorbates [46-48], where details of the method can be found. As a first demonstration of the richness of the structures that can be dealt with we reproduce model calculations [49] of the isosteric heat and TPD rates for systems with first and second neighbour and trio interactions on a hexagonal lattice. We begin with a simple system with repulsive nearest neighbour interactions only, Figure 5a. The isosteric heat, as calculated from (9,23), shows a sharp drop. at low temperature around coverage 1/3 reflecting the onset of ordering into the V"3x~Y'3 R300 structure. It results from the large increase in chemical potential as nearest neighbour sites become occupied above coverage 1/3. The feature of note is the appearance of local maxima and minima, most pronounced for temperatures less than the (reduced) ordering temperature, Tc =Tc I V2 - 0.34, for the system which appear as one cuts isothermally through
167
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i
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-.6
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-.9
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1
2
3
.4
5
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1
.7
.8
1
-13
.9
.~
0
.2
5
.4
(,~tL)
.5
Coverage
o
.6
.7
8
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(ML)
1
-.1
1
-.3 -.2 I -.4 -,5 >~
-.~
-~ 0 (5
-,7 -.8 -.9 <1 9-~ .0 -1.1 -I .2 -1.3
~
-1.4
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. . , i . , . 1 , .
0
.1
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,
i
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,
.
l
,
,
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,
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l
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,
Coverage (ML)
L
.7
p
8
,
,
,
t
,
,
,
.9
Figure 5: Normalised isosteric heat, AQ - Q(O,T') - Q(0,T), as a function of coverage for nearest neighbour repulsive interactions (V 2 > 0 ) between particles on a hexagonal lattice. The curves are labelled by reduced temperature, T = k B T / V ~ , with the values (top to bottom at O = 0.1) 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6, ...7, 0.85. Calculated for a) 9-site approximation to the two-row transfer matrix, b) including next nearest neighbour repulsion, V~ = 0.1V 2, and c) including trio interactions, V~ = -0.1V 2, W =0-2112- From ref.[49]. the phase diagram [50]. Thus there is an effective attraction, AQis o >0, between particles for coverages 0 _<1/3 and T = T / V 2 = 0 . 1 8 - 0 . 3 2 ; and a diminished repulsion, sig-
168 nailed by the local maxima, for T =0.32-0.35. These features result from the tradeoff between the energy and the entropy of the adsorbate in the vicinity of the orderdisorder boundary. Similarly for 0 _>1/3 the repulsion is enhanced, relatively, for these temperature ranges. This behavior is repeated at coverage 2/3 due to particle-hole symmetry around coverage 1/2. These features are also observed for the square lattice as discussed elsewhere [51] where connection is made to the structure of the two-particle correlation function. For temperatures greater than the ordering temperature all these features wash out leading eventually to a more or less linear decrease in A Qiso, as expected from mean field theory. Turning next to weak next nearest neighbour repulsion (V2'=0.1V2) we note in Figure 5b that additional drops appear at low temperatures around coverages 1/4, 1/2 and 3/4. These correspond to the onset of orderings into 2x2 and 2xl phases. These sharp decreases also disappear above the ordering temperature. Even this weak next nearest neighbour repulsion begins to suppress the v"3xV"3 structure. As one increases the next nearest neighbour repulsion to V2'=0.2V2, this suppression is complete with only period two structures remaining [52]. On a hexagonal lattice there are two kinds of trio interactions, (i) between nearest neighbours only, V,, and (ii) involving two nearest neighbour and one next nearest neighbour pair, Vt '. As Vt preserves period three structures induced by nearest neighbour repulsion, the additional effect of this (weaker) trio interaction on the isosteric heat is effectively to move the plateau region of Figure 5a above coverage 2/3 up (down) for Vt <0 (>0). The same is true when attractive next nearest neighbour interactions (V2' <0) exist as well. The situation is different for the case v2'>0 and Vt ;~0: depending on the relative strengths of these interactions, there is a competition between adsorbate structures of periods two and three above coverage 2/3. A similar situation applies for combined next nearest neighbour pair and trio interactions. Essentially, the trio interactions enhance the orderings induced by the pair interactions of the same sign and compete with them if the signs are opposite, for coverages above 1/2. We show an example of this situation in Figure 5c. We next turn to thermal desorption showing in Figure 6 the three corresponding TPD spectra, plotted as a function of reduced temperature and for various initial coverages. The three broad desorption peaks of Figure 6a (for 00 >0.75) correspond to the three plateaus of Figure 5a over this temperature range, with the valleys in the rate occurring at coverages 2/3, 1/3, where the adsorbate repulsive energy reduces dramatically. We compare the trace for 00 = 0.70 with the variation of A Qis o in Figure 5a: the sharpness of the low temperature peak, occurring at 0=0.67, arises from the excess repulsion reflected in the local minimum in zXQ~,o just above this coverage; the next peak occurs at 0 = 0.56 and T = 0.23 following, again, an excess of repulsion, relative to lower temperatures, in AQ~ o ; similarly the shoulders at 0 = 0.40, T = 0.27, and 0 = .22, T = 0.32. The effects of the local maxima of A Qi~o are harder to discern here but, compared to more approximate calculations of tza , they appear as a steepening of the leading edge of the desorption trace out of the valleys. Figure 6b shows the desorption traces obtained corresponding to the ratio V 2 ' / V 2 = 0.1 of Figure 5b. Again, the existence of essentially six 'plateaus' in A Qi,o, for low to intermediate temperatures, implies six desorption 'peaks' for high initial coverage, with the positions of the local maxima of F ~ u r e 6b reflecting the local minima of Figure 5b occurring about the 2 x 2 and v"3 x v 3 orderings. Figure 6c contains the effects of competition between these two orderings induced by the interactions corresponding to Figure 5c. The sharp peaks at T = 0.20 for the traces 00 = 0.85, 0.70 clearly arise from the large excess of repulsion at this temperature for 0 >__2/3. The asymmetry of the two desorption peaks for the trace 00 =
169
. . . .
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. . . .
'
a
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b o
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0 0 •
x
3
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.
.15
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.25
.30
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.15
.35
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~
9 9
.20
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.
.
.
.
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Reduced Temperature
,
.
.
,
,
,
.
.
.
.
,
.
.
.
.
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.
.
.
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T/V 2
.
c
oo 3
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.
25 JO .20 Reduced TemperGture
.
3~ T/V2
Figure 6: Temperature programmed desorption rates as a function of reduced temperature (T/V2) and initial coverages, for three sets of adsorbate interactions. The site parameters assumed are typical for those of CO on (111) faces of transition metals: V0 = 156 k J / m o l , ,,., = ~'xy = 5 x 1012 s- ' , nearest neighbour repulsion V2 = 12.5 kJ/mol = 1500 K. Heating rate is 5 Ks -1. Initial coverages (left to right) 0o = 0.85, 0.7, 0.55, 0.40, 0.25, 0.1. (a) nearest neighbour repulsion only; (b) next nearest neighbour repulsion included, V~ = 0.1V 2; (c) next nearest neighbour attraction V~ = -0.1V 2, and next nearest neighbour trio repulsion 11; = 0.2V 2. (For this interaction set the lowest temperature desorption peak for 00 = 0 . 8 5 lies below T~ V 2 = 0 . 1 5 . This is suppressed to maintain the scale.) From ref.[49].
170 0.55 nicely displays a general effect of the local maxima/minima structure of ~Q~,o. Such asymmetries are absent in mean-field type calculations. Although multi-peaked desorption spectra will occur with nearest neighbour repulsion (on a triangular lattice) alone, two lessons should be taken from these examples: the presence of additional, and weaker, interactions easily gives rise to multi-peaked spectra of more complicated and varied shapes; and the local minima of the isosteric heat significantly affect this spectral structure. To directly associate such structures with different binding states, as frequently done in the literature, is too naive and often misleading. As an application of the above theory we now look at the adsorption of CO on Ru(O001). In a number of respects this system is an ideal one to model: Menzel's group has performed a complete set of measurements [53-55], producing superb data, on the low temperature structural properties of the adsorbate (LEED), its equilibrium properties, and adsorption and desorption kinetics (the latter in both isothermal and temperature programmed modes). The system displays a number of interesting features that demand detailed analysis for their modelling. There is convincing evidence that CO is mobile on Ru(0001) for temperatures above 200K, so that the adsorbate is in quasi-equilibrium throughout desorption. LEED data [55] suggests that the CO molecules move off their original binding sites above coverage 0 = 0.4. The simple lattice gas model with one type of adsorption site, as specified in (12), is therefore inapplicable above this value, but can be used to fit the experimental data below it, with a trio interaction introduced to mimic effects in the data above 0=0.4. One can estimate the nearest neighbour and next nearest neighbour interaction strengths by making use of the equilibrium isobaric data, Figure 7, and the (derived) isosteric heat, Figure 8a [54]. An alternate, and common procedure, is to use the structural data and compare with model phase diagrams. This is always much more difficult computationally. From Figure 8a we have the following estimates: Q~o(O = 0,T=700K) = 154 kJ/tool, Q~,o(O = 1/3, T <700K) = 118 kJ/mol implying V2 = 12 kJ/mol. A fit to the low coverage isobars yields the site parameters V0 = 154.6 kJ/mol, ~'z Pxy ~" Phr = 2.64 x 10 ~2 s-~. A hindered rotation, of frequency ~'hr, has been included as the additional degree of freeAom of the molecule. All frequencies are set equal for simplicity. The behaviour of the isobars elsewhere in the 0-T plane is now determined by particle interactions alone. Figure 7 shows the fit assuming a next nearest neighbour attraction, V2' = -0.07 V2 and a next nearest neighbour trio interaction, V,' = 0.07 V2. The fit is excellent for 0 _<1/3, but is poorer in the vicinity of 0 = 1/3 (the flatter portions) as reflected in the calculation of Q~,o(O,T), which is shown in Figure 8b for several temperatures in the range 300-700 K. With the parameters which produced Figure 7 one now generates TPD spectra according to eq. (11). With the "standard" assumption for the sticking coefficient, i.e., S(O,T) = S(0)(1-0) with S(0)=0.7, one gets the desorption traces as depicted in Figure 9a displaying several features not present in the experimental data. However, the sticking coefficient varies dramatically as a function of both temperature and coverage [53]. If one parametrizes the experimental sticking coefficient, approximately, according to the insert in Figure 9b and folds this information into the calculation of the desorption traces, we get Figure 9b. The agreement with the experimental data, Figure 9c, with regards to peak positions and relative peak heights is excellent for 0 _<1/3 and still good elsewhere. On performing an Arrhenius analysis of the desorption rates in the standard manner, i.e., 'plotting' (-1/0 dO~dO vs. 1/T, thus averaging Ea(O,T) over the desorption temperature range, we obtain the solid line in Figure 8c. The dashed line plots Q~,o(g), obtained by averaging the isotherms of Figure 8b over the temperature range of the isobars at coverage 0. The hump below coverage 1/3 can only be obtained in an infinite lattice calculation. An earlier attempt [56] based on the generalized quasichemical approximation to a lattice gas with nearest neighbour repulsion only is too simplistic. =
171
. . . . . . . .
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400
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600
650
Tempereture
Figure 7: Isobaric plots for CO/Ru(O001): comparison of theory (curves) and experiment [54] (points) using a 9-site approximation to the two-row transfer matrix. Interaction parameters: V2 = 12 kJ/mol = 1440 K, V~ =-0.07V2, V; =-V~, site binding parameters as in text. The isobars are P = 1.3 x 10 -6 mbar (left) and 1.30 x 10 -4 mbar (right). Coverage and change in work function, A~, are related by 0 = 7.58 x 10 -4 A~ + 8.01 x 10 -7 ( A 4 - 4 4 0 ) 2. From ref.[49]. The isothermal desorption kinetics of CO adsorbed on Rh(111) has also been analyzed employing a lattice gas with up to third neighbour interactions [57]. Here again the form of the sticking coefficient, varying as 1-30, is important for the proper modelling of the kinetics. The transfer matrix method can be extended to deal with adsorbates with more than one adsorption site or state per unit cell. As an example we refer to hydrogen adsorption and desorption from R h ( l l 0 ) [58]. A lattice gas model with first, second and third neighbour and various trio interactions between hydrogen atoms adsorbed on two inequivalent sites on the reconstructed surface is capable of reproducing all observed ordered structures, i.e. lx3 - H at 0=1/3, lx2 - H at 0=1/2, lx3 - 2H at 0=2/3, lx2 -2H at 0=1 and l x l - 2H at 0=2. It also reproduces the experimental TPD spectra, Figures 10 and 11. There are few other applications of the transfer matrix method to the modelling of desorption kinetics [59]. Much of the modelling is performed using Monte Carlo simulations [14,60-68]. While this method has obvious advantages for simulating effects such as fast diffusion and relaxation of the local environment of the adparticle , under quasi-equilibrium conditions the transfer matrix method of calculating the chemical potential (which yields both equilibrium and kinetic data) is definitely superior for the desorption rate. This is also evident by comparing the quality of TPD spectra produced by the two methods. This and the fact that the
172 Eeff or
EB
o
{KJ/rr~
~
o
150
~:.~
9
.
oo o
100
4
i
......
,
....
,
, . . . . . . .
9
o
. . . .
o
o
o:2 o3 o'.4 ols o.6
; ....
e-
; .... L .
i
.
.
.
'
9i
- -
~;
....
, ....
i
....
t
b
150
!50
>,
140
i
Q) 130
g
13C
. _
t~
110
-4,'
100
120 110 ~-
~oo F ............................... 0
.1
.2
=
Coveroge
.4 (ML)
.5
.6
0
"
2
3 Coverage
.4
1. .5
.6
(IdL)
Figure 8: a) Experimental data on heat of adsorption (full circles) and desorption energy from ref.54, b) Temperature and coverage dependent isoteric heat, Qi,,, (e, 7) for CO/Ru(O001) for the interaction and site parameters of Figure 7. The curves are labelled by temperature (top to bottom at (9 = 0.2) T = 300, 380, 460, 540, 620, 700 K. c) Temperature-averaged desorption energy, Ea(~ (solid curve), obtained from a linear Arrhenius analysis of the TPD spectra of Figure 9b; and isosteric heat, Qi,o(~) (dashed curve) obtained by proper averaging over the curves of Figure 8b. transfer matrix computation is relatively fast (and definitely faster than Monte Carlo) is important for an accurate fit of experimental desorption data. We stress again that in the analysis of the thermal desorption data, the role of the sticking coefficient is crucial. Here a caution regarding the simulation of desorption rates by the Monte Carlo tech-
173 I
.
.
.
.
I
.
.
.
.
.
.
.
t
.
.
.
.
t
"'
"
"
" ' t
.
.
.
.
d__ar
o •
dt
o r~ .
2
!
0
4.00
350
300
500
450
Tem,,~eraLure s,o~
.
.
.
.
.
.
.
.
.
.
:
.
.
.
.
"'
";
'
~
"
"
"
b f
o ~
"
\ . ~
g2
j 300
0
500
350
4-00
4.50
Temperature
500
t,00
500
--T(K)
600
~"
~
Figure 9" Temperature programmed desorption spectra for CO/Ru(O001) for two forms of the sticking coefficient (a) S(O,T) = 1-0, (b) S(0,7") given by the inset to this figure. Parameters as for Figure 7. Heating rate 5 Ks -]. Initial coverages (left to right): 00 = 0.66, 0.58, 0.49, 0.43, 0.38, 0.3, 0.25, 0.15, 0.05. c) Experimental spectra from ref. [54]. nique is in order: by the principle of detailed balance it implicitly assumes an adsorption rate with a sticking coefficient S(O)=So(1-0) (unless precursors are invoked). Thus in any system where the sticking coefficient does not have this simple form, simulation by standard Monte Carlo is inappropriate. For example, the recent attempts [63,67] to fit the desorption data of CO~Ru(O001) are unacceptable.
174
H/ah(l 0) Tad= 90K 1.36
d
0.57
j[3
0.74
~
@
0.36 026
'
100
i
150
I
i
'
'
'
!
. . . .
200 250 300 Temperature in K
I
. . . .
350
400
Figure 10: Temperature programmed desorption spectra of H 2 from gh(110) [58]. Initial coverages are indicated. Heating rate: 10Ks -1. Before closing this section, we mention a widely used phenomenological approach to adsorption-desorption kinetics based on transition state theory. In this approach one views the desorption process as a chemical reaction where a particle A bound to a surface site I:, forming a "molecule" (A+I:), is removed into the gas phase via a transition state or an activated complex (A +I:)* leaving an empty site behind. One then assumes that the transition state is in equilibrium with the reactants and calculates the corresponding equilibrium constant. Arguing next that the transition state vibrates along the reaction path, one can calculate its partition function in the high temperature limit to arrive, eventually, at a formula for the desorption rate constant (which no longer contains the vibrational partition function of the transition state). These remarks are not meant to discredit transition state theory as such, which no doubt has its role in chemical reaction kinetics, but to point out that for desorption kinetics simpler arguments, as presented e.g. from (8) to (11), lead to answers in a straightforward way. A case in point is the several pages of arguments needed in ref.[69] to arrive at the expression for zero order desorption, already contained in (11) for constant chemical potential.
175
LJ
-9
045
,
,
|
o81111.
040 035
1
i::
>-"(1)
||
030
~
i
0.6
.........
ii :."
~.~'~'. " ..
'~" t..:
I I I I i
025
0.4
I!
Co 020
""- . . . ~ ,,/ I ....~:
._
015 L
o s
. ....
,
,
c_
010
~
0.2
!.'" ~.i
{[I
005
t
x x
C)
03
0 100
150
Temper
200
& ture
250
300
350
o of ................ o.o
(K )
o.s
1.o
1.5
!i'i
2.0
Coverage
Figure 11: TPD spectra (left panel) and isosteric heat (right panel: dotted at T=100K, dashed at T=150K and dashed-dotted at T=200K) in the "best" inhomogeneous 2-site model [58]. Solid line in the right panel gives the desorption energy (up to 0=1.36) obtained by a linear Arrhenius analysis of the TPD data. a- and b-site binding and interaction parameters: V0(a)=2.50eV, V0(b)=2.45eV, VabI =0.09eV, Vboo=-0.013eV, Vba I -'Vaa 1 = 0 . 0 4 3 e V , Vob2 =0.013eV, Vo~2 =0.0043eV, Vba~ =Vo.b~2 =0.013eV, Vba.a2-----'O.OO86eV, Vo~a=0.0065eV, V'aa=V'bb=-O.OO86eV, V~a=-0.0021eV. Initial coverages as in Figure 10 with the addition of 00=1.36.
3.5. Multilayer adsorption and desorption Whereas adsorbates with mainly repulsive lateral interactions are generally restricted to the submonolayer regime, those with mainly attractive lateral interactions exhibit multilayer growth. Examples of the latter are most physisorbed systems such as rare gases and inert molecules on metals or insulators, but also strongly chemisorbed systems such as noble metals on transition metals and others [37]. Although such systems have been studied experimentally for many years and are obviously of great interest and importance, little progress has been made, until recently, on the theoretical side despite major advances in our understanding of adsorption and desorption in monolayer adsorbates. Early work [7,9] showed that even a small population of second layer particles can in.fluence the overall rate when desorption from the islands, on which" the particles reside, is restricted. A bilayer model for zero-order desorption has also been advanced by Asada and Masuda [70]. Some non-trivial lattice-gas models have been developed for the equilibrium properties of multilayers [71,72]. Recently we have presented a theory of multilayer adsorption and desorption based on a lattice gas [73,74]. The theory is based on a lattice gas with a hamiltonian
U-'Esl~rl
i dffEs2~Ilim
i
i
i "+ I Vii
~
n.n.
ni nj
l'lillj "+" ~ V'll n .n .n.
176
+
+
n.n.
+
n.n.
~"
niminjm J + ...
(33)
n.n.n.
Here the occupation number ni = 0 or 1 depending on whether site i is empty or has a particle adsorbed in the first layer with a single particle energy Es~ , c.f. (13). Likewise, m/ = 0 or 1 depending on whether the site i in the second layer is empty or occupied with an energy Es2. Here we assume on-top sites for the second layer for simplicity. In (33) we identify vl~ (V'l~) and V22 (V'22,) as the lateral interactions between two particles in nearest neighbour (next-nearest nelghbour) sites in the first and second layers, respectively. The interaction V~ is between next-nearest neighbours with one particle in the first layer and the second m the second layer. The nearest neighbour interaction between a particle in the first layer and another one on top of it in the second layer is accounted for by Es2 which also contains the residual interaction with the substrate. As an example we quote results for Cu on Mo(110). Experimental TPD traces [75] are reproduced in Fig.12a with a theoretical fit given in Fig.12b. Although isotropic nearest neighbour interactions alone with on-top adsorption for the second layer is assumed, the model reproduces the experimental data surprisingly well, with regard to both the shape of the desorption traces as well as the absolute magnitude of the rates. Lateral nearest neighbour interactions in the first and second layers are attractive and give a critical temperature of the first layer of Tc = 1125K in agreement with an estimate by Paunov and Bauer from TPD data [75]. Desorption from the coexistence region of the first layer is quite clearly displayed in the leading edge of the high temperature TPD peaks indicative of zero order desorption. The growth mode of the Cu bilayer is shown in Fig.12c as the partial coverages 01 and 02 of the first and second layer, respectively, as a function of total coverage for temperatures spanning the desorption range. Layer-by-layer growth is clearly in evidence. This has an interesting effect on the partial rates dO1~dt and dazl dt, Fig.12d in the intermediate temperature range of TPD where first and second layer peaks overlap: although the partial rates show apparent independent desorption of the two layers with the first layer trailing the second at higher temperatures, desorption from the first layer is now constrained by that of the second layer, resulting in a sudden onset of the former and a shift of the first layer peak for initial converages larger than one monolayer to higher temperatures to accomodate the total amount of material to be desorbed. Desorption of Au from Mo(110) is also analyzed in detail in [74]. Most of the applications of the lattice gas model to crystal growth have been concerned with calculating phase diagrams, critical properties and growth mechanisms within the solid-on-solid model. While the literature on crystal growth is enormous it has mainly been concerned with phenomenological descriptions, see e.g. [76]. The theory developed in [73] is distinct in that it considers the details of the adsorptiondesorption kinetics together with equilibrium properties of the first few layers where effects of the substrate still occur. In particular, the interplay of structure and kinetics can be studied within the one framework.
177 7.S r
7.5
o
!
i
~.so
6.0 d4.5 *o w
~3.0
.
0 ' 6 )0
g
~ 2OC
900
1000
1 1 O0
1200
Tempercture
(K)
TEMPER~TUR E [K}
75 i i,
.0
i . . . . .
, "
......
:
"
'
"
0
"
"
'
.
.
.
.
.
'
"
"
"
'
""
" ' "
"
'
'
"
"
"/i
III
/
/i
. - . 6.0
1
/il
i Id i ',I i ',e
3.0
6 ~,I !.5
O
.2
.4
.6
.8
:.0
1.2
', 4
Coveroge (ML)
~.6
!.8
2.0
I
0 t ...... 800
t
=~. 900
6 , ~'1~
~000
Tempercture
, ',,,S
11O0
1200
(K)
Figure 12" (a) TPD data of the first two monolayers of Cu on M o ( 1 1 0 ) obtained with a heating rate of 2.43Ks -1 [75]. The curves differ in initial coverage by approximately 0.07 monolayers between 0.07 and 2.2 monolayers. (b) Theoretical fit for initial coverages between 0.07 and 2 monolayers in steps of 0.14 with the following parameters: V0O) = 3.2eV, Vo(2) = 2.98eV, Vl~=-0.172eV, Vj2= 0 eV, V22=-0.129eV, vx = v = v z = 5x1012s -]. (c) Partial coverages for first (sohd lines) and second (dashed lines~ as a function of temperature. (d) Partial rates for first (solid lines) and second (dashed lines) as a function of temperature. From ref.[74].
178 3.6. Competition between atomic and molecular desorption A gas in equilibrium contains atoms and molecules at partial pressures that, at low densities and high temperatures, are controlled by the mass action law. Thus in a onecomponent gas of atoms and diatomic molecules we get for the respective pressures in terms of the chemical potentials #rag and #og of the molecular and atomic gas,
-Pa --- kBT 3 et~ag !tBr Nh
(34)
_ Pm-
(35)
kBT kBr kBT T e(2Uag +edis)1kB 1" X23 Zin, eumg! - h23 2T~ Qv (T)
edis is the (positive electronic) dissociation energy of the gas phase molecule and =X,h/~'2 is its thermal wavelength. Zi,,, is the partition function accounting for the rotations and vibrations of the molecule. Thus Tr is the rotational temperature of the molecule, and Q,, (79 is an expression like (15). A molecular gas adsorbs atomically if the energy released by binding its constitutent atoms separately is large enough to supply the dissociation energy. Entropy considerations also play a role because in equilibrium the Gibbs free energy of the atomic adsorbate must be lower than than of a molecular adsorbate. This brings in vibrational partition functions of the atoms or molecules with respect to the surface and also of hindered internal motion of the molecule. For practical reasons the released energy must also be enough to surmount a possible activation barrier in order to achieve equilibrium on an acceptable time scale. The question remains under which conditions an atomic adsorbate desorbs as either atoms or molecules. The theory has recently been developed for homonuclear diatomic molecules and applied to the desorption of Te from ~ 100) [77]. Two models have been considered: (i) In the direct dissociation model one assumes that the gas phase molecules become dissociated on impact with the surface, so that only atomic species are present. (ii) For precursor-mediated adsorption one assumes that molecules adsorb in a precursor state from which dissociation occurs into the atomic state. If fast surface processes maintain the adsorbate in quasi-equilibrium, the system is completely characterized by the two partial coverages, i.e. of the precursor and chemisorbed states, and the theory can be set up quite straightforwardly. We consider the case of direct dissociation only [58]. The atomic species has a coverage 0. Its time rate of change is due to an excess flux of molecules and atoms hitting the area, as, of an adsorption site and sticking with probabilities Sm (0,7") and S,, (0, T), i.e. .o
_
dt - S m
(0, T)
(Pro - Pro) -t- Sa(O,T ) (
)]k(P a - Pa)
(36)
Her~ Pm and Po are the instantaneous molecular and atomic pressures above the surface and Pm and Po are the equilibrium pressures needed to maintain a coverage Oof atoms at the temperature T. Next one uses (34) and (35) and the equality of the chemical potentials of the atomic gas above the surface, #,,g, and the atomic adsorbate, #,,(o), to write the rate equation (36) as
dO a-7 = R,,d - Rd~,
(37)
179 where the rates of adsorption from the atomic and molecular gas components are, respectively,
RaaO0 = S,, (0, T) a, -h P"
(38)
Rad (m) = S m (0, T~ 2a, -~ I'm
(39)
and the rates of desorption as atoms or molecules are
kBT (I.ta(a))/kBT
Rdes (a) = S a (0, ~ a, " ~ - e
(40)
kBT T (a) .t.~dis)lkB T Rdes (m) = Sm(O,T) 2a, hX22 2T r Qv(T) e (2ua
(41)
To complete the theory one needs to specify the coverage dependence of the sticking coefficients and the chemical potential, #o
e
0 1 <-Vo+l,ti)lkBT - 1-0 q3(T) e
(42)
so that (40) and (41) read 0 a~kBT (#i-Vo)IkBT Rd,~
0z
askBT T
Q~(T)
Re,,(m) = S m (O,T) (1_0)2 hk22 Tr (q3(T)) 2 e
(43) (2ui-2Vo+eais)/kBr
(44)
Whether an atomic adsorbate desorbs as atoms or molecules obviously depends on the relative magnitudes of the prefactors and the desorption energies. Lateral interactions can also play a major role as we now discuss for Te on W(100). From LEED data [78] it is known that this system has a 3x3 structure at 0=1/3 below room temperature, a c(2x2) and p(2xl) structure at 0=1/2, a (3x3) structure at 0=2/3 and some more complex structures close to monolayer coverage. To explain these structures adsorption at hollow and bridge sites is necessary. The necessary two binding site lattice gas model with first, second, third neighbour, trio and quarto interactions has been set up and the chemical potential has been calculated as a function of temperature and coverage using a 12-site transfer matrix. In Figures 13 and 14 we reproduce the experimental data and a theoretical fit that also explains the ordered structures. It is very gratifying that the molecular desorption peak is exactly in the right position and of the right magnitude, i.e. about an order of magnitude smaller than the lowest atomic peak. We stress that no additional parameters are available for the fit to the molecular peak. Considering the desorption energy for Te we note that V0 =5eV and qi, =2.38eV for Te~. Thus in the absence of lateral interactions the difference in desorption energies tor atomic and
180
I
|
|
[
|
1
1
i
1
a Is
z
Te~ {1001
/
...190
Tom :500K t =20Ks" i ~ m e t e r : Exposure time (s} Te. 9
,/~
,,-- ~ ~ 160
6o 6~
I~5
~s
Z'tO$! Z~Z
]OOs Z6.3 3'm~ Zt,O
b
Te/W (100) :500K ~mn ..2gKff~ l:~-Qmet~ : Exi)osure time (s) Te.
w
t,-
~S
t
r
A
<
II II
w
Z20
w
/ / --
//
I~mmeter: Ex~:msure time (s) lez
195
w
<5
~
Tetw (lOOl I,:~: SO0K f : ZOKs"
i~
6OO
18o
8OO
1000
/
1200 ~ TEMPERATURE (K)
!
1600 --
I
1800
20C0
Figure 13: TPD spectra for Te 1 (a,b) and Te 2 (c) from Te adsorbed on ~ 1 0 0 ) at 500K. Mass spectrometer/electrometer ranges 10 -s A (a), 3x10 -8 A (b), 3x10 -9 A (c). Heating rate 20Ks -1. Parameter: exposure (s). From ref.[78]. molecular desorption would be roughly 2.6eV, i.e. molecular desorption would occur at much higher temperature and would never be significant. However, strong lateral repulsion in this system amounting to about 2.5eV at full coverage reduces the molecular desorption energy to the same magnitude as the atomic one in the high coverage regime leading to the occurrence of atomic and molecular desorption at low temperature. Here the molecular prefactor is about 3 orders of magnitude larger then the atomic one, also helping in the competition. In closing this subsection we want to stress that competition between atomic and molecular desorption should be observable in a number of metal-on-metal systems [79] but has rarely been reported most likely for lack of looking. Of course, in such situa-
181
ij oo
.12 9
.10 9 .08
r6
.06
C 0 o--
i:).. L. o E]
.04
9 .02
I
600
800
1000
1200
T emperature
1400
160Z
(K)
1800
2000
Figure 14: Temperature programmed atomic (solid lines) and molecular (dashed lines) desorption of Te from V~100) with a 2-site model. From ref.[77]. tions a separate Arrhenius analysis of the atomic and molecular rates must be performed. 3.7. The role of diffusion in desorption The quasi-equilibrium theory presented so far can easily be extended to systems with more than one adsorbed species and several adsorption sites. If, however, precursors are present as in activated chemisorption, quasi-equilibrium may not obtain during desorption as intermediate steps in the desorption process may be rate limiting. These problems have been discussed very clearly by Cassuto and King [80] at the phenomenological level, and not much has been published since apart from an excellent review by Ehrlich [81]. We briefly comment on one aspect of diffusion in desorption kinetics, following an analysis of the system N2/Ni(430) [82]. We assume that there are two distinct adsorption sites on the surface, referred to for simplicity as step and terrace sites, with partial coverages 8, and 0,. The phenomenological rate equations in the absence of precursors and lateral interactions are assumed to be
182 dOs/ dt = SsasP/ (27rmkBT) l/ 2(1-Os)-rsO s +rs._ t Ot (1-Os)-rt._sOs (1-Ot )
(45)
dOt / dt = St at P/ ( 2 mnkB T) l / 2 (1- Ot) - rt Ot- rs._ t Ot (1- Os) + rt.. s Os(1- Ot)
(46)
Here Si for i=s,t are the sticking coefficients at zero coverage with a simple linear decrease assumed as a function of coverage. The rate constants can be obtained from as
(19)
l"i = SiOiZint/ qin, 2mnail kBT vxiuyi~zi exp(-Voi/ kBT)
(47)
r ~ , = J,~., exp(-Q~_,/k B73
(48)
where Q ~ , is the activation energy for hopping from the terrace to the step site. Detailed balance demands that rz.. s = r~_,r,/ rt
(49)
implying in particular that the activation energy for hopping from the more deeply bound step site to the terrace site is enhanced by the difference in binding energies. To calculate TPD curves we assume that all parameters are such as to produce two distinct .10
F-"
.09
'
"
'
,
,..
9
,
9
,
9 ,
;
j
a
,
~
.
~
~
,
9
,
.
,
,
170
180
.'
',
b
~.o7
~.07 v
.10 .0'9
.06
v
.06
g -~
g
~ .o3 ~ .o2
-~ .o3 ~ .ca
0 1.90
0
'
17,'0
140
1,50
.~
160
Temperoture
170
180 (K)
190
200
120
,/I
"
,
I~
!~.(3
!I \,,,, t
-"
150 Temperature
t
160
190
.
200
(K)
Figure 15" Temperature programmed desorption spectra for the phenomenological model of equ.(45-49): (a) Parameters from a fitting to equilibrium data of experimental data of N 2 / N i ( 4 3 0 ) (M. Grunze, private communication); no hopping ( ~ , =0), initial coverage 00 = 2/3, heating rate 3Ks -1. (b) As for (a) except u~_, =10 s s -1, Q~.,=lO kJ/mol. The solid curve is the sum of the rates from the terrace (long-dashed curve) and step (short-dashed) sites; the net diffusive rate (step--, terrace) is the dash-dotted curve. These three components have been scaled down by 0.25. desorption peaks in the absence of diffusion, see Figure 15a. Conventional wisdom then has it that, in the presence of fast diffusion, particles trapped in the deeper-bound step sites will diffuse into the terrace sites as the latter desorb. One therefore expects a lowering of the high temperature peak and a shift to lower temperatures, thus filling up
183 the valley between the peaks, as borne out in Figure 15b. We note, however, that the transfer from steps to terraces only sets in at temperatures above the maximum of the lower peak, i.e. when the blocking factor l-O, approaches one, and this for all values of J'~ t, and for all heating rates. Indeed, the effect saturates for hopping prefactors us._t larger than 10Ss-~. In Figure 15b the solid curve represents total desorption; the shortdashed curve is desorption from the step site, i.e. the term -rsO, in (45). Diffusion replenishes the terrace sites maintaining desorption from the terrace sites even at higher temperature, i.e. due to the last two terms in (45), the sum of which is shown as the dash-dotted curve in Figure 15b. Indeed, this is the fastest route for depleting the population of the step site, as indicated by the terrace site desorption rate, -r, O, (long-dash curve). The total desorption rate thus shows the high temperature peak reduced and shifted to a slightly lower temperature with considerable filling of the valley. We condude: if system parameters are such that in the absence of diffusion the TPD curves would have two separated peaks, then the two-peak structure remains in the presence of diffusion, no matter how fast it is. 4. N O N E Q U I L I B R I U M T H E R M O D Y N A M I C S OF S U R F A C E PROCESSES So far we have assumed that quasi-equilibrium is maintained during the desorption process. This assumption breaks down when the time constant of surface diffusion is not appreciably faster than that for desorption, as it happens e.g. for rare gases on metals, in which case nonequilibrium effects show up in desorption experiments. To deal with such situations it is advantageous to formulate the kinetics of adsorption, desorption and diffusion using the methods of nonequilibrium thermodynamics. We will demonstrate the approach by considering, in more detail, gas-solid systems in which the adsorbate exhibits coexistence of a dilute, gas-like phase together with islands of a condensed phase, both of course, being two-dimensional (2-d). The 2-d gas phase consists of two parts, adsorbed, respectively, on the bare surface or on top of the condensed islands. Taking the system out of equilibrium by, e.g., changing the temperature of the substrate or the ambient gas pressure, will induce surface processes such as adsorption, desorption and diffusion. Looking at desorption from such a two-phase adsorbate, it can proceed via several channels, namely (i) out of the 2-d gas phase on the bare surface with a time constant ta, (ii) out of the 2-d gas phase on top of the condensed islands with a time constant ta', and (iii) directly out of the condensed phase with a time constant t~, a process particularly important around monolayer coverage. On the other hand, as long as desorption proceeds predominantly via the 2-d gas phase, the depletion of the condensate islands takes place by evaporating into the 2-d gas phase, with a time constant tev, from where particles then desorb into the 3-d gas phase. This will result in density gradients leading to surface diffusion. Two rather different situations may obtain: (i) if tev '~ ta, evaporation is so fast that during the desorption process a quasi-equilibrium is maintained between the adsorbed phases. In such a situation there will be a coverage regime where the desorption kinetics is roughly zero order. (ii) If t,v >) td then evaporation is the slowest process in a chain and thus rate determining. With evaporation proceeding via the rim of the condensed islands one expects roughly half order kinetics. These ideas were first put forward many years ago in a number of papers [83-88].) From the above discussion it should be obvious that adsorbates under nonequilibrium conditions can no longer be described by a single macroscopic variable, i.e. the coverage, but that, as a minimum, one must consider the partial coverages of the various phases and components. To set up a macroscopic theory of surface processes, it is then expeditious to follow the Onsager approach to nonequilibrium thermodynamics [89]. Our first task is to identify the proper set of macroscopic variables to describe the
184 adsorbate. To describe typical experiments involving adsorption, desorption and surface reactions, it is usually not necessary to introduce local densities at the macroscopic level. If the adsorbate forms a single phase, it will remain homogeneous throughout so that diffusion plays no role. If, on the other hand, the adsorbate shows coexistence of two phases, mass transport is limited to particle exchange between the two phases which can be described as 2-d condensation and evaporation. It is then rather dubious to introduce density gradients in the dilute phase, because, e.g., at half coverage, the number of molecules in a typical patch of dilute gas is at most a hundred, so that number fluctuations amount to at least 10%, and the number of particles per patch of dilute gas, and even more so the local particle density, can hardly be taken as macroscopic variables in the two-phase region. To set up the balance equations controlling energy and mass exchange in a two-phase adsorbate, we consider the total system, consisting of gas phase, adsorbate, and substrate, as closed in a volume V, and isolated with total energy U,. As extensive variables we consider the particle numbers X 1=N1 in the condensed 2-d phase, X2 =N 2 in the dilute 2-d phase on the bare surface, X3 =N2,in the dilute 2-d phase on top of the condensed islands, X4 = N 3 in the 3-d ambient gas, X 5 =N, in the substrate, and the respective energy variables X6 =U1, .... Xa0 =Us. Following Onsager we can write the macroscopm balance equations as 10
dX i -- Z dt
OS
L,j ~ j
Ut , Vt ,N, Ns
j=l
(50)
Such a description is valid (i) in the linear regime, and (ii) as long as local equilibrium pertains. The latter condition in particular implies that the entropy of the system is given by
S(Ut,Vt,N, Ns) = SI(U1,A1,NI) + S2(Uz,A2,N2) + S2,(U2,,A2,,N2, ) + ss(v3, V, N3) + S,(U,, V,,N,)
(51)
Here A~ are the areas occupied by the respective phases. Thus we get in (50) OS
vj,Aj(
=-
~rj
OS
1 m
(52)
(53)
introducing the chemical potentials, #j, and the temperatures, Tj, in the various phases and components. Noting mass conservation in the system dNs/dt = O, ( d / d t ) ( N 1+N2+N2,+N3) = O, and energy conservation d U t / d t = 0, one gets 30 conditions on the 100 phenomenological coefficients Lq, because the coefficients in front of each thermodynamic force have to vanish independently. Onsager's reciprocity relations Lq =Lji eliminate another 43 coefficients.
185 Under isothermal situations the equations simplify considerably and can be written as L12
dN1 _ at
-
-
T
L12' (~-~")
- -'T
L13 (~'-#~)
- 7"
(~-~")
dN 2 LI2 L23 L22' at - - r (#l-g,2) - " ~ (P,3-/z2) - " ~ (/z2'-P'2) dN2' dt - -
L12' T
(#l-~
d N 3 -__ d ( N l dt dt
,) L22' , ) - L2'3 ,) - " ~ (V'2-V2 " ~ (~-V'2
W N2 + N2' )
(54)
(55) (56)
(57)
Note that equilibrium conditions, i.e. v~ =t~j, imply vanishing fluxes. The next task is to relate the 60nsager coefficients in (54)-(56) to experimentally accessible quantities like sticking coefficients and activation energies. This has been done for the individual processes, such as adsorption, desorption, and 2-d evaporation and condensation [7,9]. The resulting equations are dN 1 dt - R13 4- R12 + R12,
(58)
(59)
dN2 dt -- R23 - R12 + R22' d~,
(60)
- R2'3 - R12, - R22'
dt
Here Ri3 = SiAi
[:
kBT
~h - " - ~ ~h
2
e~i
/kBr]
(61)
are the net rates, i.e. adsorption minus desorption, of mass exchange between the 3-d gas phase above the surface at temperature T and pressure P and the adsorbed components,/=1,2,2'. The chemical potentials, v~, are those apropriate for the instantaneous values of Ni(t) and Si is the sticking probability, to the i-th phase. There is some arbitrariness in deciding whether particles impinging from the 3-d phase onto the condensed islands become instantly part of the condensed phase (sticking coefficient S 1) or equilibrate first with the 2'-gas phase on top of the islands (sticking coefficient $2,). This seems irrelevant because adding (58) and (60) implies adding (61) for i = l and 2', which, with ~ ' = t ~ , results in an overall sticking coefficient S1 +S2,, since A~ ----'A2,. The rate of mass exchange between the condensed phase and the 2-d gas. phase (2-d condensation minus 2-d evaporation), summarizing diffusive processes, is gwen, within the coexistence region, by v RI2
=
1
S c f AI~ -Tr A-A 1 IN2- ~V2(Na'T)]O(N1)O(N2)
(62)
186 as an excess flux of particles, moving with an average speed v and sticking, with probability So, to the rim of_ the condensed islands, the boundary length of which we approximate by fair. Here N2(Na, 7) is the number of particles that would be in the 2-d phase if the adsorbate were at equilibrium with a total number of particles, Na=N ~+N 2 +N2,, and O(N)=I for N>I and zero otherwise. Although the phenomenological geometric quantities of f, and ~', must change as the coexistence region is traversed, approximations are possible, e.g. if the islands are n in number and circular then f -- 2V'mr, ~" = 1/2; for irregular perimeters f = fo n~-~, 0.5 < g" < 1, fo >2~r. Likewise, particles in the 2'-phase can be incorporated into the condensed phase at the island rim according to R12' = Sc'f'AI
t.,v 1 ~r Z IN2' - N2'(Na'T)IO(N1)O(N2')
(63)
Lastly, we get for the rate of equilibration between the 2'- and the 2-phase w
v 1 R22 , = (1-Sc'Rc)fAl~ -TrA-A l [N2 - N2(Na, T)IO(N~)O(N2)
(64)
Here it is assumed that particles skipping along the bare surface will either be reflected back with a probability R,, or stick to the rim of the condensed islands with a condensation coefficient Sr or hop on top of them with a probability 1-S,-R,. As is evident from the rate equations (54)-(57), the thermodynamic forces responsible for mass transport are differences in chemical potentials. To specify the latter for a two-phase adsorbate one can resort to analytic models, e.g. in simplest form, by treating a mobile adsorbate as a 2-d van der Waals gas, and a localized adsorbate as a lattice gas with nearest neighbour interactions within the Bragg-WiUiams and the quasichemical approximations [7-9]. This being specified, the macroscopic theory is complete. It has been used successfully to describe adsorption-desorption kinetics in a number of gassolid systems, such as metals on metals and rare gases on metals [7-10]. We comment briefly on a few of the results. One example of the new insights gained from the rate equations (58-60) that was not contained in (11), is the desorption from the coexistence region for a system in which the sticking probability on the bare surface, $2, is different from that on top of the adsorbate, S~ +$2,=S~,, both being assumed constant, for simplicity. This appears to be the case, e.g., for Xe on Ni and Ru where S 1=0.6 and $2=1.0 [90]. In quasi-equilibrium the chemical potentials of the various adsorbate phases are equal, i.e. ~1 = ~ =tza in (61), so that after summing (58-60) we get dNa/ dt = (SItA 1 + S2A2)[P~h/ h - kBT/ hXt2hexp(#a/ kBT)]
(65)
Within the coexistence region one can use the lever rule and re-express (65) under desorption conditions, i.e for P=0, as
dO 1 kBT 2 dt =-[($1'-$2)0+$201~-$It02~] 0~-02~ as "--~,~ exp(#a/ kB T)
(66)
The new variables here are the lower and upper boundaries to the two-phase region, 02c(T) <0it(T) We note that, within the coexistence region and at constant temperature, there .~..o coverage dependence outside the square brackets. This implies zero order kinetics, provided that the sticking coefficients are the same for the dilute and the dense phases. However, if this is not the case, quasi-equilibrium in the coexistence region does not imply zero order kinetics. As an illustration we present in Figure 16
187
0
!
-
_J
o
/1 0
9
~
|
9
I I/"
-
.
~
|
I i "i
-
(D~ 0 _..j
/"
! I
! t/3
-
03 I
--
-
0.0
o. 2
o. 4
o.s
Coveroge (ML)
o. 8
1. o
Figure 16: Isothermal desorption of X e / N i ( 1 1 1 ) for temperatures (top to bottom) T = 82,80,78,76 K and a two-component adsorbate in quasi-equilibrium (solid lines); and T = 80,76 K for a nonequilibrium situation (dash-dotted lines). Parameters for the Bragg-Williams approximation: V0 = 3200 K, cV2 = -375 K, Vz = 5xl011s-l, vx = v. = 1013S -1, a s = 5 . 0 A 2, $1, = 1 . 0 , S 2 = 0 . 6 . D a s h e d lines s h o w coexistence boundaries m quasi-equilibrium. From ref.[9]. 9
9
9
)'
an approximate fit to data on Xe desorbing from Ni(111) [90], calculated in the BraggWilliams approximation 9 Nonequilibrium effects are introduced by increasing the size of condensed-phase islands and decreasing the hopping rate away from the perimeters of these islands, such that Rl2 .~R23, R13. The effect on (two of) the curves of Figure 16 is indicated by the dash-dotted lines, corresponding to imposing the extreme condition,a~2 < R23, R13. In this case coexistence continues down to 0 = 0, i.e. it is desorption from the islands that contributes predominantly to the total rate. Such isothermal modelling also shows that the concept of a fixed desorption order (x in (3)) is found wanting: in the quasiequilibrium case, x may not be fixed at zero during coexistence, c.f.(66) and cannot be unity outside coexistence due to adparticle interactions; in nonequilibrium situations x varies markedly with temperature, and it does not necessarily approach the value of ~'. Even the utility of the Arrhenius parametrisation (4) is questionable when desorption is not a quasi-equilibrium process - isosteric Arrhenius plots are no longer one or more straight lines, but smooth curves. Figure 6 of ref. [90] contains an excellent example of
188 this; its significance was overlooked by all. This sounds acaution against the 'blind" fitting of a single line through scattered isosteric data points, so common in the literature. Now the quantity Ed(0, T), defined by (24) is a necessity if one insists on a parametric analysis of the data. Again, though, the temperature dependence of Ea (0, T) can be so great as to render that analysis almost useless unless a detailed comparison is also made with specific models of the adsorbate. In such cases it is far better to model the o
O O
x,._
o
~
t c;-i 0.0
",,\
J'/ / 0.2
0.4
0.6
Coverage (ML)
0.8
1.0
Figure 17: Desorption at T = 80 K of a two-component adsorbate with desorption from condensed phase suppressed (S~ =0); quasi- and nonequilibrium situations otherwise as Figure 16 Initial coverage 6(t=0) = 0.8. From ref.[8]. rate data directly. As a final example, we show in Figure 17, the result of setting $1 = 0, N2, = 0 in the rate equations (58-60). For quasi-equilibrium, c.f. (66), there is a linear growth of the rate as 0 decreases, as far as 0zc(7) then a nonlinear decrease as the dilute phase desorbs. The extreme nonequilibrium cor~dition produces a variable order process, however (dash-dotted line). Since the first phenomenological kinetic models describing zero-order desorption and the effects of islanding of the adsorbate were advanced [83-88], surprisingly little improvement of the theory has occurred despite the large number of experiments exhibiting these effects. Of the few groups that have modelled desorption trajectories through a phase coexistence region, Nagai and co-workers [91] have been restrictive in their use of transition-state theory (quasi-equilibrium), with a constant prefactor, and no distinction between the coexisting phases, e.g. differing sticking coefficients. That
189 thermal desorption experiments can detect unequal sticking coefficients, as described by refs. [7-10], has been shown recently by Meyer et a/.[92] in their modelling of H/Mo(110). The characteristics of the sticking coefficient at the two-phase interface can also be probed [93]. Desorption experiments clearly illustrating nonequilibrium effects are more scarce. Arthur and Cho [83] observed half-order kinetics, long ago, for Cu and Au desorbing from graphite. Their Arrhenius plots are not linear but, as has occurred with other experimental analyses t94] since, they derive a desorption energy from the straightest portion of the isosteres. Such fitting procedures, coupled with experimental uncertainties, must be partially responsible for this scarcity. As many systems are in the quasi-equilibrium regime at the approach to peak desorption temperatures only a careful examination of the low temperature-high coverage rates will illustrate lack of equilibration [17-18,95]. 5. K I N E T I C L A T T I C E GAS M O D E L To go beyond a macroscopic description of time-dependent phenomena, we explore a mesoscopxc approach entailing the kinetic lattice gas model. It was originally set up in close analogy to the kinetic Ising model for magnetic systems [96]. To set up the kinetic lattice gas model we restrict ourselves to gas-solid systems in which all relevant processes, like diffusion, adsorption, desorption etc, are markovian. We introduce a function P(n;t) which gives the probability that a given microscopic configuration n=(nl,n2,...,nt) is realized at time t, where I=N s is the total number of adsorption sites on the surface. It satisfies a master equation
dP(n;t)/dt = Z
[B(n;n')P(n';t)- B(n';n)P(n;t)]
(67)
1l'
where B(n';n) is the transition probability that the microstate n changes into n' per unit time. It satisfies detailed balance
B(n';n)Po(n ) = B(n;n')Po(n 9
(68)
where
Po(n) = Z -1 exp(-H(n)/ kBT)
(69)
is the equilibrium probability. In principle, B(n';n) must be calculated from a hamiltonian that includes, in addition to (12), coupling terms to the gas phase and the solid that mediate mass and energy exchange; this has been done for phonon-mediated adsorption-desorption kinetics [6]. Rather, we follow the procedure initiated by Glauber [97] and guess an appropriate form of B(n';n). We briefly indicate possible forms below, and their consequences for the solution of (67). Assuming that the residence time of an adsorbed particle in an adsorption site is much longer than the transition time into or out of that site, we can write B(n';n) as a sum of terms accounting for individual processes of adsorption, desorption and diffusion, i.e. ~,~,(n';n)= Wa.d(n';n ) + Wd/f(n';n)
(70)
The simplest choice has been termed Langmuir kinetics [98] for which one assumes that adsorption into a site i takes place provided that site is empty, irrespective of whether
190 neighbouring sites are occupied. We then get
nn,= oZIlni+ onlX+ Zni a + ania n + ] *~ni ', l-hi) -[-[iS(hi',nt) l ;ai
(71)
where a and a' enumerate the neighbours of site i. Similarly we write for diffusion
1
Waif(n';n) =J0 ~ ni(1-ni~) 1+C1 ~ ni.o, + ' " l b~a j i,a * ~ n i ', 1 - n i ) ~ n i + a ',
1-ni.~)
]-[
~("l',nl)
(72)
l~i,i+a Inserting (72) into (70) one gets
Co = exp(E,/kBr)
(73)
Cr = [exp(VJ k s r ) - 11r
(74)
Langmuir kinetics, in particular, results in a sticking coefficient
S(0, I9 = S0(1-0)
(75)
Other choices, and their physical or unphysical implications, have been discussed in ref.[98]. As long as the master equation (67) is used in Monte Carlo simulations solely to determine the equilibrium properties of the system, it does not matter what choices are made for the transition probabilities ~n';n) as long as they satisfy detailed balance (68). However, if we are interested in the adsorption, desorption and diffusion kinetics of a particular physical system, the choice of transition probabilities l~(n';n) is rather narrowed [98]. To solve the master equation (67), Monte Carlo techniques have been invoked [99-102]. The renormalisation group has also been used successfully [102,103]. Recently, a perturbative approach has been suggested that constitutes a time dependent generalisation of Kikuchi's method [104] in equilibrium statistical mechanics. In a systematic approach one can derive a hierarchy of coupled equations of motion for average occupations and correlation functions [97,98,105-115]. To this end we define
(ni) = ~ niP(n;t) n
(76)
191
(ninj) = ~ ninjP(n;t)
(77)
n
and obtain from (67)
d(n,)ldt=Wo(1-(n,))-WoC0[(n,)
(78)
+ C~ Z ( n , n , + = ) + ...]
I.
a
J
and similar equations for the higher order correlation functions. Comparing (78) to (1-2) for the case of a homogeneous adsorbate with (n~) = 0, allows one to identify W0 as
(79)
Wo = So pas / ( 2 7vnkB T)1/ 2
implying, with (73) and (13), that the desorption rate constant is given by
rd(o=O)
= woco
(80)
= SO as kB T/hXt2h61 Zi,,, / qi,,, exp(- Vo/k B T)
The infinite hierarchy (78) etc., is obviously exact. To solve it, approximations must be made; in particular, the hierarchy must be truncated, e.g. by invoking the 2-site or Kirkwood closure approximation, which states that (n irlj...n m
) =
(ninj )(rljn k )...(nIn m )/(n i)(rlj)(nk )...(nl)(nm
)
(81)
In higher, n-site closure approximations the right hand side would involve n-site correlation functions in the numerator and (n-1)-site functions in the denominator. In the simplest (Kirkwood) approximation, and for a homogeneous adsorbate, one is left with two coupled equations for 0 and for the average correlation function ~k = (nini+a), which read for a square lattice [98] (82)
dO/dt = W0[1 - 0- CoO(1 + C1~//0)4] d~k/dt = 2Wo[O- ~k- Co(1 + C~)r + 2./o(1 + C~r
+
q~/e)31
3(1 + C~)~/+ 6"107/+ 5C~r
q~(1
+ 2r
(83)
We note that the equilibrium solution of these equations yields, at least in the absence of the diffusional terms, the quasichemical approximation to the isotherms. Other treatments of the closure, in 2-d, correspond to quasichemical, Kirkwood or maximum entropy approximations [109,116,117]. In one dimension the truncation of the equations of motion has been worked out and examined in detail [112,115,118]. This has allowed an accurate examination of the role of diffusion on desorption and implications for the Arrhenius analysis in non equilibrium situations. The largest deviations from the desorption kinetics of a mobile adsorbate obviously occur for an immobile adsorbate where surface diffusion is negligible throughout desorption. If the time scale of surface diffusion is of the order of that
192 of desorption we expect results intermediate between those of fast and negligible diffusion.
~'-5
............
4.0
'
I . . . . . . . . .
(3
"-~
3.0
~:2.5
7
~
61
~
5
~(
s
i
9[
,
:'/
4
t
)
!
1
o 90
.
'i'f 0
10(3
110
120
130
Temperoture
1( 0
150
. . . . . . . . . .
90
~9(:;
: 10
120
130
140
150
Temperoture
Figure 18:(a) Temperature-programmed desorption rate for a 1-d immobile adsorbate with repulsion V2 =400K. Solid lines: 3-site closure. Initial coverages (left to right) 00=0.9, 0.7, 0.5, 0.3, 0.1. Dashed lines: 2-site closure (for initial coverages 00=0.9, 0.5; for initial coverages less than 0.3 2-site and 3-site closures coincide). (b) Corresponding evolution rates d <-->/dt (solid lines) and d < . - - > / d t (dashed lines) in 3-site closure for 00=0.9, 0.7, 0.5. From ref.[115]. Figure 18a presents temperature programmed desorption spectra for an immobile adsorbate with nearest neighbour repulsion for varying initial coverages [115]. The dashed lines are obtained in the 2-site closure, while the solid lines result from the 3-site closure which, for an immobile adsorbate, is exact. Clearly the 2-site approximation is inadequate. Evans et al. [118] have interpreted the three peaks in the desorption spectra as "staged" desorption, with the low temperature peak reflecting desorption of particles from an environment of two occupied neighbouring sites and the smaller middle peak as desorption from the ends of chains. This picture is nicely confirmed by looking at the time evolution of <-- > = (nini+~) and <... > = (nini+ln~+2), Figure 18b. One can perform an Arrhenius analysis of the desorption spectra in Figure 18a by plotting isosteric rates vs. T-1. Although, for an immobile adsorbate these curves are not expected to be straight lines, one does a linear fit for simplicity, anyway. The resulting (temperature averaged) desorption energy and prefaetor are plotted in Figure 19a,b for the 2-site closure and the exact immobile and mobile cases. The mobile case correponds to the quasichemical approximation, c.f. (28). Although Figure 18a shows a two-peaked desorption spectrum for an immobile adsorbate calculated in the 2-site approximation, similar to the mobile case, the desorption energy looks quite different with the appearance of a peak at half coverage. The exact (3-site) calculation exhibits two peaks in the desorption energy. In the analysis of experimental desorption data, such features might be interpreted as arising from attractive interactions. This is clearly not the case here. The corresponding prefactors also show sharp peaks, which would be interpreted as an ordering above that for equilibrium. These features are an artifact of the Arrhenius analysis, and their form depends on the strength of the interaction,
193 ~4.5 ~. 0
39
[i
E~
"~ v
i
14.0
37 36
:3.5
c:~ 3 5
o
I,i
~
0 _.J
t 41
L
~
i L
,
4 i
:
i
125
31 3o
b
(3. ~.3.0
c 33 0 ".7, 3 2 O. m
,.i ' ,'
r L i
:
I
29 ~
0
.
.
.
.
.
.~
.
.
.
.
.
.2
.
.
.
.
.
.
.3
.
.
.
.
.4
.
.
.
.
.5
Coveroge
.
.
.
.
.
.
.
.6
.
.
.
.7
.
.
.
.
.
.8
.
.
.
.
.
.
:2.0
.
i
ij
0
.9
L'\
;
.2
.3
.4
.5
"Coveroge
(ML)
.6
.7
.8
.9
(ML)
Figure 19: (a) Temperature-averaged desorption energy, Ed((~), for adsorbate with V2=400K for cases" immobile, 3-site closure (solid); immobile, 2-site closure (dash); mobile (long/short dash). (b) Corresponding prefactors v(~). From ref.[115]. the initial conditions and the range of desorption temperatures over which the analysis is performed. Clearly, the Arrhenius analysis should be reserved for systems that remain in quasi-equilibrium where the interpretation of the desorption energy as the heat of adsorption and the prefactor as a measure for the entropy is rigorous. Figure 20 exhibits the transition from an immobile to a mobile adsorbate in the TPD spectra for a system with repulsive interactions. The variation of J0 between the immobile and mobile limits is approximately five orders of magnitude. Relaxation of correlation functions to equilibrium in the absence of desorption has also been examined [113,115]. In the remainder of this section we want to briefly survey other applications of the kinetic lattice gas model. The effects of adatom clustering on desorption rates,[106] and desorption of dimers [107] have also been considered. A specific application, in the one-dimensional case, has been to N2/Ni(430) [82], including multi-particle interactions and diffusive terms completely. Recently the kinetic lattice gas model has been used to find a time-dependent generalization of the grandcanonical ensemble to describe inhomogeneous adsorbates within the coexistence region [119]. If the adsorptiondesorption kinetics are precursor-mediated, one can extend the kinetic lattice gas model by introducing three sets of occupation numbers per lattice cell, namely ni =0 or 1, mi =0 or 1 and li =0 or 1, depending on whether the chemisorbed state, the intrinsic precursor and the extrinsic precursor are empty or occupied, respectively. The hamiltonian of the system is then [120]
H=Es~'~ ni + e ~ Z mi + ~ ' i
i
Ii + ~ V2 ~ ninJ i
(V)
For the transition probability in (70) we then write
(84)
194
o (2) -9 3 x
0
c
0
2
CL 0 r~
f
o 90
I
L
IO0
110
120
.
I
130
.
.
.
.
1
.
140
.
.
.
.
.
.
150
Temperature.
Figure 20: Temperature-programmed desorption rate for a 1-d adsorbate with repulsion, V2---400K, for 00=0.9 and varying hopping rates Jo=uoexp(-Q/kBT). Hopping barrier Q=0, and po=10-4s -l (solid, three-peaked) (effectively immobile), 10-3s -1 (- -), 10-2s -1 ( . . . . . . ), 10-1s -1 ( . . . . . . ), ls -1 (-), and 10s-1 (solid) (effectively mobile). From ref.[115].
T~n',m',l';n,m,l) = Wt + We + Wc + W c t + Wce + Wee + Wit + Wet
(85)
The significance of the individual terms is illustrated in Figure 21. As an example, we can specify adsorption into, and desorption from, the intrinsic precursor state with
Wt(n,m',l;n,m,l ) = wt ~ (1-ni)(1-mi +Dtmi)8(m,', 1-mi) ]-[ 6(ink',ink) i k~i
(86) where the first factor ensures that the intrinsic precursor only exists over an empty site. The kinetic lattice gas model with precursors has been used recently to study the temperature and coverage dependence of the sticking coefficient as a consequence of lateral interactions [121,122]. A similar formalism is also appropriate for the kinetics of rnultilayers.
195
W~
~
Q "i~ ~
Figure 21: Schematic illustration of the various processes between extrinsic precursor (E), intrinsic precursor (I), and chemisorbed (C) states included in the kinetic lattice gas model. 6. PHYSISORPTION KINETICS A monograph on physisorption kinetics was published by Kreuzer and Gortel [6] which covers the field up to 1985. The book starts with a review of the gas-solid interaction of physisorption. Next the master equation is derived and the transition probabilities are calculated for phonon-mediated adsorption and desorption processes. On this basis desorption times are calculated as well as time of flight spectra of the desorption flux, and is followed by a discussion of sticking and energy accommodation. In the final chapter the Kramers equation is derived from which the macroscopic laws are obtained, together with microscopic expressions of the friction coefficient. Thus the complete programme of nonequilibrium statistical mechanics, as envisaged by Boltzmann, has been completed for physisorption namely, starting from a microscopic hamiltonian, one derives kinetic equations and, finally, calculates macroscopic transport coefficients. The only other example where this programme has been completed before is the theory of dilute gases. This is not to say that there does not remain a large amount of work still to be done. In particular, the above theory is largely restricted to the low coverage regime, except for helium where the kinetics has been worked out for multilayer adsorbates. Although nonequilibrium thermodynamics and the kinetic lattice gas model are being applied to study the kinetics of physisorbed multilayers, little progress has been made on its microscopic foundations. Brenig [123] has reviewed the kinetics and dynamics of the gas-surface interaction in the zero coverage limit stressing the importance of the principles of detailed balance and unitarity. Jack et al. [124] have derived the FokkerPlanck equation for physisorption kinetics, again in the zero coverage limit, and presented a general formulation for its solution under isothermal desorption conditions. The good news in physisorption kinetics comes from Menzel's group where an exhaustive experimental study of rare gases on Ruthenium has been completed [17-19].
196 7. C O N C L U D I N G
REMARKS
In this review we have tried to give a concise overview of the physical processes underlying adsorption and desorption. We have reviewed the phenomenological approach and methods of evaluation of experimental data. For systems that remain in quasiequilibrium throughout desorption an essentially exact theory now exists, and accurate fitting of data is now possible. We have also outlined the current status of kinetic lattice gas models. We have only made passing reference to experimental aspects of thermal deso~tion, but mention some recent work as exemplifying a new standard. We note the margin by which such good experimental technique leads the many over-simplified theoretical models and analyses of data in the literature. It is time the simple but fundamental phenomenological concepts and results outlined in Sections 2 and 3, were understood by all interpreters of TPD data. Specific examples bear re-emphasis: the assumption of a constant prefactor is no longer tolerable, nor must high temperature limits be presumed; nor should a peak temperature analysis of experimental TPD data be considered sufficient - a complete analysis is mandatory if sense is to be made of the phenomenological parameters, E d and p, including their coverage and temperature dependence [31]. The complete theory at the macroscopic level, including non.equilibrium effects, has now been formulated for the submonolayer and multilayer regimes: apart from restrictions on one's knowledge of the chemical potential of the adsorbate, there is no need for further oversimplifed modelling of TPD spectra. In the quasiequilibrium situation transfer matrix techniques can give essentially exact results (apart from sticking coefficients), as long as the adsorbate can be described by a lattice gas model. Although originally conceived to deal with adsorbates on a fixed lattice of adsorption sites and simple two-body interactions, the model is also capable of dealing with reconstruction which is mimicked through the introduction of multisite interactions [125-127]. Employing these techniques one can now also model coadsorption accurately [128,129]. To make contact with microscopic concepts, the kinetic lattice gas model provides an alternate, and perhaps more satisfactory description. One hopes for further analytic work on this model in the monolayer and multilayer regime along the lines of the closure approximations. The application to heterogeneous surfaces holds great promise. This review has addressed developments in the theory of adsorption and desorption. Only cursory mention has been made of Monte Carlo simulations. They can, in principle, produce the exact kinetics from well-defined transition probabilities. It is, however, our view that such '~umerical experiments", although very useful in providing "data ~, are not a substitute for a statistical theory in which one first derives a small set of kinetic equations from which observable quantities and relationships are calculated. Even for very complex systems such as adsorbates with precursors and multi-particle interactions for which Monte Carlo simulations were deemed to be the only practical tool, progress has now been made in the statistical theory that promises a better understanding of their kinetics. ACKNOWLEDGMENT Funding for this research was originally provided by the Network of Centres of Excellence in Molecular and Interfacial Dynamics, one of fifteen Networks of Centres of Excellence no longer supported by the Government of Canada.
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W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
Theory of adsorption-desorption on flat heterogeneous surfaces
201
kinetics
Yu. K. Tovbin Karpov Institute of Physical Chemistry Vorontsovo Pole Str. 10, 103064 Moscow, RUSSIA
1. I N T R O D U C T I O N Adsorption processes in gas - solid systems turn out to be fundamental to technologies of purification and separation of gas mixtures as well as being stages of more complex catalytic processes, membrane separation of gases, corrosion of solids in chemical gas mixtures, etc. In many cases, the time evolution of those processes is determined by the rates of adsorption and desorption. The investigation of those processes has been given, of course, quite a lot of attention. In view of the intensive experimental research, it appears necessary to develop a theory of adsorption rates. The simple theory of Langmuir describes the kinetics of adsorption on homogeneous surfaces, based on the law of mass action. One assumes that every adsorbed particle occupies one and only one site on the surface. The change of the surface coverage in time T can be described by the kinetic equation [1-4] dO dr - UA -- UD, UA = K A P ( 1 - 0) TM ,
00
=
0(~- = 0)
(1)
UD = KD0 TM ,
(2)
where UA and UD are rates of adsorption and desorption; KA and KD are rate constants for adsorption and desorption; P is the pressure of the adsorbing gas; O = N A / N is the degree of coverage of the adsorbent surface, 0 < 0 < 1, NA is the number of the adsorbed particles, N is the number of sites per unit area of surface, m is the adsorption order with m - 1 corresponding to adsorption without dissociation, m = 2, to adsorption with dissociation of the adsorbed molecules into two identical particles. The solution of equation (1) for the adsorption of non-dissociating molecules at a constant pressure in the gas has the form:
O(r) = 0o e x p ( - k r ) + 0o~[1 - e x p ( - k r ) ] ,
k = KAP + K D ,
0o~ = K A P / k ,
(3)
and for dissociating molecules in cases when the process of desorption prevails over adsorption (and the adsorption can be neglected) or vice versa, we have: Oo 0(~-) = 1 + 0oKDr '
0(T) = 1 -- (1 -- 00)/[1 + rKAP(1 - 0o)].
(4)
202 The kinetic relationships (3) and (4), however, do not describe well most of the experimental data [1-5]. It is not by accident that in the course of chemisorption, two stages can be separated, fast and slow [2]. Often enough, the slow adsorption process also can be observed for physical adsorption. It was Titov [6] who attempted to describe the slow course of the physical adsorption of gases in coal. McBain [7] described preliminary research concerning the sorption of iodine by activated coal, where the absorption did not stop after several years. The factors determining slow penetration in adsorption are related to the system, but the most important are considered to be the high heterogeneity of the adsorbent [1-5] and the strong non-ideality of the adsorbate [8,9]. To describe adsorption kinetics, one frequently applies empirical equations. The course of many sorption processes can be satisfactorily described by [10,11]
(5)
0(~) = a7 ~/~
where a and n are parameters. One discovers that the value n is analogous to the parameter in the Freundlich adsorption isotherm [1]. It seems that equation (5) was presented for the first time by Bangham and Bart [12]. A differential form of equation (5) is the empirical power law UA = KAP8 -~ ,
UD = KD~9k ,
n,k - const.
(6)
The equation for the rate of desorption corresponds to the above equation. These equations were used in papers [13-15]. Experimental data show a wide spread of the exponential dependence of the adsorption and desorption rates upon the coverage. A relationship of the type UD = KD exp(h~) ,
h = const ,
(7)
for the desorption of Th and Cs from the surface of tungsten was observed by Langmuir [8,9]. In order to describe the kinetics of adsorption in the system Co/Mn02, the following equation [16] was proposed UA = KA exp(--g~) ,
g = const ,
(8)
Methods of the determination of the constants in a given equation were considered in papers [5,17]. The integral form of the Roginski-Zeldovich equation (8) has the following logarithmic form [18,19]
O(T) = k'ln(w + ~'0),
k', r0 -
const ,
(9)
Equation (9) is often called the Elovich equation because it was he who gave its the first interpretation. It was shown in [19] that this equation is connected with a linear change of the energy of activation with surface coverage: EA(O) = EA(O = O)+gO. This equation describes the slow processes which often follow a fast initial stage of chemisorption. Equation (9) was confirmed many times. Its application and the methods for the determination of its constants are discussed in [20-26]. The necessity of explaining the origin of the empirical equations stimulated the development of the theory. In the first place, it was necessary to consider the fundamental physical factors affecting adsorption systems: the heterogeneity of the surface and the
203 lateral interactions between the adsorbed particles. This resulted in development of the theory in two directions. In some papers the main part was allotted to surface heterogeneity and the adsorbed particles were considered not to interact with one another [1-4, 27-32]. In some other papers, on the contrary, lateral interactions of the adsorbed particles were considered on homogeneous surfaces [9,27,33-38]. (A more detailed review of all those papers is given in [39,40]). The task of incorporating the influence of lateral interactions upon the kinetics of adsorption was found to be complex because non-ideal adsorption systems are characterized by co-operative behaviour. So it was necessary to use the methods of statistical physics of the condensed state in order to study this problem. That is why, at the very beginning, the theories of the first group were most widely studied and developed. In absence of lateral interactions, the description of kinetics was based on the law of mass action which essentially does not need a new method in spite of the procedure of averaging over the energy distribution function which is well elaborated in the theory of probability. Intensive development of the work of the second group started in 1973-1974. A new step in the development of the theory of kinetics of the adsorption processes was presented in 1982-1984, after elaborating equations describing the rates of adsorption and desorption on heterogeneous surfaces with lateral interactions taken into consideration [41-45]. In the present paper a review is given of the development of the theory of kinetics of the adsorption processes on heterogeneous flat surfaces. At first, we consider the theories of the first group, then we consider those of the second group. Then, we present the theories which take account of the simultaneous influences of the heterogeneity of the adsorbent surface and the lateral interactions. Because of the limited length of this review, we consider only the case of the equilibrated distribution of the adsorbed particles on a surface for all models. The state of the surface layer of an adsorbent is assumed to be unchangeable during adsorption-desorption processes (absence of reconstruction of the surface layer); also not considered is the possible solution of the adsorbed particles in the solid substance, and the possible occurrence of a precursor state. All these limitations can be calculated with the help of the kinetic equations, elaborated in terms of the lattice gas in papers [45, 47] (see also [40, 48, 49]). While considering also the moments which are obtained in current experimental investigations by the method of thermo-programmed desorption and the necessity of the molecular interpretation of thermo-desorption spectra (TDS), special attention shall be given to the application of the theory in the calculation of TDS in this review. The values of the rate constants are connected with the energy of activation are given by the Arrhenius law: =
exp(-ZE
),
KD = K~ exp(--/3ED) ,
/~ = (ksT) -1 ,
(10)
where K~4,Dare preexponential factors of the rate constants, and EA,Dis the energy of ac0 tivation of adsorption or desorption. While calculating TDS it always assumed that KA,D hardly depends on temperature. This results from rate theory of elementary processes [28,50,51] 9 In the molecular interpretation of K A~, D for surface processes, one most often uses the collisional model and transition state theory [1-4, 28, 52-55]. In the collisonal model, the rate of reaction is proportional to the probability of collisions of the reagent hard spheres which can be calculated from kinetic theory. The model of the transition
204 state [56], aside from a geometrical factor, also includes the redistribution of energy within the internal degrees of freedom of the reagents. This difference is important for the description of the kinetics of the non-ideal adsorption systems. In such systems the reagents constantly occur in the "field of action" of neighbouring particles so that lateral interactions can influence the energy of the transition state.
2. I D E A L A D S O R P T I O N
ON H E T E R O G E N E O U S
SURFACES
In his first work on the theory of adsorption, Langmuir pointed out the possibility of the existence of crystalline and amorphous surfaces in places which are characterized by various numbers of residual valences. Energetic heterogeneity produces differences in the adsorption and kinetic capabilities of various fragments of such surfaces. The sources of the energetic heterogeneity can also be conditioned by the chemical nature of the surface atoms of the solid (for example, by the occurrence of various atoms in alloys or by the occurrence of mixtures), by different crystallographic faces of the solid and their structural imperfection, etc. Such heterogeneity depends on the history of the formation of the surface of adsorbent. Expressions for the macroscopic rates of adsorption and desorption of nondissociating molecules on unit surface of adsorbent can be written as: t
t
UA = E
fqU~,
t
UD -- E
q--1
fquD'
q--1
E
t
fq = 1,
q--1
8
"~ E fqeq, q--1
(11)
where the UA(D are local rates of adsorption (desorption) on the sites of type q, 1 < q < t, t is the number of types of surface sites, fq is the fraction of sites of type q, and 8 is the macroscopic fractional coverage of the surface. The function fq (the discrete surface site distribution of the adsorption energy) characterizes the surface composition of the adsorbent. The local rates of adsorption-desorption have an analogous form to eq. (2), UqA
A =Ka(1-Oq),
D =KqOq, D Uq
KqA,D = K qA,Voexp(_~E~,D)
(12)
where the K A'D~ are the pre-exponential constants of the rate K {'D for a site of type q, E A'D is the energy of activation of the process for the site q; 0q is the probability of coverage of the sites of type q (i.e., the local coverage). The local rate constants are connected with the local Langmuir constants aq = aq0 exp(~Qq), where Qq is the adsorption energy on a site of type q, by aq = K A / K D, E D = E A + Qq, aqo: K{o/KDo If the number of types of sites is large, then the sums in (11) can be changed into integrals. It is usually also assumed that the pre-exponential factors hardly depend on the type of site so the change of the adsorption capability while passing from one fragment of the surface to another is determined by the differences in the adsorption energies on those fragments. Let x be the value of the adsorption energy. Then, formulas (11) and (12) can be re-written as: X2
UA,D = / f(x)Uh,D(X)dx, Xl
X2
0 : / f(x)0(x)dx, Xl
x2
/f(x)dx : 1 Xl
(13)
205 UA(X)
--"
KA(X)[1 0(x)] --
~
UD(X) KD(x)0(x) "--
~
KA,D(X)= K ~
A,D
exp[--BEA,D(X)]
(14)
where xl and x2 are the smallest and the largest adsorption energy. The first statistical description of the rate of reaction on heterogeneous surfaces was given by Constable [58]. He assumed the site distribution function to be an exponential function of the activation energy of the reaction: f(x) = exp(~/z)/n, where "7 is the width parameter of the distribution and n is a normalizing factor. He also assumed that the probability of a local coverage does not change while passing from one fragment of the surface to another. Later on, the statistical description of the rates of the surface processes was also treated by Cremer and Schwab [59,60]. To calculate the rates of adsorption-desorption, it is necessary to estimate the character of the changes of the energies of activation with the change of the adsorption capability of various fragments of the surface. In other words, it is necessary to know how the values KA,D(X) depend upon the value of x which in turn determines the relationship cwbetweenof KA,D(X) and 0(x)). In order to construct the function EA,D(X) (14), Temkin proposed to use an analogous relationship by Br<nsted [63] for homogeneous catalysis. Br<nsted proved that for the reaction of acid catalysis in solutions of various monovalent acids, we have the relationship ln a = a ln k + C, where g i is a rate constant which characterizes the catalytic reactivity of those acids, k is a dissociation constant for those acids, cr and C are constants. An analogous relationship was also observed for basic catalysis. Temkin proposed that one should consider various fragments of the surface as similar to compositions. Then the process of adsorption (desorption) on various fragments of the surface can be considered as a reaction, and the values of the rate constants KA,D(X) and those of the adsorption coefficients a(x), are linearly related to each other" AEA(x) = - ~ , A x ,
AED(x) = a A x .
(15)
A graphical interpretation of the Bronsted relations, with the help of the potential energy diagram, was given by Horiuti and Polanyi [64] (see Fig.l). If two monovalent reactions are characterized by an increase of the potential, then the change of the activation energy A E consists of a certain fraction of the change in the energy. At the same time, if- over a small distance- the curves can be considered as straight lines, these values turn out to be proportional. In this way, ~, < 1, and the change of the adsorption activation energy is a certain fraction of the change of adsorption energy. An analogous relationship is also valid for desorption. Then a + "7 = 1, because ED(x) = x + EA (x). The relationships (15) state that the fragments of the surface with the greatest adsorption capability possess the smallest adsorption activation energy and the greatest desorption activation energy. A decrease in the adsorption capability increases the energy of activation of adsorption and decreases the energy of activation for desorption. At "r = 0, the energy of activation for adsorption hardly depends upon the type of sites on the surface, and at a - 0, the desorption activation energies do not depend on the type of the surface fragment. In many adsorption, catalytic and electrode processes, the coefficients a and ~, turn out to be close to 0.5 [3, 65, 66]. Formulas (13)- (15) can be reduced to the following expressions for the rate of adsorption" X2
X2
/ .
Xl
Xl
206
A{__ Q ~ .~
AQ' --
AQ"
Distance Figure 1. A graphical illustration of the relation between the changes of the activation energy AE and the adsorption energy AQ (-= Ax)[64]. These expressions allow us to obtain some simple analytical formulas if one assumes a site distribution function. The most simple distribution function is the regular function f ( x ) = (x2 - xl) -~ [27]. If we assume that -y = 1/2, the following expression can be obtained"
UA = 2 K ~ P { a r c t g ( u 2 ) - arctg(ul)}/[a(aop)X/2] , ul,2 = (al,2P) 1/2 ,
al,2 = ao exp(/gXl,2 ,
a =/9(x2 -- Xl).
(17)
The value of the equilibrium pressure P, corresponding to the given coverage of the surface O, can be written with the help of the quasi- logarithmic isotherm of Temkin: 8 = ln{(1 + a2P)/(1 + a l P ) } / a .
(lS)
In this way, equations (17), (18) determine UA(O). In a similar way, we arrive at an equation for Up(O). If we limit these considerations to the range of middle coverages a l P << 1, a 2 P >> 1, then empirical equations (7), (8) can be found and we obtain a molecular interpretation of the constants in these equations [27]: rg~ KA = sin(Tr)er '
g = 7a,
7rK~) KD = sin(a~r)a '
h = ha.
(19)
The molecular interpretations of the empirical equations (7), (8) are not equivalent in meaning. Considering the lateral interactions on a homogeneous surface leads to the same kind of the concentration relationships [32,27]. The interpretation by Langmuir [32] corresponds to the values of the parameters h = 1 and g = 0 in equations (7), (8) (i.e. the model of collisions has been used). The interpretation by Temkin [27] was made with the help of the transition state model which accounts for the interaction of the activated complex with the environment. Equation (8) can also be obtained if we assume that the
207 number of sites on the surface changes during adsorption [67-69, 21]. In a similar way, with the help of exponential distribution functions (with positive and negative values of the exponent), one can explain various power dependencies of the rates of adsorption in equations (5), (6), [3,30,31]. Expressions (16) allow us to formulate the task of finding a distribution function f(z) from measured adsorption data. The first such work was carried out by Roginski, Lewin and others [1, 70-74]. There are many methods of solving the integral equations (16). The current state of this field is given in the monographs [75,76]). Fig.2 shows a distribution function obtained in [72] from data [72-74] on adsorption of hydrogen on sugar coal at 557 and 669K. Such work, based on application of the methods discussed in [1,70-72] was carried out by analysis of the kinetic data for the decomposition of methanol on zinc oxide. It was found that the distribution function obtained depended on the temperature of the experiment [72]. Based on these results, together with the data on isotopic exchange [73,74], the authors concluded that isothermal measurements are not effective for strongly heterogeneous surfaces, because at T = const., only a small part of the surface "works". This problem can be avoided when the method of programmed heating of the surface is applied.
I
I
'
9
16 18 20 x, kcal Figure 2. Site energy distribution function for hydrogen adsorption on the surface of an active carbon from sugar as obtained by Levin [72] from the processing of experimental data on the kinetics of adsorption [72-74]: f(x) = 640 exp[A-9.85-l0 s exp(a)], a = -7.19-lO-4x, x=energy in kcal/mole.
3. T H E R M A L D E S O R P T I O N S P E C T R A 3.1. Principles of t h e a n a l y s i s o f t h e r m a l d e s o r p t i o n s p e c t r a The method of thermal desorption was originally proposed in papers by Becker and Hartman [77] and Ehrlich [78]. It was widely developed and is presently used in many studies of adsorption processes. Its essence is contained in the fact that an adsorbent containing a certain amount of adsorbate is heated according to a given time-law of changing temperature T(r). The corresponding changes of pressure are measured in the experimental cell. The dependence of the pressure change (or of the number of molecules which desorb) on temperature is called the thermal desorption spectrum. Contrary to the lengthy measurements at a fixed temperature, here, within a short period of time, one can follow the process of desorption throughout the whole range of coverages from the full surface to practically zero. The mass material balance in the cell can be described in
208 the following way [79,80]:
(20)
an/dr = -AdNa/dr - nS/V - L,
where n is the number of molecules in the volume of the thermal desorption system V, N~ is the surface concentration of molecules, A is the area of the adsorbent, S is the speed of pumping the gas from the cell (liter/sec.), L is is the flow of gas into the cell from all the sources of molecules with exception of the sample itself. One assumes that at the beginning of the experiment: 1) the molecules in the cell are at thermal equilibrium with its walls, having a temperature To; 2) unadsorbed molecules obey the equation of the state of an ideal gas n = PV/RTo; 3) a stationary state occurs in the cell (dR~dr = O) for the gas pressure P equal Po = LRTo/S. If we write A P = P - P0, then the equation (20) can be re-written as:
(21)
d A P / d r = ARToN(-dO/dT)/V- S A P / V ,
where N is the number of adsorption sites on unit surface. For dO~dr one can use any equation of adsorption. Heating of the sample can be carried out according to any law T(r). The most widely used is linear T(r) = To + b r ,
(22)
where b is the velocity of heating (degrees/sec.). Other relationships considered include: 1/T = l/T0 +bT; a sequence of stepped increments of temperature; or a sequence of linear dependencies (22) with various velocities of heating. The description of the experimental data requires a simultaneous solution of equations (21) and (22).
a
b
~3
(
f
\ rl
__
t
t
Figure 3. (a) Desorption of nitrogen from tungsten after adsorption at T = 115~ Gas desorbed is proportional to the nitrogen ion current ip. 1 c m = l . 10 -9 amp. Time scale: 1 cm = 100 msec. Adsorption interval At = 10 min. Surface concentrations: n.y = 50.1012 molecules cm -2, n~ = 11.1012, nz = 136.1012. (b) Desorption of CO from W. Adsorption at 336~ At = 35 min. Time scale of desorption: 1 cm = 100 msec. Surface concentrations: nzl = 206- 1012 molecules cm -2, n& = 202.1012, n~3 = 62.1012 [81].
209 i
I
I
I
I
I
I
i
I
I
I
I
.,,--4
V
< "~ "0
10
_
8
6
r162
4 o E
2
<
0
-
!11
D
-_
!
__ _
(~1.Zt- ,~?.~r ,: ~ '
.......
.
I
1O0
~2
-
I
!
I
300
I
700
900
I
I
1100
i
: ............... ~,, I
I
I
1300
Temperature (K) Figure 4. Desorption spectrum of the nitrogen/tungsten system for a full virgin layer deposited at 770 K. (o) Heating time per step is 5 sec. A's represent alpha layer adsorbed at 77~ K after heating the full virgin layer to 600 ~ K. For comparison, a spectrum from polycrystalline tungsten is shown as dotted lines [82]. One can distinguish between two extreme cases of the outflow of gas from the cell. In the first case, the velocity of the outflow is small (S ~ 0) and the velocity of desorption can be calculated as the first derivative of pressure. On the curve AP(T) one can observe the leaps (Fig.3) which are connected with the occurrence of various phases of the adsorbate. With increasing temperature, the phase which is most weakly attached desorbs first and the phase desorbs next which has a stronger binding, etc. until the whole surface is free. In the second case, the velocity of the outflow is large and the velocity of desorption is proportional do the difference of pressures AP. The curve AP(T) is formed of several peaks, each of which corresponds to an adsorbed phase (Fig.4). In the real situation, the experiment can be carried out over a wide range of change of the velocity of the outflow [78-81]. (Discussion of the influence of the temporary delay upon the shape of TDS can also found in the papers [83,84]). Redhead [85] (see also Carter [86]) has presented a method of analysis of the TDS peaks to determine the molecular properties of the adsorbent-adsorbate system. Let us limit our considerations to conditions of heating (22) such that the temperature dependence of the the surface coverage due to desorption of molecules from a homogeneous surface in absence of re-adsorption has the form:
dO/dT = -K~O exp(-ED/RT)/b. TM
(23)
Let us observe that the pre-exponential K~ and the energy of activation ED correspond to every value of m. When these values are independent of the surface coverage. Redhead related the temperature Tp corresponding to the peak maximum to the energy of activation
210
a, u
dO
dO dT
a, u !
! !
Tp T,K T,K Figure 5. The TDS curves for different initial coverages 00 of the ideal adsorption system, m = 1 (a) and 2 (b): 00=0.1(1), 0.2(2), 0.4(3), 0.6(4), 0.8(5), 0.99(6). Dashed lines show the half-widths of the curves. of desorption by: ED/RT~ = K~ e x p ( - E D / R T p ) f m / b ,
fl = 1 ,
f2 = 20p ~ 00,
(24)
where 0p corresponds to the peak maximum. For a qualitative evaluation, one uses the fact that the peak is roughly symmetrical for dissociative adsorption (see Fig. 5) so that 0p ~ 0o/2 and f2 ~ 00. The value 00 can be determined by the measurements of the area under the TDS curve 0o = fo(dO/d'r)d'r. It follows from the formula (24) that the position of the peak for first order desorption does not depend on the initial coverage. For second order desorption the position of the peak moves toward the range of lower temperatures as the value of 0o increases (Fig. 5). For non-dissociating molecules, the position of the maximum can be found by considering the meaning of K~, ED and b. For them, the half-width of the peak AT1/2 ' - T~/2, " (Ti/2 ' > T"a/2,,~ is the width of the peak at half the peak's (where AT1~2 = T;/2 height) does not depend on the value of 00 [85]. Kislyuk et al. [87] obtained an approximate relationship between the half-width of the peak and the energy of activation for desorption: ATI/2
=
1117
ln(01/02) ,
7 = RTp/ED ,
(25)
where 01 and 02 are surface coverages corresponding to temperatures T~/2 and T~)2. Hence the simple relationship AT1~2 = 3.2. IO-3ED/R. More nearly exact is the relationship given by Edwards [88] ATx/2/T = 2.44647(1 - 1.40057- 3.532572...),
(26)
All values appearing in the above formula are dimensionless. Equation (26) can be accurate to within several percent over a wide range of model parameters 1011 < K~ < 1015, 102 < Tp < 2.103, 10 -2 < b < 102 degree/sec. The TDS curves for non-dissociative desorption are asymmetric with respect to their maxima but for dissociative desorption, they are symmetric. This criterion is used for the
211 determination of the order of desorption if the peaks do not overlap. Usually, the order of desorption can be determined graphically from the relation of the experimental data to the coordinates ln((mT]/b)- 1/Tp. For this purpose, equation (24) must be re-written in the form: ln(~mT2p/b) = ED/RTp + ln(ED/K~R)
(27)
The slope of the left-hand-side of equation (27) gives an energy of activation ED at fixed values of b and K~9. Fig. 6 shows the results of such an analysis for the desorption of //2 molecules from the phase i /~2 on the (100) face of tungsten [88]. One can avoid the preliminary assumptions concerning the value of K~9 in the determination of ED if one varies the velocity of heating b and shows the experimental data in the coordinates ln(~mTp/b)- 1/Tp [90,91]. The exactness of this method can be satisfactory if the velocity of the heating changes by more than one or two orders of magnitude. If we know ED, the value K~9 can be found from relationship (24). 1020 0
[-. 0
o
0
1019
E = 32.3 kcal 2
v2 =4.2*10
1018
-2
cm
molecule * sec
!
!
I
1.60
1.70
1.80
1 9103 (deg K) "1 T Figure 6. Plot to determine rate parameters for the ~2 state of H2 on the (110) face of W at 300 K. The coverage varies from 3.9- 1012 mol/cm 2 to saturation, iwhich is 2.5- 1014 mol/cm 2. The straight line gives ED = 135 kJ/mol and g~9 = 4.2.10 -2 cm2/(molecules, sec)[89].
In spite of the movement of the peak maximum toward lower temperatures (for second order desorption) with an increase of 80, its half-width remains practically constant. The interpretation is connected with ED through the approximate relationship AT1~2= 4.8. IO-3ED/R [87] or by the relationship, analogous to equation (26)[88]:
AT1/2/Wp = 3.52557(1 - 2 . 0 0 0 7 - 6.94172 + . . . )
(28)
212 It was shown in [85], that 7 in eq. (25) hardly depends on temperature and, if ED is given in units of Kcal/mole, then 7 is found in the interval from 30 to 34. With the help of this approximation, equation (24) can be changed into a more concenient expression for an approximate calculation [92]: ED/RTp
=
ln(K~(mTp/bT)
(29)
Quantitative analysis of the theoretical spectra proved [92] that one can construct some practically linear correlations for the half-width of the peaks: ATx/2/Tp = C1 - C2 l n ( K ~ m T p / b ) ,
(30)
where C1 = 0.20 and C2 = 0.00845 for m = 1 and C1 - 0.233 and C2 = 0.0083 for m = 2 (Fig.7). 0 -
1st ORDER
-
3
3
1 109 2 10 l~
40 ATI 2 80 -
21
3
- 120 -
2
100 l
l 0 ll
80
0
-
~
120 -
-
-
~
~
60 o ,.~
-
40
160 200
-b
m
t 20
i
0
_o
I
,
i
400
,
l
i
]
I
l
,
I
,
I
|
I
,
I
|
0
800
1200 1600 2000 Tp, K = Figure 7. Computer-generated graphs of the flash-desorption peak temperature Tp and peak width ATx/2 vs ED for a binding state with first-order desorption kinetics [92]. The majority of experimental TDS curves have a complex shape and cannot be described by the simple methods described here. The simple model of desorption which is the basis for these methods does not express the real properties of the experimental systems such as the heterogeneity of the surface and the lateral interaction between adsorbate molecules. Many adsorption spectra that are constituted of several peaks have been investigated - every peak was considered to belong to a separate phase and was described independently [92]. Such an approach turns out to be justified when it concerns various mechanisms of adsorption (with dissociation and without it) as, for example, for the system nitrogen/tungsten [79,81]. In some other situations, an even more detailed analysis is necessary. relationships for a h e t e r o g e n e o u s surface For the calculation of the TDS of non-dissociating molecules from a heterogeneous surface, one must replace the expressions for UD in section 2, by equation (20). The shapes of the TDS curves depends on the number of types of sites and the fractions of sites of various types on the surface. The desorption of molecules from the sites of each
3.2 Correlation
213 type can give a peak in the TDS. If the energetic features of the sites of various types are much different, the individual peaks do not overlap, or they hardly do. Then the TDS curve consists of several maxima and minima. If the energies of the sites of various types do not differ much from one another, the individual peaks overlap and one can observe a widened peak. The first case is considered as corresponding to a discrete distribution for fq, and the second case, to the continuous distribution function f ( x ) . In the first case, the main question is the relationship between the number of types of sites and the number of TDS peaks. In the second case, the relationship between the characteristics of the peak and the parameters of the distribution function is studied. The influence of the various kinds of distribution functions on the shape of TDS for discrete distribution functions was considered in the papers [86-87,93-96] and in the papers [79-81,86,87,92,97-101]. For the continuous distribution function f ( z ) = ( z 2 - Zl) -1, this was done by determining the doncentration dependence of the energy of activation of desorption; ED(0) =
E~ -
E~ =
~:0,
ED(O : 0 ) .
(31)
The simplest way of interpreting TDS curves is to seek for the correlation between the observed characteristics of a peak and the energetic parameters of an adsorption system. Carter [86] obtained a relationship between the differences of the activation energies of i neighbouring peaks AED (discrete distribution function) and their energy for first order desorption: AED = 3ED6/(0.91 -- 1.86), where 6--~ ( 1 . 5 - 1.7). 10 -2. While considering the conditions for peak splitting, it was assumed that the maximum of the smaller of the two neighbouring peaks should surpass e times the value of the minimum between them. This condition turns out to be analogous to that for the splitting of optical spectra. Kislyuk et al [87] obtained as a condition of splitting AED = ED/14 for non-dissociating molecules and AED = ED/9 i for dissociating ones. As a criterion for splitting, one used the condition of the occurrence of a minimum in the TDS at the increase of AED. The difference between the two evaluations is related to different criteria and to the degree of the mobility of the adsorbed molecules (see below). a
u, a.u.
U, a.u. 1
500
2
700
900
T,K
I
I
I
300
500
700
T, K
Figure 8. (a) TDS curves for atomic nitrogen on platinum: curve (al) is for the a form with N,n~ = 3-1013 atom/cm 2, b = 30 K/s; curve (a2) is the fl form with Nm~, = 5" 1013 atom/cm 2, b = 5 K/s. Part (b) is the TDS curve of CO on platinum with N,naz = 7.1014 molecules/cm 2, b = 38 K/s [87]. The same authors studied the correlation for an uniformly heterogeneous surface which is characterized by equation (31). In paper [86], a relationship was obtained between the
214 half-width of the peak and the temperature of the maximum in the TDS" ATll2 = 0.37Tp. 1/2, where In paper [87] yet another correlation was obtained: AT1~2 = [(~rT)2 + (AT~ 800
45
(2) VO no
13
700
11
1
= 1o10 deg 40 35
600 Tv (K) 500 25 400
300
200
100 !
I
I
I
I
I
I
25
50
75
100
125
150
175
AT, (K) 2
Figure 9. Computer-generated graphs of Tp azld AT1~2 for flash desorption from a second-order binding state with ED of the form ED - E~9 - aO [92]. AT~ is the peak half-width for a homogeneous surface. The value of a T characterizes the interval of the energetic heterogeneity of the surface given by the equation a E = aTT, cr ,-, 30 (25). Kislyuk et al applied this relationship to the experimental data on the atomic adsorption of nitrogen [102] and CO [103] on platinum (Fig.8). While analyzing these experimental data [104] and assuming that ln(0x/02) = 2.6 for m = 1 and 1.6 - 1.9 for m = 2, they obtained AT~ = 1050 from equation (25) for the low-temperature phase a and 900 for the high temperature phase 13 of nitrogen on platinum. The experiment gives the value AT1~2 ,,, 950 (a) and 630 (~). These authors conclude that for the a phase the agreement is satisfactory, and for the j5 phase the deviations are possibly due to non-linearity of the heating. This is why the surface of platinum is assumed to be roughly homogeneous (within every phase) with respect to the adsorption of nitrogen. For the system C O / P t , calculations based on equation (25) yield AT1/2 = 60 ~ compared to the experimental value AT1~2 = 190~ This means that the surface is nonuniform. The interval of heterogeneity evaluated by the above mentioned formulas as AED = 7 kcal/mole, agrees very well with the data on the adsorption heats [105].
215 The correlation of AT1~2 with the temperature of the peak for various meanings of E~ and a in equation (31) was studied in the paper by Schmidt [92]. The results of this analysis for second order desorption are shown in Fig. 9. In papers [106,107], attention was focused on the fact that a knowledge of only the value of the half-width of the spectrum is not enough to determine the molecular parameters of the model. This view was popular even previously, see, e.g. the paper [95], where one observed the indispensability of performing a preliminary comparison of the calculated and experimental data. In fact, it is not a complicated task to elucidate additional information from the TDS curves. In the paper [106], the width of the spectrum AT3~4 was obtained from two points where the rate were 3/4 of the maximum rate of desorption. In the paper [107], as additional information, the derivative dUD/dT, taken at two points of the spectrum where the rate of desorption was equal to 1/2 of the maximum gave another measure of peak-width. Drawing more detailed information from insufficiently separated spectra using simple relationships seems to be problematic. In paper [108] it was shown that a large number of parameters makes such an interpretation dubious (not having one meaning only). In order to better analyze the molecular nature of an adsorption system, more attention is paid to o b t a i n - from the TDS curves - data on the concentration dependencies of the effective energies of activation and on the pre-exponential factors of the rate of desorption.
3.3. Effective energies of activation and the pre-exponential constant of the desorption rate equation The equation for the rate of desorption can be represented by : UD = K0(O)O~ exp[--f~ED(0)] ,
(32)
where the pre-exponential coefficient Ko(O) and the desorption activation energies ED(O) are functions of the concentration of the adsorbed molecules. The technical possibilities to determine ED(O), if the order of desorption is known as well as Ko(O), were elaborated by Ehrlich [81]. His method has been applied to a whole series of systems [109-112]. King [112] proposed the so-called "complete analysis" for the determination of the coverage dependence, ED(O). For this purpose, one uses a sequence of TDS curves corresponding to various initial coverages 00. For every curve, an Arrhenius plot is constructed In UD -- l/T, from which one determines the value of E,I, related to the given initial coverage. The intercept of the Arrhenius plot with the coordinate axis gives the value B(O) = l n K 0 ( 0 ) + m In 0. From the plot of B(O)versus In 0, one estimates the order of the desorption and the value of Ko(O). If this plot is linear, i then the order can be determined from its slope, and Ko(O) is found from its intercept (independent of 0). If this plot is not linear, then the order m should be determined independently, which allows one to obtain the function K0(0). The method needs high quality measurements at high values of 0. It was applied successfully in numerical studies [113] for the range of coverage 0.04 < 0 < 0.3. The authors of [113] proved also that even a weak dependence of ED(O) (~ 6% on ED) provokes a considerable deformation of the concentration dependence of the isothermal rates of desorption which are obtained by calculation of the rates from the TDS curves while using the coordinates In UD - In 6 at T = const. The analysis of such isothermal rates of desorption does not require a knowledge of the order of desorption. That method
216
b a
I
I
0.s
4
hl %
I
I
I
x\5
3
02
"~e ~
~
I"
0-02 I
.~//
~
.~/"
]
0 01 ~ 0.01 0.02 0.05 0.10 0.20 0.50 Relative coverage
I
o.1
~-~ 0.02 0.01 2.15
(d) 00:24 (e, 0.6 i
i
2.25
\ i
,
2.35 103/T(K)
I
2.45
Figure 10. (a) Experimental desorption rate isotherms for CO desorption from clean Ni(110). (b) In N versus lIT for CO desorption. Data points obtained from the desorption rate isotherms in Fig. 10a. The curves correspond to relative coverages in Fig. 10a of 0.07, 0.10, 0.20, 0.40, 0.60
[113].
was applied to the analysis of the desorption of CO from the surface of Ni(110) (Fig. 10) and for the system N2/hydrazine. An improvement of this method of determining the functions ED(O) and KD(O) at various velocities of heating for fixed initial coverage was presented by Taylor and Weinberg [114]. In their method, the temporal law of heating T(r) can be arbitrary. In this way, one excludes mistakes connected with deviations from the chosen regime of heating. The method is applicable for any coverage, but it requires that the change of the velocities of heating be not less than two orders of magnitude. The authors applied their method for the description of desorption of CO from It(110). The concentration-dependent functions ED(O) and KD(O) obtained are shown in Fig. 11. They show a compensation effect. This kind of relationship can also be observed for the system 02/W(llO), for which a modified expression for the rate of desorption was used: Ud = K00exp{[-ED(0)/R](1/T- 1/Ts)} ,
(33)
where T, is a parameter. For the system 02/W(llO) it was estimated that K0 = 7.10ss -1 and T, = 1230K; the dependence ED(O) was approximately described by equation (31) with ED(O) = 35 and x = 16 kcal/mole, i The problems accompanying the determination of the desorption parameter T~ were discussed in paper [115]. A simpler way of describing TDS curves and estimating the function ED(0) is presented in paper [116]. Instead of analysing a family of curves, one proposes to use the phenomenological description of the energy of activation approximated by the polynomial
M ED(O) = ~ a k ( 1 - O)k, where the coefficients ak have no physical meaning, and M is k=l
the number of terms which can be determined from adjustment of the calculated to the
217
40l if,
30
=~
20
1015 ~ 10
13
10
o ~ t~
10
109
10 7 !
0
I
0.2
I
I
0.4
I
!
0.6
I
i
0.8
k.q
10 5
N
0
o=
!
1
Fractional coverage Figure 11. ED(8) and K(O) profiles obtained from analysis of TDS curves. Broken lines are Taylor and Weinberg's results [114]. Full lines are the results of Forzatti et al. [117]. experimental spectra using the method of least squares. After presenting the eight types of dependencies of ED(O) (Fig. (12)), the authors considered them as a combination of the contributions from the homogeneous and uniformly heterogeneous (31) surfaces. This approach was applied to describe of desorption of 02 molecules from the surface of ZnO and from a silver catalyst. In Fig. 13, the TDS curves are shown along with the constructed functions ED(O). The main problems which were studied by the authors were to how to eliminate the necessity of a large number of TDS measurements and how to select parameters of the model from one of r several curves. A method for determining ED(O) and K0(0) from one TDS curve was presented in paper [117]. The curve is normalized with respect to the maximum of the peak and one constructs a parabolic approximation around its maximum. This allows one to obtain a linear correlation between certain values which can be determined from the experimental curve. From this correlation, one can evaluate the energy of activation of desorption corresponding to the point of the maximum, and determine - approximately - all the functions ED(8). As a result one can calculate both m and K0(0). This method was been tested on the curves shown in Fig. 12; it gave an accuracy of about ~5%. The results of its application to the desorption of CO from the face of I r ( l l 0 ) are shown in Fig. 11. Habenschaden and Kuppers [118] proposed a method where the parameters may depend on temperature (though the importance of this factor is not great [119]). It was suggested that one choose a small fragment of the curves in the low-temperature range of the spectrum (leading edge analysis), so that the changes of 6 and T were insignificant.
218
3
40
,_q m
o
E r
\9
-.-.-
,,
gd
30
_
\ ~
1
_ _-~.~_,__ _-,,,.
\
" . . ~ ---~ .
\
4 I
0
"7
0.5
.7
1,4: l/2scale Il
r
o
I I
31
/I
4,1
_
i i
!,,, "~ !
i:
I
i
9
[
f ,6,
# i
"~x
~
.
I
,
i i
~
' l " . i "~.
_
-~
-" I.
~ ~
!
I
373
]l
I
I,
],"i: 1
0
I
8,
r~
473
573
673
373
473 573 673 T/K Figure 12. Upper graph: Distribution profiles of ED(e). Lower graphs: Thermal desorption curves simulated for first-order kinetics with K ~ = 1013 s -1, b = 10 K/s [116]. For this chosen fragment, a graphical analysis was carried out as in method [112]. In the papers [120,122], the role of compensation effects was studied and the prospects of using the various methods of analysis of the TDS curves to find the molecular parameters of an adsorption system were discussed. Fig. 14 shows the influence of the parameter Ts in equation (33) on the shape of the TDS curves in a system with a linear change of energy of activation of desorption (31). If one considers the coefficient x as a contribution of the lateral interactions, then the variation of Ts leads to a change of the sign of the interaction parameter (such a situation was observed in the system Au/W(llO) [109] and in a series of other systems). A detailed analysis of the various methods of determination of m, ED(O) and Ko(O) has been presented in paper [121]. Testing was carried out for desorption of the first and second order with calculation of the linear dependence (31) and of the compensational effect (33). The conclusions drawn by these authors is that the methods of [112] and [118] turn out to be preferable.
219
a
13/K/min
eq
o
_A
1;
6
13/K/min
4 _
9
1.0
0
1~/~ ^
2
1;
4 9
0
20.5 1-
0 373
473
573
I 373
473
573 T/K
T/K "7
13/K/min
O
"~ 35
b
13/K/min
A / s "1
E o
11.1
~ I\
l;
4
~--"~ 30
10
E
A/s
-1 10.5
35
10.4
' ~
10
3,4; 10
10 11.4 10 11.1 10
30
--t----.
25
ols
|
t 0
t\ l 0.5
Figure 13. Part (a): Thermal desorption curves for oxygen on zinc oxide. Part (b): Results obtained by the analysis of thermal desorption curves of oxygen on zinc oxide. Part (c): Thermal desorption curves of oxygen on silver catalysts. Part (d): Results obtained by the analysis of thermal desorption curves of oxygen on silver catalysts [116]. Equation (32) allows us to carry out a numerical analysis of the rates of desorption from the TDS curves, but does not yield much information about the molecular nature of an adsorption system. It is not by coincidence that this approach is used for the description of the process on single crystals where a large part is played by the lateral interactions as well as for processes on poly-crystalline and amorphous adsorbents. The influence of a certain molecular property of an adsorption system on the shape of the TDS curve will be considered in the following chapters of this survey: lateral interactions in Section 4, and the common influence of the heterogeneity of adsorbent and lateral interactions in Section 5. Right now we shall illustrate briefly the influence of the molecular characteristics of heterogeneous adsorbents on the shape of TDS in absence of lateral interaction.
220
r .s o. -o t_
Attractive interaction~ EO=300kJlmol J~
ld 3Is ~11 JP~t W= -25kJ/mol ~ \ 1 riO=
Repulsive interactions
%= 300kJ/tool no= ld3/s w= +25kJ/mol
t-
O
I,
To= 2000
To=
100~
Tc= 600K
1000 1100
attractive interactions
"i~88
"
T (K)
~" 115oL r~'--.~~
o
0.5
~
To= 1 0 0 0 ~
Tc= 600K
900
Tc= 2
--
1000
1
150
900
~.~!I~iI
1000 1100 TIK repulsive interactions
11501.-
lOO%
o'.s
150
9
i oo
.
Q (Mr.)
Q (ME)
Figure 14. Part (a): Simulated TDS for first-order kinetics and attractive (left) and repulsive (right) lateral interactions, and different values of the compensation effect at temperature To. The desorption energy varies with coverage as ED(O) = Eg - wO, and the preexponential factor varies according to the compensation effect as ln K = -wO/RTc + I n K ~ Parameter values: E~ = 300 kJ/mol, w = +25 kJ/mol, K~ = 1013s -1. The coverages for each set of spectra vary from 0.1 to 1 M1 in steps of 0.1 M1. (b) Dependence of the peak maximum temperature Tp and the full width at half maximum (s upon initial coverage for TDS corresponding to first-order kinetics and attractive (left) and repulsive (right) lateral interactions. Curves are for different values of the compensation effect temperature Tc with parameter values as in part (a) [120].
221 3.4. D e s o r p t i o n of n o n - d i s s o c i a t i n g molecules While investigating TDS for a surface characterized by a discrete site energy distribution function, the main problem is the explanation of the conditions for total splitting of the TDS peaks. This is related to the number of peaks Np and the number of the types of sites t. Examples of such splitting are shown in Figs. 15 and 16. These curves were calculated from equations (11) and (12). To the equilibrium distribution of the adsorbed molecules corresponds the following "chaining" of the local coverages aq/% = Oq(1-Op)/[Ov(1- 0q)]. Fig. 15 shows the influence of the change of the surface composition on the shape of the TDS for t = 2 and a = 1. The positions of the two peaks do not change but the height of every peak changes in proportion to the contribution of the sites of every type. Fig. 16 shows the simplest TDS curves for a discrete uniform distribution fq = t -1. The first peaks are the most distinct. With increasing temperature, the maxima decrease whereas the the minima increase. Such a form of the curves is connected with surface migration of the adsorbed molecules. Fig. 17 gives a comparison of the TDS curves for an equilibrated distribution of the adsorbed molecules and for the case of no surface migration [122]. (The methods of describing the surface migration of molecules are discussed in paper [45-48, 122, 123] for heterogeneous surfaces and in papers [124-126, 122] for homogeneous ones.) a.Ll.
dO dT
I
Figure 15. TDS curves for the inhomogeneous surface with sites of two types (t=2): Curve 1 is for fl = f~, f~ < 0.5; 2, for fl = 0.5, 3, for fl = 1 - f~, 00 = 0.99 [40].
222
dO dT
a.u.
I
I !
!
T Figure 16. Completely split TDS curves for an inhomogeneous surface with fq = t -1, t = 2(curve 1), 3(curve 2), and 5(curve 3); here, 0o = 0.99 [40].
dO dT 0.6
2 0.3
-
0 500
! 600
i 700
T
Figure 17. TDS curves for the inhomog;eneous surface with random distributions of the different type sites, t = 2: Curve 1 - adspecles are immobile, Curve 2 - equilibrium distribution of admolecules; K1D~ = K D~ = 1012 s -1, E ~ = 168 kJ/mol, E ~ = 126 kJ/mol, 0o = 0.99, b = 50
K/~ [122].
223 Numerical studies of the conditions for the total splitting of the TDS curves for heterogeneous surfaces are reported in paper [127]. There, the case AQ = Q q - Qq+l = const. is assumed, where Qq is the adsorption heat on site q, 1 < q < t.
AQ* kJ/mol
~Q* 20J
40,
I0i
i
i
I
l
3
I
I
6
l~t
9
20
0
I
I
0.2
0.4
,,
I
I
I
0.6
0.8
1.0
f2 Figure 18. Condition for the complete splitting of the TDS curve for nondissociating molecules on a heterogeneous surface (t=2) for Q1 = 126 kJ/mol, a = 1 (1,3) and 0.5 (2,4). Inset: dependence of the complete splitting upon the number of the site types t for a = 1 (curve 3) and 0.5 (curve 4) [127], calculated using the uniform site distribution function (h = t-l) [127]. The decrease of AQ leads to a situation where the high-temperature peak disappears first, below a certain value of AQ*. If AQ = AQ* is the critical value for the total splitting of the TDS, then at AQ _> AQ*, the TDS consists of t peaks, and when AQ _< AQ* there will be (t - 1) peaks, i.e. the case of partial splitting (t > 2), or of no splitting at all (t = 2). Instead of the high-temperature peak, there will appear a "shoulder" with a characteristic curvature. Any further decrease of AQ leads to the situation in which the shoulder becomes only weakly visible, and later on, the high-temperature peak disappears, etc. The determination of the conditions for total splitting is possible for arbitrary mobilities of the adsorbed molecules, and for arbitrary values of AQq :~ const. In the ge-
224 neral case, any local maximum can disappear, and not necessarily the high-temperature peak (the dependence for the condition of a total splitting on the shape of the distribution function). Diminishing the initial coverage of the surface results in a decrease of the low temperature maxima of TDS and in their complete disappearance. That is why, in order to show the total splitting of TDS, it is necessary to have the initial coverage 00 --~ 1.
2
2 2 09 1
""
22.0
'x~,,
2
29"4 l ~ X 9
3
29.4~ 37"8 V
a
b
Figure 19. C~176 for the complete splitting of the TDS curves for t=3, Q1=126 kJ/mol, a = 1 (part a) and 0.5 (part b). The cross sections are given for fq = 0.03, q= 1,2,3. The contour lines are given for AQ = 14.7(3), 16.8(4), 18.9(5), 21.7(6), 25.2(7), 29.4(8), 33.6 (9), all in kJ/mol [127]. Searching for AQ* can be done by numerical variation of AQ value and following the number of the local maxima of the curve dS/dT. That procedure permits us to investigate the lower limit of the differences in the adsorption energies of the various nodes that lead to total splitting of the TDS, and the dependence of this lower limit upon the molecular parameters of the adsorption system (a generalization of the criteria of paper [87]). The "optical" criterion of Carter [86] allows one to determine the conditions for a strong splitting of the peaks. Note the difference in the "optical" criterion and those obtained in paper [86] for AED (see Subsection 3.2) for the case when no surface mobility of the adsorbed molecules is possible [127]. The analogy of the TDS curves with the optical spectra in the case of a equilibrium distribution of the adsorbed molecules is not complete: it does not take into consideration the redistribution of the molecules between sites of various types. For a surface with two types of sites an approximate interpretation of 6E has been developed that relates it to the energy of activation of desorption from the sites of the first type: SE = aE + b, where a, b are constant values which depend on the surface composition and on the coefficient a (see Table. 1). That relationship holds with an
225 accuracy of 0.8 k J / m o l e for the range 4 < E12 < 500 KJ/mole. Fig. 18 shows how the conditions for the total splitting of the TDS are related to the surface composition. The minima of the curves are situated in the vicinity of fl "~ 0.5. When the number of sites of the first or second type increases, the value of AQ* increases too. W h e n a = 1 AQ* increases faster than f2 increases, and when a = 1/2, AQ* increases more than fl.
Composition
Parameters
1.0
0.75
0~50
0.25
0.15
0.10
0.206
0.162
0. I 16
0.072
0.055
0.048
0.641
0.536
0.406
0.272
0.242
0.222
0.161
0.124
0.088
0.053
0.038
0.030
0.515
0.440
0.306
0.204
0.155
0.138
O.120
0.089
0.060
0.042
0.032
0.026
0.398
0.297
0.201
0.168
0.130
0.113
0.7
0.128
0.108
0.086
0.062
0.052
0.047
0.490
0.222
0.264
0.226
0.201
0.268
0.9
0.170
0.145
0.119
0.096
0.091
0.090
0.649
0.113
0.302
0.327
0.377
0.394
0.1
0.3 0.5
,,
Table 1. Parameters of the approximate equation for
6ED(ED) [127].
Analogous relationships can be observed while investigating the conditions for a total splitting of TDS when t = 3 (the results for the splitting are shown in Fig. 19). The composition of any point in the concentration triangle (fa, f2, ]'3) can be determined by the usual rules describing three component diagrams of state: if a line is drawn across the point, parallel to any of the side of the triangle (let it be the side of the triangle which unites the corners f2 = 1 and f3 = 1), then on the other two sides of the triangle some fragments are cut out (they are along the triangles f2 = 1 and f3 = 1) which determine the part of the component occurring in the third angle (here it is q = 1). The value A Q ' , in the considered point, can be determined by the values which are on the isoline passing through the given point. The minimum value of AQ* can be determined in the vicinity of the point fq ,~ 1/3. The most rapid increase of AQ* value takes place when the value f2 increases (if a = 1), or at f~ (if a = 1/2). The conditions for the total splitting of the TDS for a uniform site distribution fq = t -a are shown in the supplement to Fig. 18. The total splitting of TDS takes place at practically one and the same value of AQ* (some differences can be observed at t = 2).
226 dO dT
1.0-
I
I
I
I
300
400
500
600 T,K
Figure 20. TDS for uniformly heterogeneous surfaces Qx=30 kcal/mol, Ax = 5(1,4), 10(2,5), 15(3,6) kcal/mol; ~ = l(Curves 1-3) and 0.5(4-6)[128].
dO
a
1.0
dO dT
1.0
6
4
4
1
I
200
I
400
I
I
600 T,K
I
200
400
600 T,K
Figure 21. TDS for heterogeneous surfaces with exponential distribution function f(z) = Here, n is a normalization coefficient, 7 = 0.33(part a) and -0.33(part b); Q1=30 kcal/mol, Ax= 5(Curves 1,4), 10(2,5), 15(3,6); a = 1(1-3) and 0.5(4-6) [128].
exp(Tz)/n.
227
b 1.0-
d_9_O
d.__OO dT
1.0-
dT
1
!
200
400
200
600
T,K T,K Figure 22. TDS for heterogeneous surfaces with the gaussian destribution function f(z) = exp[-(x - 5c)2/2a]/n, Q2 = Q1 - Ax, ~ = Q2 + $Ax, Q1 = 30 kcal/mol, (part a) a = 1, a = 2, 5 = 0.25 (Curves 1-3) and 0.75 (4-6); Az = 5 (1,4), 10 (2,5), 15 (3,6) kcal/mol; (part b) = 0.5, a = 2 (1-6) and 0.5 (7), a = 1(1-3,7) and 0.5(4-6); Az = 5 (1,4), 10 (2,5), 15 (3,6,7) kcal/mol [128].
Eef (O)
lg Kef(O )
30
12.8
~
5
1
4
6
12
18.7 0
0.5
I 11.3 l 1.0 0
! 0.5
1 ~
1.0 O O Figure 23. (a) Effective activation energies for desorption E~I(O) = -d{ln Uo(O)/O}/d~ for Q1 = 30 kcal/mol, Az = 12 kcal/mol, a = 1(1,3-5) and 0.5(2). The distribution functionis are: uniform (1,2), exponential with 7 = 1(3), and symmetric gaussian with a = 1(4) and 2(5); homogeneous surface(6). (Part b) Effective constants of the desorption rate K~s(0) = UD(0)exp[~E~s(0)]/0, corresponding to the curves of part a [128].
228 A relationship between the various conditions for the splitting of the TDS can be given in the form AQo = AQ* + nQ1, where AQ0 is a difference of the energies of desorption from the sites of the first and the second type, corresponding to the "optical" criterion. Here Q1 is the adsorption heat on the "strong" sites. Such a correlation holds within an accuracy of 20%, but the values ~ depend on a: ~(1) = 0.085, a(0.75) = 0.1, ~(0.5) = 0.13 and a(0.25) = 0.3. The most significant deviation from that correlation can be observed in the vicinity fl = 0.5 - 0.7. This correlation can be used for a qualitative evaluation with the largest number of types of sites. Thus, for the surface with three types of sites and a = 1, the conditions for the splitting of the TDS into two peaks yield AQ0 = 23 KJ/Mole and AQ* = 12.4 KJ/Mole and the conditions for the splitting into three peaks yield AQ0 = 27.1 KJ/mole and AQ* = 14.7 KJ/mole. Let us focus attention on some peculiarities of the use of continuous distribution functions f(z) to calculate TDS curves. Fig. 20 shows the spectra for a uniformly heterogeneous surface. The calculation was carried out by using equation (16), but not using the effective activation energy of desorption (31). An increase of Ax = x2 - xl makes the spectra wider and flatter. However, at a certain critical value of Ax* there appears a sharp low-temperature peak. Its appearance depends also on the value of the parameter a. Figs. 21 and 22 show the TDS for exponential and Gaussian distributions. Depending on the parameters of the distributions, there will also be possible various variants for the peak widening, and in some cases sharp low-temperature peaks can also appear. This can be related to the part of area of the comparatively weak adsorption sites which can be considered as analogous to the weak sites for a discrete distribution function, and by the redistribution of the adsorbed molecules due to surface migration. This migration causes the main stream of the desorbing molecules to go through the weak sites. Fig. 23 shows some concentration dependencies of the effective desorption activation energies for TDS curves calculated with continuous distribution functions. At a small value of Ax, the dependence of ED(O) for the uniform and the Gauss functions does not differ much from the linear ones which can well be approximated by the formula (31). In all the case one can observe the similarity of changes in ED(O) and KD(0), which usually is considered as the presence of a compensation effect. 3.5. D i s s o c i a t i v e a d s o r p t i o n The elementary processes of dissociative adsorption take place on neighbouring sites of the surface. On homogeneous surfaces the adsorption process is the same for any pair of sites whereas on a heterogeneous surface, the pairs of the sites differ from one another. In consequence, depending on the type of the pair of sites, the rate of adsorption will be different. A statistical description of the contributions of the elementary processes on various pairs of sites of the heterogeneous surface will involve introducing the distribution function of pairs f~q, 1 ~_ q, p <_t, t
t
fqp = 1 , q,p----1
~ q--1
fqp = fp,
(34)
229
dO dT
1.2
0.8
0.4
300
500
700 T,K
Figure 24. The TDS curves for dissociating molecules on heterogeneous surfaces with fq = 1/t, t = 2, Ell = 126, Qa = 84, AQ = 14.7 kJ/mol, a = 1. The surfaces are: regular(i), chaotic (2), patchwise(3) [130]. With these distribution functions one can express the rates of adsorption of dissociating molecules [129] as: t
U ~ = K ~ P ( 1 - 8n)(1 - 0p), q,p=l U D = K~0q0r, D ,
A,D = Kqp A,Do exp(--/~E~D), Kw
(35)
where t h e VqA'D are rates of t h e elementary processes on the pairs of sites qp, K A'D~ are the pre-exponential factors, and E ~ 'D are energies of activation.
230
AQ*, kJ/mol
AQ, kJ/mol 16.8
m
33.6 12.6
~
7
I
!
I
2
4
6
t
4
21.0
8.4
6
0.5
Figure 25. The conditions for complete splitting of the peaks for dissociating molecules on patchwise (1,4), chaotic (2,5), and regular (3,6) surfaces; Ell = 126, Q1 = 84 kJ/mol, a = 1 (1-3,7) and 0.5 (4-6,8)[130]. The function fq~ characterizes the structure of the surface. In general, it describes the correlation of various sites, with a given energy on the surface. If identical sites are situated together, we have to deal with a homogeneous or patchwise surface. Then, fqp = 1 for q = p and fqp = O for q :~ p, fqp = fqh. These two cases are very well known in the literature [3,27,72]. To describe them, the equations (35) should be re-written as: t
UA,D = ~fqqUqAr D , q= 1
t
UA,D= ~
fqfpU~;D ,
(36)
q,p= 1
The second equation (36) can be simplified if one introduces a multiplicative approxitrA'DAn'A'D mation for the rate constant of the elementary process K~ 'D = K~ 'D. "A ---'*J'ql "**~pl "
231 The essence of this approximation is that the contributions AK~ 'D and Agp4 'D to the change of the rate on various sites is observed independently on every site of the pair qp (relative to a pair of sites on the homogeneous surface of type "1"). Such an expression, for example, is obtained if we assume independence of the pre-exponential constants on the types of pairs qp and additivity of the contributions of the energy of activation on every site of the pair. This assumption was presented first in paper [27] as a consequence of the linear relationships (15), and it was frequently used in recent work [3,29-32, 72]. Determining the energies of activation using this assumption seems to be rough, but it greatly simplifies the description of the reaction rates. In consequence, the expression for the rate of desorption for a chaotic surface, for example, can be written as: UD = U1D~
fqAK~0 n
,
AKqD1= exp[-~(E~ - E~)]
(37)
q=l
In the case of patchwise and chaotic surfaces, the macroscopic rate of dissociative adsorption can be calculated, knowing only the function fq characterizing the surface composition of the adsorbent. Generally, this information is not complete [129]. For a large number of types of sites, expressions (34), (35) can be re-written by introducing two-dimensional distribution functions f(z, y): X2
Xl
Y2
Yi
ll 'x'y' xdy:'' X2
Y2
Xl Yl
where xi = yi, i = 1 and 2. Let us limit ourselves to analysing the conditions for the total splitting generated by a discrete distribution function. The shape of the TDS curves for dissociative adsorption is similar to that for the shape of the curves for non-dissociative adsorption discussed above [130]. The influence of the construction of the surface on the shape of TDS and on the conditions of the total splitting for the case t = 2 is shown in Fig. 24 (f~ = f2) and Fig. 25. For surfaces having ordered structure 1 (d12 = 1), the TDS begins at higher temperatures, and for a surface with a patchwise structure (d12 = 0), the TDS begins at lower temperatures. For the surface with a chaotic structure the TDS curves occupy an intermediate position, nearer however to the TDS for patchwise surfaces. On a strongly ordered surface, the desorption of molecules takes place when a pair of molecules occupies two similar sites, which is the reason that, for such surfaces with a given value of AQ, splitting does not take place (curve 1). Fig. 25 shows the conditions for total splitting depend on the surface composition. The most comfortable conditions for the splitting are realized on patchwise surfaces, and the least comfortable ones, on an ordered surface. At a fixed structure of the surface, the value of AQ*(f2) increases with a decrease in the coefficient a. No matter what the structure of the surface, all the curves of AQ*(f2) sharply increase as the number of sites of the first or second type diminish to zero. In the medium composition range 0.2 < ]'2 _< 0.8, the curves AQ'(f2) strongly depend on the structure of the surface and on the value of the parameter a. Thus, the conditions for total splitting can be determined both by the surface composition and by the surface structure (at a fixed surface composition). The conditions for total splitting of the TDS for the uniform distribution function fq = 1/t
232 depend on the number of types of sites on the surface and are given in the supplement to Fig. 25. An increase of a leads to a complete splitting of the spectrum, and the increase of the number of the types of sites t at t > 3 is practically constant. In general (also for non-dissociative adsorption), all the molecular characteristics of the adsorption system influence the shape of the TDS. A more detailed discussion of the influence of these parameters on the shape of the TDS curves is presented in paper [130].
4. L A T E R A L I N T E R A C T I O N
ON A HOMOGENEOUS
SURFACE.
4.1. P e c u l i a r i t i e s of n o n - i d e a l m o d e l s In addition to the heterogeneity of an adsorbent surface, the lateral interactions of the adsorbed particles influence the kinetics of adsorption. It was Langmuir who considered this for the first time [33, 131,132] while studying the desorption of thorium, cesium, and hydrogen from the surface of tungsten. Assuming the presence of dipole-dipole interactions between the adsorbate atoms, he described desorption with the help of an effective rate constant of desorption which depends on the surface coverage 0: UD = --KD(0)0,
KD(0) = K~ exp[--/3ED(0)],
ED(0) = E~ - H0
(39)
where ED(O) is the effective activation energy of desorption, H is a constant depending on the potential of interaction, and K~9 is the pre-exponential factor of the rate equation. It was assumed in papers [33, 131, 132] that the effective energy of activation coincides with the energy of adsorption ED(O) = Q(O). In the theory of the kinetics of surface processes, the model of the elementary event plays an important part. As already mentioned, in most cases one uses the model of collisions together with transition state theory. In calculations of the effects of lateral interaction, the differences between these models appear as different dependencies of the energy of activation on the surface coverage. This is due to the effect of lateral interactions upon the energetic states of neighbouring particles. Fig. 26 shows the differences of the effective adsorption potentials in the models based on collisions and on transition states for various surface coverages. These curves represent the averaged values of the potential curves corresponding to the local environments of the particles participating in the elementary process. Fig. 27 shows the local configurations of the neighbouring particles around a site where adsorption takes place without dissociation on lattices with z = 4 and 6 nearest neighbours. While describing the adsorption equilibrium when particles are in their ground states, this produces a change of the adsorption heat Q with surface coverage. This is due both to the lateral interactions and to the energetics of the transition state ED(O) (see Fig. 26a). If one assumes that the lateral interactions do not influence the transition state, one arrives then at another dependence ED(O) (see Fig. 26 b). Such an assumption corresponds to the conditions of the model of collisions, since then the adsorption activation energy EA(O) does not depend on the surface coverage 0. In transition state theory, EA(O) turns out to be a function of the surface coverage even
if EA(O= O) = O.
233 v(e)
a
E D (0)
A (0) e
Q(1)
/
O(O),
/ .
.
.
.
.
.
.
.
.
.
.
Figure 26. Potential curves for the model transition state (part a) and the impact model (part b). EA and ED are the adsorption and desorption activation energies, Q is the heat of adsorption, and I is the distance from the surface (horizontal axis of the figure).
4.2. Principles of t h e calculation of the a d s o r p t i o n rate Most often, the effects of lateral interactions are treated in terms of the lattice gas model. The particles localized on the sites of the lattice interact with each other if their separation does not exceed the radius R of the interaction potential. The energetic parameters of the adsorbed particles, being in the ground state, can be expressed as e(r) where r is the number of the coordination circle (in two dimensions) around any site (Fig.28). In the lattice model, the distance can be conveniently measured by the numbers denoting the coordination circles, 1 _< r _< R. The interaction of an activated adsorption-desorption complex (adsorption and desorption have the same activated complex) with the neighbouring particles in their ground state at a distance r, can be expressed as e(r). In consequence, the effective energy of activation of adsorption is a function of the difference =
_
234 n=O
1
2
3
4
1
2
3
4
5
~=1
n=O
6
Figure 27. The local configurations of neighboring admolecules around any central site (re=l) for the planar lattices z = 4 and 6 [40,49]. The theoretical description of adsorption, taking account of the interaction of nearest neighbours and based on the model of collision, was given by Roberts [133]. (The first calculation of TDS was carried out by Toya [134]). Analogous expressions for the model of the transition states were obtained much later independently in 1974 by Adams [135] and by Tovbin and Fedyanin [136-138]. In paper [135], the interactions between the nearest neighbours are considered. In papers [136-138], a more complex potential was discussed: a direct interaction between nearest neighbours and a collective interaction with the remaining adsorbed particles. A detailed review of the papers from 1937 to 1990 dealing with the kinetics of surface processes (adsorption, desorption and surface migration) and based on the lattice gas model, was given in paper [40]. Most of these papers are concerned with homogeneous surfaces. Here, we consider the basic factors influencing the kinetics of adsorption processes on homogeneous surfaces. These factors can be divided into two groups. The first group of factors characterize the physical properties of an adsorbent-adsorbate system calculated from kinetic equations. These properties include the lateral interaction potentials, the aggregated (phase) state of the adsorbed layer, and the surface mobility of the adsorbed particles. The second group of factors characterizes the accuracy of the calculated physical parameters. The most difficult problem in describing non-ideal adsorption systems lies in the fact that these systems possess cooperative properties whose calculation is a traditional many-body problem [109, 110]. Such problems seldom have an exact solution. For practical purposes one has to use approximate methods. One of the main difficulties of the theory of
235 non-ideal adsorption systems consists in the choice of an approximate method and in the estimated accuracy of the results obtained. We shall discuss this problem here in detail because it is quite important for the description of kinetics on heterogeneous surfaces. The many-body problem dscussed above concerns adsorption not only on homogeneous surfaces but also heterogeneous ones. In order to understand such complicated situations, one has to analyze the advantages and disadvantages of various methods of calculating the effects of lateral interactions for simple cases.
gl
1
2
3
r~ fxJ
r~ gxJ
g3
9
4
7 6 5 10 Figure 28. The neighboring sites belonging to various (r) coordination spheres g~ are indicated for a central site f. For the central pair of sites f,g, the sites (1-6) belong to the nearest neighbors of the unified first coordination sphere, and the sites (7-10) are second neighbors. The rate of desorption of non-dissociating molecules on a homogeneous surface can be expressed as:
UD=--KDOS(O i R , z ) ,
KD = K~ exp[-/~E~] ,
(40)
where KD is the desorption rate constant defined in (10), the function S(0 I R,z) describes the non-ideality of an adsorbed system by taking account of: 1) the structure of the surface (z is the number of the nearest neighbours); 2)the radius of the pair potential for lateral interactions R: 3) the model of the elementary process (for the model of collisions e*(r) = 0, whereas for transition state theory, e*(r) ~: 0); and 4) the calculation of the probabilities of the various configurations of neighbouring particle. If n is the number of the configuration, calculation of the probabilities 8z+x(n) introduces certain assumptions that make it possible to close the systems of equations with respect to the probability of the probable single, binary etc. configurations. Fig. 29 shows schematically the result of applying approximate methods of calculating the probability 0z+l(n) for four approximations: chaotic, polynomial, quasi-chemical and an approximate calculation via indirect correlations. The latter is the best, followed by the quasi-chemical approximation. The method of calculating the probability of triplet configurations can be related either to the superposition approximation [141] or to the approximation proposed by Hill [142]. In the
236 chaotic approximation the probabilities of the many-particles configurations are expressed by the singlet probabilities 8q (= 8). Effects of the correlations between the interacting particles are absent. However, this situation can be treated by different approximations: the mean field approximation [143-145] or a special chaotic approximation [146, 147]. In the polynomial approximation [148] one calculates the effects of the interactions between nearest neighbour particles though equations that are closed with respect to the function 81. In the quasi-chemical approximation [149-151], the values 8z+l(n) are closed through 81 and 82, which is why we then calculate the effects of the direct correlations between the interacting particles.
oj iO
On
io--o i o--Ok
Ok
J
3..........
~ i
e
k
~
oj
e
iO
0
Ok
k
Oe jO
io
Ok
% 0 P
o~ P
O
in
In
i p
rn ~ j O
Ok Oe
iO 0 P
O m
/ ~
e p
Figure 29. The schemes of the many-particles configuration probabilities for admolecules on planar lattices with z = 4 and 6 in various approximations: 1 is for the mean field and chaotic approximations, 2 is for the polynomial approximation, 3, for the quasi-chemical approximation, and 4 takes account of indirect correlations; o denotes 81, o-o, 62, triangles denote 83 [40,49]. For nearest neighbours (R = 1) the function accounting for non-ideality of an adsorption system S(8 [ R,z) takes, in the chaotic approximation (CA), the mean field approxi-
237 mation (MFA), and the quasi chemical approximation (QCA), the following forms: S(0 I R,z) =
(1 + xO) z , x = exp(/~Ee)- 1 , (CA) exp(/~zEeO) , ~ = [(1 - 20) 9. - 40(1 - 0)exp(fle))] 1/2 ( M F A ) (1 + xt) ~ , t = 1 - 2(1 - 0)/(1 + or), (QCA)
(41)
In the polynomial approximation, the function S(O ] R, z) has a more complicated form [135, 138, 49] which is not discussed here. However, we shall discuss certain peculiarities of the TDS curves that are connected with the effects of correlations and the application of the lattice gas models to analyze experimental systems. A more detailed discussion is given in [40]. 0
0.5
1
1.0
12
0.5
r 1.0 Figure 30. The concentration dependencies of the heats of chemisorption in various approximations for z = 4, r = [Q(O)-Q(O)]/[Q(O)-Q(1)]; 1 denotes PA, 2 denotes QCA, 3 denotes MFA, 4 denotes CA [147]. The differences in the methods of calculating the effects of correlations are reflected in the concentration dependencies of the adsorption heats (Fig. 30) and of the rates of desorption (Fig. 31). These figures show the relation between Q(0) and In Up(O) for the various approximations. The existence of correlations results in the appearance of points of inflection in those curves (if there are no correlations, then there are no points of inflection). The TDS curves calculated using these approximations are shown in Fig. 32 [118]. The main difference between the approximations considered here and those where the effects of correlation are ignored, consists in the existence of split TDS. The absence of correlations leads to curves with a maximum of one peak. One should observe significant
238 differences in the positions and heights of the maxima in the chaotic approximation relative to the MFA. The existence of correlations affects the splitting of the spectrum and the number of peaks is equal to (k + 1), where k is the number of the points of bending of the functions Q(0) and in UD(O).This is the main reason that, in the chaotic approximation, the coverage dependence of the adsorption heat Q(0) is strongly non-linear (curve 4). The linear dependence predicted by MFA (curve 3). (Curve 1 in the polynomial approximation) is situated nearer to curve 3 than to curve 4, but anyhow there is a splitting in it. 0
20
]
0.5 ,
!
I
1.0 I
!
O
12
-4 -!I In V 2
Figure 31. The influence of the lateral interaction of admolecules upon the concentration dependence of the desorption rate when ~e = -5, ~Se = 5:1 is MFA, 2 is PA, 3 is QCA, 4 is CA
[147].
The non-ideality of an adsorption system S depends upon the difference 6e. In equation (3), repulsion between molecules corresponds to a negative e(1). If the interaction of an activated complex with the neighbouring molecules is the same as that between adsorbed particles in the ground state, then S = 1 and the kinetics of desorption is equivalent to an ideal adsorption system, regardless of the method of approximation. Fig. 33 shows TDS curves calculated in the QCA for various values of the parameters of the lateral interaction and the energy of activation for desorption at zero coverage. The width of the TDS turns out to be proportional to the value of 6e, with an increase of 6e producing
239 a stronger splitting of the TDS, and a decrease in the temperature of the initial TDS, with a corresponding increase of the share of the particles desorbing at lower temperature values.
d_..~e dT 0.4
0.2
300
400
500
600
T
Figure 32. TDS curves for 80 = 0.9 in the approximations-polynomial (1), quasi-chemical (2), chaotic (3), and mean field (4) for g~9 = 1012 s -z, E D = 126 kJ/mol, e = -16.8 kJ/mol, e* = -8.4 kJ/mol, b = 50 K/s, z = 4 [147]. Repulsion between nearest neighbours is typical for chemisorption systems and results in a decrease of Q(0) with increasing coverage 0). In such systems the condition [ e~ I__0. To physical adsorption there corresponds attraction to nearest neighbours which leads to an increase of the heat of adsorption with increasing surface coverage. In such a case, e, e* > 0. However the effects of the repolarization of the binding (decrease of
240 the binding with the surface coverage and increase of the binding with the neighbouring particle) can lead to the situation where e > e* and. vice versus. An increase of the parameter e at *e = const, provokes an increase of the splitting of the spectrum (Curves 4-6), but its width remains constant. Curves 4 and 8 correspond to e* = 0. In this case the width of the spectrum is maximized. The diminishing energy of activation E~9 moves the spectrum toward lower temperatures and makes it possible to increase the splitting of the spectrum due to an increase of the contribution of the lateral interactions to the energy of adsorbent-adsorbate binding z~e/E~9 , E~9 ,~ Q(O = 0)). dO 0.4~- dT
0.3
0.2
0.1
0 300 600 900 1200 Figure 33. TDS in the quasi-chemical approximation, z = 4, b = 50 K/s, 00 = 0.9. 21]ae energy perameters are given in Kelvins. Curves 1 and 2: ED = 15- 103, ~e = 103, e = - 2 . 1 0 3 (1) and -103 (2). Curve 3: ED = 20.103, ~e = 103, e = -2-103. For the other curves (4-8), ED = 30-103. For curves 4-6, ~e = 10 3, e = --103 (4), --2.103 (5), -3-103 (6). Curve 7 is obtained with ~e = 0 at any E. Curve 8: e = -3-103, ~e = 3.103 [147]. Curve 5 qualitatively reproduces features of the experimental TDS curve for the C O / W system[152]. (This fact was not mentioned in paper [134].) By coincidence, the TDS curves calculated in [147] correspond to the experimental data for the systems C o / M o ( l O 0 ) [153], and on a series of metals [154], C s / W [156], etc.
241
4.3. P r a c t i c a l a p p l i c a b i l i t y of t h e cluster m e t h o d s A detailed analysis of the papers published in the years 1970-1980 [40] shows that the calculation of the interaction between nearest neighbours can assure only a qualitative description of the experimental TDS curves. For quantitative agreement, it is indispensable to use the full set of lateral interactions. This conclusion is physically obvious, because the contributions of the nearest neighbours amount to 60 to 80% of the total energy of binding of a particle with its neighbours in any condensed phase. For the first time, a quantitative description of the kinetics of the desorption of atoms from the surface of tungsten [156-158] was given by considering both the direct interactions between nearest neighbours and the collective interaction with all the remaining adsorbed molecules [136,137]. The rates of desorption (Fig.34) obtained from the experimental TDS curves were estimated for 840 K and compared with calculations done using three approximations at f l s - - -3.6. The contribution from the collective interactions was equal to 30% of the total and the marked areas in Fig. 34 correspond to the permitted values of the parameter ~c~. g which range from -0,38 to 0. That comparison shows that the approximations, after taking account of the effects of correlations, lead to considerably better agreement with the experimental data than those ignoring the correlations.
0.5
1.0
0
..
0
-8
I-.-!
I--4 1
-24 "
fp'Ar 3
In V
Figure 34. The desorption rates K/W for T=840 K, calculated for the chaotic (1), polynomial (2), and quasi-chemical (3) approximations [137], V = U ( ~ ) / U ( O = 1); symbols denote experimental values of the desorption rates with their errors [156-158].
0
~"
0
I~
~
,.~"
,-,-
'
0
---.;'l
0
,
~
~
0
v
0
0
0
....
0
.-'I.
i l
0
v
,_..
~-- ~
,..,
~.
',I.,
~
'_.
-,
i,0
--,
~
v
~
~
i
0
"
o
,---, 0
o -' ~--'
~
EE
~
(D~cl
~ 0
L,O
o
0
o
243
rl
dO dT 1.0
O.
0
200
1
2
350
3
4
5
400
450
500
550
Figure 36. Experimental (1) and calculated (2-4) TDS curves for C O / P t ( l l l ) , corresponding to the versions 2-4 of the interaction parameters in Table 2. Inset" the concentration dependence of the sticking coefficient r/(0): curve (p) - calculation, the another curve - experiment,[159]. A satisfactory qualitative agreement with experiment is obtained when one includes interactions with the first and second neighbours [159] in the description of the data for desorption of Co~It(Ill) [160], 02/Ir(lll) [160] and 02/Pt(lll) [161] (Fig. 35). The values of the lateral interaction parameters that lead to a quantitative description of the TDS have been collected for a series of systems in Table 2. These parameters correspond to the initial values 00 < 8*, where 8" is the maximum measured value of the initial surface coverage. Calculations [159, 168] have been carried out without applying special optimization procedures (see for example [169]). The search for the parameters was ended when the deviation between the experimental and calculated curves was less than 3-5%. The accuracy of the experimental data does not allow one to ask for still more accuracy. The procedure used for estimating the parameters is not precise enough. For the system Co/Pt(lll), several sets of parameters were obtained, all corresponding to a given accuracy in the description of the experimental TDS curves, but the procedure of carrying out such calculations proves that for a quantitative description of the experimental data one needs, as a minimum, calculations of the contributions for the first and second neighbours. The indispensability of including the contributions not only from the nearest neighbours, but also from more distant neighbours, has been shown in the description of
244 the rate of adsorption [159]. The concentration dependencies of the rate of adsorption are even more sensitive to contributions from further neighbours. For the system Co/Pt(111), a comparison of two procedures for searching for the lateral parameters was carried out. In the first case, the parameters e(r) and e'(r) were determined from the TDS curves and accordingly, the concentration dependencies of the sticking coefficient was evaluated (variants 1-3). In the second case, these parameters were determined from the sticking coefficients, and the TDS curves were calculated (variant 4). In the first case, the calculated sticking coefficients were in poor quantitative agreement with the experimental data. In the second case the agreement was much better (see. curve 3 in Fig. 36). In the supplement in Fig. 36 the experimental and calculated concentration dependencies of the sticking coefficients are given. -1
dO dT
0.8 0.4
0.5
1
~..~
0.1
!
I
I
I
400
500
600
700 T,K
Figure 37. TDS calculated with (solid lines) and without (broken lines) taking account of ordering, R = 1, e =-8.4 (Curves 1) and-12.6 (Curves 2) kJ/mol with e* = e/2. The phase diagrams (broken line -exact calculation) and the trajectory 0(T) in the thermodesorption process [174] are given in the inset. From general physical ideas it follows that the contribution from the more distant neighbours can not surpass the contribution of the nearest neighbours. Anyhow, their role in the kinetics of the adsorption processes is essential. The analysis of of their influence on the TDS curves [168] showed that: 1) the width of the spectrum is proportional to the value ~r=lnz(r)~e(r); 2) a consideration of the attraction with the second neighbours for chemisorption systems brings about an increase in the the splitting of the spectrum; and 3)
245 the corresponding changes in the contribution from the second neighbours (i10-15%) can considerably shift the positions of the maxima and minima of TDS to higher temperatures. At the same time, the contributions from the second and following neighbours do not split the spectrum additionally, so that the number of peaks in the spectrum is determined by the interactions with the nearest neighbours. Strictly speaking this concerns the curves with 60 -~ 1 for values of the parameters which are distant from their critical values for the splitting of the TDS. These conclusions, of great importance for the interpretation of TDS, were drawn not only in the QCA but also in the calculation of the indirect correlations [170], as well as in all recent calculations [40,171]. Finally, it should be pointed out that contributions from the second and following neighbours show a strong influence on the state of the multilayer film. The phase transformations connected with the ordered phases in these films and also with the stratification of the ordered or disordered phases, lead to changes in the character of the distribution of adsorbed particles on a homogeneous surface. Instead of a homogeneous distribution of the particles on the surface, there is a locally heterogeneous distribution in the presence of ordered phases and macro-heterogeneous in the presence of the stratification of the disordered phases. As a consequence of these changes of state, there are also changes in the character of the local surroundings of the particles in the phases and, correspondingly, of the rate of adsorption. A direct calculation of the rate should precede the calculation of the phase diagram of the adsorbed particles in order to determine the number and amounts of the phases present at a given temperature, density of the monolayer film (0) and potential of lateral interaction [172-174]. Fig.37 shows TDS curves obtained using the quasi-chemical approximation without calculation of ordering of the type C(2 x 2) on the (100) crystal face for non-dissociating particles. In the supplement, the phase diagram of this ordered system is shown, as calculated in the quasi-chemical approximation, and in an exact calculation [139] taking account of only nearest neighbours interactions (R = 1). The ordering phase transition significantly changes the shape of the TDS curves (the arrows show the disorder-order and order-disorder transitions occurring during thermal desorption). The splitting of the spectrum considerably increases (the minimum decreases), the high-temperature peak becomes sharp, and the low-temperature peak can have shoulders, the nature of which is not connected with the existence of an additional state. The attraction with second neighbours increases the range of existence of the ordered state and the shoulders cannot be observed on the rapidly increasing front of the low-temperature peak [40,174]. The indispensability of the calculation of the phase changes while considering the kinetics of adsorption was illustrated by the description of the desorption of Hg atoms from the (100) surface of W [175, 176]. That system is characterized by the presence of a common edge for a family of the curves with different initial values of 00, which indicates two-dimensional condensation of the adsorbed atoms. Previously, the existence of such a common edge was explained on the basis of double-layer models [40, 177, 178]. The calculation of a condensed phase allows to one explain the existence of a common edge, if equations (40), (41) for a monolayer model along with the quasi-chemical approximation at c*(1) = ~(1)/2 [179]. The authors [175, 176] described their experimental data using equation (39) where ED(8)(= Q(0))is the adsorption heat, calculated in the quasi-chemical approximation [180]. As a result of that description, these authors arrived at the values: K~ = 10x6 cek-Xr; ~Hg-Hg(1) = 5.85KJ/mole; and E~ = 172KJ/mole. However, their in-
246
7
I !
/ /
I I I I I I I I |
,t1,I10 I
I i I !
3
, I
I
I
II I
50
630
590 T,K
Figure 38. The TDS curves for the system Hg/W(100) coincide with the experimental data (broken curves)when calculated with 801 < 8 < 802 (1-6), 8o1 ,,~ 0.45 and 8~) ,,~ 0.8 [175]; they deviate from the experimental data at 80 = 0.99 (7) [179]. teraction parameters did not assure the creation of islands of Hg atoms[176], the existence of which was found in Monte-Carlo simulations. In this way, the thermodynamic and kinetic characteristics of the adsorption system were not selfconsistent. The calculation of the condensation of the Hg atoms lead us to a satisfactory agreement with the experimental data [175, 176] (Fig.38) at other values of the energetic parameters E~ = 136.5K J/mole, eHg-Hg(1) = lO.4gJ/mole, K g = 1013 cek -1, e*Hg_Hg(1)/eHg_Hg = 0.55 [179]. The parameters obtained have reasonable values: an increase of the attraction eHg-gg results in condensation in the range of temperatures investigated and allows the anomalously great value of K~ to decrease by three orders of magnitude and consequently, a considerable decrease in the estimated value of the energy of activation for desorption E~ was observed. Thus, the values of the parameters obtained lead to consistency between thermodynamic and kinetic adsorption properties. The deviation of the calculations from experiment can be observed only at 80 ~- 1. It is possible that this is connected with the formation of the second layer of adsorbed atoms which is ignored in the model.
4.4. Calculations using cluster methods Cluster methods of calculation of the equilibrated distribution of the particles have some well-known limitations [181-183]: in the critical areas of phase transitions they give only qualitative descriptions, with differences in predictions of critical temperatures from the exact values by 15-30%. (e.g. for the (100) face, the exact value (~e) = 1.78, and the
247 quasi-chemical approximation gives the value 1.38). In adsorption, the concentration of the adsorbed particles changes over a broad range and in many situations, the trajectory of thermal desorption O(T) crosses areas with various states of aggregation. Thus, there appears a question: how well can the cluster methods describe the kinetics of adsorption? A review should give a comparison with analogous calculations carried out with the help of other methods that assure a more precise calculation of the effect of correlation. Monte Carlo simulations belongs to such methods as well as the matrix method. Monte Carlo simulation is at present a fundamental method of calculation in statistical physics (see for example [184, 185]). The algorithm is simple enough and the computations allow one to investigate lattices, containing up to 106 sites. In many situations the difference between the solution obtained in this way and the exact value is insignificant. The matrix method of investigating lattice systems, allows to one obtain an exact solution for a periodically repeating representation of the lattice [186, 187, 139]. In general, with the help of those methods one can investigate a slab of sites of width L, where L is the number of lattice parameters. In certain situations,one can investigate the thermodynamic limit at L ~ ~ [188, 139, 140]. As mentioned in paper [189], in the absence of phase transitions the numerical analysis of a small slab when L > 4 gives practically the same results as for the macroscopic L. This was a good reason to apply this method to the construction of a scaling procedure for values of L corresponding to the critical range of phase transitions. Such an approach increases possibilities of calculating effects of correlation in studies of surface phenomena and for the construction of phase diagrams [172, 173]. While applying the two methods to investigate the kinetics of adsorption, one can basically evaluate the reliability of the results obtained by cluster methods. For the first time, the calculation of the kinetics of desorption by the Monte Carlo method was reported in ref. [190], and the matrix method for L > 1, in ref. [191]. For a comparison of the calculations of the adsorption kinetics carried out with the help of cluster methods and with matrix and Monte Carlo method, it is indispensable to view the differences in two ways. The first one is connected with the influence of the neighbouring particles (through the non-ideality of the adsorption system SD(O[R,z)) upon the local rate of the elementary process. The second aspect is connected with the influence of the state of aggregation of the adsorbed monolayer. Usually, the influence of these two factors cannot be separated. Equation (41) shows that in the quasi-chemical approximation, the influence of the neighbouring particles is manifested through/92 = /?It which represents the probability of finding two neighbouring particles together. Depending on the level of accuracy of calculation of this function, the same accuracy is required to calculate the contribution from the lateral interaction. The exact solution for/92 is known only for 19 = 0.5 [139, 140]. Fig. 39 shows the comparison of the exact dependence 09(~e) for interactions of nearest neighbours with an analogous dependence obtained by the approximate cluster methods. With increasing interaction, the difference between curves 2 and 3 increases. The calculation of indirect correlations considerably diminishes this difference and the curves become similar, though the difference still remains observable. This is obvious because phase transformations occur with increasing interactions, and the character of the distribution of the particles changes. In consequence, the expressions for/92 and/93 calculated without taking into account the change of the aggregate state of the system are not exact. Considering the ordering of the particles (with the formation of a C(2 • 2)
248 structure) even without taking into account indirect correlations (curve 4)) considerably improves the agreement between 82 calculated in the quasi-chemical approximation with the exact solution. It was observed [49], that considering both the ordering of particles and the indirect correlations leads to an approximate relation 82(fie) which differs from the exact solution by only several %. An analogous influence on the accuracy of calculation of 62(~e) by the cluster method shows the effect of stratification of the particles in the case of attraction between adsorbed particles.
O2
0.5- -
5 1
0.3-
I
-3
I
I
-l
i
1
I
I
3
Figure 39. The dependence of 02 on ~e for 0 = 0.5 on a planar square lattice, calculated by various approximate methods: 1, with indirect correlations in t-he quasi-chemical approximation, 2, exact solution (after Onsager), 3, quasi-chemical approximation, 4, with ordering in quasi-chemical approximation, 5, taking account of two-dimensional condensation in the quasi-chemical approximation [40,49]. The duster methods assure a satisfactory accuracy of the description of the local distribution of the interacting particles with any interaction. It is just this fact which determines the non-ideality SD(8[R,z) and in consequence, an accurate local description of the kinetics of adsorption. The solution of the problem of calculating the aggregate state of the adsorbed layer is more complicated. For a correct description of the critical region, it is indispensable to decrease the phase transition region in comparison to the exact solutions (see the supplement for Fig. 37). This effect of the cluster methods is usually avoided by using phase diagrams obtained by applying some other theoretical methods or determined from
249 experimental data while applying the law of corresponding states (see the example in [195]). An analogous problem exists, however, in the matrix methods. These methods cannot basically describe the change of aggregation even when the calculation is made for large (but finite) values of L [140]. Scaling procedures on L give approximate results, even if they are additionally controlled by the fluctuations in the correlation functions 0~(r) [172]. In Monte Carlo simulations, the problem lies in searching a compromise between the necessity of increasing the number of the sites of the lattice (in order to allow "renormalization-group" transformations) with the assurance of attaining the equilibrium state on the lattice. If we consider that while calculating TDS curves such a procedure should be applied at every comparatively small temporary change of temperature, keeping these conditions correctly demands much care even with contemporary computers. I
I
I
I
Noool
1
_=
i 400
500
400
50O
400
500
T,K
Figure 40. Thermal desorption spectra calculated o using a combination of quasi-chemical and mean-field approximations (curves 1), the Monte Carlo method (curves 2) and the transfer-matrix technique (curves 3) for g ~ = 10x5 s-x, E~9 = 35 kcal/mol and different laterm interactions: ex = -1.4 and e2 = -0.6 (a), ex = -2.0 and e2 = 0 (b),ex = -2.6 and e2 = 0.6 kcal/mol (c). The initial coverage is equal to unity and the heating rate is 40 K/s. The inset (upper right) shows adsorbed particles (dark circles) on a square lattice [191]. Comparison of the results obtained using cluster methods and the Monte Carlo method was carried out in a large number of studies. Looking at the results (see [40]), one can conclude that in the majority of cases, the calculations basically agree with one another. This agreements has a quantitative character when the calculations are carried out for the entire range of a phase transition, while they have only a qualitative character in the cases where phase transitions occur. It is, however, necessary to emphasize that in all cases the Monte Carlo calculations are comparable with calculations done using the
250 analytical expression of type (40), (41) where the aggregate state of the adsorbed particles is considered even when the comparison was carried out outside of the expected range of applicability of the expressions (40) and (41)).
a
b 35 o
2
~
400
3o
25
500
T,K
0.2
0.6
O
Figure 41. Part (a)" thermal desorption spectra for a square lattice calculated for K~9 = 1014 s -1, ED = 35 kcal/mol, b = 50 K/s, and E = - 2 kcal/mol (see fig. 5c [197]). (b) The coverage dependence of the desorption activation energy for (fig. 5f [197]). Curve "c" corresponds to the repulsion of only nearest neighbors (the case under consideration). The other curves reflect the contribution of triple interactions.
Q 1
23-
21-
19-
0.1
0.2
0.3
0.4
O
Figure 42. The heat of adsorption of repulsive molecules (z = 4, e = - 2 , Q(0 = 0) = 23 kcal/mol) in the quasi-chemical approximation taking account of ordering (1) [198] and without taking ordering into consideration (2); curve 3 - calculation by the matrix method [199].
251 In order to illustrate the current state of the problem, we limit our considerations to the calculation of the characteristics of the ordering of type c(2 x 2). Paper [196] reported on the calculation of the TDS curves by Monte Carlo and then in the quasi-chemical approximation without considering of the effect of ordering. The main conclusion of the paper consists in the statement that in the range beyond the phase transitions, both methods give practically the same results, but in the range where the phase transitions occur, their results differ. Moreover, in the Monte Carlo method one can see the a qualitatively new effect which is the presence of an intermediate peak that cannot be obtained when one uses the quasi-chemical approximation. However, in paper [191] these calculations were also compared with calculations based on the matrix method (see Fig. 40). The results show that the intermediate peak is not observed, and that it was due to an incorrect Monte Carlo calculation. In spite of the qualitative agreement of the matrix method with the quasi-chemical approximation, one can observe considerable differences; in the matrix method, the minimum is more profound and the peaks are sharper. Such a result (for strongly separated peaks) can be obtained from the quasi-chemical approximation with explicit considering of ordering [159]. The peaks, however, are a bit more diffuse than in the matrix method, and the low-temperature peak has a jagged structure as shown in Fig. 37. The absence of the jagged structure in the matrix method means that the phase transition of the ordering has not been taken into account. In paper [197], the Monte Carlo calculation no longer contains the additional peak and the TDS curve is similar to the TDS of paper [191]. The ED(0), however, (see Fig. 41), strongly differs from the isosteric heat of adsorption Q(8) (Fig. 42) [198]. Fig. 42 also shows the curve of Q(0) calculated by the matrix method [199]. In these calculations [191,196,197], the collision model was applied. In this model either ED(6) = Q(O) or the curve of ED(0) is similar to the function In UD(O). (The difference is related to the two possible methods of determination of ED(O), see [39,40]). The function In Up(O) has break at the points of the phase transition [159] which also is absent in Fig. 41. Thus, these Monte Carlo and matrix calculations do not give the ordered state. In the fundamentals of the matrix method there is assumed the impossibility of describing the ordered state for finite L (calculations [191] executed at L = 4). That is why instead of a sharp jump at the point of the phase transition, a bend in the line appears in the function Q(0). With increasing L, Q(8) does not change its character. Finally, both methods give artificial effects. The cluster method assures the appearance of a jump in the heat of adsorption at the point of the phase transition, but after that jump we have QCM(~) > Q(~ - 0). The matrix method not only does not give the jump Q(O) at the phase transition, but also leads to the value QMM(O)> Q ( 0 - 0) (an increase of L to 12 keeps these artificial effects [200]), even though QcM(O) > QMM(O). The flattened character of the curves of Q(O) is expressed in the shape of the TDS: the jagged structure of the low-temperature peak disappears and the high-temperature peak has a flattened maximum. These differences are mainly observed near the critical temperatures ([ /~e ]> 1.7). With increasing repulsions between the molecules ([ f~e ]> 3), the cluster method and the matrix calculation give practically the same results. Fig.43 shows the rates of desorption, calculated using both methods [199] for the transition state. The generalization of the matrix method assures the inclusion of the interactions of the activated complex with its neighbours, and was presented in paper [201].
252 Thus, the description of even a simple adsorption system having c(2 x 2) ordering on a homogeneous surface leads to difficulties when applying any method. Outside the critical range, the cluster method is not inferior in its accuracy in comparison to the matrix method and to Monte Carlo. The calculations carried by the cluster method are 2 to 3 orders of magnitude faster than either of the other two. Of course, within the critical ranges it is better to use the combined methods (see [40]) but this is a separate question not considered here. For interactions on heterogeneous surfaces, the problem becomes much more complicated, and the use of the cluster methods [40-49] gives a possibility of considering lateral interactions with the same degree of accuracy as they provide for homogeneous surfaces. lg
S
65-
6
43-
f
2b-
-1 -2 -3 ~4 i
0
I
I
I
I
I
0.5
I
I
I
I
O
1.0
Figure 43. Concentration dependence of the desorption rate for z = 4. ~e(1) --2 (1-4) and -5 (5-6); ~(2) - 0 (~,4-6) ~ d - ~ / 3 ~ ( ~ ) (2,3); c(~) - 0 (~,2,5) and ~(~)/2 (3,4,6), r-X ~nd 2. The solid lines are given by the quasi-chemical approximation, and the broken lines by the matrix method [199].
253 5. T H E T H E O R Y O F A D S O R P T I O N ON HETEROGENEOUS SURFACES
WITH LATERAL INTERACTIONS
5.1. The problem of calculating lateral interactions Real adsorbent surfaces are characterized by a large variety of compositions and structures which depend on the preparation of these materials. Their surfaces are heterogeneous and in many cases, the description of adsorption on them over a wide range of gas pressures and temperatures, will require that one consider the effects of lateral interaction. Even between nearest neighbours, this involves a complex analysis of the whole adsorption system. The state of occupancy of any lattice site (denoted as "f') depends on the type of the site, as well as on the occupancy of its nearest neighbours, which depend in turn on the type and state of occupancy of the more distant neighbours h, including also the lattice site f, from which the consideration began. As a result, the probability of localization of a molecule on a particular site depends not only on the type of this site, but also on the types of the vicinal sites. Then, depending on the required accuracy of the evaluation of the effect of the distribution of vicinal sites upon the local filling at each lattice site, different approaches to the description of the equilibrium distribution of the particles and the kinetics of adsorption will be required. On a homogeneous surface, the distribution of the of adparticles depends on the structure of the surface and the potential of intermolecular interaction. On heterogeneous surfaces an additional effect is observed that is connected with different adsorption and kinetic features for different lattice sites as well as their spatial distribution on the surface. Changing the spatial character of the distribution of different lattice sites (at a fixed number), yields surfaces differing in their adsorption and kinetic characteristics. Obviously for very low coverages where the contribution of lateral interactions can be neglected, such surfaces will be indistinguishable in adsorption experiments. However, at higher coverages where the contribution of lateral interactions cannot be neglected, these interactions will influence the course of adsorption. The theory of adsorption taking simultaneous account of the effects of surface heterogeneity and of lateral interactions on the equilibrium and kinetic characteristics of adsorption has been discussed in [41-45]. This theory makes it possible to consider the local properties of individual surface lattice sites, and leads to macroscopic expressions
,I ,1 i f a] hi
hi
h2
gl
fig 4
g3
f
g31 l~
h 4, g4: h 3 14
gl
=~
h2 f
g3 ha
h2
g2 f
ti 4
g2
11 81]
12
f 13
ha
gJi 14
Figure 44. The sites g, h and 1 are first, second, and third neighbors of the central site f. The mapping of a fragment o~ the surface onto a cluster consisting ot one central site and its nearest neighbors ( R=I )[202].
254 for properties such as isotherms, heats, adsorption rates, desorption, surface migration etc. by considering the local coverages of surface sites. In order to estimate these local coverages, there were constructed systems of algebraic (for equilibrium) and kinetic (for nonequilibrium distribution of molecules) equations. Several levels of the description of adsorption exist for heterogeneous surfaces. These describe the probability of coverage of individual surface sites. Although the considered fragment of the surface is small, it allows for a precise estimate of the effect of the spatial distribution of sites on the adsorption processes. At the next level, which is a cluster description of the surface, it allows for recognition of the local surface structure. In this case, only nearest neighbor correlations are retained in the distributions of sites of different types. A third level coupled description of the surface is produced by mapping in terms of site pairs. Such description is least exact when the surface is expressed in terms of individual sites, where lateral interactions of vicinal molecules can become impossible. The minimum dimensions of the fragments and clusters as well as the distances between individual site pairs cannot be smaller than the range R of the lateral interaction. In order to estimate macroscopic characteristics of adsorption, one must average the local characteristics for different fragments, clusters or site pairs of different types. For this purpose, it is necessary to introduce approximate distribution functions of fragments, clusters, and site pairs that characterize the structure of a heterogeneous surface. The binary distribution function fqp(r) for r = 1 was already discussed when the adsorption kinetics of dissociating molecules on heterogeneous surfaces in the absence of lateral interactions was described. Analogous functions were introduced also for distances r < R. The second level of the models introduces the cluster distribution functions f(qmR) describing the probability of finding a cluster on the surface containing the central site around which there are locahzed at the distance r mqp(r) sites of p-type, 1 _< q p >_ t where t is the number of site types; the symbol mR denotes the totality mqv(r) 1 > p > t 1 > q > t 1 >_ r > R. The condition of normalization of the cluster function and the relations between the choices of mqp(r) are given by: t
f(q{m}R) = fq bq
~
rr~(r) = 2q(r)
(42)
p=l
where bq is the total number of different choices of mqp(r) for central site q for different surface clusters, 2q(r) is i the number of vicinal sites in the r - th coordination shell of site q. A detailed description of different distribution functions and examples of the construction of adsorption isotherms and heats is given in [49]. In the following sections of this article we will review the results of the applications of this theory, mainly by presenting examples of the estimation of desorption processes.
5.2. Kinetic equations of adsorption. In the case of heterogeneous surfaces, the parameters of the lateral interactions between different adsorbed molecules may depend on the type of sites on which these molecules are locahzed. For this reason we will denote these parameters for the pairs of molecules localized on q and p sites at distance r by %~(r). The interaction of an activated complex localized above q site with vicinal adsorbed molecules localized above q site at distance r will be denoted by %~(r). For physical adsorption, the dependence on the site type is
255 significantly smaller than for chemisorption. In the final expressions we will assume that e~(r) = e(r). Macroscopic values of adsorption and adsorption rates for the case of non-dissociating molecules localized on a heterogeneous surface are expressed by the equations (11) in which the local rates of elementary processes including lateral interactions are given by A = K ~ P ( 1 -- 8q) Sq A Uq
D = KqD 8qSq D Uq
(43)
where therate constants K {'D are defined by equation (12), and the s~'D function includes the effect of the non-ideality of the adsorption system on the rate of elementary processes. In the absence of lateral interactions, this function is unity and equation (43) reduces to equation (12). For homogenous surfaces, the equation defining UD transforms into equation (40). Apart from the four factors mentioned above and referring to a homogenous surface, the functions of non-ideality derived for heterogenous surfaces also take account of the surface structure and its effect on the distribution of adsorbed molecules. We will limit our consideration here to the third level of the binary distribution function fqp(r) for the description of this structure. In this case, the non-ideality function derived for desorption rate including interactions of the vicinal neighbours has been derived using different approximations in chapter 4. It may be written as: t
[1 + ~ d~x~8,] =~
x;~ =
exp(fl&~)- 1
(CA)
p=l t
Sq
=
exp[flZq E
dqp&qpOp]
&qp = Qo - eqp
(MFA)
(44)
p--1 t
dqpxqptqp]
t ~ = 2Qp/(A~ + bqp) (QCA)
p--1
A ~ = 1 + x~(1 -- 0q-- 8p)
Xqp = exp(-fleqp) - 1 bqp =
where dqp = fqp/fq characterizes the conditional probability of localization on a q-site at a distance r from a p-type site; tqp characterizes the equilibrium conditional probability (in the quasi-chemical approximation) of localization of an adsorbed molecule on a q-type adsorption site when the p-type is occupied. It was shown in ref. [147] that the disordered approximation is not correct, because in this approximation the symmetry relating to the sequence of selection of molecules of different types is disturbed. For this reason this approximation is presented only to illustrate the fact that the cluster methods developed for a homogenous surface can be adapted fully to a heterogenous surface without loss in correctness. As in the case of homogenous surfaces, the mean field approximation includes the mean energy of interaction of vicinal molecules. In the quasi-chemical approximation, short ranged correlations are also considered. Estimation of the rates of adsorption on the basis of the equations (43) and (44) requires knowledge of the local coverage 8q. These values are obtained from solution of the equations aq(1 -8q)SpSp = ap(1 -8p)SqSq, where Sq includes the effect of the non-ideality of the adsorption system on the coverage of q-type
256 sites in the absence of Sq = 1 interactions (the form of Sq depends on the approximation). For example in the case of the quasi-chemical approximation we have t
Sq = [1 + ~
dqpxqptqp]~q
(45)
p----1
where aq is the Langmuir constant for a q-type site. In following parts of this paper the main results of the complex consideration of the effect of surface heterogeneity and of lateral interactions using the quasi-chemical approximation will be considered.
5.3. Effect of vicinal neighbours. In numerous instances, the expressions for desorption rates derived by considering the conditional distribution functions of clusters and site pairs cover each other and the analysis of the complex effects of the factors affecting the form of the TDC simplifies. Especially in the case of the square lattice (2g=4) such superposition takes place for fl = f2 = 0.5 and for the three following surface structures: a) ordered (chess-board) b) disordered c) patchwise. In the case of this last structure, the contribution of the boundary existing between different homogeneous fragments is neglected. In the first case, each site of the first type is surrounded by sites of the second type and vice versa: d(1 [0.4]) - d12 = d21 = 1; and the remaining functions d(qm) and dqp are equal to zero. In the second case, d(q[mql, rnq2]) = c2q mql f~ql f~q2 and dqp = fp, where c2q mq~ is the number of connections derived from zq in relation to mq~. In the third case dn = d22 = d(l[4,0]) = d(2[0.4]) = 1 and the remaining distribution functions are equal to zero. One of the major problems considered in TPD analysis is the number of peaks obtained. Surface heterogeneity, repulsions between adsorbed molecules, as well as porosity may lead to splitting of the TPD into two peaks. Their common effect may be illustrated by the model calculations presented in Fig. 45 for the three surfaces mentioned above. (Curves 1 correspond to an ideal adsorption system [203].) On the ordered surface (Fig. 45a), an increase of the repulsion between molecules does not change the number of the peaks in comparison to an ideal adsorption system but rather leads to an increase of the height of the low-temperature peak and to a shift of this peak toward lower temperature. Such behaviour also takes place with increase of the differences between Q1 and Q2 on a heterogeneous surface even without considering lateral interactions [127], and on a homogeneous surface where these interactions exist [147]. In the latter case, either the magnitude of the TDC minimum or the widths of the low and high temperature peaks are significantly larger than for the ideal case. Significant qualitative differences are observed for heterogeneous ordered surfaces. With increasing repulsions, the maximum of this peak increases and shifts toward higher temperatures. This is related to the fact that with stronger repulsions, larger site coverage is characterized by lower bond energies compared to that in the absence of lateral interactions at 0 = const in the range of 0 < 0.5. Such behaviour of heterogenous ordered surfaces leads to an "anomalous" dependence of adsorption heat on concentration [49,205]; in the case oflow i coverages and repulsive forces between admolecules, there will exist a range of coverage starting from 00 = 0 in which the adsorption heat increases from Q(0 = 0) to Q1.
257 4
a
dO dT
1
dO dT
1
2 1
,
360
I
300
660
450
450
600 T,K
T,K 1 dO
C
dT
2
tI / 250
.I 400
I
550
T,K Figure 45. TDS for heterogeneous surfaces with ordered (part a), chaotic (part b), and patch-wise (part c) arrangements of different types of sites; t = 2, fl = 0.5, Q1 = 113.4, Q2 = 84 kJ/mol, a = 1, g D~ = 1012 s -1, 80 = 0.99, b = 50 K/s, eqp = e, e~p = e/2;e = 0 (curve 1),-4.2(2), -8.4(3),-12.6(4) kJ/mol [203]. On heterogeneous disordered and patchwise surfaces (Fig.45b and 45c), increasing repulsions between admolecules leads to formation of a more jagged structure for the TDC (an increase in the number of peaks). Similar to the case of ordered surfaces, an increase of the repulsions affects the width of the TDC peak and shifts its initial part toward lower temperature. Simultaneously, the heights of the high temperature peaks decrease but their position remains practically constant. The positions and heights of the low temperature and intermediate peaks depend in the same way on surface heterogeneity and on lateral interactions. On disordered surfaces, the number of peaks for fixed model parameters increases to 4. This fact can be interpreted as meaning that on the different homogenous domains of the surface, there occurs a splitting determined by lateral repulsions between adsorbed particles. Such interpretation must, however, be treated with caution since patchwise surfaces characterized by the same parameters, can produce not 4 but 3 peaks.
258 Assuming, thus, that the height of the intermediate peak exceeds significantly both the low and the high temperature peaks, one can conclude that this is due to some superposition of contributions to the "high temperature" peak for surface fragments characterized by a low energy bond, and the "low temperature" peak for the surface fragments characterized by a high energy bond. In order to clarify such an interpretation one must analyze the dependences of the local contributions dOq/dT and dS/dT. The calculations show that, except for the high temperature region in which 8 < 0.1, the relation dQ2/dT > dQ~/dT holds. That means, that during the whole process, the main stream of the desorbed molecules flows through the sites characterized by a lower bond energy. This permanent "leaking" of the molecules from the sites of the second type is assured by the condition of their equilibrium distribution. For this reason we cannot say anything about the effects of the splitting of the TDC on different fractions of a homogeneous surface. Analogous changes of the number of peaks may also be caused by changes of other molecular properties of an adsorption system. The effect of the surface composition of a disordered surface on the form of the TDC, is shown in Fig. 46 [205]. The decrease of the fraction of strong sites leads to an increase of the number of peaks from 3 to 4.
dO dT
1
290
390
490
590 T Figure 46. The influence of the composition of a heterogeneous surface with a chaotic distribution of sites of different kinds upon the shape of the TDS. E - -8.4 kJ/mol, fl = 0.2(curve 1), 0.5(2), 0.8(3). The other the molecular parameters are the same as in Fig. 45 [205]. Fig.47 shows a TDC picture for the case of attractive interactions between adsorbed molecules [203]. In the case of weak interactions, differences in the sites of different types prevent the molecules on an ordered surface from getting closer because for these molecules it is more comfortable to be localized in the regions situated between second neighbours and thus not to interact with other molecules. However, when the attractions increase, the contribution of the lateral interactions increases. This in turn decreases the splitting of the TDC. At stronger interactions, the form of the TDC can be superposed
259 "structurally" with the TDCs for homogenous surfaces. Similar effects will also be found for other heterogeneous surface structures. dO
dT
4
1.2-
0.8-
0.4-
300
I
I
!
400
500
600
T Figure 47. TDS of attracting molecules on a strictly-ordered surface fl = 0.5, e = 0(curve 1), 4.2(2), 8.4(3), 12.6(4) kcal/mol, with the rest of the parameters taken to be as in Fig. 45 [203]. It should be emphasised that in the general case, the fragment, cluster or pair method of describing the structure of a heterogeneous surface are not equivalent. Different modes of desorption may change the form of the TDC curve. As a simple example, we will consider the surface of a model ordered alloy, assuming, that on the usual "chess-board" (t=2), each fourth series consists of the sites of a type characterized by a lower adsorption energy. Surface composition is represented by fl = 3/8 and f2 = 5/8 functions. Calculations of the TDC for that system (Fig.48) were done using the various methods of description
260 of surface structure. Curve 1 corresponds to the first level method: an elementary cell (fragment) consists of eight sites (see the remark to Fig.48). Such a description of local coverage of any fragment leads to 8 local coverages. Curve 4 corresponds to the pair method of the description of surface structure (dqv functions are then employed). Curves 2 and 3 correspond to cluster descriptions of the surface. For curve 2, the number of types t = 2, as for the pair method of description, whereas for curve 3, it was assumed that the sites situated in the 4th series can be characterized by different coverages. Such situations are due to the fact that different coverages of vicinal sites in the first and third series can, owing to lateral interactions, influence the coverage of the sites localized in the fourth series in different ways. In the last case the number t is equal to 3. (adsorption energies for the sites of first and third type are the same). dO dT 1,3
1 x
20
3 x
40
x
50
6x
70
80
0
x
O
x
O
x
O
x
O
O
O
1,3
I
I
I
I
300
400
500
600
Figure 48. The influence of the structure of a heterogeneous surface on the shape of TDS curves for R = 1, e = -8.4 kJ/mol. Distributed model (curve 1), cluster models (2,3), averaged model (4). The rest of the molecular parameters are as in Fig. 45. The fragment of the surface under consideration is shown in the inset.
261 Curves 1 and 4 show different results; curve 4 does not contain the intermediate peak and there can be seen only a thin structure in the low temperature peak. This is related to an incorrect application of the third level method. Cluster methods occupy an intermediate position between the fragment and the pair methods of description of surface structure. Due to differences in the coverages of the sites characterized by the same adsorption energy (Fig. 3), one obtains an almost exact solution. Such methods of introducing sites of competing types permits one to solve successively for the correct distribution of adsorbed molecules on a heterogeneous surface [202] while simultaneously retaining the small dimensions of the system of type (45). (For curve 3 this dimension is 3 and for curve 1, it is 8.) Thus, the structure of a heterogeneous surface influences significantly the form of the TDC and the number of TDC peaks. This number increases for chemisorption systems (repulsions between neighbouring molecules) and decreases for physical adsorption (attractions between neighbouring molecules). These circumstances must be taken into account in the interpretation of experimental data. The results obtained agree with Monte Carlo calculations [206,207]; the nature of splitting as well as the form of TDC are dependent in typical ways on the surface heterogeneity and on the lateral interactions. In the case where t=2, similar changes are observed in the number of TDC peaks, depending on the sign of the parameter describing the interactions between adsorbed molecules. Note also that in ref. [208], the nature of the TDC splitting is determined only by the term resulting from site heterogeneity (t=2), and the lateral interactions influence the peak width and the position of minima without changing the nature of the splitting. This is related to the application of the disordered approximation while including the lateral interactions because that approximation does not include correlation effects [147]. 5.4. P e a k s p l i t t i n g . Including lateral interactions increases the number of TDC peaks and requires very precise definition of the conditions for full splitting [127]. The problems which appear in the investigation of the conditions for full splitting will be analyzed for the special case of surfaces consisting of two types of sites (t+2). In this case AQ* denotes a value of AQ for which (at AQ < AQ*) corresponds to n = l (lack of splitting) and at AQ > AQ* n/2 (instead of n=2 because of the absence of interactions). Such a definition involves the possibility of the appearance of a "thin" TDC structure. In order to explain this definition, we will consider the TDC for the disordered surface shown in Fig. 49. The increase of AQ (curves 1-3) or the repulsion parameter modulus [ e [(curve 4-6) leads to successive n values equal to 2, 1 4. The splitting conditions are determined by the presence of local minima in the TDC curve because when these minima are not deep, there appears a plateau (curve 3) or weakly outlined (curve 4). Note also that on a homogenous surface in the absence of interactions, only one TDC peak will appear, whereas in the presence of interactions, two peaks may appear which under certain circumstances can be considered as jagged structures. For a homogenous surface the appearance of a local minimum on a TDC curve depends on the e and e* parameters. The values of ~ such that, f o r [ e [<[ ~ [, the local minimum in the TDC curve is absent, are given in Table 3. The importance of the value of e* is also related to the fact that, at e* = e, no splitting occurs (for any e values) on a homogenous surface.
262
dO dT 0.5
3 ..._/_//'kTP~
2
0.1
~'~
~
~"
J'~-~
# #
I
300
I
~th
I
500
700 T,K
Figure 49. TDS for a chaotic surface, t = 2, fl = 0.5, Q1 = 126, E1 = 139 kJ/mol, eqp = e, e~p = e/2, zl = z2 = 4, b = 50 K/s, 00 = 0.99, Q2 = Q1 - AQ. The values of e and AQ are given by the same numbering scheme as that used in Fig. 51 [205].
6 2
3
,
4
6
,.
0
-8.7
-7.3
-6.3
'5.1
0.25
-8.8
-7.4
-6.5
-5.4
-7.0
-5.8
0.50 .
0.75
-8.9 .
.
12.3
-7.7 .
- 10.2
.
.
-8.9
-7. I
Table 3. The condition of complete splitting of TDS on homogeneous surfaces [205]. T h e dependences of AQ*(fl/e) presented in Fig. 50 permit one to determine the effect of surface composition on the conditions for full splitting. These regularities are different for different surface structures. On a regular surface, m u t u a l localization of the sites and repulsions between molecules "occur" in the same direction. This permits one to determine the contribution of molecules desorbed from the sites of different types, decreasing in
263 AQ*, kJ/mol 50.4
33.6
15.8
!
0.5 fl Figure 50. The condition of the complete splitting of TDS curves for various compositions of a regular (curves 1-5), chaotic (6,7), and patch-wise (8,9) surfaces with e =-4.2 (4,6,8)-2.1 (3), 0 (1), 4.2 (5,7,9) kJ/mol [205]. '
'
~
this way the magnitude of AQ*. Increased attractions favours the appearance of vicinal molecules on sites of different types, which leads to an increase of AQ*. Such a trend is observed for any fx value. A more complex dependence for AQ*(fx/e > 0) is connected with competitive contributions to Eq originating from other pairs (11 and 22) of vicinal sites when fl values deviate in both directions from 0.5. On patchwise surfaces, the increase of attractions (repulsions) between admolecules causes ~Q* to decrease (increase). The different nature of the contributions to a TDC curve originating from different sites localized on regular and pathwise surfaces should be emphasized. In the first case the individual peaks are related to isolated sites of different type. (We assume that F1 = 0.5 and d12 = 1.) In the second case, the peaks are related to the contributions of individual macroregions. In the latter case the attractions between molecules favour appearance of admolecules in the "homogenous" phase, and show more precisely the differences between individual macroregions. The repulsion (less than g) promotes the transfer of molecules between vicinal macrophases and causes the suppression of TDC peaks. On a disordered surface, the effect of interactions between admolecules on the full splitting is slight. This, however, does not mean that the form of the TDC is due to the absence of interactions.
264 A more impressive picture of the AQ*(e/fl = 0.5) dependence is presented in Fig. 51. For other ]'1 values these interrelations, are qualitatively similar. With a common effect of surface heterogeneity and of lateral interactions, the splitting of the TDC depends on AQ, e, and on e*. All curves presented in Fig. 51 start from e* typical for a homogenous surface (AQ = 0). Fig. 51 shows the function ?.(ED) (ED is the activation energy of desorption on homogenous surface) at e* = e/2. The simplest situation is realized on regular surfaces: AQ* values increase monotonically with the increase of e, although the form of this function can change slightly depending on fl. In region A there are always two peaks, and in region B, one. 168 I
AQ, kJ/mol
33.6
-
64 I
I
Ed, k J / m o l
-2.1
-6.3
a
^
e
4-/]~"
b
"
A
" 16.8
I
A
~
.
A
B
I
-8.4
7.
"1~
0.0
IB B
" I
~
-8.4
6 C
3.
0.0
7 I
111
I
l
-8.4
l
I
0.0
I
e, kJ/mol Figure 51. The dependence of AQ* upon e on regular (part a), chaotic (b), and patch-wise (c) surfaces. In the region A, the TDS have two peaks. There is one peak in region B (splitting is absent). The coordinates of the points correspond to the parameters AQ and e for the TDS curves in Figs. 49 and 52 with the same numbers. The inset shows the dependence of ~ upon ED [205]. On patchwise and disordered surfaces the situation is more complex. In the case of a patchwise surface, the AQ* curve consists of three easily distinguishable fragments. For a disordered surface, these fragments are less distinguishable. For low AQ, the TDC curves differ slightly from those observed for a homogenous surface in that they have two peaks connected with lateral interactions. The increase of AQ leads to a decrease of the TDC splitting (curves 1 and 2, Fig. 49 and 52). At weak repulsions between admolecules the splitting of the TDC is determined only by surface heterogeneity (curves 5 and 6 in Fig. 49, curve 6 in Fig. 52). In region B there is one maximum (curves 2 and 4, Fig. 49, curves 2 and 5, Fig. 52); such curves are described usually by continous distribution functions defined for different surface compositions. While considering only the form of these functions, it is difficult to draw any conclusion about the number of different surface sites. The analysis presented here shows that for a representative description of the spectrum, a knowledge of the two types of sites assumed in order to estimate the lateral interactions between admolecules is sufficient.
265
d._.~O 0.5
dT
4
0.1-
,
300
I
500
I
I
700
T,K Figure 52. TDS curves for a patch-wise surface. The parameters axe as for Figs. 49 and 51 [205]. Further increase of AQ or c (transition from curve 2 to curve 3 in Figs. 49 and 52, as well as from curve 5 to curve 4 in Fig. 52), leads again to splitting in the TDC. This is connected with the cooperative contribution of both factors (surface heterogeneity and lateral interactions). At the same time these curves can contain typical (or "suspicious") fragments: a "shoulder" plateau (curve 3, Fig. 49) or a high temperature "tail" (curve 3-5, Fig. 52). The TDC curves can have a jagged structure with three or four peaks (curves 7 in Fig. 49 and 52). The true nature of these peaks becomes clear by analysis of the contributions of individual components to the whole stream of desorbed molecules [203]. In particular the differences in the maximum number of peaks on patchwise and on disordered surfaces may be explained by the equilibrium distribution of molecules between sites with strong and weak bonds. [203] 5.5. Effect of m o r e d i s t a n t n e i g h b o u r s . Figs. 53 and 54 show the effect of the second neighbours on the TDC for the case of attractive and repulsive interactions betweeen the adsorbed atoms. In both cases, increasing second neighbour repulsions (curves 3, 5, 9, 11, Fig. 53, curves 4,5 Fig. 54) leads to a shift of the initial part of the spectrum toward lower temperature (plus an increase of the width of the TDC) and to decreases of the peak maxima. At the same time, for disordered surfaces characterized by c(1) < 0, the low temperature (fourth) peak is more distinct (curve 9, Fig. 53). An increasing second neighbour attraction leads to a decrease of the width of the TDC, and to an increase of the spectral maxima. The effects due to
266
dO
12
~Y 1.6 5
61
8
2 11 ~ "
4
3
400
5
1
~
600
400
600
T,K Figure 53. The influence of second neighbor interactions upon the TDS for patch-wise (1-6) and chaotic (7-12) surfaces with a = 1, t = 2, fl = 0.5, zl = z2 = 4, b = 50 K/s, 00 = 0.99, g D~ = 1012 s - l , Q1 -- 113.4, Q2 = 84, E, = 126 kJ/mol, eqp(r) = e(r), e;~(r) = e(r)/2, e(1) = -8.4 (curves 1,3,4,7,9,10) and 8.4 (2,5,6,8,11,12) kJ/mol, 7 = e(2)/e(1) = -1/3 (4,5,10,11), 0 (1,2,7,8), and 1/3 (3,6,9,12)[209]. interactions with second neighbors were considered at I" ~ I< 1/3 where 7 = e(2)/e(1). This is probably reliable for most real adsorption systems, because with increasing distance, the interactions between adsorbed atoms decrease. It was concluded that the nature of the TDC splitting is determined mainly by the interactions with nearest neighbours, but the positions and heights of the maxima depend strongly on the interactions with the second neighbors. Similar conclusions concerning the role of the second neighbours were drawn earlier for homogenous surfaces [168]. Nevertheless situations are also possible where interactions with second neighbors lead to some qualitative changes such as change of number of peaks for values of the parameters close to those obtained for the abovementioned conditions for full TDC splitting [127]. In Fig. 54 one can see the TDC curves corresponding to different values of the Br<nsted-Temkin parameter (curve 7 and 8) which characterizes the changes of the activation energy of desorption from different types of sites due to the changes of their bond energy. If a < 1, the changes in the desorption activation energy are smaller than the changes of bond energy. In other words the lower a, the lower the difference between this and the activation energy of the strongest sites. That means an increase of the activation energy in comparison to the case where a = 1 which leads to a shift of the whole spectrum toward higher temperatures. The other conclusions
267
dO dT
6
_
3 7\
1 300
600
Figure 54. The influence of second neighbor interactions upon the TDS shape for the ordered surface with fl = 0.5, e(1)= -8.4 (curves 1,3,4,7) and 8.4 (2,5,6,8) kJ/mol, a = 0.5 (1-6) and 1.0 (7,8); 7 = -1/3 (1,2,7,8) and 1/3 (4,6). The rest of the parameters as in Fig. 53 [209]. about the role of the composition and structure of the surface, as well as of the lateral interactions and heating rate, remain practically unchanged. The effect of the anisotropy of the interaction with first neighbours and of the averaged desorption due to interactions with second and more distant neighbours were obtained using a simpler version of the R approximation according to which ~qp = ~=R1+1 zq(r)dQp(r)eqp(r)/zq(1). Here R1 is the distance at which the interactions are considered in the quasi-chemical approximation ; parameter used during the exchange of %p(r) for e;p(r)0 shown (the same form has a e~p in Fig. 55. Such an analysis was made for real surface structure in the description of experimental data obtained for K on Si(100)-2xl [210-212]. The heterogeneity of the surface of the Si(100)-2xl edge is caused by the dimerization of surface silicon atoms. This leads to the formation of two types of sites shifted with respect to the other by a lattice semiperiod [210]: fl = f2 = 0.5 m11(1) = m22(1) = 2, m21(1) = 4 (cluster distribution functions); zx (1) = z2(1) = 6. The variations of the energetic parameters were considered for curve 1. The parameters of this curve were selected on the basis of experimental TDC curves
268
dO
1.4
d-T
1
2
3
0
620
860
T,K
Figure 55. The influence of the anisotropy of the parameters of the nearest neighbor interactions (curves 2-5) and of the averaged description of distant neighbors upon the shape of the TDS. The parameters with respect to those of curve 1 are :2 - e~l(1) = -6.3, 3 - e22(1) = -12.4, 4 el2(1) --4.2, 5 - el2(1) - - -8.4, 6 - (eqp - f.qp) 0.7, 7 - (~qp - f.qp) 2.3 (all energies in kJ/mol) [209]. -~
:
obtained for different starting coverages [211] (Fig. 56): K ~ = 1013c-1, Qo = 0.99 b = 7 . 7 K / c E 1 = 2 3 5 , 2 E 2 = 2 0 7 . 9 , A Q = 2 7 . 3 e 1 1 ( 1 ) = - 1 0 . 5 e~1(1)=8.4 e~2(1)= e2~(1) = -12.6, e~2(1) = e;,(1) = -6.4 e22(1) = ei~(1) = 0, e ; p - eqp = 4.6 for all qp pairs (all quantities are expressed in kJ/mole). The increase of repulsions between adsorbed atoms localized in the series consisting of the sites characterized by a strong bond (Fig. 55, curves 1 and 2) at az~ = const, where aqv = e;p(1)/eqp(1) leads to a significant increase of the width of the high temperature part of the TDC, and to a slightly less significant increase of the width of the whole spectrum. However, the minimum separating the low and high temperature parts of the spectrum decreases. An increase of the repulsion between atoms localized in the series of sites characterized by a weak bond (Fig. 55, curves 1 and 3) at a22 = const does not cause any changes in the high temperature part, and leads to spliting and expansion of the low temperature part of the spectrum. Increasing repulsion between adsorbed atoms localized on vicinal sites (Fig. 55, curves 1 and 4) at Dd12 = const increases the total width of the central part of the spectrum. One can assume that each eqp(1) parameter is "responsible" for its TDC part. The role
269 of these parameters is determined by the nature of the surface s t r u c t u r e - in this case all contributions are mutually commensurate. In general, the variation of each parameter of the anisotropic interaction %p(1) and r causes a partial change of the TDC curve analogous to the changes caused by these parameters in the case of a homogenous surface [147]. The effect of the averaged interactions of more distant neighbours is illustrated by the curves 1,6,7 in Fig. 55. An increase of their contribution broadens the TDC uniformly but does not introduce qualitative changes. This results from the specificity of the molecular field approximation [47]. Its use on a homogenous surface does not lead to spectrum splitting, although increasing the repulsions does cause anincrease of the TDC width. dO dT
I
!
I \
I
!
\ / /
J 3 ~, / /
2,,/
I
350
I
I
550
I
]
750
950
T Figure 56. The TDS curves for K on Si(100)(2xl). Broken lines are experimental data [211] for 00= 0.03 (curve 1), 0.11 (2), 0.32 (3), 0.56 (4), 0.90 (5) and 0.99 (6)[209]. Fig. 56 illustrates the correlation of experimental and calculated TDC curves. A model taking account of the anisotropy of interactions between atoms localized on vicinal sites and the averaged description of the interactions between more distant neighbours gives satisfactory correlations. It reflects the basic relations between the curves near their maxima and minima, the width of individual parts of whole spectrum, and the presence of an inflection at 0o ~ 0.5. The discussion of the "resistant" curve 1 was not included in this paper because of the necessity of verification of other hypotheses: the possibility of a change
270 in the aggregate state of the potassium atoms as well as the possibility of reconstructing the silica surface owing to adsorption of potassium atoms. Neverthless, the parameter analysis permits one to conclude that an adsorption model for K on (100)Si-2xl [212] that accounts for the existence of the anisotropic structure with one type of site is not realistic because it does not permit one to arrive even at a qualitatively correct description of the experimental data [211].
5.6. Dissociative adsorption. Dissociative adsorption occurs on two vicinal surface sites. To describe the rates of the elementary processes in the absence of lateral interactions on homogenous surfaces, one must pass from the distribution function fq (or f(x) ) to fqp (or f(x,y) ), functions characterized by larger dimensions. The same increase of dimensions of the distribution functions is necessary for description of this process on heterogeneous surfaces when lateral interactions are taken into account. (This increase takes place in the first level model). In the cluster description of surface structure, the f(q{m}R) with one central site of q-type and R coordination shell corresponding to this site, is replaced by the f(qpAR) function with two central sites and R coordination shells [44,45]. In the same way the binary Probable location of hydrogen on Ir (110)-(1 x2)
dO
rx
dT
1/ ~ , \
b
a
(
)
(
~
[
,'ll
,'1_,1
,,'1,7
,t
,'/ /
',~/I ." I ./ 200
il
-;'/I
.Y" J
~"~
300
z
-,\/
l
Z"
!
Top view
,Y
'\
~~.
)
~'~
.
400
Sideview
I
500 T,K
Figure 57. (Part a) Probable locations of hydrogen on Ir(110)2x1. Part (b) shows the TDS curves for H2on Ir(l10)2xl, calculated for various initial coverages [214]. Broken lines- experiment [213] with e0 = 0.99 (curve 1), 0.86 (2), 0.61 (3), 0.48 (4), 0.37 (5), 0.27 (6), 0.068 (7). It was assumed that adsorption occurs at position B (sites with strong adsorption bonding) and C (sites with weak bonding).
271 distribution function fqp(r) is replaced by the triplet distribution function fqpo(rl,r2) in which rl and r2 are the distances between vicinal r/ site and central q and p sites respectively. These distribution functions were discussed in detail in [40,44,45,49]. An example of an application of the theory taking into account both lateral interactions and surface heterogeneity is the TDC for D 2 o n ( l l O ) I r - 2xl described in [213]. The elementary lattice consists of three sites (Fig.57a) - one "strong" (site of the first type) and two "weak" (sites of the second and third type t=3). The sites of the third type are separated only formally for an easier presentation of the distribution function - the molecular characteristics of these sites are the same as those of the sites of second type. The lateral interactions considered are limited to three coordination shells R = 3, and the related distribution functions have the form: fq = 1/3 Zq(r) = 4 li < q < t 1 < r < R [214]. In order to present kqAA, a multiplicative representation is used, leading to the conclusion that e;p(r) = e;q(r) and %p(r) = %q(r). It implies also equality of the parameters relating to the sites of the second and third type. All parameters related to lateral interactions for the third coordination shell are assumed to be zero except e11(3) and e~1(3). Calculations show that the presence of these two factors is sufficient to produce first order desorption for the high temperature peak (see below). Fig.57 shows a comparison of the experimental and the calculated TDC curves. The calculations were done using"/~AA = 2-10~3c -~, E~ A = 71,4; AQ = 17,2 k J / m o l {ElA - E A } / A Q = 0.8 [fA/KIA = 0.07, en(1) = e22(1) = e23(1) = --2.1 e12(1) = --0.2 e12(2) = e 2 2 ( 2 ) - - - 0.2 e11(3) = 2.1 a11(1) = 0.9, a23(1) = 0.4 a~2(1) = 0.5; eqv(r) and %p(r) not mentioned here are equal to zero (all energy quantities are expressed in kJ/mol), hap(r) = eqp(r)/%p(r) q,p = 1,2,3). The curves presented in Fig. 57 show reasonable agreement. This model describes all the pecularities of the experimental TDC: splittings of the peaks, correlations between the intensities and the areas under the low and high temperature maxima, the nature of the peak shifts along the temperature axis due to increases of the initial coverage, the broadening of the low temperature maxima, etc. The features related to the splittings of the peaks are determined by the heterogeneity of the (ll0)Ir 2xl edge and the features of the self peak are determined by the lateral interactions. Among these features one should emphasize a typical broadening of the low temperature peak as well as the fact that the position of the low temperature maximum is shifted to lower temperature (second order desorption) whereas the position of the maximum of the high temperature peak does not change (first order desorption). This suggests the existence of strong repulsions between adsorbed atoms locahzed on "weak" sites and a strong attraction between atoms on "strong" sites. Because the "strong" sites are isolated, the attraction between adsorbed atoms localized on these sites involves only the third coordination shell. This also follows from the value used for the interaction parameter e11(3). (For this reason the contributions Q1(3)and e~1(3)) are separated.)It should be noted that the authors reporting the experimental TDC (Ibbotson, Wittring and Weinberg [213]) have commented upon these curiosities of peak evolution and have drawn some conclusions about the the lateral interactions. However, they have not arrived at any quantitative estimates.
5.7. Continuous distribution functions. With many types of sites localized on a heterogeneous surface, macroscopic rates of adsorption processes can be studied using continuous distribution fonctions instead of
272 discrete ones. In this case, when estimating the lateral interactions it is reasonable to limit consideration to models of the third level. Fig.58 shows the effect of lateral interactions on the form of the TDC for non-dissociating adsorbed molecules on surfaces with different distribution functions f ( x ) . Increasng repulsion causes an increase of the spectral width and a decrease of the maximum height. At the same time, the maximum shifts toward lower temperatures. The degree of such changes depends on the ratio of the number of the sites characterized by higher (~ x2) and lower (.-- zl) adsorption energies. In the case of an exponential distribution function, the shift of position of the maximum for 7 > 0 is significantly less than for 7 < 0. Lateral interactions also can change the form of the TDC. Uniform and Gaussian distribution functions give, in the absence of lateral interactions, different forms for the TDC, but including repulsive interactions between the adsorbed molecules leads to TDC curves which become similar (curve 3). 0.29
0.33
dO
dO
dT
T 220 0.50 -
3 89
4 89
5 89
T
C
420 0.70
520
620
d
de
dO dT
dT
220
320
420
520
T
2:20
320
420
520
Figure 58. The influence of repulsion between nearest neighbor admolecules on the shape of TDS for heterogeneous surfaces with chaotic distribution of sites; f ( z ) - uniform (a), symmetric Gaussian (b), exponential 7 = 1/3 (c) and 7 = -1/3 (d); e = 0 (1),-0.5 (2) and-1.0 (3) kcal/mol [214]. Continuous distribution functions were used to describe the desorption of C O molecules from the surface of polycrystalline Rh [215]. The authors of [215] concluded that their TDC curves are similar to those determind for (111) and (11) edges [216,217]. The analysis of these experimental data has shown [218] that the process of desorption from the polycrystalline surface is not an exact superposition of desorption processes from the above-mentioned edges. The results of the description of the TDC curves for individual edges and for the whole polycrystalline surface are presented in Fig. 59. Among the most important factors taken into account in that study was the complete saturation of all
273 surfaces considered in the experimental work. The questionable point is that traditional TDC analysis deals usually with the position of peak maxima and the magnitude of the activation energy of the process connected with Tp, as well as with the estimation of the desorption order. In a given experimental system, the spectra cannot be described by assuming an ideally homogenous surface (either with or without consideration of lateral interactions) because of the broadening of both ends of the peak, especially at low temperature. For this reason, it was even necessary to use continuous distribution functions to describe the desorption from the (111) edge. DTC curves determined for both edges are characterized by commensurable intervals of different desorption activation energies. Continous Gaussian distribution functions were employed also for the C O / M g O system in a Monte Carlo study. The resulting TDC for this system is shown in Fig. 60. The spectra [219] consist of two peaks. The differences in the surface composition are due to different temperatures of sample treatment (500 and 1000~ The data deduced from isotope e-
d_o
a
dO dT
dT 1 II
300
!
!
400
500
i
T,K
300
400
500T, K
dO c
, ,/ , , / , ,/, 300
400
500
600 T,K
Figure 59. TDS curves for CO/Rh [218], solid lines - calculation, broken lines - experiment. (Part a) (110) face with z = 4, b = 50 K/s, Q = 31.2 kcal/mol, g ~ = 1013 s -1, c o o - c o = -2.5 kcal/mol, ebo_c o = O.14eco-co; 80 = 0.50 (curve 1), 0.24 (2), 0.15 (3), 0.07 (4), 0.02 (5); characteristics of the heterogeneous face with f ( x ) = exp(0.bx), Ax = 8 kcal/mol. (Part b) (111) face: z = 6, b = 5.7 K/s, Q = 32.5 kcal/mol, K~ = 4.1013 s -1, e c o - c o = -2.5 kcal/mol, ebo_co = O.14eco-co; 80 = 0.46 (curve 1), 0.36 (2), 0.18 (3), 0.09 (4), 0.046 (5); f ( z ) = exp(0.1z), Ax = 3 kcal/mol. (Part c) A polycrystal described as a superposition of (110) (fraction 0.2) and (111) face (fraction 0.77), and an additional peak with energy Q = 38 kcal/mol with the same values of the parameters: K~9 = 4- 1013 s -1, e c o - c o = -1.8 kcal/mol, e~o_co = 0.25eco-co; 8o = 0.47 (curve 1), 0.36 (2), 0.22 (3), 0.15 (4), 0.09 (5), 0.055 (6).
274 xchange of C 13 have shown that adsorbed molecules localized on strong and weak sites do not exchange one for other (essentially no migration on the surface). The Monte Carlo calculations reproduce totally the experimental TDC curves. For the samples treated at 500~ the amount of strong sites is 95% and for those treated at 1000~ - 33%. a
Q 350
450
550
T, K I
350
450
550
T,K
Figure 60. Experimental data (dots) and model predictions (lines) for the desorption of CO from MgO outgassed at 500~ (part a) and 1000~ (part b) [207]. The mean adsorption energies for a gaussian site distribution are E1 = 42, E2 = 25.5 kcal/mol, g D~ = 9-109 s-1, g D~ = 1014 s -1 for both surfaces. The energy dispersion for each kind of site is (part a) al = 2.19 and a2 = 1.48 kcal/mol (500~ and (part b) a~ = 1.1 and a2 = 1 kcal/mol (1000~ While considering the application of continuous distribution functions we should mention the simplified description of the kinetics of adsorption taking lateral interactions into account. If one introduces certain assumptions about the molecular properties, it becomes possible to arrive at relatively simple analytical expressions for the rates of elementary processes containing both evaluated factors. We will limit here ourselves to the application of the molecular field approximation and will consider disordered surfaces, assuming simultanously that the lateral interactions do not depend on the type of sites on which adsorbed molecules are localized. As a result of such simplifications, expressions are obtained [43] assuming that the contributions are additive. For example, the formula which is used for the rate of adsorption of non-dissociating molecules is written in the form UA = UA(r = O) exp[~ze*Q], where UA(e 0) is the adsorption rate for an ideal adsorption system described by equation (17) in which al,~ coefficients must be replaced by a12(0) = aoezp[~(xl,2 + z E/9]. In the same way, the form of the adsorption isotherms (18) changes [220]. The expressions for the adsorption rates are also simplified. Neverthless, discussion is given in ref. [43,40] of the combined effect of surface heterogeneity and of lateral interactions. =
6. C O N C L U S I O N S . An advanced theory taking into account both the heterogeneity of the surface and the lateral interactions between adsorbed molecules allows one to define the basic physical factors characterizing adsorption and creates possibilities of either a quantitative description of the kinetics of adsorption or of a molecular interpretation of some selected parameters.
275 Estimation of thermal desorption spectra on the basis of molecular models allows one to explain numerous rules of TDC i.e the nature of the peak splitting, the characteristics of peak shifts due to change of surface properties, the change of the desorption order with change of starting coverage etc. Computer studies increase the possibilities of modelling real processes in comparison to traditional theoretical methods and make it possible to clarify the basic physical factors of the system under study. Cluster methods have permitted the development of theory that reflects numerous factors of adsorption systems. Complex investigations of molecular models taking into account molecular properties of real adsorption systems are still at an initial stage. This review concerns only the kinetics of adsorption of individual components. Adsorption kinetics of a mixture of molecules on a heterogeneous surface taking account of lateral interactions will also be elaborated and presented in a future paper. Reconstruction of the surface and the effect of mobility of adsorbed molecules on the kinetics of surface processes will also be considered. Detailed studies of the role of all of these factors will allow a better understanding of the nature of these processes as well as eventually producing their quantitative description.
Acknowledgement Athour wishes to thank Profs. W.Rudzinski, W.A.Steele and V.A.Bakaev for preparing the English version of this manuscript.
276 References
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W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
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S T A T I S T I C A L R A T E T H E O R Y AND THE M A T E R I A L P R O P E R T I E S C O N T R O L L I N G ADSORPTION KINETICS ON W E L L DEFINED SURFACES J. A. W. Elliott and C. A. Ward Thermodynamics and Kinetics Laboratory, Department of Mechanical Engineering, University of Toronto, Toronto, Canada M5S 1A4 The Statistical Rate Theory approach for predicting the rate of kinetic processes is developed from a simple quantum mechanical model of an isolated, thermodynamic system. The approach leads to an interpretation of irreversibility and to an expression for the rate in terms of molecular or material properties of the system. The approach is evaluated by examining the predictions that follow when it is used to examine two types of reactions: those occurring homogeneously within one phase and those occurring when a non-dissociating, diatomic molecule is exposed to a single crystal solid surface with one type of binding site. In the former case, the material properties are known. In the latter, it is found that they can be evaluated from the equilibrium adsorption isotherms. As evidenced by the agreement between the predictions and the measurements for both types of reactions, the expression for the rates obtained from Statistical Rate Theory appears to contain explicitly the concentration dependence. The adsorption kinetics of CO-Ni(111) was the adsorption system considered. When examined with Absolute Rate Theory, it was found that, as with many other systems, agreement between the measurements and the predictions can not be obtained without introducing coverage dependence into the rate expression through the introduction of the concepts of precursor states and of a coverage dependent sticking probability. Since the Statistical Rate Theory expression for the adsorption kinetics does not require the concepts of precursor states or of sticking probability, but nonetheless leads to the correct prediction of the adsorption kinetics for this system, it is not clear that these concepts need to be introduced. Other systems need to be examined before this possibility could be established.
1.0. I N T R O D U C T I O N When a molecule coming from a gas phase adsorbs on a solid surface, it must both cross a gas phase boundary and form an adsorptive bond as it encounters the solid. Similarly, when a molecule desorbs it must break the adsorptive bond and enter the gas phase. Although the collision of the molecule with the solid surface can be treated using either classical or quantum mechanics, the prediction of the rate of bond formation or breaking requires a quantum mechanical description because this process fundamentally involves the probability of the event occurring. The theoretical approach with which we are primarily concerned, Statistical Rate Theory, is based on quantum mechanics and thermodynamics. This theory was originally proposed in 1977 [1] and was published in its more current form in 1982 [2] and 1983 [3]. It has been used to predict the rate of gas absorption at liquid-gas interfaces. Its application to that rate process essentially involved predicting the net rate at which gas molecules cross the liquid-gas interface - a process similar to the first step in the adsorption process. The expression obtained for this rate from Statistical Rate Theory allowed certain anomalies in the reported values of diffusion coefficients in liquids ("surface resistance") to be resolved [4-6]. In the case of gas
286 absorption at a liquid-gas interface, it was found that the complete rate expression could be predicted in terms of molecular and material properties. The formation or breaking of a bond between an adsorbed molecule and the solid surface is the more complex of the processes involved in adsorption-resorption kinetics. It is basically a chemical reaction, and the Statistical Rate Theory approach leads to the definition of a new material property, the cross-section for adsorptive bond formation, that describes both the formation and breaking of bonds. Herein, we concentrate on the application of Statistical Rate Theory to the bond breaking or formation process, and examine the predictions that follow from this theory by using it to examine two types of "chemical reactions". The first chemical reaction considered is an electron transfer reaction between ionic isotopes dissolved in electrolytes. This case was chosen because it allows us to examine the theory in a form that requires a minimum of assumptions. The derivation of the rate equations makes use of the concept of an isolated system. Since there is no heat of reaction associated with electron transfer reactions between isotopes in solution, such intensive properties as temperature and pressure are constant throughout the reaction period, and since the reaction is between isotopes in solution, the activity coefficients do not appear in the expression for the reaction rate. These characteristics of electron transfer reactions between isotopes also lead to the value of the equilibrium constant being unity. As a result of these simplifications, the rate equation does not contain any unknown material properties; thus when the theory is compared with the data, a very "pure" form of the theory can be examined because questions associated with evaluating the material properties do not arise. Further, the structure of the equations is simple enough that some of the fundamental assumptions of Statistical Rate Theory can be examined quantitatively. In the next case that we consider, Statistical Rate Theory is applied to predict the net rate of molecular adsorption on single crystal solid surfaces. The evaluation of the material properties appearing in the rate equations is a major consideration. A second consideration is that the adsorption and desorption processes are assumed to take place isothermally. This requires that the interaction of the system with a reservoir be taken into account so that the total system considered is again an isolated system. A new approach for taking the interaction with the reservoir into account is presented. The final equations that are obtained from the Statistical Rate Theory approach for predicting the net rate of adsorption are the same as those that have been used in the past [7-10], but one major assumption is removed from their derivation. The objective of the Statistical Rate Theory approach is to obtain the explicit coverage dependence of the rate expression so that the other properties appearing in the rate expression are molecular and material properties. If successful, this approach would lead to a very different conceptual circumstance than has been obtained from the Absolute Rate Theory approach [11, 12]. It has been found by comparing the predictions from Absolute Rate Theory with experiments that the explicit coverage dependence is not obtained from the theory. This has led to the introduction of the concept of precursor states [ 13]. Precursor states are then used with either the Absolute Rate Theory approach [ 14] or with the sticking probability concept [ 15, 16] to try to derive the correct coverage dependence. As a first step toward determining whether it is necessary to introduce the concept of precursor states, we apply Statistical Rate Theory to a system that others [17] have reported not to be described by Absolute Rate Theory without empirically introducing coverage dependence. The system chosen for this purpose was CO-Ni(111). In addition to it having been studied using Absolute Rate Theory, the system has been subjected to other experimental studies and these can be used to establish the material properties for this system.
287
1.1. Absolute rate theory approach to adsorption kinetics The traditional theory used to describe adsorption kinetics is Absolute Rate Theory which is based on a particular model of reactions. This model involves an activated state [11, 12] and is based on the premise that in order for a molecule to take part in a reaction, it must first form an "activated complex". This requires the input of energy, the activation energy. It is assumed that molecules in the activated state are in equilibrium with the reactants and the rate of formation of products is calculated as the rate at which molecules pass through the activated state. The assumption of equilibrium between the reactants and molecules in the activated state and is one of the fundamental assumptions of Absolute Rate Theory, and its validity has never been established. In the case of the adsorption process, the reactant is a gas phase molecule which is approaching a vacant site on the surface. In order to become adsorbed, the molecule passes through a transition state which will be located approximately at the position of maximum potential energy on the path to adsorption. The activation energy for adsorption, E a, is thus defined as the energy difference between a free gas molecule and the molecule in the transition or activated state. Clark [12] has clearly described the development of the Absolute Rate Theory expression for the adsorption rate: (1.1,1) where k is the Boltzmann constant and T is the temperature; v is the rate of molecular collision with the surface; f(O) defines the fraction of surface that is available for adsorption and is, as yet, an unspecified function of the relative coverage, O" exp]-Ea/kT[ is the fraction of molecules striking the surface that have an energy greater J the product v f (.-0 ) e x p[- E a / k T ] is. the .rate at which molecules with thanL.E a . Thus sufficient energy to adsorb strike vacant adsorptxon sites, and a , the "condensation coefficient" [ 12], is the fraction of those meeting this condition that actually adsorb. The collision frequency, v, is given by v
P = ~ 42mnkT
(1.1,2)
where P is the pressure in the gas and m is the mass of an individual gas molecule. The relative coverage, 0, is expressed as a fraction of the coverage at saturation, i.e., ~= N a
Mr
(1.1,3)
where N ais the number of adsorbed molecules and M T is the total number of adsorption sites. For desorption, it is assumed that the desorbing molecule must pass through an activated state before desorbing. The activation energy for desorption, E d, is defined as the difference in energy between the adsorbed molecules and the activated state for desorption (see Fig. 1.1, 1), and it is assumed that the adsorbed molecules are in equilibrium with the molecules in the activated state. The expression for the desorption rate that is provided by Absolute Rate Theory is [12] (1.1,4)
288 where K d is called the desorption rate coefficient and g(0)is another unspecified function of coverage.
m
--
transition or activated state
I--4
r O
initial state (adsorbed molecules)
Figure 1.1, 1. Schematic diagram showing the activation energy for desorption. The allowable energy levels are shown for the initial and transition states. The activation energy is shown as the difference between the ground state energy level for the adsorbed molecule and the ground state energy level for a molecule in the transition state. A similar diagram can be drawn to represent E a. It should be noted that Absolute Rate Theory does not lead to an expression in terms of coverage for the condensation coefficient a , for the activation energy E,,, for the desorption energy E a, or for the desorption rate constant Kd . Nor is there any reason from Absolute Rate Theory to think that these are material properties and thus independent of coverage. Many experimental studies have indicated that additional coverage dependence must be added to the Absolute Rate Theory equations. This has affected the conceptual understanding of adsorption kinetics and has led to the introduction of concepts such as precursor states that may not be necessary.
1.2. Incorporation of the coverage dependence in the absolute rate theory equations through the assumed existence of precursor states Several approaches have been used to formulate the additional coverage dependence that is required by the Absolute Rate Theory equations. One approach has been empirically based. Coverage dependence is simply introduced into the activation energies and into the pre-exponential factors as required to obtain agreement between the measurements and the predictions. Another has been to introduce the concept of precursor states. For example, Gorte and Schmidt [ 14] used the idea of precursor states to derive an additional coverage dependence in the desorption term. They assumed that adsorption was a two step process in which the molecules first formed a weakly bound precursor phase before proceeding to the adsorbed phase. By evaluating desorption rates in a regime where the number of molecules in the precursor phase was constant (valid when the number of molecules in the precursor phase is small), and assuming desorption to the precursor phase to be the rate determining step (i.e., the rate from the precursor state to the gas is rapid), Gorte and Schmidt found that the desorption rate could be written
289
Jdes = k*kd ka
0 _
(1.2, 1)
1-0
where k*, k a and k a are the rate coefficients for the transition from the activated state to the gas, the adsorbed state to the activated state and the activated state to the adsorbed state respectively. The rate coefficients are made up of exponentials of activation energies and rate constants for the different processes. Gorte and Schmidt note that the only evidence for the existence of the precursor is that the above equation better describes experimental desorption results. It will be shown below that Statistical Rate Theory leads to the factor 0/(1 - 0) in the expression for the desorption rate, but it is obtained from the theoretical approach without introducing the concept of precursor states.
1.3. Precursor states and the sticking probability Kisliuk [15] also applied the idea of precursor states to derive a coverage dependent equation for the sticking probability which he defines as the number of molecules being adsorbed divided by the number of molecules striking the surface. Kisliuk considers the probabilities for a molecule to arrive at the surface above either an empty or filled adsorption site. There is then some probability for the molecule to be adsorbed on an empty site, evaporate from a filled site or move on to a second site. Kisliuk assumes that the probability of the second site being filled is independent of the state of the first site. The sticking probability is then found as the sum of the probabilities of adsorption on the first, second, third, etc. sites that are visited by a molecule. The equation Kisliuk found
was _ s so
1+
/ -1 K
(1.3, 1)
1- 0
where s is the sticking probability, s o is the sticking probability at the initial time, and K is yet a new parameter. Note that if K equals unity, the sticking probability reduces to the Langrnuirian form s=s0(1-0 )
(1.3,2)
King [ 16] considered two types of precursor states: one type was proposed to exist over a filled adsorption site and the other type over an unfilled site. He obtained a more complicated expression for the sticking probability that contains parameters that can depend on coverage. There is no way to derive the coverage dependence of these parameters from the theory he proposed [13, 16].
2.0. P R O B A B I L I T Y O F A T R A N S I T I O N IN " P A R T I C L E " C O N F I G U R A T I O N IN AN I S O L A T E D S Y S T E M In the Statistical Rate Theory approach, the expression for the rate of a particular process is obtained from the transition probability concept as defined in quantum mechanics. It has been previously shown that the expression for the transition probability of interest can be obtained from a first-order perturbation analysis of the Schr6dinger equation [2, 3]. In this section, we show that the same expression for the transition probability can be obtained from a simple physical argument.
290 To implement the Statistical Rate Theory approach, our first objective is to predict the rate of "particle" (molecule, atom or electron) transitions in an isolated system. The problem can be stated in terms of the particle configuration, A,j, that exists in the system at a particular instant. The particle configuration of the system defines the number of particles of each species that are present in each phase of the system and their spatial distribution within each phase. The phases considered may be either bulk or surface. Herein, we shall restrict our attention to cases where the spatial gradients within each phase are negligible. Consider an isolated system at an instant in time during its evolution toward equilibrium. Suppose that the particle distribution is known at that time and the system energy at that instant is known to within the energy uncertainty. Although the possible quantum mechanical energy levels for the system are degenerate, they are discrete in an isolated system. The restriction that the system energy lies within the energy uncertainty effectively limits the range of quantum mechanical states to which the system has access. This is indicated schematically in Fig. 2.0, 1, where the number of quantum mechanical states available when the system is in a particular particle distribution A,j is denoted as ~(~,j).
Quantum States of Z~
Quantum States of Z i
Quantum States of 2k
E
K[au,a,] J_ ~ f~( Z~)
] K[au,av] f~( Z j)
f~( Zk )
Figure 2.0, 1. Schematic of quantum mechanical states within a thermodynamically isolated system. The particle distribution within the system at an instant is indicated by ~,j. The virtual distributions that would result from the transfer of a single particle are ini:licated by ~'k and A,i, the total number of states corresponding to a given distribution by f~(&), the rates of transfer from one quantum mechanical state to another by K [ a u, a v ] and K[ a u, a t ], and the energy uncertainty by AE. We now introduce the probability, K[a(A,i)u,a(Ak)v], of a transition at any instant between the quantum mechanical state a ( 2 i ) u of the distribution A,i and the quantum mechanical state a(A k)v of the distribution ~,k where both quantum ~echanical states lie within the energy uncertainty. The numbers of states corresponding to these two particle distributions are denoted as f~(A,j) and s k); thus 1 < u < f~(~,j)
(2.0, 1)
and 1 < v < ~(&k)
(2.0, 2)
291 We now make two assumptions. The first is that if at any instant the quantum mechanical state of an isolated system is measured and its energy is found to be within the uncertainty AE, the system is as likely to be found in any one state as in any other. Except for its context, this assumption is not very different from one of the standard assumptions of statistical mechanics that was originally introduced by Gibbs in his study of equilibrium statistical mechanics [18]. When the assumption introduced by Gibbs was proposed, it was based on a system description that used classical mechanics. However, its equivalent has become a standard part of statistical mechanics that is based on quantum mechanics. It should be noted that we make this assumption for a system that is not in thermodynamic equilibrium. The second assumption, and one that is central to Statistical Rate Theory, is based on the probability of a transition between any two quantum mechanical states that lie within the energy uncertainty of the isolated system, but are associated with different particle distributions. We assume this rate, denoted as K[oc(Xj)u,O~(A,k)v],has the same value for all combinations of quantum mechanical states within the energy uncertainty range of the isolated system. We take its value to be Ke:
K[ot(A,r)u, Ot(Zs)vl = Ke for 1 _
(2.0, 3)
and for 1 < v < f2(& k)
We now wish to use these two assumptions to determine the expression for the probability of a transition from one particle configuration to another, independently of the system being in any particular quantum state initially. Suppose it is known that at any particular instant the particle distribution in an isolated system is ~,j and that the number of states within the energy uncertainty is f~(A,j), then the probability of measuring the state of the system and finding that it is in any of these states is (f2(A,j)) -1 . The product of this probability with the probability of the system making a transition from one quantum mechanical state to another would be the probability, P(&j; Otu), of a system making a transition from the particle distribution ~j tO any quantum mechanical state a u that lies within the energy uncertainty but corresponds to a different particle distribution. =
(2.0, 4)
However, of more interest to us is the probability of a transition from particle distribution ~,j to particle distribution A,k at any instant, z(2j, Ak), and since there are s k) quantum mechanical states corresponding to Ak, this latter transition probability would be given by
292 At the same instant that there is a probability of a transition from ~, to ~k there is a probability of a particle transition in the opposite sense, say to &i, (see Fxg. 2.0, 1) and the probability for a transition to this latter configuration from /],j would be given by
~(/]'i)
"c(/],j,/],i)= K e ~(/]'J)
(2.0,6)
Before examining these expressions for the transition probability, we note that if the Boltzmann definition of entropy is adopted:
S(l],r)
= k ln(f~(~,r))
(2.0, 7)
then the transition probability may be written in terms of entropy. For example, Eq. (2.0, 5) gives
Z()~j,2k ) = Ke exp[ S(,~,k) -k S()~j) ]
(2.0, 8)
We would emphasize that the expression for the transition probability given in Eq. (2.0, 8) is the same expression for this transition probability that was obtained from a first-order perturbation analysis of the Schr6dinger equation [2, 3]. Thus, in summary, the two assumptions listed above appear to be consistent with the more fundamental approach through the Schr6dinger equation. The expression for the net rate of particle transitions may now be obtained. We suppose that the transition from A,j to/t, k involves the transfer of one particle and that the transition probability z(A,i,A, k) is approximately constant as the result of the transfer of a single particle. The validity of the latter part of this assumption will be investigated below. The number of particles transferred is equal to the number of times this transition and if z(A,j,A, k) is the probability of this transition taking place at a takes place, 8Nik, J particular instant, then the nurhber of times the transition takes place in the interval d~t is given by
~Njk = ~(A,j, &k )St
(2.0, 9)
and the uni-directional reaction rate is given by
J(~j,/]'k ) = qd(Aj, ~k)
(2.0, 10)
When the system is initially in particle distributionAj, the probability of a particle transition occurring that leads to the system making a transition to A,k is given by Eq. (2.0, 8); however at the same instant, there is a probability of a particle making a transition in the opposite sense. This would cause the particle distribution in the system to be, say, ~,i- Considerations similar to those above lead to
T(i~j,t~i) = Ke exp[S(Xi)-k S(A,j) ]
(2.0, 11)
Since at any instant, there is a probability for the system to move in either the positive sense ( Aj to A,k) or in the negative sense ( A,j to Zi), the most probable transition when the system is in particle distribution Zj is to the particle distribution corresponding to a
293 large number of states._If this rate is denoted as J, then it would be the difference between J(~i,Ak) and J(~i,,~i). From Eqs. (2.0, 5) and (2.0, 6), the most probable, net rate at which'the system pro6eeds in the forward direction from A,j may be written
J(Zj) = n(K~~i[n(,~k)- n(Z~)]
(2.0, 12)
Or
J(Aj) = Ke(exp[ S(Ak) -k S(J~j) ]-exp[S(Ai ) -k S(Aj) ])
(2.0, 13)
To apply this relation to a particular kinetic process, it is necessary to obtain the expression for the change in entropy associated with that particular process. We shall consider two kinetic processes in subsequent sections.
2.0.1. The increase in entropy of an isolated, nonequilibrium system From Statistical Rate Theory, one obtains a simple interpretation of the "irreversible" increase in entropy that occurs in an isolated system as it evolves toward thermodynamic equilibrium. The Boltzmann definition of entropy has been adopted. This algebraic relation alone does not lead to the prediction of an increase in the entropy of an isolated system as the system evolves to equilibrium. It does, however, define thermodynamic equilibrium as corresponding to the condition existing in the system when the number of states is maximized. From Eq. (2.0, 12), it follows that if the system is in particle distribution 2i and if f~(~,k) > f~(A,i), then the most probable evolution is to particle distribution 2k .~At any instant then, the most probable transition is to the particle distribution corresponding to the larger number of states. Since this direction is also the direction of increasing entropy, the most probable evolution of the system is one corresponding to an increasing entropy. This increase would continue until the system reached a particle distribution where the number of states in either direction from the existing distribution was the same. This condition would correspond to a maximum in the number of states. In other words, if the equilibrium particle distribution is denoted as ~eq and those on either side as ~t and ~u, then the fact that ~"~(,)t,eq ) was a maximum would mean:
~(Zt) = ~'2(~eq ) = ~"~(Zu)
(2.0.1, 1)
Thus, from Eq. (2.0, 12) it may be seen that the most probable, net rate at equilibrium, J(/~eq), vanishes, but from Eqs. (2.0, 5), (2.0, 6) and (2.0, 10), it may also be seen that the uni-dxrectional rates reduce to Ke:
J(,~,eq,Zt) = J(Zeq,,~u)= K e
(2.0.1, 2)
This latter relation provides an interpretation of the rate of exchange between quantum mechanical states. Under conditions of thermodynamic equilibrium, it is the exchange rate between particle distributions. For example, for a reaction occurring homogeneously within one phase, as will be seen, it is the equilibrium exchange rate for the reaction. For the case of adsorption kinetics (i.e., particle exchange between two phases), it is the equilibrium exchange rate between the adsorbed phase and the gas phase. It should be noted that the expression for the rate given in Eq. (2.0, 13) is conceptually different than that obtained from either Absolute Rate Theory or the sticking probability approach. For example, in Statistical Rate Theory the view is taken that under equilibrium conditions in an isolated system a kinetic process takes place in either the
294 forward or negative direction at the same rate, K e. Then if that system is displaced from equilibrium in either direction and released, at any instant, there is a probability of the system moving in the equilibrium direction, and at the same instant, a probability of the system moving in the direction away from equilibrium. The rate in either direction is the equilibrium exchange rate multiplied by an amplification factor. As indicated in Eq. (2.0, 12), if ~(&i) is the number of states available to the system when it is in the displaced particle cofifiguration and f2(/~k) is the number of virtual states in the particle configuration closer to equilibrium, then the amplification factor for the rate in the direction of equilibrium is ~(&k)/f~(A,i). Similarly, one finds the amplification for the rate away from equilibrium is given by" f ~ ( & i ) / ~ ( s Thus if f2(~ k) > f2(~,i), then the rate in the direction of equilibrium is augmented relative to the equilibrium exchange rate, whereas the rate away from equilibrium is reduced relative to the equilibrium exchange rate. The most probable, net rate is then the difference between these two rates. Thus there is only one equation for predicting the net rate of the process. For example, when this procedure is applied to adsorption kinetics, the same equation is used to predict the net rate at which particles are transferred from the gas phase to the surface (adsorption) or the net rate at which particles are transferred from the surface to the gas phase (desorption). Only the values of the physical properties appearing in the rate equation will be needed to predict the net rate in either direction. Both Absolute Rate Theory and the sticking probability approach treat these process as two separate processes. There are, for example, different values of the pre-exponential factor and of the activation energy for adsorption and for desorption in the Absolute Rate Theory approach (see below). 2.1. Expression for the rate of an electron transfer reaction We now propose to obtain the expression for the rate of the electron transfer reaction between ionic isotopes in solution. Such a reaction may be expressed:
(2.1, 1)
a+b--+c+d
where components a and b are isotopes with charges of n + and (n + 1)+, respectively; c is an isotope of b, and has the same charge as b and d is an isotope of a and has the same charge as a. Physically, the reaction represents the transfer of a particle (an electron) from one component to another in a homogeneous, single phase, isolated system. There is no heat of reaction or expansion associated with the production of the products of this reaction; thus the temperature and pressure would be constant. The other intensive properties, the chemical potentials, are also approximately constant as a result of transferring a single electron, except in the limit where there are no products present and the transfer of one electron gives rise to the presence of the first product particles. This limit will be considered as a special case. We first consider the case where there is a sufficient amount of the product present so that the creation of an additional single particle of each product type is negligible compared to the amount of product already present. In this limit then, all of the intensive properties are unchanged as a result of one electron transfer. However, the transfer of one electron does give rise to a change in total entropy as may be seen from calculating the entropy change. The Euler relation for the entropy may be written l (2.1,2) where T and P are the temperature and pressure and #n is the chemical potential of component-n; U and V are the internal energy and the system volume, and N n is the
295 total number of particles of component-n. There are a total of l components present 9The particle distribution %j may now be stated explicitly.
Xj. Na,Nb,Nc,N d
(2.1,3)
There may be other species present but their numbers do not change when the reaction takes place and are thus not listed. The particle distribution that results from the transfer of an electron so that the reaction proceeds in the positive direction, i.e., from the distribution ~,j to the distribution Zk is Zk:N a -
1,N b - 1,N c + 1,N d + 1
(2.1, 4)
The change in entropy may be calculated from Eq. (2.1, 2). One finds 1
S(Zk ) - S(Zj ) = T(Zj ) [#a (Zj ) + #b (J~j ) - #c (J~j ) -- ~d(J~j )]
(2.1, 5)
It should be noted that the change in entropy between the particle distributions %, and Xk has been expressed only in terms of the chemical potentials evaluated whex4 the particle distribution is Xj. That is, the change in entropy is a function of the instantaneous particle distribution /~,. This result is obtained because of our assumption that the intensive propemes do not change as a result of one particle making a transition. It will have important consequences when the expression for the rate of the reaction is obtained. From Eqs. (2.0, 8) and (2.1, 5), one finds that the probability of a transition from ~,j to A,k at any instant is given by 9
,
"((Xj,Zk)
,
= K e
.]
exp[ ~ a ( z j ) + ldb(Xj)- l l c ( Z j ) - /2d(~,j)]
kT
(2.1,6)
Note then that the transition probability depends only on the intensive properties and the exchange rate between quantum mechanical states, K e. Thus, the assumption that the intensive properties do not change as a result of one electron transfer is consistent with the assumption of the transition probability being constant over an interval of time St. These assumptions will be discussed further after explicit expressions for the chemical potentials are available. From Eq. (2.0, 10), one finds that the uni-directional rate, J(2,j,)~ k), is given by J ( A j , X k ) = K e exp[#a(Aj) + ~2b (J~j ) -- # c ( Z j ) -- #d(J~j ) ]
(2.1, 7)
kT
At the same instant that there is a probability of the reaction proceeding from ~j. tO ~'k, i.e., in the positive direction, there is a probability of an electron transfer occumng that would cause the reaction to move in the negative direction. This would mean that the particle distribution would move from A,j to ~,i where
Xi:Na + l, Nb + l, Nc - l, Nd - 1
(2.1,8)
The corresponding change in the entropy may be obtained from Eq. (2.1, 2):
S(Zi) - S(Zj )
1
- -
T(Xj'j [-12a(Aj ) - #b(Xj ) + l.tc(Xj ) + btd(Xj )]
(2.1,9)
296 The probability of a transition from /~j to A,i is given by
v(~,j,X i) = K e exp[
--/aa(~j ) --/ab(~j)-F/ac(~j ) +/ad (~j) ] kT
(2.1, 10)
and the uni-directional rate in the negative sense is given by
J(~j,~i)=Keexp[ -/aa(')t'J)-/ab(ZJ)T/ac(ZJ)W/ad('~l'J)]
(2.1, 11)
kT
From Eqs. (2.1, 7) and (2.1, 11), one finds as the expression for the net, most probable rate
J(~j) = 2Kesinh[/aa(&j) + /ab('~'J)- /ac(~J)- /ad(~J)] kT
(2.1
12)
Note that the expression for the net rate at an instant only depends on the intensive properties evaluated when the instantaneous particle distribution is A,j and the constant K e. This is a reflection of the fact that the entropy change between A,j and Xk or ~i only depended on the value of the intensive properties evaluated in this condition and the constant K e. 2.2. The expression for the rate of an electron transfer reaction To examine the Statistical Rate Theory expression for the rate of a chemical reaction, the expression for the rate will be used to predict the concentration as a function of time for electron transfer reactions between isotopes dissolved in electrolytes and then these predictions will be compared with experimental results [3]. It is necessary to treat the solutions as nonideal. The chemical potentials must be written in terms of activity coefficients, rather than the concentrations, and we shall take the chemical potentials to be of the following form [19]:
/aa = /a~ (T,P) + kTln(r,~x~)
(2.2, 1)
where /a~ (T,P) is the reference chemical potential of component a , x a is the mole fraction of this component, and Ya is its activity coefficient. When the system is in thermodynamic equilibrium, the intensive properties must satisfy certain relations. These relations may be obtained by requiring that the entropy of the system be a maximum. As a necessary condition for equilibrium, one finds
#a ( ~eq ) +/ab(l'~eq)= llc ( ~eq ) + ~ld( ~,eq)
(2.2, 2)
and if the equilibrium constant, E c, is defined as
E c = exp(/aa~ + lab~ -/ac~ --/ad~ kT
(2.2, 3)
then as may be seen from Eq. (2.2, 1), it is only a function of temperature and pressure. If the reaction defined in Eq. (2.1, 1) has reached equilibrium, then Eq. (2.2, 2) must be valid and after inserting the expressions for the chemical potential of each of the
297 reacting components into Eq. (2.2, 2), one finds that the result may be written in terms of the equilibrium constant Ec: (2.2, 4)
Xa~'aXb~b eq where the subscript eq indicates that the term in the brackets is to be evaluated at equilibrium. After introducing the expression for the chemical potential given in Eq. (2.2, 1) and the expression for E c, one finds from Eq. (2.1, 5) that the change in entropy as a result of a single particle transition is given by S ( ~ k) - S(&j) = k In[ Ecxayaxb 7b ] x~7r
(2.2, 5)
and from Eq. (2.1, 12) that the net rate may be expressed
j = ge[Ecxa~taXb'Yb XcTcXd~td
Xc~tcXd~td ] Ecxa~'aXb ~'b
(2.2, 6)
Since components a and d are ions of the same charge, but are isotopes, the ratio of their activity coefficients is unity. The same would be true of the activity coefficients of components b and c, and hence 7a 7b = 1
(2.2, 7)
?'cTd
Also, the ratio of mole fractions may be written in terms of numbers of particles, since (2.2, 8)
XaX~b = NaNb
X~Xd N~Nd The number of reactant particles present in the system, after the reaction defined in Eq. (2.1, 1) is initiated, may be expressed in terms of the reaction variable r N a = N a (0)- r
o~ = a,b
(2.2, 9)
where N a (0) is the initial number of particles present. For the products, one may write N o = NI3(O ) + r
fl = c,d
(2.2, 10)
To establish the equilibrium constant for the reaction, we again take advantage of the fact that components a and d are isotopes of the same charge, as are b and c. If equal quantifies of components a and b were added initially, then under equilibrium conditions it would be expected that there would be equal concentrations of component a and its isotope, component d. Since the system we are considering is spatially homogeneous,
(Na)eq "- (Nd)eq
(2.2, 11)
298 A similar argument could be applied to components b and c:
(2.2, 12)
(Nb)eq = (Nc)eq
As may be seen from Eqs. (2.2, 4), (2.2, 7) and (2.2, 8), if these conditions are to be met, then Ec = 1
(2.2, 13)
Since the value of the equilibrium constant has been found in this special case to be unity and since its value is independent of the initial concentration, as may be seen from Eq. (2.2, 4), its value may be taken as unity for all initial concentrations. After making use of Eqs. (2.2, 7) to (2.2, 10) and Eq. (2.2, 13), one finds from Eq. (2.2, 5) that the expression for the change in entropy between particle distributions Xj and A.k is given by
S(~, k) - S ( Z j ) = k
r)(Nb(O)-~]
ln[(Na (0) -(~cc(O) + r ) ( N d ( O ) +
(2.2, 14)
where the initial concentration of each species is indicated by Na(0). From Eq. (2.2, 6), one finds that the expression for the net rate of the reaction may be expressed j = K e [ ( N a ( O ) - r)(Nb(O ) - r) (0) + r)(Nd(O ) + r)
(Nc(O)+ r)(Na(O)+ r) ] (Na(O) - r)(Nb(O ) - r)
(2.2, 15)
The latter equation is the one we shall use to make predictions of the concentration for particular electron transfer reactions that can be compared with a set of measurements. We would emphasize that two important simplifications have resulted from the fact that the reaction being considered is an electron transfer reaction between ionic isotopes in solution: 1) Since the reactants and products are isotopes, the activity coefficients do not appear in the final expression for the rate. No assumption was made regarding the value of the activity coefficients. Their elimination occurred because of the system being considered. 2) The expression for the rate has been written in terms of the number of particles of each component that would be present in the system at any instant in time and the equilibrium exchange rate for the reaction, K e. For the isolated system that we consider, once the initial concentrations of the components are given, K e is fixed, although its value is unknown. The predicted expression for the net rate of the reaction can be examined by determining if there is one value of K e that leads to a prediction of the concentration that is in agreement with the measured concentration as a function of time. Equation (2.2, 15) gives the expression for the rate in terms of the reaction variable r. As may be seen from this relation, if the initial concentrations of the products are both zero, the initial rate of the reaction is predicted to be infinite. This infinity results from the breakdown under these conditions of the assumption that there is no change in the chemical potentials of the products as a result of the reaction going one step in the forward direction. Under these conditions, the creation of the first products gives rise to a significant change in the value of the chemical potential of the products.
2.2.1. Expression for the net rate at very short times In preparation for calculating the rate in the very initial period, we first develop the expression for the initial rate without assuming that the chemical potentials are unchanged
299 as a result of one electron being transferred. The particle distribution, &(r), existing in the system at any time may be represented in terms of the reaction variable r and the initial amount of the reactants: (2.2.1, 1)
~(r):(Na(O ) - r),(Nb(O ) - r),r,r
The distribution that would exist after the reaction had proceeded one step in the forward direction would be (2.2.1, 2)
X ( r + 1):(Na ( 0 ) - r - 1),(Nb (0) - r - 1),r + 1,r + 1
The change in entropy may be calculated from Eq. (2.1, 2). If it is assumed that the reaction variable r and the initial concentrations are measured in moles, then one finds that S(r + No-l) - S(r) = Na (O)ln[
( N a ( O ) - r) (ga(O) - r - No-l)
(Nb(O) - r)
]+ gb(0)ln[ ( N b ( O ) - r - N o
+ r l n [ ( N a ( O ) - r - No - 1 ) ( N b (0) - r - No -1) r2 ( N a ( O ) _ r)(Nb(O) _ (r + No -1 )2
+No_ 1ln[(ga (0) - r - No-1)(Nb(O) - r - No-l)] (r + No-1) 2
-1)
]
r) ] (2.2.1,3)
where N O is Avogadro's number. For macroscopic systems, one may suppose that N a , N b >> No -1
(2.2.1, 4)
The change in entropy that results from the first electron transfer is obtained from Eq. (2.2.1, 3) by imposing the condition given in Eq. (2.2.1, 4) and then taking the limit of r going to zero: S(No -1) - S(0) = kln[ Na(O)Nb(O)) ] No -2
(2.2.1, 5)
In this limit then, there would be no product present and the initial rate may then be calculated by combining Eqs. (2.0, 8) and (2.0, 10) and inserting Eq. (2.2.1, 5). One finds J(O) = Ke(No2Na(O)Nb(O))
(2.2.1, 6)
In the latter relation, the term in parentheses is the product of the initial number of particles of the reactants. The rate J(0) is measured in the same units as the equilibrium exchange rate. Unless the equilibrium exchange rate is very small or the initial number of particles of the reactants is very small, the initial rate may be very large, but not infinite. The initial rate will be considered in specific cases in the following section.
2.3. Experimental examination of the expression for a reaction rate The electron exchange reaction between isotopes of A g has been examined experimentally by Gordon and Wahl [20]. This reaction may be written
300 *Ag + + A g 2+ --> *Ag 2+ + A g +
(2.3, 1)
When it took place in a 5.87 f H C I O 4 solution, measurements of *Ag2+/(*Ag2+)ea at different times were reported [20] and are shown in Fig. 2.3, 1. There wei'e no prod[acts initially present. The theoretical prediction of these measurements may be made from Eq. (2.2, 15) after it has been integrated. We first write the equation in terms of the reaction variable, r. dr = K e [ ( N a ( O ) d--7
r)(Nb(O ) - r) r2
r2 - ( N a ( O ) - r ) ( N b ( O ) - r) ]
(2.3, 2)
After separating the variables in Eq. (2.3, 2), integrating and applying as the initial condition that r(0) is equal to zero, one finds
get =
cl r
7.2
4C~
4C 2
C~ arc tanh[ ~ -~~l + C~ arc tanh[ C2~4 ] - 4r
8~4
8~4
Ca log(-Cs) _ G l o g ( G ) _ G 2 l o g ( - G + G r ) 2C23 16 2C23
-t C2 l~
- C2r + 2r2 ) 16
(2.3, 3)
where the values of C i are determined from the initial concentrations of the reactants: C 1 = (Na(0) 2 + Nb(0) 2) C2 = (Na(O)+ Nb(O)) C3 = (Na(O)- Nb(O))
(2.3, 4)
C4 = Na(0) 2 -6Na(O)Nb(O)+ Nb(0) 2 C5 = N a (O)Nb (0)
If the theoretical expression for the rate is correct, then there should be one value of K e that gives a prediction of the concentration of each of the products throughout the period of the reaction. To determine the value of K e that gives the best description of the
data, we shall take as the definition of the error, E, between the n-measurements and the corresponding calculations: n
E 2 = ~ [ t c ( r i ) - tm(ri)] 2 i=1
(2.3, 5)
where tc(ri) is the calculated time at which the reaction variable has the value r i and t m (r i) is the time at which the reaction variable is measured to have the same value of r i. Equation (2.3, 3) may be viewed as being of the following form: Ket = F(r)
(2.3, 6)
301 The time at which the concentration of the products is predicted to reach a value of r i may be obtained from this latter relation. After this expression is substituted into the expression for the error, one finds
F(ri) E 2 = ~[ - tm(ri)] 2 i=1 ge
(2.3, 7)
After differentiating the latter relation with respect to K e and requiring the result to vanish, one finds that the value of K e that minimizes the error, t is given by
~[F(ri)] 2 ge = i=1
(2.3, 8)
~ F ( r i ) t i ( r i) i=1 It should be noted that the minimization of the error has been based on minimizing the difference between the predicted time and the measured time at which a particular concentration is reached. By proceeding in this fashion, we avoid the necessity of inverting the function F(ri).
0.8 0
@ 0
0.6 = @ .m I.
=
0
0.4
=
o
0.2 .
.
.
.
i
0.2
.
.
.
.
i
9
0.4
I
0.6
.
9
0.8
9
i
1
Time, s Figure 2.3, 1. Comparison of the predicted and measured concentration ratio *Ag2+/(*Ag2+)eq for the reaction indicated in Eq. (2.3, 1) taking place in a 5.87 f HCIO 4 solution at 0.2~ with initial concentrations of *Ag § and Ag 2+ of 0.01062 f, and 0.00179409 f and no product initially present. The solid dots indicate the reported measurements of Gordon and Wahl [20] and the solid line the predicted concentration ratio made from Eq. (2.3, 3) for the stated initial concentrations. t In Ref. [3] the expression for K e was incorrectly given, but the calculations were made on the basis of the correct equations.
302 Equation (2.3, 8) and the reported experimental results may be used to calculate the value of K e. One finds
K e =0.00017044 f
(2.3,9)
S
A necessary condition for the theory to be valid is that when this value of K e is used in Eq. (2.3, 3), the prediction of the product concentrations be in agreement with the data. The predictions for one set of experiments are shown as the solid line in Fig. 2.3, 1. The data are said to be accurate to approximately 10%. Thus, as may be seen by comparing the experimental data and the predictions, there is no measured disagreement between the theory and experimental results for this case. In Ref. [20], data are given for the electron exchange reaction between isotopic ions Statistical Rate Theory has been used to of Ag at a different temperature (11.4~ predict the results at this second temperature. The close agreement between the theory and experiment was found at the second temperature as well. Further, Statistical Rate Theory has been applied to examine the electron transfer reactions:
*Mn042- + MnO4- .__>*Mn04- + Mn042-
(2.3, 10)
and *V 2+ -!-V 3+ --->* V 3+ + V 2+
(2.3, 11)
using data reported by Sheppard and Wahl [21] and by Krishnamurty and Wahl [22].
~"*0"8 o
0.6
.~
0.4
'~
0.2
r." .
.
.
.
.
5
-
-
i
i
i
10
.
.
.
.
9
i
i
15
20
25
30
Time, s
Figure 2.3, 2. Comparison of the predicted and measured concentration ratio Mn04-/( MnO4-)ea for the reacuon indicated m Eq. (2.3, 10) taking place m a 0.16 f NaOH solution at 0.1~ with initial concentrations of the reactants of 0.00004 f, and 0.000096 f. The solid dots indicate the reported measurements of Sheppard and Wahl [21 ] and the solid line the predicted concentration ratio made from Eq. (2.3, 3) for the stated initial concentrations.
303 Only in the case of the manganate-permanganate reaction is there any indication of disagreement between the theoretical predictions and the experimental results. A comparison for this case is shown in Fig. 2.3, 2. It is not clear that this difference is significant. For the other five systems for which there is data available, there is no indication of any disagreement between the experiments and the predictions made from Statistical Rate Theory [3]. 2.3.1. Examination of the predicted initial rate If the approximation is made that the chemical potentials are unchanged as the result of one electron transfer taking place, one obtains the expression for the rate shown in Eq. (2.2, 15). As may be seen from this equation if the initial concentration of the products is zero, Eq. (2.2, 15) indicates that the initial rate will be infinite. However, when the initial rate was calculated without introducing this approximation on the chemical potentials, the expression for the rate is that given in (Eq. 2.2.1, 6) and as may be seen from this latter equation, it indicates a finite rate initially when no product particles are present. From the information obtained in the previous section, we may calculate the initial rate from Eqs. (2.2.1, 6) and (2.3, 9). One finds J(0) = 1.7806 x 1038f
(2.3.1, 1)
S
Thus when the initial concentration of the products is zero the initial rate is finite, but very large. Assuming the rate stays constant over the initial period, the period of time required for the reaction variable, r, to become large compared to one t~article can be estimated. For r to be a thousand particles would require only 10-`59s. Thus the approximation that the chemical potentials do not change as the result of the reaction going one step in the forward direction appears to be valid except at the very shortest times. Using the prediction of the number of product and reactant particles as a function of time that is obtained from Statistical Rate Theory, the entropy as a function of time may be predicted. From Eq. (2.1, 2), one finds that the difference between the entropy at the time t and its initial value is given by
Na(O) ) + N b ( 0 ) l n ( N b ( O ) ) S(t)- S(O)= k[Na(O) ln( Na(O)-------~r Nb(O)-------~r +rln((Na(O)- r)(Nb(O ) - r) r2
)]
(2.3.1, 2)
If one uses Eq. (2.3.1, 2) and the predicted expression for the reaction variable given in Eq. (2.3, 3), then for the electron transfer reaction between Ag ions, one finds the entropy as a function of time that is shown in Fig. 2.3.1, 1. It should be noted that although there is a rapid initial rate of entropy increase, the entropy is predicted to remain finite at all times, unlike the reaction rate. The predicted and measured rates of the reaction are shown in Fig. 2.3, 1. In this case then, the prediction is that the most probable path from the initial nonequilibrium particle distribution to the equilibrium particle distribution corresponds to an increase in entropy at every instant of time for the isolated system.
304
0.005 o r~
i
0.004
r~
0.003 0.002 -
0.001
0.25
0.5
0.75
1 ]]me, s
1.25
1.5
1.75
Figure 2.3.1, 1. Predicted entropy change as a function of time resulting from the reaction *Ag + + Ag 2+ --->*Ag 2+ + Ag + taking.~lace in a 5.87 f HCIO 4 solution at 0.2~ with initial concentrations of *Ag + and A g z of 0.01062 f and 0.00179409 f and no product initially present.
2.4. Predicted rate at the initial time from absolute rate theory and statistical rate theory As may be seen in Fig. (2.4, 1), for the electron transfer reaction between isotopic ions of Ag in solution, Statistical Rate Theory leads to the prediction of a very large rate initially and also to a rate that approaches zero as the system approaches equilibrium. Although there is an approximation at short times, the approximation appears valid except for the very shortest times. The prediction of both of these rates is one of the characteristics of Statistical Rate Theory, and one that distinguishes it from previous theories. When Absolute Rate Theory is applied to electron transfer reactions, the concentration dependence of the rate expression is obtained from an interpretation of the relation for the equilibrium constant. As seen in the previous section, for the type of system that we consider, the equilibrium constant may be expressed
c~ca
Ec = [ CaCb ]eq
(2.4, 1)
where C i is the concentration of component-i. The Absolute Rate Theory interpretation of this latter relation strongly involves the assumption of equilibrium between the activated complexes and the reactants and leads to the denominator being interpreted as the concentration factor driving the reaction forward and the numerator being the concentration factor driving the system in the reverse direction [11, 12]. Thus, the expression for the forward rate of the reaction, according to Absolute Rate Theory, is
305
&(AbRr) =
(2.4, 2)
klc c
and similarly the reverse rate is assumed to be of the following form
Jr(abRT) = krCcCd
(2.4, 3)
Under equilibrium conditions, Jf.(abRT) and Jr(AbRT) must be equal and since the equilibrium constant, E c , is unity, it follows that the rate constants must be equal. Thus, the net rate, according to Absolute Rate Theory [ 11 ], is
J(AbRT) = kAbRT(CaCb -CcCd)
~
0.8
(2.4, 4)
S tatis ti cal Rate T heor y
0.6
g
o i
0.4
;olute Rate Theory
o.2
0.2
0.4
0.6
0.8
1
Time, s
Figure 2.4, 1. Comparison of the predictions from Absolute Rate Theory and the measured concentration ratio *Ag2+~(*Ag2+)eq for the reaction indicated in Eq. (2.3, 1) that took place in a 5.87 f HCI04 solution at 0.2~ with initial concentrations of *Ag+and Ag2+of 0.01062 f and 0.00179409 f. The solid dots indicate the reported measurements of Gordon and Wahl [20] and the solid line the predicted concentration ratio made from Absolute Rate Theory for the stated initial concentrations. Note the poor agreement during the initial period. We note that the interpretation of the equilibrium constant (i.e., Eq. (2.4, 1)) used by Absolute Rate Theory is arbitrary [23]. For example, if K e is the equilibrium exchange rate, then
XetqCd Ec Cafb ]eq
(2.4,5)
306 would also be the equilibrium exchange rate. Further, if (1/Ec)[CcCd/CaC b ] were greater than unity, then it would mean that there was an excess of products compared to reactants and it would be reasonable to expect that the rate at which the reaction would proceed in the reverse direction, Jrv, would be
Ke [C~Ca Jrv--Ec CaCb]
(2.4,6)
If (1/Ec)[CcCd/CaCb] were less than unity, then there would be an excess of reactants compared to products and one would expect the reaction to be driven in the forward direction. As the driving force, one might reasonably assume that it would be the inverse of the less-than-unity (1/Ec)[CcCd/CaCb] or
CaCb
Jfd = KeEc[ CcCd ]
(2.4, 7)
Thus the net rate of the reaction would be
e~CaCb_ Ccq
Jnet = Ke[ "Ccc-~d EcCaCb]
(2.4, 8)
To compare this latter expression with the Statistical Rate Theory expression, we may rewrite Eq. (2.2, 6) that was obtained from Statistical Rate Theory. If Eqs. (2.2, 7) and (2.2, 8) are used to simplify Eq. (2.2, 6), then it is found that the latter equation reduces to
j= K~tE~UaUb_ UaU~
EcNaNb ]
(2.4,9)
which is the equivalent of Eq. (2.4, 8). From the point of view of interpreting the equilibrium constant, the Absolute Rate Theory approach does not appear to offer any advantage compared to the latter interpretation and the second one leads to the same expression as the Statistical Rate Theory approach. To see that the expression for the rate that is obtained from the Absolute Rate Theory approach leads to poor agreement between theory and experiment during the initial period, it has been applied to electron transfer reactions [3]. An example of the type of results obtained is shown in Fig. 2.4, 1 where the results predicted from Absolute Rate Theory may be compared with the measurements reported by Gordon and Wahl for an electron transfer reaction between isotopes of Ag and with the results obtained from Statistical Rate Theory. The value of kabRr was determined from an equation that was obtained in the same manner as Eq. (2.3, 8). When the results shown in Fig. 2.4, 1 are compared, one notes a much better agreement between the Statistical Rate Theory approach and the experimental results in the initial period than between the Absolute Rate Theory and the experimental results. Both theories give an adequate prediction of the experimental results when the concentration ratio is near its equilibrium value. For the electron transfer reactions, the Absolute Rate Theory approach was found to lead to poor agreement in the initial period for each of the reactions examined [3]. It can be made to agree with the measurements in the initial period, but it does not then agree with the measurements near equilibrium. The disagreement in the initial period was originally thought to arise from experimental error; however Absolute Rate Theory also gives a poor prediction of adsorption kinetics in the initial period (as will be seen in sub-
307 sequent sections) and the experimental techniques used to examine adsorption kinetics are of a different nature than those used to study electron exchange reactions. 3.0. A D S O R F H O N K I N E T I C S We shall now consider the problem of non-dissociative adsorption in a constant volume, isothermal system. This represents a change in constraints as compared to the case of the reaction occurring in a homogeneous, isolated system. Since adsorption is an exothermal process, if the system is to be isothermal, then it is necessary to include a reservoir so that the system and reservoir define the isolated system to which Statistical Rate Theory may be applied. To obtain the expression for the net rate of adsorption, we again turn to Eq. (2.0, 13), this time determining the change in entropy associated with the adsorption of one molecule within the system. The entropy changes of the reservoir, the solid, the gas phase and the adsorbed phase must be taken into account in order to derive the total entropy change of the isolated system. We assume that there are no spatial gradients in intensive properties within each phase and that the molecular transport rate between the gas and the adsorbed phase is the rate determining step in the evolution of the system to equilibrium. We will assume that the reservoir maintains the entire system at a constant temperature, T. The Euler relation for the entropy of the gas phase may be written
S g "-
U g -b
Vg -
N g
(3.0, 1)
where the superscript g on a quantity refers it to the gas phase. In the model that we will consider, the solid substrate will be approximated as having uniform properties up to a dividing surface which is located at a position such that no solid atoms are in the interphase. Thus, the Euler relation for the adsorbed phase may be written Sa = ( T ) U a -
(-~)A a - ( @ 1 N t r
(3.0, 2)
where U Cr is the internal energy of the adsorbed molecules, ?' is the solid-gas surface tension, A Cr is the area of the interphase, N a is the number of adsorbed molecules and /2 a their chemical potential. For the solid phase, we shall suppose that there is no absorption of the gas phase molecules and neglect any effects due to shearing stresses. Then the approximate Euler relation may be written Ss =
(11
Us +
Vs _
N s
(3.0, 3)
where the superscript s on a quantity refers it to the solid phase. When one molecule is transferred from the gas phase to the adsorbed phase, the molecular configuration is changed from &j to A,k where ~,j:Na(j),Ng(j)
(3.0, 4)
and )ck:N(r(j)+
1,Ng (j) - 1
(3.0, 5)
308 We assume that during this process the reservoir, denoted with the superscript R, is heated quasi-statically so that
AS R
=
Aug AUa AUS T T T
(3.0, 6)
where A is an operator that defines the following operation Atp -= tp(;l,k ) - tp(~j)
(3.0, 7)
We shall suppose that the intensive properties of each of the phases are unchanged as a result of a single molecule being adsorbed. If initially, there are no adsorbed molecules on the surface, this approximation would be invalid at the initial instant; however, it was illustrated for the reactions considered in earlier sections that the period of time for which it would be invalid is so short as to be negligible. We shall assume the same to be true for adsorption kinetics. By conSidering the volumes of the gas and of the solid phase and the area of the adsorbed phase to be constant, and by using Eqs. (3.0, 1), (3.0, 2), (3.0, 3) and (3.0, 6), one may then write the change in entropy associated with the adsorption of one molecule as
+As,, +As, + As,, -
,u g
/2 a
(3.o, 8)
And hence, using Eqs. (2.0, 8) and (2.0, 10) along with Eq. (3.0, 8), one may write the uni-directional adsorption rate in terms of the chemical potentials
](A'J)=Keexp
kT (3.0, 9)
Following similar arguments, one may derive the change in entropy of the system as the result of a single molecule desorbing, and then using Eq. (2.0, 11) the uni-directional desorption rate may be written (3.0, 10)
And thus one gets for the net rate of adsorption J(Aj) =
2Ke sinh[l'tg(zj ) - l'ttr(~'j)
(3.0, 11)
By requiring that the differential of entropy vanish for virtual displacements about the equilibrium state, one obtains the condition for equilibrium
(3.0.12)
309 If this equilibrium condition is substituted into Eqs. (3.0, 9) and (3.0, 10) then, at equilibrium
J(~eq)'-Y(~eq)=ge
(3.0, 13)
so that K e, the exchange rate between quantum mechanical states of different particle distributions, is seen to correspond physically to the equilibrium exchange rate between the gas and adsorbed phases. Note that in the derivation of the net adsorption rate above, the only assumptions made about the solid substrate were that the shear stresses could be neglected, that no gas phase molecules were absorbed and that its volume did not change as a result of the adsorption. Equation (3.0, 11) is, therefore, applicable to a number of different systems and is consistent with a change in both the internal energy and the entropy of the solid as a result of the adsorption taking place. In order to calculate the surface coverage as a function of time from Eq. (3.0, 11), explicit relations are required for the equilibrium exchange rate, K e, and the chemical potentials of both the gas and adsorbed phases,/~g and/1 ~. The equilibrium exchange rate has been formulated as [ 1, 9] (3.0, 14)
K e = VeAeff~
where v e is the equilibrium collision frequency of molecules in the gas with the solid surface, Aeff is the area available for adsorption to occur per unit surface area and ~ is the probab~ity that a molecule striking an available adsorption site will adsorb. As defined, the equilibrium exchange rate has units of molecules per time per unit surface area. The equilibrium collision frequency may be found from the Maxwellian velocity distribution to be
Pe
(3.0, 15)
v e = ~itcmkT e
where m is the molecular mass and T e and Pe are the equilibrium temperature and pressure respectively. We shall assume the solid surface is a single crystal surface that is uniform in composition and consists of only one type of bonding site, that the total number of adsorption sites per unit area is M, and that at equilibrium nee of these sites per unit area have molecules adsorbed on them. The fractional area available for adsorption under equilibrium conditions would then be given by Aeff = ( M -
(3.0, 16)
n~e ) a a
where act is the area of an individual adsorption site. The equilibrium exchange rate may now be written
"e
g e = 42'~kZe
4)Oe
(3.0, 17)
where the area of an adsorption site and the probability that a molecule striking an available site adsorbs have been combined into a new quantity, the equilibrium adsorption cross-section
310 cre = aa~
(3.0, 18)
The quantity tr e is particularly important to our considerations. The subscript e has been included to emphasize that this cross-section is an equilibrium parameter. As such, its value does not change as the kinetic process proceeds. Since the adsorption cross-section is to be evaluated only at equilibrium, it appears to be a material property. If this is true, it may be tabulated for a given, well defined (single crystal) interface that has one type of bonding site? and used to predict the adsorption rate in a completely independent circumstance. We shall investigate this possibility in subsequent sections. An approximate expression for the chemical potential of ideal diatomic, nonsymmetric molecules in the gas phase can be obtained using the Born-Oppenheimer approximation and Boltzmann statistics [24]
l.tg = kTln(P(~)
(3.0, 19)
where P is the pressure in the gas phase and ~(T) is given by
[1
l]ex l /h
=
(3.0, 20)
(2~rkT)~ 2mlm2r2 (ml + m 2)1//2 where h is Planck's constant, COg is the characteristic vibration frequency of the gas molecules, D O is the dissociation energy, r e is the separation distance of the two atoms and m 1 and m 2 are the masses of the two atoms. These molecular properties of CO have all been previously established [24] and they are listed in Table 3.0, 1.
Table 3.0, 1 Gas Phase Prop.erties of Carbon Monoxide
Ogg(Hz)
re(nm )
DO(J / molecule)
6.394 x 1013
11.28
1.464 x 10 -18
3.1. Thermostatistical formulation for the chemical potential of the adsorbed molecules Following Ref. [ 10], we shall approximate the adsorbed CO molecule as a bound, double harmonic oscillator and obtain the expression for the chemical potential of the adsorbed molecules from statistical thermodynamics. The possible energy levels of such a molecule may then be represented in terms of the six characteristic frequencies as
fIf the single crystal surface has more than one type of bonding site, then there is one value of O"e for each type of bonding site.
311
eijklmn= E~ +(i +2)h(01+(j +l)h(02+...+(n+l)h(06 i,j,k,l,m,n
(3.1, 1)
=0,1,2 ....
where E~) is the minimum potential energy of the adsorbed molecule and co1, (02 . . . . . ( 0 6 are the six characteristic vibration frequencies. The potential seen by the adsorbed molecule can be thought of as consisting of two parts; the first arising from the interaction of the adsorbed molecule with the substrate atoms, and the second arising from the interaction of the adsorbed molecule with the rest of the adsorbed molecules collectively. Since changing the number of adsorbed molecules will affect this interaction, both because newly adsorbed molecules will add to the adsorbate-adsorbate interaction and because the addition of the adsorbate may affect the relative positions of the substrate atoms, E 6 will be allowed to depend on surface coverage 0 0=~
n ~
(3.1,2)
M0
where na is the number of adsorbed molecules and M 0 is the number of surface substrate atoms, each per unit surface area. Note that although interactions with neighbouring molecules are taken into account through E 6 , the vibration frequencies are assumed not to be affected by neighbouring adsorbed molecules and the individual harmonic oscillator degrees of freedom are assumed to be independent of one another. The allowed energy levels of the collectively adsorbed molecules are then
E(II,I2 ..... /6) = NaEo +
Na ~ ( h(0i ~ /=1~ , T j +
6
i=lEIih(0i
(3.1,3)
where Ii is the number of phonons with the characteristic frequency (0i" The canonical ensemble has been chosen to treat the problem of determining the thermodynamic properties of the adsorbed gas. The ensemble consists of a collection of surfaces, each with the same number of adsorbed molecules, the same surface area and the same temperature. The canonical partition function may be written
where Ea is the energy of the system when it is in state a . To change from a summation over states to a summation over energy levels, degeneracies arising from the ways of distributing the phonons of each type over No. oscillators and the ways of distributing N a adsorbed molecules over a total of M r adsorption sites must be included. After changing the summation to include these degeneracies and performing the required summations, one finds as the expression for the partition function of the adsorbed molecules
Qa= where
MT! (qlq2...q6)NCr Na ?(Mr - Na ) ?
(3.1,5)
312
q/
--
oxp( ) i exp(h~
(3.1, 6)
and where 9
,
eoi = E~
CO
oi
hr i 2
+
(3.1, 7)
~, ooj j=l
The Helmholtz function for the system may be written (3.1, 8)
F = Fg + Fa + Fs
where F g, Fcr and F s are the Helmholtz functions of the gas phase, interphase and solid respectively. The Helmholtz function of the adsorbed molecules may be found from statistical thermodynamics [24] to be (3.1, 9)
F a = - k T l n Qcr
According to thermodynamics, the chemical potential of the adsorbed molecules is given by (3.1, 10)
O N cr A tr , T
Thus, the chemical potential of the adsorbed molecules may be written (3.1,11)
where
9 6
( h(,oi "~
a': expr ]17 ~xpt'2-ff)
t, kT )i=1e x p(ho~i ~ - - ~"~ )- 1
(3.1, 12)
and where fl' is a function of coverage that is defined as
oNcr
r
(3.1,13)
313 Note that 8'(T, 0) contains as parameters the six fundamental vibration frequencies. All of the vibration frequencies have not been resolved for most systems. The method that we describe below can be applied when any number of frequencies have been resolved. We shall suppose that only two frequencies have been resolved by electron energ3, loss spectroscopy (EELS), that they are denoted coj, (j = 1, 2) and that the unresolved frequencies are denoted toi, (i = 3 to 6). Then S' may be factored so that all of the unknown frequencies appear in one factor. From Eq. (3.1, 12), one may write ~' = ~'V
(3.1,14)
where
r
~'(T, 0) -= exp
(~-T)/I~3 =
expt 2--~) ('ho)i ~
exPt,,--k-~-J-
(3.1, 15)
and exp h ~ J / zKs) j=l
Now .u a (T, N ~
(3.1, 16)
-- 1
can be written
/Ia (T,N a) = kTln (M r _ N a ) ~ ,
Note that in order to have the expression for the chemical potential of the adsorbed molecule, all that remains is to determine the unknown function ~'(T, 0). This function plays a fundamental role in the determination of the material properties.
3.2. Determination of the material properties necessary to predict the adsorption kinetics of CO adsorbing on Ni(111) For a given gas-surface combination for which there are unresolved vibration frequencies, we now introduce a method by which ~'(T, 0) may be determined from the empirical isotherms. The system of CO-Ni(111) is one such system and we shall assume that one type of bonding dominates in this system. The vibration frequencies for CO adsorbed on Ni(111) that have been resolved using EELS [25] are thought to correspond to the stretching of the adsorptive bond and to the stretching of the C-O bond when the CO molecule is adsorbed. The values of the fundamental frequencies for these two bonds correspond to (1.23 x 1013Hz) and ( 5.57 • 10 '~ Hz) respectively. The C-O stretching frequency is known to vary slightly with coverage [25]. This will be neglected herein. By the nature of the isotherm fitting procedure, any error resulting from this assumption will be included in ~'(T, 0). The number of surface atoms per area for Ni(111), M 0, may be found from geometry to be 1.86 x 1019a t o m s / m 2 . This is for a uniform, single crystal and does not include any surface roughness. The number of adsorption sites available in
314
this system, M, is known only approximately from the maximum coverage observed. The value we will use is 0.53M 0 [26]. Christmann et al. [26] measured the work function change of the Ni surface as a function of temperature and CO pressure. The measured data can be converted to isotherms if a relationship between work function and coverage is assumed. Christmann et al. state that the work function is a linear function of coverage up to a work function value of 1.1 eV, and that above 1.1 eV, the work function may be correlated with coverage using LEED patterns. They also state that the maximum value of the work function (1.31 eV) corresponds to a coverage of 0.53. Using a linear relationship* between work function and coverage, we have converted the reported data [26] to the isotherm data shown in Fig. 3.2, 3. Now we propose to use this information to determine the function ~'(T, 0). The first step is to determine the values of this function from the measured isotherm data. This requires an analytical expression for the isotherm. Under equilibrium conditions, the chemical potential of the molecules in the gas phase must be equal to the chemical potential of the adsorbed molecules. This condition allows us to obtain an expression for the equilibrium isotherms. We first introduce n~, M 0, and 0e: the equilibrium number of adsorbed molecules, the number of substrate atoms (each per unit area) and their ratio. 0e = n~
(3.2, 1)
M0 The chemical potential for the gas phase molecules is given in Eq. (3.0, 19) and that for the adsorbed molecules in Eq. (3.1, 17). After equating the chemical potentials and using the definitions of 0 e and M 0, one finds
0e Pe = (0M --
(3.2,
where we have also introduced the number of adsorption sites per substrate atom, 0 M M
oM = . .
(3.2, 3)
/no
The analytical expression for the isotherm, Eq. (3.2, 2), plays a fundamental role in what follows. It may be inverted to obtain an expression for ~'(T, 0) =
0e
(3.2, 4)
Pe ( OM - O, ) I[I"* To obtain an empirical isotherm, values of 0e are measured for each of several different pressures. Thus from each empirical isotherm, several values of ~'(T, 0) can be calculated from Eq. (3.2, 4) and the values of the parameters listed in the upper portion of Table 3.2, 1. Next, note that without loss of generality, Eq. (3.1, 15) may be written in the form
?Another possibility for determininga relationship between work function and coverageis to make use of the shape of the work function versus coverage plot measured by Wedler et al. [27], even though the measurementwas done on a polycrystalline Ni surface. This was the method used in a previously published paper [9].
315
/ where b is a function of temperature only and /3' is a function of coverage only. Then, from Eq. (3.2, 5) one finds kTln(~') = b - / 3 '
(3.2, 6)
so that the coverage and temperature dependence have been separated and thus to obtain the function ~(T,O), it is necessary to obtain the functions b(T) and fl'(O). To obtain the values of b(T) from the empirical isotherm data, one isotherm was selected as a reference (we chose T = 370 K). The reference isotherm was arbitrarily assigned a value of b (we chose b = 0). There is no loss of generality in the choice of the reference value of b, since b wilJ only be determined to within a constant. Once these ~rej were calculated at each of 7/points on the reference choices were made, values of isotherm from Eq. (3.2, 4). To select the value of b(T i) for the i th isotherm, the following equation was used
10 ~
5
~
0
~
-5 -10
I
,~ -15 -20 .
.
.
9
300
.
.
.
.
9
325
.
.
.
.
|
350
.
.
.
.
I
.
.
375
.
.
,
400
.
.
.
.
,
425
.
.
.
.
9
450
Temperature, K
Figure 3.2, 1. The function b as determined from the isotherms obtained from the measurements of Christmann et al. [26]. The data points were obtained from the empirical isotherms by the procedure described herein. The solid line is a fourth order polynomial fit of the data points.
316 7"/i
7"/i
Z bj(zi) Z b(Zi)=j=l ~i = j=l
(3.2, 7)
17i
where ~i is the number of coverage values selected on the i th isotherm at which to compare the values of krln(() with the reference isotherm. The superscript ref on a quantity refers it to the reference isotherm and the subscript j on a quantity refers it to the jth measured point. As may be noted from Eq. (3.2, 7), the value of b(T i) selected by this procedure is an average of the values measured at the different coverages. Since the empirical isotherms determined from the measurements of Christmann et al. [26] do not all span the same coverage range, isotherms that did not have enough points with coverages in common with the reference isotherm had to be referred to an intermediate isotherm. The value of b(Tn), already determined for.the intermediate isotherm, could then be added to determine the required value of b(T ~) with respect to the reference isotherm. Equation (3.2, 7) was used for each measured isotherm to obtain a value of b(T i) at the temperature of the i th isotherm. In Fig. 3.2, 1, the values of b(T ~) obtained by this procedure are shown as data points. Since each isotherm has several measured points (at different values of coverage), there will be several values of ~'(T, 0) and hence several values of (kTln(~')-b) or fl'(O) are obtained from each isotherm. In Fig. 3.2, 2, the
1.75 [ l
'~
1.74 1.73
1.72
"r
~e, I
1.71 .7
t .
0.1
-
.
0.2
l
l
.
0.3
,
-
9
0.4
l
,
0.5
Coverage (na / Mo) Figure 3.2, 2. The function fl' as determined from the isotherms obtained from the measurements of Christmann et al. [26]. The data points were obtained from the empirical isotherms by the procedure described herein. The solid line is a fifth order polynomial fit of the data points.
317 values of fl'(0) obtained in this manner are shown as data points. All of the measured points on an isotherm yield values for fl' even though only selected points (those with coverages in common with the reference or intermediate isotherms) are used to determine the single value of b for a given isotherm. Table 3.2, 1 Properties o f C arb.on Monoxide Adsorbed on Ni(111) Property
Value
Source
Mo(atoms / m2)
1.86 • 1019
geometry
M(sites / m 2)
0.53M0
Christmann et al. [26]
tOl(HZ)
1.23 x 1013
EELS [25]
o92(Hz)
5.57•
EELS [25]
bo - Co(J / molecule)
-2.0980 • 10 -19
b1(J / moleculeK)
2.0996 x 10 -20
b2 (J / moleculeK 2 )
-8.6513 x 10 -23
b3 (J / moleculeK 3 )
1.6017 x 10 -25
t)4 ( J / moleculeK 4 )
-1.1128x10 -28
c1(J / molecule)
1.5423•
c 2 (J / molecule)
-1.3471 x 10 -18
c3( J / molecule)
6.2692 x 10-18
c4 (J / molecule)
-1.3580 x 10-17
Cs(J / molecule)
1.1572x10 -17
-19
present work, obtained by fitting the measured data of Christmann et al. [26]
318
296 K --.------------- 315 K ~ _ ~ ~ 332 K
0.5
~
3
5 _~
4
347 K K 362 K
0.4 370K 377 K 384 K 0.3 391 K O
r~
399 K 0.2 405 K
412K 0.1
418K 425 K 431 K 437 K 2
4
6
8
10
Pressure, 10 -9 ton"
Figure 3.2, 3. Comparison of the measured and recalculated isotherms. The data points represent the values measured by Christmann et al. [26]. The solid lines are calculated from the properties listed in Table 3.2, 1 at the temperatures (in kelvins) listed for each isotherm. Note that the points shown in Figs. 3.2, 1 and 3.2, 2 are a translation of the isotherms into the functions fl'(O) and b(T). The double harmonic oscillator model of the adsorbed molecules dictates that b should be a function of temperature only. If there were any coverage dependence in b (i.e., different values were obtained for the bj(T ~) for a given
319 isotherm), it would result in the functions (kTln(~')-b) not lying exactly on top of each other for the same value of coverage. This would result in a temperature dependence in fl'(O). Indeed, in Fig. 3.2, 2 a slight temperature dependence can be seen in the data points; however the scatter in the data in Fig. 3.2, 2 is very small (note the expanded scale of the ordinate). To obtain the function ~'(T, 0) corresponding to a set of measured isotherms, we shall introduce the approximation b - fl' = b0 + bIT + b2T 2 + b 3 T 3+...-c o - q 0 - c 2 02 - c 3 0 3 . . .
(3.2, 8)
where the coefficients bi and ci will be found from the method of least-squares applied to the data obtained from the isotherms and shown in Figs. 3.2, 1 and 3.2, 2. Polynomial fits of the functions b(T) and fl'(O) are shown in Figs. 3.2, 1 and 3.2, 2 along with the data points. A fourth order fit was used for b(T) and a fifth order fit was used for fl'(0).t Note that the functions b(T) and fl (0) obtained in this way are only obtained to within an additive constant because the choice of the reference isotherm is arbitrary. This does not cause difficulty because in the equations in which these parameters will be used, they appear as b - f l ' and the arbitrary constant is eliminated when the difference is taken. Once the coefficients b i and c i have been determined from the empirical isotherms, they may be tabulated along with other material properties (see Table 3.2, 1) and used to obtain an expression for the chemical potential. The fitting technique that has been used to obtain the values of these coefficients has a number of advantages over techniques that fit individual isotherms. By introducing a reference isotherm and then including all of the available data points, one is able to use higher order polynomials in the fitting procedure than would be possible if only a single isotherm were used. Often on a single isotherm, there are a limited number of points available and the order of the polynomials used in the fitting procedure would, therefore, be restricted by the number of points on the isotherm. In addition, separating the coverage and temperature dependence allows one to introduce only one function, fl'(O), that applies to all isotherms so that one need not have the complete coverage range for a particular isotherm in order to use this procedure. The coverage range for which the function ((T,O) may be used is the entire coverage range spanned by the set of isotherms. An example of these aspects of the fitting procedure is illustrated in Fig. 3.2, 3 by the isotherm at 296 K where there are only two data points spanning a very small coverage range. This isotherm was included in the fitting and indeed the two points are reproduced from the empirically determined function ((T, 0). The function ((T, 0) was also obtained over the complete temperature range of the data in Ref. [26]. This is important to this study because we wish to obtain isotherm information over a temperature range that overlaps the kinetic data of Ref. [ 17]. This will then eliminate the necessity for extrapolation of the chemical potential function so that any error in the predicted kinetics can be attributed to the Statistical Rate Theory approach. In order to determine if the chemical potential expression that is based on the function ~'(T, 0) leads to a good description of the isotherms, it may be used to reproduce the isotherms from which it was determined. In Fig. 3.2, 3, the measured isotherms are given along with the isotherms that were calculated from the values of the coefficients listed in Table 3.2, 1. The agreement is seen to be remarkable. This result provides support for the double harmonic oscillator model of the adsorbed CO molecule on Ni(111). It was this model of the adsorbed molecule that led to the form of the isotherm equation, and it was this equation that allowed the coverage and temperature dependence of ~(T,O) to be separated. tThe fitting was done using the routine "Fit" in the softwarepackageMathematica. |
320 3.3. Comparison of statistical rate theory predictions with kinetic data We are now in a position to apply Statistical Rate Theory to the CO-Ni(111) system. The relations for Ke,/z g and /zCrgiven in Eqs. (3.0, 17), (3.0, 19) and (3.1, 17) may now be substituted into Eq. (3.0, 11) to obtain the expression for the net rate of adsorption
I(M-na)(llrP~p na ] j=dn.....~a= Pe e (M_nae)tYe dt ~/2~'mkT na - (M-n~) (~eO
(3.3, 1)
Note that in the above theoretically derived equation, the term ncr / ( M - n a), introduced by Gorte and Schmidt [14] for purely empirical reasons (see Section 1.2), appears in the desorption term in the rate expression. This coverage dependence in the desorption term has not been previously predicted from a theoretical formulation other than Statistical Rate Theory. The coverage dependence predicted by Statistical Rate Theory for the adsorption rate, particularly the ( M - n ~) / nCr factor, is different than that predicted in
1.1 1.o
0.9 0.8
0.7 ~]
0.6 ~
'0.5
.•••___,
0.4
r~
0.3
__ .~
0.2 0 .lu
0.1 r,r.,
0.0 -0.1 0
2
4
6
8
10
12
Time, s Figure 3.3, 1. Fractional coverage of CO on Ni(111) during isothermal (395 K) isobaric (5 x 10 -3 torr) adsorption (left branch) and isothermal (395 K), isobaric (5 x lb -6 torr) desorption (fight branch). The data points were reported in Ref. [17]. The solid line was calculated from Statistical Rate Theory using an equilibrium adsorption cross-section, o"e, of 0.083/~ 2. For the adsorption curve, the starting time used was 0.19 seconds and for the desorption curve, the starting time was 0.45 seconds.
321 the other theories. The coverage dependence in both of these terms will be examined by comparing the Statistical Rate Theory predictions with the experimental results reported by Rubloff [ 17]. In his experimental procedure, a fast-acting valve and nozzle were used to suddenly expose the surface to a high pressure / ) o n . This allowed adsorption to take place isothermally and lsobarlcally. After a period of ume, the valv~ was suddenly closed; thereby rapidly reducing the pressure to a lower value, P~ p. After the pressure reduction, isothermal, isobaric desorption took place. The coverage as a function of time was measured using ultra-violet photoelectron spectroscopy. Adsorption-desorption cycles were recorded at several different temperatures. Four of the experiments reported by Rubloff are shown in Figs. 3.3, 1-4. These are all of the experiments reported by Rubloff that are within the temperature range of the isotherm data that we have examined herein. After making use of the function definitions given in Eqs. (3.0, 20), (3.1, 16), (3.2, 5), and (3.2, 8) and the parameters listed in Tables 3.0, 1 and 3.2, 1, the only unknown quantity in the kinetic expression for the net rate of adsorption, Eq. (3.3, 1), is the equilibrium adsorption cross-section, cre . We shall assume it is a material property that is 9
o
.
'
9
,y
~
1.1 1.0 0.9 ~
D DO D 9 D 9
0.8 0.7 0.6
~ ~
P
0.5
P D
0.4
P
t,.
~.
0.3
O
--
0.2
.~
0.1
L.
0.0 -0.1 0
2
4
6
8
10
12
Time, s
Figure 3~3, 2. Fractional coverage of CO on Ni(111) during isothermal (408 K), i~obaric ( 5 x 10 -~ torr) adsorption (left branch) and isothermal (408 K), isobaric ( 5 x 10-" torr) desorption (right branch). The data points were reported in Ref. [ 17]. The solid line was calculated from Statistical Rate Theory using an equilibrium adsorption cross-section, cre, of 0.12~t 2. For the adsorption curve, the starting time used was 0.19 seconds and for the desorption curve, the starting time was 0.45 seconds.
322 only dependent on temperature. From Statistical Rate Theory, it is predicted to be an equilibrium property so it could also be pressure dependent. To examine this aspect of the theory, the value of a e will be inferred from the desorption portion of an adsorptiondesorption curve. If the coverage dependence of the predicted net rate of adsorption has been obtained explicitly from Statistical Rate Theory, then one value of (re should provide a prediction of the coverage as a function of time that is in agreement with the measurements for all values of the coverage during the desorption process. And if a e is, in fact, a material property that is only temperature dependent, then since the adsorption measurements were performed at the same temperature, the value of (re that was determined from the corresponding desorption measurements should also lead to a good prediction of the adsorption measurements [9]. Thus, the type of data shown in Figs. 3.3, 1-4 potentially would allow us to examine two aspects of the Statistical Rate Theory approach. As will be seen, there is a difficulty with the data that prevents them from serving this dual purpose. To infer the value of (re from the desorption portion of the experimental curves, we first integrate Eq. (3.3, 1). This operation gives an equation of the form (3.3, 2)
C a e ( t - t o ) = r i n g)
where
c= q2
Pe
kr,
(3.3, 3>
(M_nee)
and f ( n a) is given by
:<.~
n-f E
n~
ha(to)
na
-(M-na)~ll/P~p
],
dna
(3.3, 4)
The latter equation may be integrated numerically once a value of na has been specified. Thus, the time at which the system is predicted to reach the value na is given by (3.3, 5)
t = t o + f(na-----2) Cae
Next, it is necessary to determine the experimental time, tm, at which the system was observed to reach the coverage n a. The square of the difference between the predicted time to reach a particular coverage and the measured time summed over several data points is a measure of the error. To determine the time tm requires that the ordinate of the figures reported by Rubloff be converted to coverage. We take this ordinate, Y ( t ) , to be N (:r(t)- g e (P'samp)
a
off
(3.3, 6)
" "e \" samp )
Once the pressure and temperature are known, the isotherm equation, Eq. (3.2, 2), may be used to calculate the necessary equilibrium coverages indicated in Eq. (3.3, 6). The
323 experimental procedure adopted by Rubloff was to equilibrate the system at a low CO pressure with the valve off and then to suddenly expose the surface to CO at a pressure that was three orders of magnitude higher by suddenly opening the valve to the nozzle. It is indicated that the higher pressure is less than 5 x 10 -3 torr. We investigated several different possible pressures that span the range of pressure that could be reasonably expected to occur in the experiment For the results that follow we take p~ to be off 9 "~" 5 • 10- 3 torr and Psamv to be 5 • 10" - 6 torr. (The effect of errors' an these pressures on determining the adsorption cross-section will be discussed below.) Note that the fractional coverage is equal to unity when the surface has equilibrated at the higher pressure and is equal to zero when the surface has equilibrated at the lower pressure. The figures published by Rubloff were enlarged so that n values of the fractional coverage corresponding to n experimental times could be read approximately. For the i th data point, we then have a measured time, tmi, required for the system to reach the
1.1 1.0 0.9 0.8
~
0.7
~
0.6
~:~
0.5 0.4
@ --
@
0.3 0.2 0.1 U
I..,
r.r.,
0.0 -0.1 0
2
4
6
8
10
12
Time, s
Figure 3~3, 3. Fractional coverage of CO on Ni(111) during isothermal (422 K), isobaric ( 5 x 10-" tort) adsorption (left branch) and isothermal (422 K), isobaric (5 x 10 torr) desorption (fight branch). The data points were reported in Ref. [ 17]. The solid line was calculated from Statistical Rate Theory using an equilibrium adsorption cross-section, o"e, of 0.16,~ 2 . For the adsorption curve, the starting time used was 0.19 seconds and for the desorption curve, the starting time was 0.45 seconds. TM
324 particular coverage and a calculated time, tci, found from Eqs. (3.3, 3), (3.3, 4) and (3.3, 5). The square-error, E 2 , is then defined by E 2-
~(tmi--tci) 2
(3.3, 7)
i=1
If Eq. (3.3, 5) is substituted into the above equation, then requiring that the derivative of Eq. (3.3, 7) with respect to cre vanish, yields an equation for the value of cre that minimizes the square-error
1.1 1.0 09
,~ ~
0.8
0.6 0.5 0.4 0.3
0.2 .~,
0.1 0.0 m
-0.1 0.0 I
9 |
i
0.5
|
i
|
1.0
i
1.5
|
l
2.0
|
,
i
2.5
Time, s
Figure 3.3, 4. Fractional coverage of CO on Ni(111) during isothermal (435 K), isobaric ( 5 x 10-3 torr) adsorption (left branch) and isothermal (435 K), isobaric (5 x 10-" torr) desorption (fight branch). The data points were reported in Ref. [ 17]. The solid line was calculated from Statistical Rate Theory using an equilibrium adsorption cross-section, o"e, of 0.37~t 2. For the adsorption curve, the starting time used was 0.15 seconds and for the desorption curve, the starting time was 0.36 seconds.
325
o.e =
n
i=1
(3.3, 8)
C~.(tmi -to)Yi i-1 The equilibrium adsorption cross-section will be taken to be the one calculated from Eq. (3.3, 8) using the desorption portion of the measurements seen in Figs. 3.3, 1-4. The inferred values of o"e at the four temperatures that we have investigated are shown in Fig. 3.3, 5. The results shown in Fig. 3.3, 4 were measured at 435 K. At this temperature, Rubloff [ 17] reports approximately 4.5 seconds of data for the adsorption-desorption cycle. In another figure of Ref. [17], however, a blow-up of the data is shown in which the adsorption portion can be seen more easily. All of the desorption data were used to determine the equilibrium adsorption cross-section. The predictions of both the adsorption and desorption portion of each experiment are shown in Figs. 3.3, 1-4 as solid lines. In assessing the agreement obtained by this procedure, it should be noted that the predicted adsorption and desorption portions of the curves are slightly dependent on the choice of the location of zero fractional coverage and the choice of the location of a fractional coverage of unity. Also, starting times must be selected for both the adsorption curve and the desorption curve. The initial time for the start of the adsorption portion of a curve was taken as the time at which the coverage began to rise. Likewise, the initial time for the desorption portion of a curve was taken as the time at which the coverage began to decrease. Even with these uncertainties in fitting the desorption curve to obtain the equilibrium adsorption cross-section, the maximum deviation in the value of the crosssection so obtained was 10%. It is expected that this is within the error to which the crosssection is known when one considers the experimental scatter in the data. Further, we note that Rubloff does not explicitly state the pressure at which the experiments were performed, but only the ratio of the adsorption pressure to the desorption pressure. The effect of changing the nominal pressure at which his experiments were performed results in a change in the inferred values of the adsorption cross-section, o"e, but not the degree of agreement between the Statistical Rate Theory predictions and the experimental data. For example, if the pressures used in the experiment were an order of magnitude lower, the calculated cross-sections would be approximately an order of magnitude higher. To assess the explicit coverage dependence that is predicted from the Statistical Rate Theory approach, one may examine the desorption portion of each of the curves shown in Figs. 3.3, 1-4. There does not appear to be any measured disagreement between the predictions and the measurements for this portion of the adsorption-desorption cycles. The results in each of these figures would support the contention that the coverage dependence of the net adsorption rate is given explicitly by the Statistical Rate Theory approach. And since a single value of the adsorption cross-section in each of these experiments allows one to predict the coverage dependence, it appears that o"e is a material property. It is clear from Fig. 3.3, 5 that a e is temperature dependent. Whether it can be approximated as being only temperature dependent as opposed to being both temperature and pressure dependent is not entirely clear at present. Although the same value of a e that was inferred from the desorption portion of each experiment was used to predict the adsorption portion of each of the four experiments, and although the pressure for the adsorption portion of each experiment was three orders of magnitude higher than the existing pressure during the desorption portion of each experiment, and although there is no measured disagreement between the theoretical predictions of the net adsorption rate in any of the four experiments, we are not able to argue that cre is independent of pressure because the time for the adsorption process to be completed is so short that a range of
326
values of tr e will give an equally good fit of the data in each case. Certainly, the results are consistent with tre being only temperature dependent, but this possibility must be viewed as unestablished. It should be noted that although it would be useful if tr e were only temperature dependent, there is no theoretical reason from Statistical Rate Theory to think that it must be the case.
o,
0.5
0.4 r~
r,.)
0.3
-N
~k 0.2
0.1
.
.
.
.
.
390
.
.
.
.
.
400
.
.
.
.
|
410
.
.
.
.
.
.
420
.
.
.
.
430
.
.
.
.
.
440
.
.
.
.
!
450
T~n per at ur e, K Figure 3.3, 5. Values of the equilibrium adsorption cross-section inferred from R.obloff' s data [17]. In order to make the calculation, experimental pressures P~ and P~ were taken to be 5 x 10 -3 and 5 x 10-" torr respectively. -
With more detailed experimental results, tr e could be examined more carefully. According to Eq. (3.0, 18), its maximum value is the physical cross-sectional area of an adsorption site. Otherwise, the probability that a molecule striking an available adsorption site will adsorb, ~, would have to be greater than unity. The maximum possible crosssection would be the surface area per site (or l/M) which is approximately 10 .~2 for CO on Ni(111). As indicated in Fig. 3.3, 5, the values of cre inferred from these experiments ranged from 0.1 to 0.4 .~2.
3.4. Comparison of absolute rate theory predictions with kinetic data Rubloff [17] pointed out that his experimental results could be used to assess the coverage dependence that is derived from Absolute Rate Theory. He examined the desorption portion of the measurements performed at 435 K that are shown in Fig. 3.4.1, 1. He assumed "first-order" desorption kinetics and examined the possibility that K d and E d are independent of coverage. However, he also made an assumption
327 regarding the equilibrium coverages at the two pressures that, in view of the isotherms, is not justified. We first present his argument. on ) >> O. - (e,~, ) O.(~.m,
3.4.1. P r e d i c t e d d e s o r p t i o n r a t e u n d e r t h e a s s u m p i o n o f -
In the Absolute Rate Theory expression for the desorption rate, Eq. (1.1, 4), RuNoffs assumption of first-order desorption means that the function g(0) is taken to be g(0)= 0
(3.4.1,1)
After equating the rate of desorption to the negative rate of change of coverage, Rubloff found
d__~Od-t -oKa exp(- kff-~>
(3.4.1, 2)
And if both K a and E d are independent of coverage, the latter relation may be integrated to give 0(t) = Bexp(-t / z)
(3.4.1, 3)
where B is the constant of integration and 1/z is related to the pre-exponential factor Kd and the desorption activation energy E d
1/z = K d exp(-ff-~)
(3.4.1, 4)
Since in the experiments, the pressure on the sample when the valve was open was three orders of magnitude larger than the pressure when the valve was closed, Rubloff assumed
o~ >> Oe(P'f/~mp)
be (PJamp) of:
(3.4.1,5)
The validity of this assumption is discussed below; however, if this assumption is made and the ordinate Y(t), defined in Eq. (3.3, 6), is required to be unity initially, then according to Absolute Rate Theory, it is given by
Y(t) = exp(-t / "r)
(3.4.1, 6)
Rubloff then compared the values of Y(t) that could be predicted from Eq. (3.4.1, 6) for various values of z with the experimental results. These calculations are shown in Fig. 3.4.1, 1 as plain lines. As may be seen there, no single value of z gives agreement between the theory and experiment throughout the experimental period. Rubloff was led to conclude that both the pre-exponential factor and the desorption activation energy must change "markedly" with coverage if one is to obtain agreement between theory and experiment.
328
1.0
0.8
0.6
o,
0.4
u
02 .im
00
0.0 0.4
0.8
1.2
1.6
2.0
2.4
2.8
3.2
3.6
4.0
4.4
4.8
Time, s Figure 3.4.1, 1. Fractional coverage of CO on Ni(111) during isothermal (435 K), isobaric ( 5 x 10 -6 torr) desorption. The data points were reported in Ref. [ 17]. The bold line was calculated from Statistical Rate Theory using an equilibrium adsorption crosssection, o"e, of 0.37/~ 2. The starting time was 0.36 seconds. Curves found from Absolute Rate Theory in which no coverage dependence was included in either Ke or E d and for o~ > > Oe(esamp off ) are shown are shown as plain lines. which it was assumed that Oe(Psamp)
3.4.2. Predicted desorption rate under the assumption of 0, (Ps, mp) = Oe(P,~,) On
"
Off
The assumption listed in Eq. (3.4.1, 5) allowed Rubloff to simplify his equation for the ordinate. However, since the analytical expression for the isotherm is now available, on we may calculate the values of Oe(Psam-) and Oe(P' soHm'.p ) 9 We note that this calculation p . . could not be made previously because an expression ~or the isotherm was not available. There is only one data point available on the isotherm at 437 K and only two data points available on the 431 K isotherm. As may be noted from Fig. 3.2, 3, the fitting procedure outlined in Section 3.2 reproduces the points on these isotherms when the properties in Tables 3.0, 1 and 3.2, 1 are used to obtain the expressions for ~'(T, 0), ~(T) and q~(T) and they are used with Eq. (3.2, 2) tO calc~alate the isotherms. Equation (3.2, 2) may also be used to calculate Oe(P~ and Oe(P~ at 435 K. One finds on Oe(Psamp) = 0.977
(3.4.2, 1)
and off ) Oe(Psamp
=
0.752
(3.4.2, 2)
329 Since these values of the relative coverage differ by less than 25%, neglecting one relative to the other does not appear justified. If the assumption in Eq. (3.4.1, 5) is not made, but one otherwise proceeds in the same fashion as outlined in the previous section, one finds that the ordinate may be expressed ""
on
off Oe(Psamp)
Y(t) = Oe(Psamp)exp(-t / "C)- ~
(3.4.2, 3)
__ Oe (Psamp) on Oe(Psamp) off
1.0
0.8
0.6
r
0.4
0 m I= omal
0.2
f,,,
00
0.0 L. 0.4
0.8
1.2
1.6
2.0
2.4
2.8
3.2
3.6
4.0
4.4
4.8
Time, s Figure 3.4.2, 1. F r a c t i o n a l coverage of CO on, Ni(111) during isothermal (435 K), isobaric ( 5 x 10--" torr) desorption. The data points were reported in Ref. [ 17]. The bold line was calculated from Statistical Rate Theory using an equilibrium adsorption crosssection, o"e, of 0.37~ 2. The starting time was 0.36 seconds. Curves found from Absolute Rate Theory in which no coverage dependence was included in either K d or E d are shown as plain lines. In this latter expression, the parameter ~: still bears the same relation to the preexponential factor and desorption activation energy as that indicated in Eq. (3.4.1, 4). Thus, we may determine if there is any constant value of 1: for which there is agreement between the measurements and the predictions. The predictions obtained from
330 Eq. (3.4.2, 3) for four values of z are shown in Fig 3.4.2, 1 along with the measurements and the Statistical Rate Theory predictions. As may be seen there, no value of z leads to agreement between the predictions and the measurements throughout the experimental period. Thus, although Rubloff concluded, on the basis of an unjustified assumption, that the pre-exponential factor and activation energy of adsorption would have to depend on coverage in order to obtain agreement between the Absolute Rate Theory predictions and the measurements, his conclusion nonetheless appears valid. This circumstance is similar to the situation found from Absolute Rate Theory when it was applied to examine electron transfer reactions between isotopes. 3.4.3. Motivation for the introduction of precursor states Results of the type shown in Figs. 3.4.1, 1 and 3.4.2, 1 have provided the motivation for introducing the concept of precursor states into the Absolute Rate Theory equation for desorption [28, 29], as such states had been introduced earlier to account for a lack of agreement between the observed and predicted adsorption rates [ 13]. However, since the Statistical Rate Theory correctly predicts the coverage as a function of time, there does not appear to be any need for introducing this concept, at least for the CO-Ni(111) system.
4.0. SUMMARY AND CONCLUSIONS To examine the Statistical Rate Theory approach, it has been applied to predict the concentration dependence as a function of time in two types of reactions. The objective of this approach is to derive explicitly, the complete concentration dependence of the reaction rate expression and to have only molecular or material properties appearing in the expression for predicting the rate of a process. The basis for the derivation is a simple quantum mechanical model of an isolated system and the Boltzmann definition of entropy. The instantaneous state of the system is described in terms of the particle (electron, atom, or molecule) distributions within a phase or phases. As a special case, the particle distributions within each phase are assumed to be spatially homogeneous. This type of system is general enough to consider either chemical reactions which take place homogeneously within one phase or adsorption kinetics where the rate of adsorption is controlled by the kinetics at the gas-solid interphase. Two assumptions are introduced that allow the expression for the quantum mechanical transition probability for a change from one particle distribution to another at an instant to be obtained in terms of the number of microscopic states in the original distribution and in the virtual distribution, and the rate of exchange between quantum mechanical states of the two distributions, K e. These assumptions lead to a simple interpretation of the reason for the increase in entropy in an isolated system. Also, they lead to the prediction of the rate of change of the particle distribution in terms of molecular and material properties and in terms of a non-equilibrium variable. In the case of the reaction occurring homogeneously within one phase, this variable is the bulk concentration and in the case of adsorption kinetics, it is the surface coverage. It is found that K e is equal to the equilibrium exchange rate of the reaction occurring homogeneously within one phase and, in the case of adsorption kinetics, it is the equilibrium exchange rate between the gas phase and the adsorbed phase. Thus, if the theory is correct, one value of K e should allow the concentration to be predicted from an initial nonequilibrium value throughout the period of system evolution. This is the basis for testing the theory in two circumstances. In the first circumstance, electron transfer reactions between isotopes dissolved in electrolytes are considered. The special properties of this type of reaction lead to an expression for the reaction rate that does not contain any material properties and the concentration dependence of the reaction rate is predicted explicitly [3]. The predictions
331 that follow are compared with concentration measurements in reactions between silver ions, between vanadium ions, and between manganate and permanganate ions. The measurements had been previously reported by Wahl and co-workers [20-22]. It is found that for one value of K e in each case, the Statistical Rate Theory predictions are in reasonable agreement with the measurements throughout the period of system evolution [e.g., see Fig. 2.3, 1 and 2.3, 2]. In contrast, Absolute Rate Theory does not give an explicit expression for the rate of a chemical reaction. The concentration dependence of the reaction is obtained from an interpretation of the equilibrium constant. This interpretation depends strongly on the concept of equilibrium between the activated complex and the reactants, but it is arbitrary. The Absolute Rate Theory prediction is such that it can not be in agreement with the measurements both initially and finally. An equally valid interpretation of the expression for the equilibrium constant leads to the Statistical Rate Theory expression for the rate of the reaction [23] and it is in agreement with the data both initially and as the system approaches equilibrium. In the second process considered (adsorption kinetics), material and molecular properties play a major role in the Statistical Rate Theory expression for the rate [9]. To examine the Statistical Rate Theory approach, it is applied to the isobaric, isothermal desorption of CO from Ni(111). The molecular properties of CO appearing in the rate expression have been previously tabulated by others (see Table 3.0, 1). To determine the material properties of the C O - N i ( l l l ) interface, the adsorbed CO molecule is approximated as a double harmonic oscillator. Based on this model, it is found that the information required to predict the adsorption rate necessitates knowledge of the number of adsorption sites and a set of equilibrium isotherms. If the fundamental vibration frequencies of the adsorbed molecule are known, they can be incorporated, but it is not necessary that they be available. This empirical information is found to be sufficient to determine all of the material properties appearing in the rate expression except one. The one remaining property arises from the equilibrium exchange rate K e. The equilibrium exchange rate is expressed in terms of the adsorption cross-section, cre (T,P). According to the theory, the equilibrium adsorption cross-section is a material property of the CONi(111) interface and as such depends only on the temperature and pressure at which the process occurs. Thus for isothermal, isobaric kinetics (adsorption or desorption), there should be one value of ae(T,P) that allows the surface concentration to be predicted throughout the period of the kinetic process. This is the basis on which the theory was tested [9]. Rubloff has reported measurements of the isothermal, isobaric adsorption of CO on Ni(111) and then at a pressure three orders of magnitude less, but at the same temperature, isothermal, isobaric desorption in the same system. The value of Cre(T,P) was inferred from the desorption portion of a cycle. In each of four experiments that were performed at different temperatures, it was found that one value of Ge(T,P) led to predictions that were in close agreement with the measurements (see Figs. 3.3, 1-4). Thus it would appear that for this system, the Statistical Rate Theory approach successfully led to the explicit coverage dependence of the rate expression. If it is assumed that O'e(T,P) is independent of pressure, then the value of Cre(T) inferred from the desorption portion of a cycle may be used to predict the surface concentration during the adsorption portion of a cycle. This calculation is performed using the same equation as that used to predict the desorption portion of a cycle. Only the initial surface concentration and the pressure are different than for the desorption portion. Although it is found that the predicted surface concentration for the adsorption portion of the cycle is in agreement with the measurements for all four temperatures (see Figs. 3.3, 1-4), we are not able to conclude that cre is independent of pressure. It is found that a range of values of cre also gives reasonable agreement for the adsorption portion. Only one equation is used in the Statistical Rate Theory approach to predict either the rate of desorption or of adsorption. It is only necessary to take into account the physical
332 conditions under which the process takes place. This is very different than the Absolute Rate Theory approach. In the latter approach, there are two different equations used, one for desorption and one for adsorption. When the Absolute Rate Theory equation is used to predict the desorption data reported by Rubloff, it has been previously reported [17] that agreement can not be found both initially and finally unless coverage dependence is introduced into the pre-exponential factor K,/ and the desorption energy E d. This observation is similar to the results found for the electron transfer reactions where Absolute Rate Theory was also found to be unable to predict results that were in agreement with the data both initially and near equilibrium. It is common to introduce the concept of precursor states with Absolute Rate Theory equations as a means of introducing the necessary coverage dependence or to use the concept of precursor states with sticking probability [13]. In the latter case, the expressions for the rate then contains parameters that can depend on coverage [ 16]. The coverage dependence that is obtained from the Statistical Rate Theory approach is obtained from the expression for the chemical potential. The coverage dependence of the latter is obtained by modeling the adsorbed diatomic molecule as a double harmonic oscillator. Then, the coverage dependence of the energy of the adsorbed molecule is required to be such that the total coverage dependence of the chemical potential expression is in agreement with the measured equilibrium adsorption isotherms. It might be argued that this is only the equilibrium expression for the chemical potential and that the expression would not be valid when the system is far from equilibrium; however no evidence is seen in the predictions that would suggest that the expression is strongly limited in this regard. Since the Statistical Rate Theory approach appears to predict the coverage dependence of the rate equation, it is not clear that there is any need to introduce the concept of precursor states. Before this conclusion can be strongly drawn, Statistical Rate Theory needs to be applied to other well defined systems besides CO-Ni(111). In its present state of development, this requires the availability of the equilibrium adsorption isotherms. The Absolute Rate Theory approach, even with the precursor states included, does not require any relation between adsorption kinetics and the adsorption isotherms of the system.
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
C.A. Ward, J. Chem. Phys., 67 (1977) 229. C.A. Ward, R. D. Findlay and M. Rizk, J. Chem. Phys., 76 (1982) 5599. C.A. Ward, J. Chem. Phys., 79 (1983) 5605. P. Tikuisis and C. A. Ward, In: Chhabra, R. and DeKee, D. (eds.), Transport Processes in Bubbles Drops and Particles, Hemisphere Publishing Co., New York (1992) 114-132. C.A. Ward, M. Rizk and A. S. Tucker, J. Chem. Phys., 76 (1982) 5606. C.A. Ward, P. Tikuisis and A. S. Tucker, J. of Colloid and Interface Science, 113 (1985) 388. C.A. Ward and R. D. Findlay, J. Chem. Phys., 76 (1982) 5615. R.D. Findlay and C. A. Ward, J. Chem. Phys., 76 (1982) 5625. C.A. Ward and M. Elmoselhi, Surf. Sci., 176 (1986) 457. C.A. Ward and M. B. Elmoselhi, Surf. Sci., 203 (1988) 463. K.J. Laidler, Theories of Chemical Reaction Rates, McGraw-Hill Book Company, New York (1969) 45. A. Clark, The Theory of Adsorption and Catalysis, Academic, New York (1970), 210. M.A. Morris, M. Bowker, and D. A. King, "Kinetics of adsorption, desorption, and diffusion on metal surfaces" in Simple Processes At The Gas-Solid Interface, eds. C. H. Bamford, C. F. H. Tippler, and R. G. Compton, Elsevier, New York, 1984. R. Gorte and L. D. Schmidt, Surf. Sci., 76 (1978) 559.
333 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.
P. Kisliuk, J. Phys. Chem. Solids, 3 (1957) 95. D.A. King, Surf. Sci., 64 (1977) 43. G.W. Rubloff, Surf. Sci., 89 (1979) 566. J. Willard Gibbs, Elementary Principles in Statistical Mechanics, Yale University Press, 1902; Dover Publications, N. Y. (1960) 16. I. Prigogine and R. Delay, Chemical Thermodynamics, Longmans, London, (1954) 357. B.M. Gordon and A. C. Wahl, J. Am. Chem. Soc., 80 (1957) 273. J.C. Sheppard and A. C. Wahl, J. Am. Chem. Soc., 79 (1957) 1020. K.V. Krishnamurty and A. C. Wahl, J. Am. Chem. Soc., 80 (1958) 5921-5924. F. K. Skinner, C. A. Ward and B. L. Bardakjian, Biophysical Journal, 65 (1993) 618-629. T.L. Hill, An Introduction to Statistical Thermodynamics, Dover Publications Inc., New York (1986) 147. W. Erley, H. Wagner and H. Ibach, Surf. Sci., 80 (1979) 612. K. Christmann, O. Schober and G. Ertl, J. Chem. Phys., 60 (1974) 4719. G. Wedler, H. Papp and G. Schroll, Surf. Sci., 44 (1974) 463. M.R. Shanabarger, Surf. Sci., 44 (1974) 297. C O. Steinbrtichel, Surf. Sci., 51 (1975) 539.
This Page Intentionally Left Blank
W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.)
Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
335
A N e w T h e o r e t i c a l A p p r o a c h to A d s o r p t i o n - D e s o r p t i o n K i n e t i c s on E n e r g e t i c a l l y H e t e r o g e n e o u s F l a t Solid Surfaces B a s e d on S t a t i s t i c a l R a t e T h e o r y of I n t e r f a c i a l T r a n s p o r t
W. Rudzifiski Department of Theoretical Chemistry Maria Curie-Sktodowska University Lublin 20-031, POLAND
1. I N T R O D U C T I O N In the theories of adsorption-desorption kinetics, the mass balance of the adsorbate over the entire heterogeneous solid surface is usually written in the following form, = - M Z x_, _
(I.I)
(~0i
-
i
where M is the total number of sites on the solid surface; 8i means the fractional coverage of sites of i-th type; Xi is the fraction of these sites on the solid surface and t is the time. Most frequently, 00 is taken to be the expression offered by the Theory of Activated Adsorption-Desorption, (TAAD) hi- = K
p(Z -
e)
exp
-
where s is the number of adsorption sites involved in an elementary adsorption-desorption process, p is the pressure in the gas phase, e= and ed are the activation energies for adsorption and desorption respectively, K~, Kd are slightly temperature dependent parameters. Further, T and k are the absolute temperature and the Boltzmann constant respectively. Equation (1.2) represents the most commonly used, particular application of that approach thoroughly discussed in the previous chapter by Elliott and Ward. Now, let us consider for simplicity physisorption and the case s -- 1. So, at the equilibrium when ~0e = 0, eq.(1.2) yields the Langmuir isotherm equation 0(e)(p,W ) =
Kpexp {k-~} 1 + Kpexp {~}
(1.3)
where K = - ~ , e = ( e d - e~), and where the superscript (e) refers to equilibrium. At equilibrium, the assumption of a discrete distribution of the fraction X of adsorption sites among corresponding values of e, expressed in eq.(1.1), leads to the following expression, 0(e)(p,T)=~Xi Kipexp{ kW } i 1 + Kip exp { kW-a}-
(1.4)
336 where now 0}*) means the "total", (average) fractional occupancy of all adsorption sites. In the case of the actual (real) solid surfaces one usually deals with a dense spectrum of adsorption energies which should be represented rather by a continuous function X(e), so that, 0~e)(p, T) = f ~g
II
Kpexp {k-~} X(e)de 1 + Kpexp {k-Y}
(1.5)
where X(e) fulfills the normalization condition
/
=1
f~
and ft is the physical domain of e. For the mathematical convenience, fI is frequently assumed to be the interval (-co, § or (0, § It was shown that replacing the true physical domain 12 E (fl, em), (et and em meaning the lowest and the maximum values of e for a heterogeneous solid surface), by (-oo, +oo) or by (0, +c~) does not affect much the behaviour of the calculated isotherm provided that extremely low or high surface coverages are not considered[l]. Hundreds of papers have been published showing how much the behaviour of the actual gas/solid adsorption systems is influenced by the adsorption energy distribution in these systems. Finally, two monographs on these surface heterogeneity effects have been published[i,2]. At small surface coverages, the second term on the r.h.s, of eq. (1.2) can be neglected. Many papers on the experimental studies of adsorption kinetics[3] were published, but the reported data did not obey the Langmuirian kinetics represented by the first term on the r.h.s, of eq. (1.2). Thus, various empirical laws were formulated to correlate the experimental data for adsorption kinetics. The first attempts by Roginski and Zeldovich [4,5] to provide a theoretical explanation for these empirical laws employed the concept of adsorption on an energetically heterogeneous solid surface. Later on that concept was more thoroughly elaborated by Aharoni and coworkers[6-8], and more recently by Tovbin[9] and Cerofolini[10]. Studies of desorption kinetics were carried out even more extensively. They were stimulated by the wide application of the Temperature Programmed Desorption (TPD) experiments to study the energetic properties of catalysts and catalyst supports[l 1]. The lack of applicability of the Langmuirian desorption kinetics represented by the second term on the r.h.s, of eq. (1.2) was detected very soon. It was observed, that the activation energy for desorption ed changed with the surface coverage, so, the theoretical analyses of TPD desorption spectra started with the following equation,
O}e)(p,T),
0t = Kd0S exp
--
(1.7)
In most cases, the dependence of ed on 0 was explained as originating from in the energetic heterogeneity of the actual solid surfaces, characterized by the dispersion of the activation energies for desorption.
337 Although every adsorption process is accompanied by a simultaneous desorption process, and vice versa, the studies of adsorption-desorption kinetics proceeded historically along two separate routes. One group of scientists studied kinetics of adsorption at low surface coverages, and neglected desorption phenomena in their studies. This group of scientists treated the surface energetic heterogeneity as a dispersion of the activation energies for adsorption, across a solid surface. The second group of scientists investigating desorption at high surface coverages, mostly in TPD experiments, treated the surface heterogeneity as the dispersion of activation energies for desorption. Provided that we accept TAAD, we should consider the energetic surface heterogeneity as a simultaneous variation of e~ and ed values, from one adsorption site to another. So, let us consider the generalization of eq.(1.2) for a heterogeneous solid surface, characterized by continuous spectra of the adsorption and desorption energies. Provided that every adsorption site is characterized by a certain pair of values (e~ and ed) for the whole heterogeneous surface, we have OOt
--s
--ed
t~ ~d
where X(e=, ed) is a two-dimensional differentialdistribution of the fraction of the surface sites among corresponding pairs of the values {c=, ca}. Following the theoretical results obtained for the case of adsorption equilibria,one may assume that K= and Kd are practicaUy the same for all adsorption sites. Apart from the mathematical problems posed by the solution of the integral equation (1.8) with respect to X(e=, ca), one faces problems of a fundamental nature. Namely, first of all, one must know the analytical form of 0 as the function of e= and ed. Statistical theories of adsorption equilibria provide us with the functions = 0(ed - e=). Thus, in order to make use of the statistical theories of adsorption, one must establish the relationship between c= and ed- Seeking for the relationship between ed and e= on different adsorption sites seems to be a difficultfundamental problem. Finally, the lack of applicability of Langmuirian adsorption kinetics was reported for typical physisorption systems in which the sense of the activation energy for adsorption seems to be difficultto interpret. All the above mentioned difficultiesdisappear when, as the starting point, one applies Statistical Rate Theory of Interracial Transport[12]. 2. A S I M U L T A N E O U S D E S C R I P T I O N OF A D S O R P T I O N E Q U I L I B R I A AND KINETICS
The starting point of our consideration is the equation developed by Ward and Findlay[12].
0o ,[ 0t = Kgs exp{
kT
]
} - exp{-(PgkT- #') }
(2.1)
where #g, #s are the chemical potentials of the gaseous and adsorbed (surface) molecules respectively, and K;s is a constant. They assumed next that the transient configurations of adsorbed molecules are close to the equilibrium ones.
338 Now, let us consider the Langmuir model of adsorption , i.e., one-site occupancy (localized) adsorption when no interactions exist between adsorbed molecules. Then, #s
0
k--T = In q~(1 - 0-----~
(2.2)
where qS is the molecular partition function of the adsorbed molecules. Accepting the ideal-gas approximation for #g, we have
~g
~0g
kT = k"-T+ In p
(2.3)
Now, let us consider the region of low surface coverages, i.e., neglect the second term within the square brackets in eq (2.1). As the adsorption kinetics is essentially a non-equilibrium process, we introduce the superscript (n) at the surface coverages appearing in the equations for adsorption kinetics. Then, 0 0 (n)
0t
/~g - #~
= K'g~exp
kT
,
~
#og ~ 1 - 0 (n)
= Kq~pq exp{ kT"
0(n)
(2.4)
The main effect of the energetic heterogeneity of the actual (really existing) solid surfaces is related to the dispersion of the minima in the gas-solid potential function across a solid surface. In the case of localized adsorption, these local minima are called "adsorption sites", and the value of the gas-solid potential at these local minima, taken with a reverse sign, is called - "the adsorption energy", and usually denoted by e. So, while considering the kinetics of adsorption on a heterogeneous solid surface we will write the molecular partition function qS as the following product, s qS= qos exp{ ~_..~}
(2.5)
where q~ is the same for all the adsorption sites, and e varies from one to another site. We will denote further by K the following product, K = q0 exp
~-~
and rewrite eq (1.3) to the following form,
[
011= 1 +
(00 /0t)
K~sp K exp{~-~}
]_1
(2.7)
The above equation describes the rate of adsorption on adsorption sites having adsorption energy equal to e. The experimentally measured surface coverage 0}n), and the mean rate
339 of adsorption (O0}")/Ot), are the values 0 ('0 and (O0('O/Ot) defined in eq (2.7), averaged over all kinds of adsorption sites taken with an appropriate statistical weight. With a dense spectrum of adsorption energies on a solid surface, that statistical weight becomes practically a continuous differential distribution of the number of adsorption sites among corresponding values of adsorption energy, X(e). Thus, in the case of a heterogeneous solid surface, the experimentally determined values O}~) and (O0}n)/Ot) are to be related to the following average;
/I 1 + /00K~spoj0t/ 0}n)= K exp{~--~} +oo
--1
(2.8)
)~(e)de
When equilibrium is attained, 0 = 0 (~) is given by the condition #~ = tt g, i.e., 0 (e)=
[l + ~ e x1p {
-e
(2.9)
}
and the experimentally measured surface coverage O~~) is given by, +co
0~e) = /
1 e x p { ~- e } ] -1 X(e)de 1 + ~pp
(2.10)
--OO
where the superscript (e) in 0~~) refers to equilibrium conditions. Both eq (2.8) and eq (2.10) can be written in the same form,
+?[
O~i) =
c~ i) _
1 +exp{
kT
]_1
}
X(e)de,
(2.11)
i = e, n
For the non-equilibrium conditions e!i) = e!'~), e~n) = kW in (00(n)/Ot) K~spK
(2.12)
whereas when equilibrium is attained, e!0 = e!'), e~~)
-
(2.13)
- k T ln(Kp)
When T ~ 0,
0 (i) tends to the step function
O~i), oo
l i m = 0 ~ i)= { 0 ' f o r e < e ! i) and then 0t(i)= f X(e)de T--.0 1, for e >_ e~i);. ' e( i )
(2.14)
340 for both equilibrium and non-equilibrium conditions. This is shown in Figure 1, where the function 8(e!0, T), written in the following form exp { ~ } 8(e~i), T) = 1 + exp { u }
(2.15)
is shown, as the function of the dimensionless variables er = ( e - e!O)/kTo, and r = T/To. For the reduced temperature r = 1, the function (2.15) is very close to the step function
(2.14). I:=1 1.00 -I f . =
.=
i
,
0.80 m
0.60 --
~###3
.
S
/,"
.=
m
"~II J
0.40 -.
.--'
0.20 --
0.00
-
-
"
,,
i
.=
jill
-20.00
I"llllllallllllllllllllllilllJlllll -10.00 0.00
10.00
II 20.00
Figure 1. The temperature dependence of the function 8(e, ec). The dimensionless temperatures are:r= 1)(--),r=5(---),andr= 10( . . . . ). In the theories of adsorption equilibria on heterogeneous solid surfaces, the step function defined in eq (2.14) is called usually the "condensation isotherm". The application of CA (Condensation Approximation) was studied thoroughly in the theories of equilibria of adsorption on heterogeneous solid surfaces[i]. It was shown there that in the case of adsorption on a heterogeneous solid surface, the essential condition for the applicability of the CA approach is that the variance of x(e) must be at least 10% larger than the variance of k--~-]. Of course, the same will be true also for
341 adsorption-desorption kinetics. Replacing the true kernel 0(0, i = e, n in eq. (19) by its corresponding step function (2.14) means assuming that the adsorption proceeds gradually on various adsorption sites in the sequence of the decreasing adsorption energy e. At a given temperature T, pressure p, and (0~___..2)),adsorption "front" is on the sites whose /
__
%
f oo(")
energy e is equal to ec given in eq. (2.12). But then, the overall adsorption rate ~ ~ )
is, in fact, governed by the local rate of adsorption, on the sites whose adsorption energy is equal to ec, through the obvious relation
( ) ~=~
00~n) = Const. x(ec) 0t ~, 0t
(2.16)
In other words, e~'~) in equation (2.15) can be considered as,
•
0t
e!~) = kT In
(2.17)
X(e!"))I~g~pK
where Kgs = Const. K'gs. One may argue that because eq. (2.1) does not apply at very small (0 --+ 0) or very high (0 --+ 1) surface coverages, one cannot accept the step isotherm in our consideration. However, it is obvious that the picture of a sharp "adsorption front" will be a very good representation for the true isotherm (2.15) at not too high temperatures, and the true isotherm (2.15) does not violate the condition that 0 cannot be very small or very high at e = e!i), i.e. on the adsorption sites where the kinetics of the "local" adsorption governs the kinetics of the "total" adsorption through the relation (2.16). For some distributions X(e), the integration in eq. (2.14) can be performed in an exact analytical way. This, for instance, is the case of the rectangular adsorption energy distribution,
X(e)
r 0,
=
1
.
forez<e<em" . . . elsewhere
(2.18)
leading to the generalized Temkin adsorption isotherm, in the case of adsorption equilibria. 0~r _
1 + exp{ ~=-r kT } ] k____~TIn -4"' era-el [l+exp{ q ]kZ}
(2.19)
The corresponding equation for adsorption kinetics is obtained from eq (2.19) by replacing e~r by e~'~)n. Then
oe~n) = 0t
Kg, pK exp{ el exp{e~-q#=)} _ exp{ e=-q } kT v t RT } 1{ ~ }
w h e r e / ( g s - Gs(em --r
kT
-1.
(2.20)
342 Eqs (2.19) and (2.20) are not subjected to any temperature limits, and are valid at high temperatures too. (Provided that the adsorption can be considered in terms of localized adsorption). Eq. (2.20) is convenient to demonstrate that it is not essentially the condition T ---, 0 for the Condensation Approximation to be applicable. The essential condition is that the variance of the adsorption energy distribution must be larger than kT. In the limit T ~ 0 or more generally when .kT is small, eq. (2.20) yields the equation, r Ote _._ ~m - er em--el
(2.21)
obtained for the adsorption energy distribution (2.18), by adopting the Condensation Approximation formulated in eq. (2.14). The corresponding equation for adsorption kinetics reads, s
0~0~
ot =
exp{-
6 m - - El
kT
(2.22)
Eq (2.22) is just the Elovich equation which is probably the most popular one to correlate experimental data for adsorption kinetics. Originally, it was launched as an empirical equation, but later on, it was associated intuitively with a constant, (rectangular), of adsorption (desorption) energies. The attempts to derive it theoretically always started with the Temkin isotherm for adsorption equilibria. But next, several additional assumptions had to be made, to arrive at the Elovich kinetic equation. This made all these deviations obscure and the nature of Elovich equation remained still a half-empirical one. The general procedure proposed here by us leads to the kinetics equations, in which the parameters have precise thermodynamic interpretation. That procedure allows one to predict adsorption kinetics from the adsorption equilibria and vice versa. The integration of eq (2.22) leads to the following expression, 8~=) =
k_.....T_TIn [apt + 11
(2.23)
6m - - 61
where c~ =
6m-
kT
61
s
K~K exp{~--~}
(2.24)
The comparison of eqs (2.22) and (2.23) must bring one to the conclusion, that investigating linearity of the plot ln(OO~)/Ot) vs. 0~~) In -ae~ ~ = const,
kT -
~
(2.25)
is to be recommended to check whether the adsorption kinetics is Elovichian. The linearity of the plot 0['~) VS. ln(apt + 1), predicted in eq (2.23) involves the necessity of making an always suspicious assumption concerning the value of a-parameter.
343 As eqs (2.19) and (2.20) represent the exact results of the integrations in eqs (2.10) and (2.8) when X(e) is the function defined in eq (2.18), they reduce correctly to appropriate equations for a homogeneous solid surface when ~ ~ 0. (To check, one must apply de l'Hospital's rule.) Though the shape of X(e) for an actual solid surface is expected to be a complicated curve in general, its "smoothed" version can, to a first crude approximation, be represented by the gaussian-like curve, x-exp{ ~-=-~} r X(e) = [1 + exp{~-~}] 2
(2.26)
centered at e = e0, the dispersion of which is related to the heterogeneity parameter c. This function has a physical background related to some universal features of energetically heterogeneous solid surfaces[I]. When equilibrium is attained, from eqs (2.13), (2.14) and (2.26) we obtain, /9~r =
[
1 + exp{ c(r - eo }
]_1
(2.27)
C
or, in another form, 0~,)_ (Kop) kT/r - 1 + (Kop) kT/r
0 < kT/c < 1
(2.28)
where s
Ko = K exp{~-~}
(2.29)
Eq (2.28) is the well-known "generalized Freundlich', (Bradley's) isotherm which is so commonly used to correlate experimental adsorption isotherms. It reduces to Freundlich isotherm at low surface pressures (coverages), lim 0~~) = (Kop) kT/r
p--.,O
(2.30)
Now, let us consider the form of 0}'0, and (00~'~)/0t) corresponding to the gaussian-like adsorption energy distribution (2.26). For obvious physical reasons, there must be a certain minimum, and a maximum value of the adsorption energy e, on a heterogeneous solid surface, ez and era. Thus, the gaussian-like function (2.26) should, for various values of the parameter c, be viewed correctly in the way shown in Fig.2. All the functions shown in Fig. 2 are normalized to unity, and given by the equation, 1 ~1 exp{ ~--=-~r l X(e) = ~NN [1 + exp{~-~}] 2
(2.31)
344 0.60 O
r~
0.48
(D (D
0.36
O ...
~
O
<
0.24 /
."
/
,
9
/
0.12 -
-%
-
/
~f
~. 9
9
%
.
9
%
"
%
o
0.00
I
0
I
2
"" 3
I
I
4
5
I 6
9* 9 .
I 7
o
8
9
Energy of adsorption Figure 2. The effect of the heterogeneity parameter c on the shape of the adsorption energy distribution X(E), given in eq. (39). Here EU is a certain energy unit comparable to kT. As it can be deduced from this figure, el = 2EU, e0 = 5EU, and em= 8EU. The values of the parameter c are: 0.5EU (. . . . ), 1.0EU ( - - - ) , and 5EU (--). One can see that as the heterogeneity (parameter c) increases, X(e) tends to rectangular adsorption energy distribution. where the normalization factor
FN reads, -1
FN=
l+exp{e]
eo}
-- l + e x p {
kT
}
(2.32)
Figure 2 shows that when c ~ oo, the function in eq (2.31) becomes the rectangular (constant) energy distribution. It means, the Elovich equation is likely to be a limiting form of all kinetic equations when the surface is strongly heterogeneous. With finite integration limits, the applicability of the Condensation Approximation was discussed in the recent monograph by Rudzifiski and Everett[l]. As it was demonstrated above, in the case of the rectangular energy distribution, the condition for the CA approach to be applied is not solely T ~ 0, but ~------Lt ~T ~ c~. Practically, that condition means, that the ratio of the variance of (00(e)/Oe), or (00(n)/Oe) to the variance of X(e), should be smaller than 0.9. In the case of the function X(e) defined in eq. (2.26), that ratio is represented by kT/c. As the function (2.31) resembles to some extent the rectangular distribution (2.18), we will accept now the relation (2.17) as an approximate one in eq. (2.14). Namely we will treat X(e!n)) as a constant, while calculating the integral (2.14). For the function defined
345 in eq. (2.26), the expression for 0~'0 reads, ~ =
[
1 + exp{ d~ c
}
1
Considering eq (2.17) and solving eq (2.33) with respect to O~
"- KgspK~ (1 -r n) 0~n))
(2.33)
(O01")/Ot),we obtain, (2.34)
though the constant Kgs in eq. (3.34) is not identical to that in eq. (2.20). The comparison of eqs (2.34) and (2.4) shows, that the gaussian-like dispersion of adsorption energies causes the term ( 1 - 0~n))/0~'0 to be powered by c/kT. Now, let us investigate the simplified form of eq (2.34) valid at low surface coverages, 00~=) 0t = Kg~pK0(O~"))-r
(2.35)
In other words, let us investigate the kinetics of adsorption corresponding to the Freundlich adsorption isotherm. After integration we obtain, (2.36) In the case of a homogeneous solid surface, instead of Freundlich region we have Henry's region in the limit of low pressures (coverages). Then instead of eq (2.36), we have, 0 (') = [2Kg~Kptexp{~-~}] 1/2
(2.37)
Thus, in the case of a homogeneous solid surface, the double-logarithmic plot In 0('0 vs. 1 In t should, (at small pressures), be a straight line with the tangent (01n 0(")/0 In t) = ~. In the case of a heterogeneous solid surface that double-logarithmic plot should also be linear, but the tangent (OInSC'O/Olnt)= (1 + ~T) -1. Because the values of c/kT are larger than unity, the tangents (0 In 0('0/0 In t) smaller will indicate adsorption kinetics on a heterogeneous solid surface. As the values thane k-Y _- ~a would be very typical for many of the actual solid surfaces, so, the tangent (OlnO(=)/Olnt) = ~ should also be typical. One can see it in Fig.3 in the paper by Aharoni and Ungarish[13], which is redrawn in Figure 3. With rising temperature the ("condensation") approach will be less and less accurate. Rudzifiski and Jagietto (RJ) have shown, that for typical heterogeneous surfaces and typical physical regimes at which the gas-solid adsorption experiments are carried out, the CA approach can be safely applied. If a question arises whether CA approach is
346
-/
, /O
/
O /
u
9
~,,~,
e
I
!
D i /
-0.5'
r 'i !
9
i
/
q !
tg c z -
/ /
1
2.5
-1.0' -3
-2
-1
0
0.5
log ((A/N o )t) Figure 3. The black circles (o 9 .) are the data for the kinetics of hydrogen adsorption on chromia drawn by Aharoni and Ungarish[13] in log(q/No) vs. log[(A/No )t] coordinates, where q is the adsorbed amount, and No, A axe some constants. The dotted line (- - -) drawn here by us shows that at the smallest surface coverages (c9log q/c9 log t) = 2-~" sufficiently accurate in some cases, one may apply the more accurate RJ approach[i], to represent the result of the integration in eq. (2.11). As we focus our attention on the most fundamental problems, we will accept the CA approach for the sake of simplicity. Now let us consider, why the model of localized adsorption along with the CA approach (assumption of a strong surface heterogeneity) leads to these generally observed laws for adsorption kinetics. (The Bangham's power, and the Elovich equations). It is now widely realized that this is the geometric nonuniformity of the real solid surfaces which is the main source of their energetic heterogeneity for adsorption. The surface geometrical distortions lead to a dispersion of the value of the local minima in the gas-solid potential function described by X(e) function. But if these local minima are the consequence of geometrical distortions, the creation of various energy barriers separating these local minima will be an unavoidable accompanying effect. These energy barriers will hinder the translational movement of adsorbed molecules across a solid surface. In other words, the stronger the surface heterogeneity, the stronger tendencies to localized adsorption. This is why the assumption of localized adsorption along with the CA appro-
347 ach work together so well. The observations of the actual adsorption systems provide an impressive support for this intuitive hypothesis. By taking into account the dispersion of the local minima in the gas-solid potential functions, (e.g. the dispersion of ~), we have arrived at a more accurate result that the rate of adsorption is proportional to t[!-0(n) e~-) ]c/kT. For low surface coverages, the above statement leads to the Bangham's empirical power law. The proportionality r~,e~")]c/kT is likely to L e(n) be valid in the case of moderately heterogeneous surfaces. In the case of strongly heterogeneous surfaces, the adsorption rate will be proportional to exp{--~T~----x'0~n)},because in this limit every (one-modal) adsorption distribution degenerates into the rectangular adsorption energy distribution. However, as demonstrated in Fig.2, this degeneration proceeds gradually, so the rate of adsorption will always be a hybrid between that described by the term rl-e<")]c/kT and the L e(n) .... rate of adsorption described by the term exp{ - ' =~-~' ~t a(n) }. And, this is why the experimental data can be correlated by both the Power Law and the Elovich equations, but always with a limited success. One can only say, that this hybrid behavior resembles more the e(,,) , than that predicted by the term exp{ - ~ Akr( ' ~"t) }, one predicted by the term trl--O(")]~lkT or vice versa. The purpose of our consideration in the present section was to provide a standard routine for a simultaneous description of equilibria and kinetics of adsorption on a non-porous, but energetically heterogeneous solid surface, characterized by a certain distribution of adsorption energy. To illustrate our new procedure we have chosen some expressions for the adsorption energy distribution, which lead to compact well-known equations for adsorption kinetics. In general, the functions O~)(p,T) and o0~") _- - f(O~) p,T, t) will have to be evaluated at numerically. Then, in the case of adsorption kinetics, numerical calculations will involve solving, for an assumed function X(~), the systems of the two equations (2.14) and (2.17) to eliminate e!'~) and to obtain ~ Ot as the function of 0~") p, T. It was Ward and coworkers [12] who proposed to express #" in eq. (2.1) by appropriate functions of coverage and temperature developed for adsortpion equilibria. Their proposal has been fully supported by the computer simulations of adsorption kinetics, published recently by Talbot et. a1114]. While assuming that adsorption is accompanied by desorption, they found, that the transient configurations of adsorbed particles can be approximated well by the corresponding equilibrium configurations at the same coverage.
3. M U L T I - S I T E O C C U P A N C Y A D S O R P T I O N : E F F E C T S O F S U R F A C E T O P O G R A P H Y ON A D S O R P T I O N K I N E T I C S . The generally accepted quantitative measure of the energetic heterogeneity of the actual solid surfaces is the differential distribution of a number of adsorption sites among the corresponding values of adsorption energy. This function, called usually - - " the adsorption energy distribution", is sufficient for a complete thermodynamic description of adsorption equilibria in the systems where one adsorbed molecule occupies one adsorption site and no interactions exist between the adsorbed molecules (Langmuir model).
348 However, in the systems where the interactions between adsorbed molecules cannot be ignored, or when one adsorbed molecule occupies more than one site, another important physical factor comes into play. This is the way in which adsorption sites characterized by different adsorption energies are distributed on a heterogeneous solid surface. In other words, this is the topography of a heterogeneous solid surface. So far, two extreme models of surface topography have, almost exclusively, been considered in theoretical works on adsorption. The first one was the "random" model, introduced to literature by Hill[15]. It assumes that adsorption sites characterized by different adsorption energies are distributed on a solid surface completely at random. The other extreme model of surface topography was the "patchwise" model, introduced to literature by Ross and Olivier[16]. It assumes, that adsorption sites having the same adsorption energy are grouped on a heterogeneous surface into "patches". These patches are large enough so that the states of an adsorption system in which two interacting molecules are adsorbed on different patches, could be neglected. Thus, the adsorption system can be considered as a collection of independent subsystems, being only in a material and thermal contact. Yang and coworkers[17-19], and Zgrablich and coworkers[20-24] have shown that even in the simplest case of Langmuirian adsorption the surface topography affects strongly the surface diffusion of adsorbed molecules. Although different expressions are obtained for adsorption equilibria or surface diffusion on surfaces characterized by different topographies, a practical discrimination between various topographies is difficult, at the present standard accuracy of adsorption measurements. Thus, experimental studies of one-site-occupancy adsorption do not practically allow at present to determine the surface topography by a suitable computer analysis of experimental data. On the contrary, the experimental and numerical studies of multi-site-occupancy adsorption seem to create such a chance[i,25]. It was demonstrated first by Rudzifiski and Everett in their monograph: "Adsorption of Gases on Heterogeneous Surfaces"[1]. That idea has been developed and elaborated further in the recent works by the author and coworkers [26 ,2 7] . Similarly to the case of adsorption equilibria, the starting point of our consideration was the Nitta's approach to multi-site--occupancy adsorption[25]. When Nt molecules are adsorbed at equilibrium on a heterogeneous surface, the system partition function Q(Nt, M,T), may be written as follows, q -
g(N (Nij}
,M,(N
j}/exp
, i
"
where r is the energy of adsorption of jth group (mer) on ith kind of sites. Nij is the number of adsorption-pairs of sites i and a group j; {N~j} is a distribution of these adsorption pairs, the subscript i0 being used for the empty site, g(Nt, M, {N~j}) is a combinatorial factor, expressing the number of the distinguishable ways of distributing the Nt molecules on M sites under the condition of a special distribution {Nij }; q~ is the product of internal and vibrational partition functions of an adsorbed molecule. As emphasized by Nitta, it is difficult to find a rigorous expression for g(Nt, M, {N~j}) by taking into consideration the mutual correlations between neighbouring sites and ne-
349 ighbouring groups in a molecule. A simple expression is obtained by assuming that all pairs of site-mer {ij ) are independent, under the constraints imposed by the distribution
{N,j}. To evaluate 0t, three kinds of surface coverages, 0t, {0tj } and
= s'iNt M , (j = 1 , . . . , s ) ,
0ij --- ~M i ~
(i =
...,
to be defined;
the fraction of sites occupied; (s is the total number of sites occupied by the adsorbed molecule), the fraction of sites occupied by the segment of type j; (sj is the number of sites occupied by the segments of type j), s), the fraction of sites of type i occupied by the segments of type j.
-, Ot -__- - ~sYt
Ot j
{O~j} are
W; j = 1 , . . . ,
The problem of evaluating Ot is not trivial, and Nitta proposed a complicated numerical procedure, in which only a discrete distribution of adsorption energy can be applied. However, that problem becomes easy when all the segments of an admolecule are of the same kind. The Nitta's approach leads then to the following expression for the chemical potential #" of the adsorbed (surface) molecules[l], Oi
#~ = -e,i - kT ln(q,(s) - (s - 1)kT In Ot + skT In 1 - Oi '
(3.2)
where Ni ei =
(3.3)
esi = seli
(3.4)
and ( is a constant related to the flexibility and symmetry number of a molecule. Thus, when all the segments are identical, the Nitta's assumption that all the pairs {i,j} are independent is true only if ith values are independent. This means a lack of spatial correlations between ith values on various sites which is true only in the case of random topography. It means, Nitta's expression (3.2) is valid for surfaces having random topography. When the surface becomes homogeneous, i.e., 8i ~ 8t, equation (3.2) reduces to the well-known Flory's isotherm for multi-site occupancy adsorption on homogeneous solid surfaces, ~ = -e~ - kT ln(q,(s) + skT In
(1
-0)
~ '
(3.5)
In the case of patchwise topography es will contain the additional index i referring to i-th patch, esi. Thus in the case of patchwise topography the isotherm equation reads, ~s = - e s i - kT ln(qs(s) + skT In
(1
(3.6) -
Oi) ~
350 The averaging of 0i in equation (3.6) with respect to the dispersion of e,i defined in (3.4) can be carried out easily by applying the Rudzifiski-Jagietto[1] method. Then, 0t = - X ( q r
Corr.
(3.7)
where
x(~) = / x(~)d~
(3.8)
X(e) is the differential adsorption energy distribution normalized to unity, and e,~ is found from the condition
( 020i~
--0
(3.9)
0'2i / ,,fi-'-r162
and Corr. is a correction term which can be safely neglected in the case of strongly heterogeneous surfaces[i]. Then, the Rudzinski-Jagiello approach becomes essentially equivalent to the Condensation Approximation. After performing the differentiation (3.9) in equation (3.2) one arrives at the following expression for e,~c, e,c = e~:) = - / ~ ' - kT l n ( q , r
(s - 1)kTln0t ,
(a.10)
where the superscript "r" refers to random topography. After replacing/z ~ by/~g of an ideal gas, /zg [pA3 l kW = In
(3.11)
[%kTJ
eq. (3.10) takes the following form, e~) = - k T ln(sK'p) - (s - 1)kW In 0t,
(3.12)
where K ' = q~(Aa %kT "
(3.13)
A is the thermal Broglie wavelength, and qg is the internal molecular partition function of the molecules in the bulk gas phase. In the case of patchwise topography the equation for e~ is obtained by applying eq. (3.9) to eq. (3.6), and then takes the following form,
e,r = e!rp) = - k T In
(s89+ ~)
,_~ K'p
(3.14)
Now let us consider the rectangular adsorption energy distribution, which is used so frequently to represent the energy dispersion on strongly heterogeneous solid surfaces,
x(~,)
' e--/-~-,,'
ror'<~,<e', ~ - -
0 , elsewhere
(3.15)
351 where e's and e~ are the lowest (l) and the maximum (m) values of the adsorption energies found on a heterogeneous solid surface. Then, according to eq. (3.7) for the random surface topography, at takes the following form O~e)=
~r kT In -~ kT In O~s-I) kT ln(sK')+ ~~ e~.(r) - ,~ - A--~ p ~ _
(3.16)
where A, = (e~ -ezs). For patchwise topography we obtain, 0~e ) -
--
e~)
kWln I / ~s(l+~)K' - - ~ ~ - - 1 1 + kT In ) ~ p ~Sg + 1)
e~-e~ = A--~
(3.17)
To emphasize that eqs. (3.16) and (3.17) describe the adsorption equilibria, we introduced again the superscript (e) in 01e). Now let us remark that for one-site-occupancy adsorption, (Langmuir model), i.e. when s = 1, eq. (3.16) developed for random topography reduces to the well-known Temkin's isotherm. For patchwise surface topography, 0t will always be the Temkin's isotherm for both one--site and multi-site--occupancy adsorption. Thus, we can see, that even in the absence of interactions between the adsorbed molecules, surface topography will affect the adsorption equilibria in the case of multi-site-occupancy adsorption on a heterogeneous solid surface. In the case of one-site-occupancy adsorption on heterogeneous solid surfaces, surface topography does not affect the kinetics of adsorption (desorption), until the interactions between adsorbed molecules can be neglected. It was shown in the series of papers published by Aharoni and coworkers[28-30]. Now we are going to show that surface topography must affect also the kinetics of multi-site-occupancy adsorption (desorption) on energetically heterogeneous solid surface. Then, for random surface topography and the rectangular adsorption energy distribution, for non equilibrium conditions we have, ~g - ~ = -A~0~ n) + kW ln(sK') + kW In p + (s - 1)kW In 0~n) = 0
(3.18)
whereas for patchwise topography, we obtain, ~ g - ~s = -A~0t + kT In
s(l+~)K , s89+ 1)(s-l)
+ kT In p = 0
(3.19)
After inserting eqs. (3.18) and (3.19) into eq. (2.1) one can express the experimentally monitored rate of adsorption \ 0t ] in terms of the actual (non-equilibrium) coverage 0~~) which is also monitored experimentally. Here, we will limit our interest to the very initial rate of adsorption, (very small surface coverages), when the second term within the square bracket in eq. (18) representing the
352 readsorption rate can be neglected. Then, for patchwise surface topography we arrive at the following equation for the rate of adsorption, -)
cOt = K'gs
(S89-[-1)(s-I)
p e x p { - }As k Ta(")V t
(3.20)
which is essentially the well-known Elovich equation. For random surface topography, we obtain,
i)O~n)=OtK'gssK'p [0~n)]s-1 exp { - ~ - ~ 0 t }
(3.21)
Comparing eqs. (3.20) and (3.21) one can see, that the surface topography will affect also kinetics of multi-site--occupancy adsorption on energetically heterogeneous solid surfaces, even in the absence of interactions between the adsorbed molecules. Now let us consider whether there might be a chance to elucidate the information about the nature of the surface topography of an investigated adsorbent sample, from the experimental data for adsorption kinetics. Our answer is yes, and to prove it we will take as an illustration the kinetics of chemisorption on scandia. There is a large body of experimental data in the literature on adsorption kinetics, but this particular example provides a very impressive proof. It is shown in Figure 4.
C02
There, in Figure 4 we show the initial rate of adsorption \ 0t ],,a~, measured at very small surface coverages, and reported by Pajares et. all31]. One has'to realize that the smaller is the pressure p at which the kinetics of adsorption is studied, the smaller is also the region of the studied surface coverages. In the case of patchwise topography, going down to smaller and smaller pressures (surface coverages studied) should lead to a better and better linear dependence of since exp { - ~-~0(' } k rt~)
tends to unity, and the derivative of exp { - ~-"-a(' } k~'t r~)
, on p, with respect to
0}'~) tends to the constant value -~--'Such a behaviour predicted for patchwise surface kT" topography is not observed in Figure 4. Let us remark at this moment that the surface of the scandia sample was very heterogeneous, as it can be deduced from the log-log plots of equilibrium adsorption isotherms measured by Pajares and owo k [a21. (Figu~ 11 in the paper by Fiero and Pajares[32].) Now let us assume that the scandia sample has random topography and adsorbs in a two-site-occupancy way by its two oxygens. Then, though the derivative of exp { - a-A(n)}kT't with respect to 0~n) tends to the constant value a_~kT when 0~n)~ 0, the
6"02
observed rate of adsorption will decrease with p, because p is multiplied by 0~") decreasing with p. (Smaller experimental pressure means smaller surface coverages studied). Our conclusion that the scandium oxide must have random topography is in agreement with the results published recently by Bakaev and Steele, carrying out computer simulations of adsorption on metal oxide surfaces[33]. They have shown that the best agreement
353 I
"7
I
I'
I
'I
f
I
I
I
~0 "7
1.5 -
~~'''~~
-,.-4
E
1.0-
~ 0
(,9 E
-
0.50-
~
C
-
1.0 0.5
m
4=
"
1.0-
[] o
0.50
I
0
!
2
I
I
4
I
I
6
I
I
I
8 p (Tort)
Figure 4. The initial rate of adsorption of C02 on scandia oxide, measured by Pajares et. al.[31] at low surface coverages. (After Pajares et. al.[31]). between experimental and theoretical adsorption isotherms is obtained for the surface model, in which surface oxygens are viewed as randomly packed hard balls.
4. D E S C R I P T I O N OF D E S O R P T I O N K I N E T I C S " A P P L I C A T I O N T O T P D SPECTRA The Temperature Programmed Desorption (TPD) is nowadays one of the most popular and frequently used methods to characterize the energetic properties of adsorbent and catalyst surfaces. The principles of that method were published by Amenomiya and Cvetanovic in 1963134], and 5 years later the first theoretical paper on the application of that experiment to study energetic surface heterogeneity was published by Cvetanovic and Amenomiya[35]. The number of the papers reporting on the application of this technique started to grow rapidly, and one can quote dozens of relevant papers published in this field. As our paper proposes an essentially new theoretical approach to the problem, we refer only to the most exhaustive reviews by Falconer and Schwarz[36], Kreuzer[37], and by Bhatia et al.[ll] presenting the approaches which have been used so far in the
354 theoretical interpretation of the TPD spectra. Tovbin[38] has reviewed the achievements of Russian scientists in this field. The theories of TPD were based commonly on the theory of activated desorption (adsorption). In fact, the TPD technique was used first of all to study the surface energetic heterogeneity of catalysts and adsorbents. The various peaks observed on TPD diagrams were ascribed to various kinds of surface adsorption sites characterized by different activation energies for desorption. In most cases, researchers were happy with such qualitative information about the surface energetic heterogeneity. Only few papers reported on more attempts to draw quantitative information about surface heterogeneity from the experimental TPD spectra. Czanderna et. a1.[39], Cvetanovic and Amenomiya[35], Carter[40], Dawson et. al.[41] and Tokoro et. a1.[42] assumed that the surface energetic heterogeneity manifests itself as a linear variation (decrease) of the activation energy for desorption with the decreasing surface coverage. King[43] was the first to assume that the activation energy for desorption may be a more complex function of the surface coverage. Also Tokaro et. al. followed that assumption in their second work on that problem[44]. An interesting paper in this field was published by KnSzinger and Ratnasamy[45]. After 1980, the papers concerning that problem were published by Davydov et. a1.[46], Unger et. a1.[47], Malet et. a1.[48], Leary et. a1.[49], Ma et. al.[50] and by Salvador and Merchan[51]. Very interesting reports concerning that problem have been presented during the "Second International Symposium on Surface Heterogeneity Effects in Adsorption and Catalysis on Solids" held in Zakopane- Levoca ( P o l a n d - Slovakia) in the autumn 1995152]. At low (partial) pressures p, and high coverages 0}"), (the condition usually found in the TPD experiments), the first term in eq. (1.2) is expected to be small compared to the second one. In this section we will limit our selves to one-site-occupancy adsorption, i.e., when s = 1. Then, to a good approximation, the experimentally observed desorption rate should be given by,
oNlo> 0t
= MKdO exp {-ed/kT}
(4.1)
where Nt(~) = M6} n). Experimentally, the desorption rate is monitored as the concentration of the desorbed species in the carrier gas, c, so, -ONt = F c 0t
(4.2)
where F is the volumetric flow rate of that carrier gas. Of course, eq.(4.1) is valid for a hypothetical homogeneous solid surface with the same constant desorption energy ed across the surface. Generally, it was observed that while applying eq. (4.1), the estimated ed values change with the surface coverage #~n) so, ed in eq. (4.1) was considered to be rather a function ed(O). The authors trying to generalize
355 eq.(4.1) for the case of the actual, energetically heterogeneous solid surfaces, wrote it in the following form[50], cgt = Kd
0t~)(ed)exp {-ed/kT} X(ed)ded
(4.3)
fl
where X(ed) is the distribution of the number of adsorption sites among various values of ed, normalized to unity, and the superscript "t" in O0~=)/Otmeans the average of O0(=)/Ot over the "total" heterogeneous surface. Then, f
X(ed)tied = 1
(4.4)
f~
where fl is the physical domain of ed. Eq.(4.3) could be treated as an integral equation for X(ed), provided that the kernel O('O(ed)is known, and provided that there exists a functional relationship between ed and ea on a heterogeneous solid surface. The existence of such a relationship is a mystery which, to our knowledge, has been considered in only one theoretical paper by Salvador and Marechan[51]. One can even ask the question whether ea and ed on various adsorption sites are correlated at all. What is only sure, is the fact, that the Langmuirian kinetics of adsorption, (represented by the first term on the r.h.s of eq.(1.2)), is not found in real adsorption systems. Instead, Elovich, Power Law, or other equations for adsorption kinetics must be applied. In terms of TAAD, it means, that there must exist a dispersion of e~ values on different adsorption sites.The concept of the dispersion of the activation energies for adsorption was accepted as fundamental by all the authors trying to explain the theoretical origin of these empirical equations. The TPD experiments, on the other hand, reveal a dispersion of ed values on the actual solid surfaces. So, generally, the correct generalization of eq.(4.1) should read, 0t
= Kd
o(n)(ea, ed) exp {-ed/kW} X(ea, ed) deaded
(4.5)
fl
where X(e~, ed) is a two-dimensional adsorption energy distribution. (The differential distribution function of the number of adsorption sites among the corresponding pairs of values of e~ and ed.) The common interpretation of the TPD spectra was based on the assumption that, at every moment, (at every adsorbed amount), the observed desorption rate can still be described by eq. (4.1). The value of ed corresponding to that adsorbed amount was still called "activation energy for desorption" and was given the same physical meaning as for a homogeneous solid surface characterized by this ed value. Looking to eq.(4.5) one can deduce easily that such an interpretation could be accepted only if the following two conditions were fulfilled:
356 1. There exists a functional relationship between
ed
and e= on various adsorption sites,
2. The desorption proceeds in an ideally stepwise fashion, in the sequence of increasing activation energies for desorption. While accepting the Statistical Rate Theory, along with the Langmuir model of adsorption, we have, 00(``) 1 - 0(") 0t = Kap 0(.) exp{e/kT} -
1 0 (~) Kd exp{-e/kT} p 1 - 0(``)
(4.6)
where !
$
(4.7)
Ka = Kgs qo exp{#~,/kT} Kd =
K
!
~ exp{-/z~/kT}
(4.8)
q~,
As it has already been mentioned, in typical TPD experiments the (partial) pressure of the desorbed species, (concentration c in the carrier gas), is usually small, so, the desorption rate can be well represented by the second term on the r.h.s, of eq.(4.6). Although we already have at hand a more general treatment taking into account the possibility of readsorption, we will neglect it in the present publication for the purpose of clarity of presenting the main idea of using the Ward's theory as the starting point. So, we assume that in the case of a homogeneous solid surface having adsorption energy e, 00(``) 1 0(``) (9t = -- Kd--p 1 -- 0(n) e x p { - e / k T }
(4.9)
Eq. (4.9) can be rewritten to the following form,
exp {(e - ~"))/kT} o(n) (s
,,
"--
1+
exv
(
-
~
(4.10)
)
where ~ p
e~``) = kT In
Kd
00(n) [l 0t J
(4.11)
Figure 1 shows the temperature dependence of the function 0('0(ec). At not too high temperatures, the desorption from a heterogeneous solid surface will proceed in a stepwise-like fashion. This assumption was intuitively made in the hitherto interpretation of TPD spectra, but this is the Statistical Theory of Interfacial Transport which provides now, for the first time, a rigorous proof for that intuitive common assumption. The step on 0(e, e!~)) will appear at e - e!~). Then the observed "total" desorption rate from the whole heterogeneous surface will be governed by the "local" rate of desorption from the sites the adsorption energy of which is equal to e!~).
357 The observed "total" rate of desorption (O0~")/cgt) will be proportional to that local desorption rate on the sites where e = e!'~), and to the fraction of these sites on the surface. Thus, lim
00~n) =
m-->o cot
Const
(O0~(e))
~=<{,,)
X(e! "))
(4.12)
where
a
1 + exp t ( e - e~ ))/kmj~
From now on, till the end of this chapter, we will consider only the desorption kinetics. (,~) Therefore, we will drop the superscript (n) in the expressions 0 (~), 0}n) and ec 9 While expressing (O0/Ot) in eq. (4.11) by (OOt/Ot) defined in eq. (4.12), we obtain, ec= _kT l n [ - p ~d where/T;'d =
0t =
1
(00t'~ ]
x(~) k-a-T)<<
(4.14)
Const Kd. Alternatively, eq.(4.14) can be rewritten to the following form,
p
The above equation is to be compared with the equation 00t0 t = --Kd0t exp { - skT
}
(4.16)
which is commonly used to analyze the TPD spectra from heterogeneous solid surfaces. (We consider here the simplest case of one site-occupancy adsorption). Although it is usually said not explicitly, or perhaps even not realized sometimes, the interpretation of the TPD spectra based on eq. (4.16) involves the assumption of an ideally stepwise character of the desorption process. For our further purposes, we will rewrite eq.(4.15) to the following form, X(ec) = exp {ec/kT} [--~Pd p ~__t.]
(4.17)
The function xc(ec) calculated from the relation,
00t _ ___1 0Nt(er
Xr162
8ec-
M
aec
(4.18)
is usually called the "Condensation Approximation" for the actual adsorption energy distribution X(e). It is defined as follows: x<(~<) =
oo /(oo) ~
0
x(~)a<
358 In the limit T ~ 0 the derivative ( ~ ) becomes the Dirac delta function 6 ( e - ec), and Xc(ec) becomes the exact function X(e). One of the common approximations used in the theoretical description of adsorption equilibria on heterogeneous solid surfaces is to replace the true kernel in eq.(4.13) by the step function 0c(e, ec)[53-55]. The essential condition for this approximation to be applicable is not solely the condition T ~ 0 but the condition (6e/6• < 1, where 6e is the variance of the derivative (00/0e), and 6• is the variance of X(e). Thus, to a certain degree of accuracy, the function Xc(ec) calculated in eq.(4.18) can be compared to the function X(ec) calculated in eq.(4.17). In the limit T ~ 0 or in the limit of a very heterogeneous solid surface, they will become equal. Then, from the comparison of eqs (4.18) and (4.17) we obtain, (4.20)
Oec
At every temperature T, the still existing adsorbed amount Nt(T), is found from the relation, T
(4.21)
Nt(T) - V o - ~F f c(T)dT To
where ~ is the heating rate, ~ = dT/dt. In typical TPD experiments ~ is a constant. Also, at every temperature T, the corresponding value of ec can be calculated from the desorption rate, if the value Kd and ec(T) are known. The preparatory steps to calculate Xc(er are the following: 1. To eliminate the noise in the experimental TPD curve, which would be multiplied in the subsequent differentiations, the TPD spectrum is approximated by a sum of gaussian-like functions: c(T) = E aici(T)
(4.22)
where ci(W) = ri
E[
exp
{
-
Ei
(4.23)
In eq.(4.23), E~ is the variance of c~(T), and r~ governs the shape of c~(T). This is a fairly symmetrical gaussian-like function for r - 3, right-hand widened for r < 3, or a left-hand widened gaussian-like function for r > 3. 2. We assume that at To the surface coverage is equal to 1, and that at the final temperature, Tk, it is equal to zero. Then, Wk
M= ~
c(T)dT To
(4.24)
359 3. The partial pressure p of this adsorbate is given by the relation p = cP (where P is the atmospheric pressure). 4. The derivative (ONt/Ot) in eq.(4.20)is replaced by, 0Nt = - F c (t(W)) 0t
(4.25)
5. For physical reasons ~c must be a one--to-one increasing function of T, we write the derivative (ONt/Oec) in eq.(4.20) as follows:
( ONt'~
fONt'~(OT)
Fc(t) Oer
(4.26)
Remembering that c = p/P, after certain rearrangements eq.(4.26) takes the following form, oqec I~d 1 [--er "l aT = fl-P c(T) exp t ~f
(4.27)
This is a differential equation, the solution of which will give the function ec(T), provided that the constant/~d is known. Let us assume for the moment that it is known. The boundary condition is the assumption that at T = To, desorption starts at the adsorption sites having adsorption energy e(To) = Co. 6. Having calculated the function ec(T), the temperature scale is converted into energy scale, and the function X(ec) is calculated from eq.(4.26), written in the following form,
x(cr =
[Oer
Fc(W(er /3vm
(4.28)
taT
or from eq.(4.17) considered as the following function,
While considering the limit T ~ 0 we had the purpose in mind to illustrate in an easy way the basic features of our new theoretical approach. In the actual TPD experiments c(T) is measured at finite temperatures. So, let us consider now this real physical situation. Eq.(4.26) does not change, as it is valid for any temperature. Changes will appear in eq.(4.14) obtained by inserting eq.(4.12), into eq.(4.11). To find the relation between the "local" rate of desorption (O0/Ot) and the "total" rate (00t/0t) at finite temperatures, we consider the following obvious relation
0r
or
f ( oe)
(4.30)
o
Then, we replace (i)O/i)T) under the integral by the following product,
oo (oo) OT-
~
(oo) OWl = -
~e
\OWJ
(4.31)
360 where, =
1
~-y exp { ~-:~ } kT [1 + exp {~-=m}]2 = -
( 00 )
(4.32)
kT
(Or
Because following form
is not a function of e, the r.h.s, of eq.(4.30) can be rewritten to the
jfo~ (i)O) ~ X(e)de =
(Oer 9~o~ (00)X(e)de = N
- f l \ 0T J
- f l (0er \ 0T,] Xr162
(4.33)
where the function X,(ec) is the same "condensation" function as that on the 1.h.s. of eq.(4.18). Now let us consider the "local" rate of desorption from the adsorption sites for which the energy is equal ec,
(O0/Ot)~=~r
(90
= fl
0T
s
= fl
~
s
\0T)
(4.34)
~:c
Because according to eq.(4.32), ~o = 4 k T ' we replace now (4.33).
and
~
~r = 4kT
(Oec/OT)ineq.(4.35),
0T)
(4.35)
by its value calculated from equations (4.30) and
0ec (o_~o ~ \Ot] 0---T = - -flXr162
(4.36)
Eq.(4.35) takes then the following form, 00
)
(oe,)
1 ~ 0t ~c = 4kW Xr162
(4.37)
In the limit T ~ 0 the fully accurate expression (4.37) reduces to the approximate expression (4.12) previously used by us. It means that the value of in eq. (4.12) is simply We should remember that for e = ec, the expression (4.9) reduces to the expre~sio. (4.11)because 0(~ = ~o)= ~. a It means, at finite temperatures eq.(4.28) is still valid, but the function X(e) calculated next from eqs (4.17), (4.18) and from (4.28) has the fully rigorous meaning of the "Condensation Approximation" Xc(ec) for the true adsorption energy distribution X(e). Having calculated Xc(e,) one can calculate X(e,) using the methods developed by Rudzinski and Jagiello[56-59]. In many cases sufficiently accurate values of X(e,) are obtained by using the Rudzinski-Jagiello approach[i],
4kT.
x(~) = Xc(~r - -g-(kT) ~ [-~2 j
Const
(4.38)
361 Eq.(4.38) is valid for the Langmuiric model of the "local" adsorption, assumed here by us. Rudzinski et.al, showed that including interactions between adsorbed molecules results mainly in using more complicated expressions to calculate X(ec) from Xc(ec), the latter function being always the key information about surface energetic heterogeneity[I,57]. Their considerations were based on the assumption, that lateral interaction parameters for a molecule adsorbed on a certain adsorption site does not depend on the adsorption energy of that site. Such an assumption seems to be fairly reasonable in the case of one-modal adsorption energy distributions. However, the actual TPD spectra from many solid surfaces are multimodal. They look like a number of more or less overlapping peaks, corresponding to various groups of sites existing on a studied solid surface. Thus, in many cases a certain generalization of the method developed by Rudzinski et. al. will be necessary, to account properly for the interactions between adsorbed molecules. A first necessary step toward such a generalization, will be "decomposition" of the calculated condensation function X~(ec) into the overlapping one-modal distributions corresponding to a certain kind of adsorption sites. In the present treatment we neglect diffusional effects, which may be responsible for a certain broadening of these overlapping peaks. Diffusional effects, however, are usually neglected in the published analyses of the TPD spectra from heterogeneous surfaces. It reflects the general belief that this is the energetic surface heterogeneity which predominantly governs the shape of a TPD diagram. A decomposition of a condensation function X~(e~), was done by Cases and coworkers[60] for a number of adsorption systems. It appeared then that such decomposition can be successfully made only for some systems, and failed as a general decomposition method. Then, these authors came to the conclusion that the general physical situation will be fol!owing. If even one may distinguish on a given surface, a number of distinct kinds of adsorption sites, (or surface domains), there will still exist a certain distribution of surface properties (adsorption energies) within each group. Therefore, one should consider the following representation for an actual adsorption energy distribution X(e), n
n
~(s -" ~ ~fiXi(s , ~ ")'i = 1 (4.39) i=l i=l where Xi(e) is the adsorption energy distribution for ith kind of adsorption sites. The condensation function X~(e) calculated from a TPD diagram will then have the form, n
Xc(s
E~ i ~-~6 ] X i ( , ) d , -- ~"[iXci(s i=1 0 i=l
(4.40)
where 0i(e) is the isotherm equation for adsorption on an ith kind of adsorption sites. Even, if it is still the Langmuir-like function, it may have different values of the constant Kd, for different kinds of adsorption sites. In their improved decomposition (DIS) method[61] Villieras et. al., used the following DA (Dubinin-Astakhov) functions to represent X~(e), (See the chapter contributed by these authors to this book), ~ci(s -- ri(s -- i ) Er
exp -
s __ 6o] Ei Ji
(4.41)
362 Although diffusional effects are commonly neglected, they can also be taken into account. Huang and Schwarz[62] showed that in the absence of intraparticle diffusion the more accurate description of mass transfer effect on the TPD spectra requires considering a nonsteady mass balance which includes both axial dispersion and convective transport. The equations that describe the concentration in the bulk fluid, c, and on the surface St, are given by, 0c 02c 0c 0-'-t = De ~ - U0z
1 -p
(4.42)
p 0Nt
0t
where D~ is an effective diffusion coefficient (m2/s), u is a temperature dependent constant, p is the porosity (cm3/cm3), and Y = Vm0t. It is known, that in addition to diffusional effects outside the solid phase, TPD spectra may be affected by intraparticle diffusion and lateral interactions between adsorbed molecules[49]. It is suspected that the two effects mentioned above may be a source of some extra peaks on the TPD diagrams. To illustrate the application of our new approach to analyze the TPD spectra, we take the experimental data obtained in the Department of Chemical Technology of Maria Curie--Sklodowska University in Lublin[63]. The Temperature Programmed Desorption measurements were conducted on AMI1 equipment (Altamira Instruments). Thermal Conductivity Detector was used as a detector. Experiments were conducted on the silica supported nickel catalyst (15%Ni) obtained by impregnation of silica Merck Kieselgel 100 by nickel nitrate solution. The catalyst made in this way was calcinated at 4000 and reduced right before the experiment. Hydrogen purified on the OXICLEAR deoxidant made by PIERCE CHEMICAL Co., and passed through the activated carbon absorber was used as a reducing agent. The carrier gas was argon (99.99%pure) additionally purified over the same OXICLEAR deoxidant and then by the molecular sieves 4A and 5A. Figure 5 shows the decomposition of the TPD diagram of hydrogen desorbed from the silica supported nickel catalyst, done by using the linear combination (4.22) of five functions Ci(T) defined in eq. (4.23). The parameters found by computer in the course of that decomposition are collected in Table 1. Table 1. The parameters found by computer in the course of the decomposition defined in eqs. (4.22) and (4.23) of the TPD diagram of hydrogen desorbed from the silica supported nickel catalyst.
r,
i=l i=2 i=3 i=4 i=5
10604 1405 2139 2004 1154
2.22 2.32 2.71 2.71 1.90
T~ (~
35.56 278.67 141.00 218.50 384.51
Ei (de9 ) 127.15 107.49 88.04 84.47 213.93
363
so oo -~
;> .,..,
60.00 l I l 1 l
40.00
l l
I I
I
20.00
f
1 I
I
i
~
i
I i
I
t
t
I I~~
t
4,
4
\
0.00 0
200
400
600
800 T (~
Figure 5. The decomposition of the experimental TPD diagram (--) by the linear combination (4.22) of the function (4.23), (. . . . ), done by using the parameters collected in Table 1. The values 1, 2, 3, 4, 5 at the maxima of the composite functions ci(T) denote the value of the index "i", in eq. (4.22) and in Table 1. Figure 6 shows the function ec(T) calculated by solving the differential equation (4.28) for three values of the parameter Ka, the temperature dependence of which was neglected. The choice of the parameters /~'d in our model calculation was dictated by the obvious requirement that the ec values should lie in a physically reasonable range. We are facing here a situation, which is similar to that in the papers reporting on calculation of X(e) from the equilibrium adsorption isotherms. There the calculation of X(e) requires the knowledge of the Langmuir constant K. Changing the assumed values of K resulted there primarily in shifting the calculated function X(e) on the energy scale toward higher or smaller values of e. In the case of X(e) calculated here from the TPD spectra, changing the assumed value of/(a results both in shifting X(e) on the energy scale, and in a simultaneous broadening (or vice versa) of X(e), as it is shown in Figure 7. The choice of the parameter e0 = e(T0) has a similar effect on the calculated function X(e). Figure 8 shows the solution of the differential equation (4.28) for three values of e0, and Figure 9 shows the corresponding calculated functions X(e). While thinking about a possibility of determining the parameters /(d and e0 in an in-
364 60.0o
E
50.0
40.02
30.0
20.0
1
10.0 .=
2
0.0-~
. . . . . . . . .
270
I . . . . . . . . .
470
I . . . . . . . . .
670
I . . . . . .
870 T (K)
Figure 6. The functions r calculated from eq. (4.28) by assuming co(To) = 0, for three values of the parameter /fd; the curve No 1 is obtained by assuming that h'a = 105, the curve 2 is for R'd = 104, and the curve 3 corresponds to/x'a = 103.
dependent way, one might eventually consider independent calorimetric measurements. Calorimetric studies of adsorption equilibria show that the main contribution to the isosteric heat of adsortpion qst comes from the energies of adsorption. That possibility deserves obviously further theoretical studies. After determining the "condensation" function Xc(ec), we decompose it in the way outlined in eqs. (4.40) and (4.41), for its further theoretical analysis. Figure 10 shows an example of the decomposition of the condensation function Xc(ec). Table 2 collects the parameters; 7i, ri, q0 and Ei found by computer, while decomposing the function Xc(cc) calculated by assuming e(T0) = 0, and/(d = 104-
365 0.800
E
0.60-
0.403
0.20-
0.00
, ,
I
I
i
I
I
0.0
I
J
I
I
I
I
I'
I
I
I
10.0
I
I
'
'
'
'
'
'
20.0
,
i
,
I
'
i
['
'i
i
30.0 r (kJ/mole)
Figure 7. The condensation functions X~(E~) calculated from eq. (4.28) with the functions (Oec/OT) = f(ec), found by numerical differentiation of the functions ec(T) presented in Fiure 6. The numbers 1, 2, 3 correspond to the same values of the parameter /~d as in Figure
g
Table 2. The parameters: 7i, ri, ei, 0 Ei found by computer while decomposing the calculated function Xc(e~) by the linear combination (4.40) of the functions (4.41).
i=1 i=2 i--3 i-4
~/i
ri
0.4 0.105 0.34 0.1
3.5 2.4 2.1 2.2
s0
(kJ/mole)
8.3 10.05 8.58 10.8
Ei (kJ/mole)
1.48 0.6 2.9 4.0
The decomposition of the function Xc(ec) can be done by assuming at least four over-
366 ~, r
0
E ~
40.0-
-
30.0-
20.0 =
3
10.0-
0.00 5 27O l
I
I
I
l
I
l
I
I
I
I
470
I
I
I
I
a
l
I
I
i
l
670
l
I
I
I
I
I
I
I
i
I
l
I
I
I
l
870
T (K) Figure 8. The functions ec(T) calculated from eq. (4.28) by assuming that /fd = 104, for three values of the parameter co, 0 (curve No 1), 5 kJ/mole (curve 2) and 10 kJ/mole (curve 3).
laping peaks to exist. The estimated variances Ei's of the composite functions Xci(ec), are smaller than the values of kT corresponding to the related range of temperatures T(ec). Meanwhile, even if Xi(e) had been a Dirac delta function 6 ( e - e,), the variance of its corresponding condensation function should be close to kT value. It means, that while calculating Xc(er for this particular chemisorption system, from TPD spectra, one should consider values of e(T0), Kd leading to broader functions X~(e~). This can be done by assuming e(To) = 0, and/~d values smaller than 103. Such parameter values will result into broader calculated X~(er functions, but also shifted on energy scale toward higher chemisorption energies. In fact, Lee and Schwarz[64] argue that for the nickel-supported silica catalysts the activation energies for hydrogen desorption should vary from 50 to 90 kJ/mole. Thus, the decomposition of the calculated X~(e~) function, and then studies of its composites X~i(ec) seem to create a chance for arriving at reasonable range of the values of the parameter/(d-
367
o
60.
40.0
0.00 5.0
10.0
15.0
- - - - ~ 20.0 25.0 r
(U/mole)
Figure 9. The condensation functions Xc(Ec), calculated from eq. (4.28) by assuming that ~'d = 104 , for three values of the parameter r The numbers 1, 2, 3 correspond to the sazne values of the parameter E0 as those in Figure 8. CONCLUSIONS While presenting our new approach to the kinetics of adsorption (onto), and desortpion from flat but energetically heterogeneous surfaces, for the purpose of clarity, the consideration was limited to the model of a monolayer localized and ideal adsorbed phase. The key problem in this approach is to relate the "local" rate of adsorption OO/Ot to the "total" rate 0StlOt. This can also be done by considering other adsorption models - the models of mobile adsorption for instance. In the case of localized adsorption, the interactions between adsorbed molecules can easily be taken into account, just in the same way as in the case of adsorption equilibria. Similarly to the case of adsorption equilibria, including the lateral interactions there will be a much more difficult problem in the case of mobile adsorption on the surfaces posessing topography different from patchwise. The proposed method is based on the assumption that the transient states of the adsorbed phase are close to those corresponding to the adsorption equilibria. This also involves an implicit assumption that the surface diffusion proceeding via "hopping" or in another way is a fast process. The easy way in which the commonly observed laws of adsorption kinetics, are developed here, may suggest that the above assumption is true for the majority of adsorption systems and physical situations studied experimentally.
368
0"501 0
E
-
II
-
I I
0.10- _
/I..I.. 3,,"Jl I " ~
" --
0.008.0
I
/
rl
I
x~
z
"12.0
16.0
20.0 r162(kJ/mole)
Figure 10. The decomposition of the condensation function Xc(ec), calculated by assuming that e(To) = 0 and ~'d = 104. The broken lines are the composite functions "TcXc~(ec). REFERENCES 1. Rudzinski, W.; Everett, D.H., "Adsorption of Gases on Heterogeneous Surfaces", Academic Press, 1992. 2. Jaroniec, M.; Madey, E., "Physical Adsorption on Heterogeneous Solids", Elsevier, 1989. 3. See the review by Low, M.J.D., Chem. Rev., 60, (1960), 267. 4. Roginski, S.; Zeldovich, Ya. Acta Physicochim., URSS 1, (1934), 554. 5. Roginski, S.; Zeldovich, Ya. Acta Physicochim., URSS 1, (1934), 595. 6. Aharoni, C.; Ungarish, M., J.C.S. Faraday Trans. I, 73, (1977), 1943. 7. Aharoni, C.; Ungarish, M., J.C.S. Faraday Trans. I, 74, (1978), 1507.
369 8. Aharoni, C.; Suzin, Y., J.C.S. Faraday Trans. I, 78, (1982), 2329. 9. Tovbin, Yu., "Lattice-Gas Model in Kinetic Theory of Gas-Solid Interface Processes", Progress in Surface Science, 34, (1991), 1. 10. Cerofolini, G.F., in "Adsorption and Chemisorption on Inorganic Sorbents", Dabrowski, A.; Tertych, V.A., Editors, Elsevier, 1996. 11. For most recent and exhaustive review see, Bhatia, S.; Beltramini, J.; Do, D.D., Catal. Today, 7, (1990), 309. 12. Ward, C.A.; Findlay, R.D., J. Chem. Phys., 76, (1982), 5615. See also proceeding paper by Ward, A., Findlay, R.D., and Rizk, M., J. Chem. Phys., 76 (1982), 5599. 13. Aharoni, C., Ungarish, M., J.C.S. Faraday Trans. I, 73, (1976), 456. 14. Talbot J.; Jin, X.; Wang, N.-H., Langmuir, 10, (1994), 1663. 15. T.L.Hill, J.Chem.Phys., 17, (1949) 762. 16. S.Ross, and J.P.Olivier, "On Physical Adsorption", Interscience Publishers, Inc., N.Y. 1964. 17. Chen, Yu-D., and "fang, R.T., "Multicomponent Diffusion in Zeolites and Multicomponent Surface Diffusion", this monograph. 18. A. Kapoor, and R.T. Yang, A.I.Ch.E.J., 35, (1989) 1735. 19. A. Kapoor, and R.T. "fang, Chem. Engng. Sci., 45, (1990) 3261. 20. V. Pereyra, G. Zgrablich, and V.P. Zhdanov, Langmuir, 6, (1990) 691. 21. V. Pereyra, G. Zgrablich, Langmuir, 6, (1990) 118. 22. V. Mayagoitia, F. Rojas, V. Pereyra, and G. Zgrablich, Surface Sci., 221, (1989) 394. 23. V. Mayagoitia, F. Rojas, J.L. Riccardo, and G. Zgrablich, Phys. Rev., B41, (1990) 7150. 24. J.L. Riccardo, V. Pereyra, G. Zgrablich, F. Royas, V. Mayagoitia, and I. Kornhauser, Langmuir (in press). 25. T.Nitta, M.Kuro-Oka, and T.Katayama, J.Chem.Eng.Jpn., 17, (1984) 45. 26. W.Rudzifiski, K.Nieszporek, and A.D~browski, Adsorption Science and Technology, 10, (1993)35. 27. Rudzinski W., and Aharoni, C., Polish J. Chem., 69, (1995) 1066. 28. C.Aharoni, and M.Ungarish, J.C.S. Faraday Trans. I, 73, (1977) 1943. 29. C.Aharoni, and M.Ungarish, J.C.S. Faraday Trans. I, 74, (1978) 1507.
370 30. C.Aharoni, and Y.Suzin, J.C.S. Faraday Trans. I, 78, (1982) 2329. 31. I.A.Pajares, I.L.Garcia Fierro, and S.W.Weller, J.Catal. 52. (1978) 521. 32. I.L.Garcia Fierro, and I.A.Pajares, J.Catal. 66, (1980) 22. 33. V.A.Bakaev, and W.A.Steele, Langmuir 8, (1992) 1372, 8, (1992) 1379. 34. Amenomiya, Y.; Cvetanovic, R.J., J. Phys. Chem. 67, (1963) 144. 35. Cvetanovic, R.J.; and Amenomiya, Y., Catal. Rev. Sci. Eng.6_, (1972) 21. 36. Falconer, J. L.; Schwarz, J.A., Catal. Rev. Sci. Eng. 25, (1983) 141. 37. Kreuzer, H.J., Langmuir 8, (1992) 774. 38. Tovbin, Yu., in "Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces", (Rudzinski, W.; Steele, W.A.; and Zgrablich, G., Editors) Elsevier, 1996. 39. Czanderna, A.W.; Biegen, J.R., and Kollen, W., J. Colloid Interface Sci., 34, (1970) 406. 40. Carter, G., Vacuum, 1_.22,(1962) 245. 41. Dawson, D.T.; and Peng, Y.K., Surface Sci. 33, (1972) 565. 42. Tokoro, Y.; Misono, M.; Uchijima, T.; and Yoneda, Y., Bull. Chem. Soc. Japan, 51, (1978) 85. 43. King, D.A., Surface Sci. 47, (1975) 384. 44. Tokoro, Y.; Uchijima, T., and Yoneda, Y., J. Catal., 5..66,(1979) 110. 45. KnSzinger, H.; Ratnasamy, Catal. Rev. Sci. Eng. 1j_7,(1978) 31. 46. Davydov, V.Ya; Kiselev, A.V.; Kiselev, S.A; Polotryuk, V.O.V, J. Coll. Interface Sci. 74, (1980) 378. 47. Unger, K.K.; Kittelman,U.R.; Kreis, W.K., J. Chem. Techn. Biotechnol., 31, (1981) 435. 48. Malet, P.; and Munuera, G., in "Adsorption at the Gas-Solid and Liquid-Solid Interface", p.383, Rouquerol, J., and Sing, K.S.W., Eds., Elsevier 1982. 49. Leafy, K.J.; Michaels, J.N.; and Stacy, A.M., AIChE J., 34, (1988) 263. 50. Ma, M.C.; Brown, T.C.; and Haynes, B.S., Surf. Sci. 297, (1993) 312. 51. Salvador, F.; and Merchan, D., React. Kinet. Catal. Lett., 52, (1994) 211. 52. Proccedings of the "Second International Symposium on Surface Heterogeneity Effects in Adsorption and Catalysis on Solids", Zakopane-Levoca, (Poland-Slovakia), held in autumn 1995 (Brunovska, A.; Rudzinski, W.; and Wojciechowski, B.W., Editors).
371 53. Roginsky, S.; and Todes, O., Acta Phisicochim. USSR, 21, (1946) 519. 54. Harris, L.B., Surface Sci.,10, (1968) 129, 13, (1968) 377, 15, (1969) 182. 55. Cerofolini, G.F., Surface Sci., 24, (1971) 391. 56. Rudzinski, W.; and Jagiello, J., J. Low Temp. Phys. 45, (1981) 1. 57. Rudzinski, W.; Jagiello, J., and Grillet, Y., J.Colloid Interface Sci., 87, (1982) 478. 58. Jagiello, J.; Ligner, G.; and Papirer, E., J. Colloid Interface Sci., 137, (1989) 128. 59. Jagiello, J.; and Schwarz, J.A., J. Colloid Interface Sci., 146, (1991) 415. 60. Villi~ras, F., Cases, J.M., Francois, M., Michot, L.J., Thomas, F., Langmuir, 8_, (1992) 1789. 61. Villi~ras, F., Michot, L.J., Cases, J.M., Francois, M., Rudzinski, W., Langmuir (in press). 62. Huang, Y.J.; Schwarz, J.A., J. Catal., 99, (1986) 149. 63. Rudzinski W., Borowiecki T., Dominko A., Zientarska M., Chemia Analityczna, (in press). 64. Lee, P.I, Schwarz, J.A., J. Catal., 73, (1982) 272.
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w. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
373
Surface diffusion of adsorbates on heterogeneous substrates G. Zgrablich Centro Regional de Estudios Avanzados, Gobierno de la Provincia de San Luis, Av. del F u n d a d o r s/n, 5700 San Luis, Argentina and D e p a r t a m e n t o de Ffsica, Universidad Nacional de San Luis, C h a c a b u c o y Pedernera, 5700 San Luis, Argentina
Contents 1-
Introduction
2 - G e n e r a l theory of surface diffusion 2.1 - Tracer diffusion coefficient 2.1.1 - U n c o r r e l a t e d r a n d o m walks 2 . 1 . 2 - Correlated r a n d o m walks 2.2 - Chemical diffusion coefficient 2.3 - Relation between D and D* 2.4 - Phenomenological a p p r o a c h to D 2.5 - Arrhenius form for D 2.6 - Lattice-gas model for the diffusion coefficient on heterogeneous surfaces 3 - Surface diffusion in porous solids 3.1 - Surface diffusion coefficient from permeability e x p e r i m e n t s in porous solids 3.2 - Simple empirical and phenomenological models 3 . 2 . 1 - Variations on t h e Arrhenius equation. 3 . 2 . 2 - Effective m e d i u m approximation 3 . 2 . 3 - Percolation m o d e l 4 - Tracer diffusion on simple t o p o g r a p h y heterogeneous surfaces 4.1 - R a n d o m t r a p s
374 4 . 2 - Random barriers 5 - Diffusion on correlated heterogeneous surfaces: an approach for the description of general topography 5.1 - Generalized gaussian model 5.1.1 - Influence of a for random surfaces (r0 = 0) 5.1.2 - Combined effect of a and r0 5.2 - Site-bond model 5.2.1 - Tracer diffusion on correlated traps and barriers surfaces 5.2.2 - Tracer diffusion on a general site-bond surface 5.2.3 - Chemical diffusion coefficient for a general site-bond surface 6 - Conclusions and open problems 1.
INTRODUCTION
Diffusion of adsorbed particles on heterogeneous surfaces is a most important phenomenon ocurring in a variety of processes of great practical relevance such as gas separation and purification by adsorption, heterogeneous catalysis, crystal growth, wetting, sintering, etc. In spite of continuous theoretical and experimental efforts dedicated to its study for many years, surface diffusion on heterogeneous surfaces still presents many unsolved problems and doubts related to the mechanisms and physical nature of the migration of particles and to the characterization of surface energetic heterogeneity. In the last ten years, researchers have recognized the importance of including the energetic topography in the statistical description of heterogeneity. This, and the progresses in the experimental techniques for surface analysis at the atomic scale, have encouraged the development of more refined models of heterogeneous surfaces capable of including the topographic characteristics of the adsorptive energy surface. One of the important problems is then to study the effects of the energetic topography both on the tracer and chemical diffusion coefficients. Another important problem, specially due to its applications to heterogeneous catalysis, is that of surface diffusion in porous adsorbents. Here, many different effects contribute to the behavior of the chemical diffusion coefficient as a function of the surface coverage and other types of phenomenological models, or semiempirical equations incorporating apparent (or resultant from the superposition of several effects) quantities, are necessary. In the present monograph, a general basic framework is first given for describing surface diffusion on heterogeneous surfaces, and then particular models are presented and discussed for the two specific problems mentioned above. 2.
G E N E R A L T H E O R Y OF S U R F A C E D I F F U S I O N
This section is devoted to state the general kinetic equations which are independent of the particular model assumed to describe the energetic heterogeneity and are, in particular, valid for homogeneous surface. We end with an explicit description of surface
,(a)
!
. (b) ,
, ,
o
~a,~
,
X
375
..
X
Figure 1: Random walks in one (a) and two (b) dimensions. diffusion on heterogeneous surfaces based on the lattice-gas model, which is the most general description available at present. 2.1.
T r a c e r Diffusion Coefficient
Tracer diffusion Coefficient, D* , refers to the migration of a single particle (the "tracer") on a surface. It can be conveniently introduced by analyzing the particle movement as a "random walk" [1-3]. 2.1.1.
U n c o r r e l a t e d R a n d o m Walks
In an uncorrelated random walk each jump is completely independent from the previous one. For homogeneous surfaces this occurs when the particle transfers its energy rapidly to the phonon bath of the substrate after a jump, in such a way as to stay in the new site for a long time compared to the periods of typical vibrational modes involved in jumps [4]. If this happens, the particle loses memory of the previous jump and the next one will be made in a completely random direction. For heterogeneous surfaces, however, in addition to the above condition, the random walk will be uncorrelated only if at each site all jumping directions are equally probable. This new condition will be fulfilled obviously only by some kinds of heterogeneous substrates (for example a random traps lattice). For an uncorrelated random walk in one dimension, Fig. l(a), we have as well known results
[2]: (x(t)) = 0
(2.1)
(~(t)) = ~ ( t )
(2.2)
where n(t) is the number of jumps in a time t. The average (...) is intended to be taken over a large number of walks of duration t. In two dimensions we will have analogously
(R2(t)> = a2nz(t) + ayny
(2.3)
376 or, if a~ = ay = a and n(t) = n~(t)+ ny(t)
(R2(t)l = a2n(t)
(2.4)
Introducing now an "effective" jump frequency I/ef f "--
(2.5)
t
the mean square displacement for the tracer can be written as (R2(t))
=
a2Vefft
(2.6)
For homogeneous media vr
is a constant and the proportionality law
(R2(t)> c< t,
(2.7)
which is valid for a medium with any Euclidean dimension d, is fulfilled (normal diffusion). For heterogeneous media, v~ff can be in general time-dependent and (2.7) is replaced by the "anomalous diffusion" proportionality law [5] 2
(a2(t)) c< t ~ ,
d~ _ 2
(2.8)
where the value of d~ depends on the system considered. Anomalous diffusion occurs typically on fractal substrates and for some kinds of energetically heterogeneous surfaces, but also in a variety of other interesting physical problems [6]. The Tracer Diffusion Coefficient, D* , for a medium with Euclidean dimension d, is defined independently of the characteristics of the medium as:
(R~(t)) = 2dD*t
for t ~ ~
(2.9)
or equivalently, D * = tlim--,~[ (R2 (t))2dt
(2.10)
Combining (2.6) and (2.10) we have D * = tlim
a21/eff ] 2d ]
(2.11)
In the case of surface diffusion d = 2. However, it is useful to keep these equations in their most general form. Definition (2.10) is independent of the nature of the jumps (for example, these can be due to an activated process, i.e. jump over an energy barrier, or through a tunneling process). If the jumping process is an activated one, we can use the absolute rate theory [7,8] to write I / e f f "-
2dr e x p ( A S / k s ) e x p ( - E , / k s T ) ,
(2.12)
377 where v = k B T / h , A S is the activation entropy corresponding to the transition of a particle from a potential minimum to the saddle point of a barrier and E~ the activation energy (the energy height of the saddle point from the bottom of the potential minimum). With this, the tracer diffusion coefficient for an activated jumping process can be now written as (2.13)
D*= Do exp(-Ea/ksT),
where
(2.14)
Do=
Eq. (2.13) is known as the Arrhenius law and is strictly valid for an homogeneous surface. For heterogeneous surfaces AS and E~ generally vary from point to point. Some times, a satisfactory approximation is to replace these values by their mean values, AS and Ea, taken over the whole surface. However, as it will be shown later on, for some heterogeneous surfaces the Arrhenian behavior is not always appropriate. For an assembly of N classical (distinguishable) particles the tracer diffusion coefficient still has a perfectly good meaning through the generalized definition D * = t~oo lim 2 d1g t ~N( [ / ~ i ( t ) -/~i(0)[ 2) ,
(2.10')
i=1
although it cannot be measured experimentally due to the impossibility of labelling the N particles, but, of course, it can be determined in computer simulations. 2.1.2.
Correlated
Random
Walks
When the relaxation time of the particle after a jump is comparable to the vibrational period, or when energetic heterogeneity makes the jump probability depend on the direction, a given jump will not, in general, be independent from the previous one and we will have a correlated random walk. To illustrate how this can be due to heterogeneity, we refer to Fig. 2 where the adsorptive energy E along a direction x on the surface is represented. If the tracer particle located at site i jumps to the right, the next jump will probably be made in the reverse direction due to the lower potential barrier. Let us write the mean square displacement of the tracer particle as: n
n
i=l
j=l
= <(X:
zJ)>
(2.15)
where r'k is the displacement vector corresponding to the k th jump and the point (.) represents the scalar product between vectors. After developing the sums and grouping terms conveniently, and taking into account that r'k.r'k = a 2, we have = ha2(1 + 9
n
a2 k=l
2
+ --
a------y--- - t- .. .) ~-2~ ~'k.~'k+2
?'t k - 1
(2.16)
378
X Figure 2: Correlated random walk due to energetic heterogeneity. Introducing now the definition of the "jump correlation factor", f, as ~
/=(1+-~
? ~ - - T?Z
~
~
rk.rk+m
aS
)
(2.17)
?2 m--1 k--1
the mean square displacement can be expressed in the form:
(R2(t)) = n a ~ f
(2.18)
In this way, if the definition of tracer diffusion coefficient, Eq. (2.10), is maintained, then the most general expressions involving D* become: = 2dD*t = na2 f D*
=
lim
na2 f ] 2dt J
(2.19) (2.20)
The value of the jump correlation factor, f, ranges from 0 to n. For an uncorrelated random walk all jump directions are distributed completely at random in such a way that (~'~.~'j) = 0 for any pair (i, j) and f = 1. If jumps are so strongly correlated that given the first jump in a given direction all subsequent jumps will be taken in the same direction, then (~'~.~'j) = a 2 for all pairs (i, j) and f = n. Another limit situation arises when, given a jump in one direction, the probability of the next jump to be taken in the reversal direction is very high. In such a case f approaches zero. A very complete discussion of the properties of correlated random walks is given in [9].
379 2.2.
C h e m i c a l Diffusion Coefficient
The Chemical Diffusion Coefficient, D, is usually defined through the Fick's First Law expressing the proportionality between the diffusive flux of adsorbed particles, J, and the particle concentration gradient ~Tp(~'): 1st Fick's Law
J = -DV,.p(~')
(2.21)
Here the adsorbate concentration p is assumed to depend only on the spatial position K This definition is generally valid even for heterogeneous surfaces where D can be a function of the position. If the adsorbate concentration p were also a function of time, then taking into account the continuity equation -
0
V.J + -~p(~',t) = O
(2.22)
we have 0
O---~p(r, t) = Yr. [DVrp(r, t)]
(2.23)
which reduces to the well known 2=d Fick's Law
~tp(r t) DV~p(~',t)
(2.24)
=
only in the case that D is independent of the spatial coordinate K In the most general case, and specially for heterogeneous media, D not only will depend on the spatial coordinate but also on the direction (anisotropic medium) being then represented by a tensor [4]. 2.3.
Relation
Between
D a n d D*
A general relation between D and D* can be obtained by expressing the two diffusion coefficients in terms of velocity correlation functions. This can be easily achieved in the case of D*. In facts, the mean square displacement for a tracer particle can be written as {R2(t)) = {[ fotV(t ') dt'12)
= =
]o'dt'
dt"{g(t').g(t")>
(2.25)
380 where 5(t) is the instantaneous velocity of the particle at time t.The velocity correlation function K~ = (5(t').TY(t"))
(2.26)
in Eq. (2.25) depends only on the time difference ~- = t" - t', in such a way that K ~ ( t " - t ' ) = (g(t').g(t"))= (fi'(0).g(7-))= K . ( 7 ) = K ~ ( - r )
(2.27)
With this, Eq. (2.25) can be written as (R2(t)) = ~0t dt' /~,____.--Td~-(g(0).~'(T)) t'
= ~0t dT j~Ot-r dt' (g(O).~7(~-))+ drKv(T)(t - T)+
=fo' dTK.(v)(t
=2]o' d~'(t -
f
d~-
dt' (~'(0).77(r))
t
"7-
f t drKv(z)(t + T)
- T)+ j~Ot d r K . ( r ) ( t -
7)
~-) (g(O).~7(T))
(2.28)
Then, using the definition (2.10), we finally obtain (2.29)
D* = -~ 1 ~o,~ dt(5(O).g(t)) For a system of N tracer particles, this result can be easily generalized to D* = - f 1- j ~ ~=~ fo
(2.30)
~ dt(gi(O).g~(t))
It is interesting to see how the simple expression, Eq. (2.11), can be derived as a particular case of Eq. (2.29). If 5(0) and g(t) are correlated only over a "jump time" t ~ 2 , then
Veff
D*
1 [~
--- -d J O
115
dt(5(O).5(t)) ~ -~
(5(0/.5(
I/elf
)
(2.31)
and, given that g(0) = 5'(~-~) ~ a~'eff/2, this reduces to D* -- 2-~a 2lJef f
(2.32)
381 The analogous expression to Eq. (2.30) for D in terms of the velocity correlation function can be obtained by a simililar but much more lengthy procedure given, for example, in [10]. The result is the Kubo-Green formula: oo
N
N
1 fo dt(~ ~'~(0). ~ ~'j(t)) D = 2kBTp2xA i--1 j--1
(2.33)
where n is the two-dimensional compressibility of the adsorbed phase and A its area. It can be shown [4] that x is related to the mean square fluctuation of the number of molecules in A through ((6N)2> =
ksTp 2xA
(2.34)
so that Eq. (2.33) can be finally written as 1 r~ D = 2((5N) 2) Jo
N
N
dt(~ ff~(O).~ gj(t)) i--1
(2.35)
j--1
As a first important difference between D* and D, we notice that, while in D* only velocity correlations for each single tracer particle are involved, D depends also on cross correlations for the velocities of different particles. By separating in Eq. (2.35) the contributions of the direct and cross correlations, and combining with Eq. (2.30), we can write
D_D= D*
(N) ((SN) 2)
[l + f~ dt(~iCj gi(O).gj(t)) ] J dtf E~(g~(0).g~(t)) o
[
(2.36)
Thus a simple relation between D and D* exists only in the particular case in which there are no cross correlations between the velocity of different particles. In that case v~(0) and gj(t) are totally independent for i-r j and Eq. (2.36) reduces to:
D (N> D--7 = ((5N)2)
(2.37)
The relation between (N) and ((SN) 2) can be readily obtained through the partition function for the grand canonical ensemble of adsorbed particles at temperature T and chemical potential/~ as:
((/~> = o-~0(k-~) where 0 = D D*
N/A
(2.38)
is the surface coverage. Thus Eq. (2.37) becomes
0 ~]
~n-~ (k-~)
(2.39)
382 known as the Darken equation [11]. The right hand side is referred to as the "thermodynamic factor". The coverage dependence of the chemical potential, the isotherm equation, can be obtained through a great variety of adsorption models, depending on the kind of system to be considered. For a general heterogeneous surface we have already seen that single particle correlations could be present over several jumps, and it is then also probable that in some cases cross correlations for velocities would be important. The validity of Darken equation for heterogeneous surfaces should then be considered only as an approximation. The effects of adsorptive energy heterogeneity on the chemical diffusion coefficient, according to Eq. (2.39), will have two components, i,e. those acting through the tracer diffusion coefficient D* and those acting through the thermodynamic factor. Of them, the latter have been intensively studied through adsorption models for heterogeneous surfaces [12, 131.
2.4.
Phenomenological Approach to D
As discussed in the previous section, the calculation of D is a difficult problem even if D* and the coverage dependence of ~t were known, except in the most simple case in which the Darken equation is valid. For such reason, the development of a phenomenological approach to D is of great importance. One of the most transparent of such approaches is due to Reed and Ehrlich [14], who introduce a "phenomenological diffusion coefficient ", D, as
b = ~r(O)a 2
(2.40)
where F(0) is a coverage dependent effective jump frequency. The basic assumption of this model is that the Darken equation is still valid for the general case in which cross velocity correlations are not negligible if D" is replaced by D. In this way the chemical diffusion coefficient in the Reed-Ehrlich model can be written as
D = ~r(O)a2[O(g/ksT)/OlnO]T
(2.41)
The reasonability of this model can be further appreciated by combining the KuboGreen formula, Eq. (2.35), with Eq. (2.38) to obtain
D = [O(~/ksT [ OlnO
(2.42) =1
Then Eq. (2.41) has the same physical meaning as the Kubo-Green formula if the factor
lfo 2N
N g~(O).gj(t)) dt(~-~ i,j
is considered as an average b = ~F(O)a 2.
(2.43)
383 The validity of Eq. (2.41) has been tested by Monte Carlo simulation in the case of homogeneous surfaces [15] against the Bolzmann- Matano and the number fluctuation methods [8]. All three methods agree in general, except when repulsive nearest-neighbor and attractive next-nearest-neighbor interactions are present among adsorbates. In such case, predictions of eq.(2.41) are in concordance with those obtained by the transfer-matrix method [8,16]. Although the Reed-Ehrlich model was formulated for homogeneous surfaces, it is intuitively clear that it can be also used as an approximation for heterogeneous surfaces. In fact, Eq. (2.41) should be valid (to the same degree of approximation as for an homogeneous surface) for one realization (sample) of an heterogeneous one. Now, the chemical diffusion coefficient for an heterogeneous surface should be obtained as an average over a large number of surface samples (all prepared with the same statistical properties). If the effective jump frequency, F(0), and the thermodynamic factor are statistically independent quantities, the averaging process will preserve the factors separation and the form of Eq. (2.41). Thus the applicability of the Reed- Ehrlich model to the case of an heterogeneous surface amounts to statistical independence of the effective jump frequency and the thermodynamic factor. We shall see in Section 2.6 what this really means in a particular formulation of the diffusion coefficient for heterogeneous surfaces based on the lattice-gas model. 2.5.
A r r h e n i u s F o r m for D
It is usual, but not always useful, to force the chemical diffusion coefficient to obey an Arrhenius law of the type D = Doe -zE~
;
(2.44)
~ = 1/ksT
where the "apparent" Arrhenius parameters, i.e. the preexponential factor Do and the activation energy for diffusion Ed , are given by Do = lim D
(2.45)
t3~0
Assuming the validity of Eq. (2.41), Ed can be written as
[a~nr(o)] Ed=-[ O~ o- E-
La-~ejr
[~J
alno -
r alnOO~
0~] -10H~
~]r
(2.47)
0l,~o
where U is the internal energy per particle, H~ the heat of adsorption per particle and the effective activation energy of jumps given by -[aznr(e)/o~]e. Eq. (2.47)showsthat /~ is a function both of 8 and T, so that linear Arrhenius plots of InD against/3 can be expected only over narrow ranges of T. It is also shown that if the heat of adsorption
384 increases (decreases) with coverage then Ed is greater (smaller) than the effective activation energy of jumps, /). Lateral interactions and heterogeneity, however, will affect, in general, both terms in Eq. (2.47), in such a way that they are not really meaningful as separate contributions. Interpretation of experimental data in terms of the apparent Arrhenius parameters Do and Ed should be handled very carefully since the validity of Eq. (2.44) can be far from reality in the case of heterogeneous surfaces or when lateral interactions are strong enough to produce order-disorder phase transitions in the adsorbate. In the last case, values of D varying through several order of magnitude with coverage can be found [17-21]. 2.6.
L a t t i c e - G a s M o d e l for t h e Diffusion Coefficient on H e t e r o g e n e o u s Surfaces
Following [8,22,23] we present here a kinetic derivation of D for a general heterogeneous surface assuming that the adsorbate can be represented by a lattice-gas of interacting particles. More precisely, we assume that adsorbed particles are located in a two-dimensional array of adsorption sites S, Fig.3, each one being either vacant or occupied by a single particle. Diffusion occurs via activated jumps of particles to nearest-neighbor empty sites through the saddle points, or bonds, B. Jumps to the next-nearest-neighbor sites through the high energy barriers H are neglected (effects of long range jumps on diffusion, discussed in [24], will not be considered here). The adsorptive energy can be described by a three-dimensional potential energy surface E(x, y), where x and y are the cartesian coordinates on the substrate. Fig.3, left hand side, represents equipotential contour lines of such energy surface for an homogeneous (a) and an heterogeneous (b) surface. When one moves along, for example, the x direction, at a fixed y = y0, the adsorptive potential profile shown on the right hand side is found. We assume that the statistical properties of the adsorptive energy surface can be conveniently described by an n-point multivariate distribution function:
6,~( El, E2, ..., En)dE1 dE2...dEn
(2.48)
which is the probability of finding an energy Ea E (Ea,E1 + dE~) at the point (x~,y~), E2 E (E2, E2 + dE2)at (z2, y2),..., E,~ E (E,~,E,~ + dE,-,) at (x,~,yn). The n points (xl, yl), (x2, Y2),..., (z~, y~) can be chosen in some convenient way to describe the energetic topography. For interacting particles, the jump rate will be in general dependent upon the particular configuration "z" of adsorbed particles in the neighborhood of the jumping particle and on the potential energy topography around it. So, we can write the flux of particles from column 1 to column 2 (see the quare lattice of Fig. 3 (b)) as 1
J,,2 = ~<E PAo,iKAo,,)
(2.49)
where PAO,i is the probability that the site in the first column is occupied by a particle A and the nearest-neighbor site in the second column is empty, with an environment of
385
S
B
H
Y
V(x,yo) A
~
a---~
S
N 1
a-----~ 2
X
X
~
(a)
B
H
X
V(X,Yo)
(b)
X
Figure 3: Equipotential contour lines (left hand side) and adsorptive potential along x direction (right hand side) for homogeneous surface (a) and heterogeneous surface (b), showing adsorption sites S, saddle points B and barriers H.
386 adsorbed particles marked by the index i, and K is the rate constant for the transition
AO, i ---+OA, i. The summation on i is taken over all possible configurations of adsorbed particles surrounding the pair AO and the average (...) is taken over the ensemble of surfaces, i.e. should be calculated with the distribution function (2.48) as:
((--.))----/---/(...)~n(Sl,...,En)dSl...dEn
(2.50)
Using the grand canonical distribution, we have
(2.51)
PAO,i - Poo,i exp [/~(#1- E 1 - WAO,i)]
where Poo,i is the probability that a pair of nearest-neighbor sites is empty, with the environment marked by the index i, #1, is the chemical potential in column 1, E1 is the energy of the site occupied by the jumping particle in column 1 and WAO,i is the lateral interaction of a particle A with the neighborhood "{'. Substitution of (2.51) into (2.49) yields 1
J1,2 = ~(exp(~#l) ~ Poo,iKAo,~ exp[-13(E1 + WAO,~)])
(2.52)
i
In a similar way, the flux from column 2 to 1 is given by 1
J2,1 -" ~(eXP(fl~2) 2 Poo,iKoA, i eX,p [-/3(E2 + WOA,~)]) i
(2.53)
where #2 is the chemical potential in column 2. Application of the detailed balance principle to the present case gives KAO,i e x p [ - ~ ( E 1 + WAo,i)] =- KOA,i exp[-13(E2 + WOA,i)] so that the summations in Eqs. (2.52) and (2.53) are equal and the total flux J = can be expressed through a gradient in the chemical potential in the form O# J = -/3(exp(/3#)~-~z ~
Poo,~KAo,i exp[-3(E~ + WAO,,)])
Introducing now the adsorbate density p =
(9..54) J1,2-J2,1
(2.55)
O/a 2, we have
Olz Op J = -a213(-~ ~. Poo,iKAo,iexp [-/3(Ea + WAO,{)]-~x >
(2.56)
Comparing this equation with Fick's first law we finally obtain for the chemical diffusion coefficient:
D = -a2~(-~
~ Poo iKAo,iexp[-~(E1 + WAo,i)]> i
(2.57)
387 We now wish to discuss under what conditions Eq. (2.51) can be reduced to a generalized form of the Reed-Ehrlich representation. In the context of the present lattice-gas model, we can write for the effective jump frequency: F(0) - 4 ( ~ QAo,iKAo,i}
(2.58)
i
where QAO,i is the probability that a given A particle in the first column, has an empty nearest-neighbor site in the second column, with the environment marked by the subscript "z". Comparing the definitions of PAO,i and QAO,i, we see that they are related through: Q ~o,~ = PAo,~ /
Y] i
P.~,~ = PAo,~ / 0
(2.59)
where PA,i is the probability that a site with environment "i" is occupied. Using (2.51) and (2.59), Eq. (2.58) takes the form
s
4 ( ~ Poo,~KAo,i exp{/3 [#- (Ex 4- WAO,i)]}/O)
(2.60)
i
which can be considered as the generalized expression of the effective jump frequency for heterogeneous surfaces represented by a two-dimensional square lattice of sites. Assume now that the t h e r m o d y n a m i c a l factor O/~#/OlnO in Eq. (2.57), which is d e p e n d e n t u p o n a p a r t i c u l a r realization of a h e t e r o g e n e o u s surface a m o n g those belonging to the statistical ensemble, has a s m o o t h variation and can be replaced, t h r o u g h a m e a n field a p p r o x i m a t i o n , by O(/~#)/OlnO, i.e. t h e a p p a r e n t t h e r m o d y n a m i c a l factor calculated from the overall a d s o r p t i o n i s o t h e r m ~ = #(0). Then, combining Eq. (2.60)and (2.57), we obtain the generalized Reed-Ehrlich expression" _~
D-
0(9~) F(O) Oln-O
(2.61)
This equation is helpful for practical calculations and is currently used in the literature. However the validity of the main assumption stated above, on which it rests, is far from being evaluated. Future simulations to test its validity are encouraged. So far, the present analysis is applicable to any kind of diffusion (for example, activated and tunnel diffusion). For gas-solid phenomena, we are specifically interested in activated diffusion. For this process the rate constant can be written as
KAO,~ - u ezp{--/3 [E~(0)+ W~'- WAO,i]}
(2.62)
where u is the usual preexponential factor, E~(0) the activation energy for a jump at low coverage and W/* is the lateral interaction of the activated complex A* with the environment "z". Using (2.62) in (2.60), the effective jump factor becomes: -
s
-
//
= 4(~ ~ Poo,i exp{-~ [E~(0)+ Ea + W/* - #]}}
(2.63)
The lattice-gas formulation developed here will be used in Section 5 to discuss diffusion on well characterized simple and correlated heterogeneous surfaces. Before this, however, in the next Section, it is useful to discuss diffusion in porous adsorbents, to be analyzed through somesimple empirical and phenomenological models.
388 I--
zH I Ii
0
~"
D ii
C3
i
i
i
i
~-
1 2 3 4 COVERAGE ( m o n o b y e ~ ) Figure 4: Log of surface diffusion coefficient versus coverage for the system CF2C4 at 240K[29], showing a typical behavior. 3.
SURFACE
DIFFUSION
IN POROUS
SOLIDS
Porous solids constitute a wide variety of materials which are of fundamental importance in gas-solid processes of great practical interest, like catalysis and gas separation. Therefore the study of surface diffusion in porous solids has attracted the attention of researchers for a very long time [25-41,82-84], but even now a general theory explaining the main observed properties is far from being available. Two kinds of difficulties contribute to this situation: i) the experimental difficulties to measure accurately surface diffusivities, which must be obtained by subtracting the contribution due to the transport of molecules in the pore space in experiments where the total permeability through a cylindrical pellet is measured; and ii) the fact that the surface of a porous material is highly heterogeneous, an important component of such heterogeneity being due to surface rugosity. A typical behavior of the surface diffusion coefficient versus coverage is given in Fig. 4 for the system CF2C4 on silica at 240K [29]. Different mechanisms are usually assumed to explain the behavior of D in different coverage regions. Below the monolayer a diffusive mechanism based on activated jumps of individual molecules over energy barriers is considered. The very fast rise of D in this region (which often is greater than one order of magnitude) is attributed to the effect of strong heterogeneity. Around the monolayer, D stabilizes and even decreases for larger coverage. In the multilayer region two kinds of mechanisms are usually taken into account: i) a hopping mechanism where jumps are allowed both in the same layer or between different layers [37]; and ii) a mechanism known as hydrodynamic model, which regards the adsorbed gas phase as a laminar-flowing film of viscous liquid [26,30]. In a third region, identified as that where capillary condensation begins, D raises again. Here the hydrodynamic model is applied. We are mainly interested in the submonolayer region, where gas-solid interactions and
389 heterogeneity effects are most important. In this section we modify Fick's law in order to take into account the "tortuosity" of the surface of a pore, relating then the diffusion coefficient to the permeability which is the quantity obtained in experiments, and review some of the empirical equations and phenomenological models which have been used in the analysis of experimental data. 3.1.
Surface diffusion coefficient f r o m p e r m e a b i l i t y e x p e r i m e n t s in p o r o u s solids
The "permeability", I, of a gas through a cylindrical pellet of cross section A and length L is defined through: - -IA~z z
(3.1)
where N is flow rate through the pellet and dp/dx is the pressure gradient along the x axis of the cylinder. If the pellet is a porous solid capable af adsorbing the flowing gas, then the total permeability It will have two components
It = / g + Is
(3.2)
where Ig is the permeability in the gas phase, corresponding to transport of molecules in the pore space, and Is is the surface permeability, corresponding to surface transport of molecules in the adsorbed phase. If the mean free path of molecules of mass M in the gas phase is much greater than the mean pore diameter (say, ten times), then the flow in the pore space should obey the Knudsen equation C b = x/MT
(3.3)
where C is a constant characteristic of the pore space and is independent of pressure, temperature and the nature of the gas. The condition for Knudsen flow is frequently fullfilled for a great variety of gas-solid systems. Thus, by measuring the total permeability of a gas such as He which will not adsorb on the given solid at the temperature of interest, C is easily determined. Now, It is measured for the adsorbable gas whose surface diffusivity we want to study and this allows the determination of the surface permeability as
Is = It - C v / M T
(3.4)
Total permeabilities are easily measured through an ingenious permeation apparatus (see for example Ref [30]) which allows the measurement of the flux on the basis of the change in the pressures on both sites of the pellet under steady state conditions, or through time lag measurement. We must now relate Is to D. To do this we first immagine the total internal surface S of the porous pellet spanning a rectangle of length TL (along the x direction) and side
390
b = S/'rL. Now the distance ~ measured on the rectangle along the x direction is related to x by ~ = Tx. The parameter ~- is known as the "tortuosity" of the porous pellet. Writing now Fick's first law, Eq. (2.21), for this spread out rectangular surface we have N. = _bDdP(~) S dp(x) d~ = - r 2 L D dx
(3.5)
where Ns is the surface flow rate along the coordinate ~. Now dx d~ = dp dp dx dv then .~s= _ S--~--Ddp dp
7"2L dp dx and, taking into account by the definition of permeability, Eq. (3.1), that the surface permeability/~ is given by/~ = -IV~/(Adp/dx), we obtain the following relation between D and 5:
....
.]
D = Sdp/dpJ Is
(3.6)
or, in a more convenient form:
D __. [[ T2VsTP OP] ppV,~ -~ Is
(3.7)
where pp is the pellet density, VSTP is the gas molar volume at standard temperature and pressure, V,~ is the molar volume necessary to fill a monolayer and Op/O0 is obtained from the adsorption pressure-coverage isotherm. Thus the measurement of the adsorption isotherm and the monolayer capacity is necessary as an additional experiment to relate D to Is. For this reason, the pressure gradient applied to the pellet must be very small, in such a way that the equilibrium pressure of the adsorption isotherm at a given coverage may correspond to the mean pressure through the pellet. The only parameter which is still undetermined is the tortuosity T. This cannot be measured directly for the gas of interest, but can be estimated by comparing the measured flux of a non-adsorbable gas with the expected value predicted by the Knudsen equation for a straight cylindrical tube [42]. Typical values of ~- determined by this method range between 1.5 and 2.5. More recently, an alternative way to estimate 7 through the fractal measure of surface rugosity has been proposed by Avnir [43]. It is interesting to explore the relation between tortuosity 7 and the porosity r (the volume fraction accessible to the gas phase) in a porous material. This is a very difficult problem and only some few semi-empirical equations have been given. However, the subject is of great importance since r is a measurable quantity. Two frequently used equations are T = 1/r
(3.8)
due to Wakao and Smith [44], and = 45/r
(3.9)
391 40
I
I
I
i
o SO2, 1 5 ~ o S 0 2 . 30oc
35 30
i
= NH 3. ZS~
CO 2, -78 ~ 9 CO 2, -50 ~ * H e , 30~
= NH3, 40oc
" N2
"
930~
v
-'h_ 25 - ~ O o ~ O
0
O
0 o
Ov
v 0
0
%,
OV
~
o
v 0v
0
o
0
0
0 0
0
0
m
E
E lS 111-
~ ~ % 9
•
_
A
~oO~o @
A
A
~
~
4kA
@A
5
I
I
I
I
I
I
100 200 300 400 500 600 700
0
P, tort
Figure 5" Permeabilities of five gases in porous Vycor glass versus mean pellet pressure. due do Weisz and Schwartz [45]. Less frequently used is the relation 1
(3.10)
developed by Wissberg [46] who considered the solid as formed by an agglomerate of overlapping spheres. Some very high values of tortuosity reported in [47] follow instead the equation 7 - 1/r 2
(3.11)
Monte Carlo simulations performed by Evans and coworkers [48], who simulated the solid by placing spheres of different radii centered at random positions (allowing overlapping of spheres), follow closely Eq. (3.10) which gives the smallest tortuosity for all values of porosity. We can only conclude that the relation between ~- and r is strongly dependent upon the nature of the porous material. Figure 5, taken from Ref. [33], representing the measured total permeabilities for five gases in porous Vycor glass, gives a nice idea of the experimental errors involved in this kind of measurements (see the statistical fluctuations of experimental points). Notice, in particular, that for a weakly adsorbed gas, like NH3 at 25~ and 40~ statistical fluctuations are nearly of the order of the difference between the total permeabilities of
392 that gas and those of the reference non-adsorbable gases _N89and He. This means that for NH3 the statistical experimental error is almost of the order of the surface permeability which is being measured. In spite of these experimental dificulties, permeation experiments have provided for a long time valuable information which has been used to improve our understanding of surface diffusion on highly heterogeneous surfaces through simple phenomenological models. We review some of them in the following section. 3.2.
Simple empirical and phenomenological models
3.2.1.
V a r i a t i o n s on t h e A r r h e n i u s e q u a t i o n
Carman et al. [29] and Barrer et al. [49] made use of the Arrhenius equation D = Doe -zE'~
(3.12)
to analyse surface diffusion in a variety of systems. Carman realized that surface heterogeneity should be responsible for the increase in D at low coverage and that the activation energy E= should decrease with coverage by similarity with the behavior of the isosteric heat of adsorption qst for an heterogeneous surface. Barter found that the ratio E=/q,t was approximately in the range 0.2 to 0.5 for solids with smooth (but heterogeneous) surfaces and in the range 0.6 to 1.0 for solids presenting rough surface textures. These results were interpreted assuming that for rough surfaces there is a contribution of long molecular flight over "evaporation barriers" created by the crevices and blind pores. Higashi et al. [50] proposed a simple modification of Eq. (3.12) to take into account that a molecule not only can jump from the site it is occupying to a neighboring empty site, but to any neighboring site and, if it is occupied, the molecule will keep migrating in a random walk from site to site until an empty site is found and it is adsorbed again. The expected number of jumps until re-adsorption is given in such a model by
n(e) = ~ i ( 1
-
i'-1
e)e ' - ' =
1 1 -0
(3.13)
Thus, since D should be inversely proportional to the time of residence at a site, and this is now shorter by a factor (1 - 0 ) , we have D=
19'o e_ZE,, 1--0
(3.14)
Even though this modification of the Arrhenius law seems to work for nearly homogeneous surfaces and at 6 < 0.6, in many cases it predicts the wrong curvature for D(0) at low coverage [32]. Following Carman's thinkings about a relationship between the activation energy and the isosteric heat of adsorption, Gilliland et al. [33] assumed the linear relationship E~ = aqst
(3.15)
393 Table 1 Correlation of experimental data to Eq. (3.16) Gas Solid Do a Range of (cm2/s) Variaton of qs~ (Kcal/mol)
Mean deviation from correlation
Ref.
(%) C02 C02 NH3 C2H4 C3H6 i - C4H10
CF2CI2 SOz
Glass Glass Glass Glass Glass Glass Silica Carbon
0.037 0.018 0.20 1.15 1.20 0.025 0.27 0.22
0.48 0.47 0.60 0.81 0.75 0.46 0.63 0.43
4.1-6.3 5.5-7.7 6.5-8.8 5.3-7.0 6.3-7.5 5.9-7.1 6.5-7.8 6.7-8.8
6 4 9 15 17 10 4 8
33 33 33 51 51 51 52 53
and proposed the equation:
D = Doe -zaq''
(3.16)
With this, they could obtain satisfactory fitting of a variety of experimental data, as shown in Table 1. This partial success of the very simple Eq. (3.16) stimulated further research, by Sladek et al. [34], who proposed what is known as Sladek's correlation:
D~ = 0.016e -~
(3.17)
where m is a parameter whose value (1, 2 or 3) depends on the type of solid (conductor or dielectric) and the type of gas-solid interactions. With these three values of m, data from 30 gas-solid systems with D varying over 11 orders of magnitude were correlated to within 131 orders of magnitude. This success, on a coarse grained scale, reflects the fact that there is undoubtedly a strong relation between Ea and qst for a strong surface heterogeneity. However, equation (3.16) or (3.17) cannot be valid in general, since, for example, for a homogeneous surface it would predict a constant D value, contrary to what is generally observed. A more complete expression for D, was developed by Okazaki et al. [37] who included other elementary processes, like the hopping from one layer to another, as depicted in Fig. 6. The total residence time is t = (1 - O~)to + O~tx, where 0~ is an effective coverage for diffusion. Each elementary process is assumed to behave in an Arrhenius form. This leads to the equation D = D 0 ~ -,m,,
(1 - e - ~ i i l -2- 0~1 - ta/to)] f(q)dq
(3.18)
where t: =
(e - z ~ q -
to
(1
-
e-~'q)(1 - e -~'q') _
(3.19)
394
t1
LO
9
~
9
9
Figure 6: Hopping modes of adsorbed molecules and their respective residence times.
10-
I
IIIII
I
I I IIII
I
I
I
I III
Key Data Key Data 9 6 [] 1 7 2 o 3 9 8
0
~D~ i 0 -2
I
9
4
v
9
5
e
10
CO
r~
~
. m
'~
10 -3
Key Data Key Data A 11 9 13
,'
v
16
[]
17
.,m
14 i l II
10
I
-3
I
I
i i IIII
I
10
F[-]
-2
i
I
I I III
10
Figure 7: Correlation of surface diffusion coefficients data of Table 2 by Eq. (3.18).
-1
395 Here, f(q)dq is the number of molecules adsorbed with heat of adsorption between q and q + dq, q, is the heat of vaporization of the adsorbate and E'a is the activation energy for all layers above the first. For an adsorbate obeying the B.E.T. isotherm:
0
=
cx/(1
-
x)(1
-
x
+
cx)
(3.20)
where x = p/po is the relative pressure, 0e is estimated as 0~ = 0(1 - x). Fig. 7 shows the correlation of experimental data given in Table 2 by Eq. (3.18). Eq. (3.18) improves the correlation of diffusivity data near the monolayer and in the multilayer region. However, like other phenomenological equations based on Arrhenius behavior it does not allow a deeper understanding of the effects of heterogeneity and, on the other hand, still predicts a constant behavior for a homogeneous surface. 3.2.2.
Effective m e d i u m a p p r o x i m a t i o n
The effective medium approximation (EMA) is an old method which has been succesfully applied to a variety of problems related to conduction in disordered media [54-57]. The idea, in a sense similar to that of mean field theory, is the following: one assumes that the mean effective diffusion coefficient Dm is known. The random medium is then replaced everywhere but in a small region by the equivalent effective medium (which is then homogeneous). One then successively assignes to the small region elements the values Di of the initial random medium, each with its own weight f(Di). These values differ from Dr~ by amounts 5D which depend both on Dm and Di
Di = D~ + 5D(Dm, Di)
(3.2~)
Dm is then self-consistently determined by the condition
~f(Di)SD(D~,Di)
= 0
(3.22)
i
which states that, on the average, the correction induced by the disorder vanishes. We now discuss more deeply the EMA in the context of a network of random resistors, of conductances g, by closely following Kirkpatrick [57]. The distribution of electrical potentials in a random resistor network to which a voltage has been applied along one axis may be regarded as due to both an "external field" which produces an increase in voltages by a constant amount per row of nodes, and a fluctuating "local field", whose average over any sufficiently large region will vanish. We shall represent the average effects of the random resistors by an effective homogeneous medium which, for simplicity, we consider to be made up by a set of equal conductances gin. Consider now a different conductance gAB = g, oriented along the external field between nodes A and B, surrounded by the effective medium, Fig. 8(a). To the effect of the uniform field, represented by a constant voltage Vm, we add the effects of a fictious current, 5i, introduced at A and extracted at B, which is chosen in such a way as to satisfy current conservation at A and B:
Vm(gm - g) = 5i
(3.23)
396 Table 2 Correlation results for experimental data Gas Porous T 0 ~ Solid C2H4 Vycor 30.0 0.13~0.31 C3H6 Vycor 30.0 0.04~0.60 iC4H~o Vycor 30.0 0.04,-~0.73 S02 Vycor 30.0 0.29,~1.07 CF2Cl2 Linde -33.1 0.47~1.61 Silica -21.5 0.21~2.66 S02 Linde -10.0 0.46~2.13 Silica 0.0 0.36~1.71 CF2C12 Carbolac -33.1 0.85,-~2.30 -21.5 0.61~1.71 0.0 0.48~1.16 20.0 0.32,~1.10 C02 Carbolac -33.1 0.13~0.53 -21.5 0.14~0.41 0.0 0.04~0.26 20.0 0.03~0.17 S02 Vycor 15.0 0.37,~0.84 30.0 0.27-,~0.67 C02 Vycor -78.0 0.63~0.89 -50.0 0.38,~0.78 C3H6 Vycor 0.0 0.14-~0.99 25.0 0.07,-~0.71 40.0 0.07~0.61 iC4Hlo Vycor 0.0 0.16~0.48 C2H6 Vycor 0.0 0.02~0.25 25.0 < 0.15 50.0 < 0.12 C3H6 Vycor 0.0 0.12~0.81 25.0 0.06~0.69 50.0 0.03~0.50 CF2Cl2 Carbon -5.0 0.26~1.20 Regal 10.0 0.12~1.02 20.0 0.07,~0.86 Call6 Graphon 0.0 0.11,~1.14 25.0 0.05,-~0.97 50.0 0.02~0.73 nC4Hlo Graphon 30.0 0.15~1.12 41.7 0.25,-~1.14
using Eq. (3.18)
D xl0 s 4.4~9.1 2.2~7.5 2.2~6.7 0.8,~4.0 2.1~5.3 1.9~7.0 2.8~7.6 2.6~9.2 3.2~10 2.7~11 3.0~15 2.8~16 3.6,~13 5.8~14 5.1~16 7.5,~17 0.8,,~3.3 0.8~2.6 0.7,-~1.3 0.6,--2.5 0.5~7.7 0.9~6.2 0.5,~6.8 0.7~2.0 2.7,-~11 3.3~15 5.7,~8.8 1.1~15 1.1~11 0.9~10 4.0,~3.4 3.5~39 5.9~39 80~730 120~500 170~300 100--~1300 90~880
a
Do
Esl
Ref.
0.39 0.45 0.48 0.38 0.49
2.i3x10 -3 5.41x10 -3 ll.9x10 -3 2.16x10 -3 9.20x10 -3
4.98 7.79 10.4 8.75 7.49
[37]
0.41
8.14x10 -3
8.75
[52]
0.41
7.67x10 -3
7.49
[52]
0.54
3.41x10 -3
7.83
[52]
0.46
5.02x10 -3
8.75
[33]
0.51
9.95x10 -3
7.83
0.45
4.29x10 -3
7.79
[30]
0.56 0.54
1.06x10 -2 1.47x10 -2
10.4 6.95
[30] [32]
0.52
1.47x10 -2
7.79
[32]
0.66
5.75x10 -2
7.49
[35]
0.63
5.57x10 -1
7.79
[32]
0.48
6.39x10 -1
7.39
[52]
397
'i e
I
Ca)
B
i
(b)
Figure 8" Constructions used in calculating the voltage induced across a conductance g surrounded by a uniform medium. The extra voltage, 5V, induced between A and B, can be calculated if we know the conductance G~B of the nework between points A and B in absence of the conductance
(rig. S(b)): =
i/(g +
(3.24)
Now, G~B is related to the conductance GAB between A and B in the uniform effective medium through GAB = G~AS + gin, and this can be calculated by a simple symmetry argument: express the current distribution in Fig. 8(a) with gAS = g~ as the sum of two contributions, a current 5i introduced at A and extracted at a very large distance in all directions, and an equal current introduced at infinity and extracted at B. In each case, the current flowing through each of the z equivalent bonds at A and B is r so that a total current 25i/z flows through the A B bond. Then, it follows that GAS = (z/2)gm, or G'AS = ( z / 2 - 1)gin. Using (3.23) and (3.24) we obtain
5V = Vm(g~ - g ) / [g + (z/2 - 1)gm]
(3.25)
If the values of g are distributed according to a probability density function f(g), the requirement that the average value of 5V must vanish leads to the folowing condition for determining gm:
f dgf(g)(gm - g ) / [ g + (z/2 - 1)gin] = 0
(3.26)
Since in the equivalent diffusivity problem D is proportional to g, we may write for the EMA for the diffusion coefficient:
f d D f ( D ) ( D m - D ) / [ D + (z/2 - 1)Dm] - 0
(3.27)
We notice that Eq. (3.27) is valid both in 2 and 3 dimensions for a lattice of adsorptive sites with coordination .number z. Two nearest-neighbor sites are connected through a bond characterized by an energy barrier for molecular jumps.
398 100
100
50
5O
10
I0
5
Dm(e ) 5
Dm(e) Hom D O=O
IDe=o
0.5
0.5
0.1
I
I
I
0 0.2 0.4 0.6 0.8
(a) I
I
0.1
i
i
I
I
0 0.2 0.4 0.6 0.8
(b) I
0
Figure 9: Behavior of D~(O)/DH=~ ~ a function of 0 for different values of the heterogeneity parameter s. (a) E~ = e; (b) E~ = ~e. Through the derivation of Eq. (3.27) it is clear that the EMA is applicable to heterogeneous surfaces whose energetic topography is such that bond energies are distributed totally at random and such that the activation energy to jump from any site A to a nearestneighbor site B is the same as that for the reverse jump, i.e. a random barriers surface (see section 4.2). However, by considering that each resistor in Fig. 8(a) is really representing a homogeneous patch, it is also clear that Eq. (3.24) is applicable to a patchwise topography. Monte Carlo simulations performed by Kirkpatrick [57] show the shortcoming of EMA for a correlated bonds network corresponding to intermediate topographies. From a practical point of view EMA may provide an adequate basis (compared with other existing models) to describe diffusion on highly heterogeneous surfaces like those corresponding to porous solids. Kapoor and Yang [40] applied this approximation to the analysis of a variety of experimental data. They assumed for D the Higashi modification of Arrhenius law, Eq. (3.14), with a uniform distribution of activation energies given by
1
Emin_E~a z
f(E~) =
0
Eamin Emax for __~E~ < otherwise
(3.28)
They also assumed a Langmuir isotherm
o=
pb
(3.29)
b = boez'e
(3.30)
1 +pb
with
399
,r
i" (a)
(b)
Figure 10: Adsorptive energy contour lines: (a) thermal energy lower than critical energy; (b) thermal energy higher than critical energy. where ~ is the mean adsoption energy. Under these assumptions, integration of Eq. (3.27) over activation energies yields" eS+pb+(~-l)Dm(O)/DH=~ e -~ + pb + (~ - 1)D,-,.,(O)/DH~
[ = exp
e S - e -s ] ~D2 m (O)/DH=~ ~ '
(3.31)
where s is the "heterogeneity" parameter ( E y ~'x- Ey~'~)/2kT, and DH=~ = D'o e-~E~ Figure 9 shows the behavior of Dm(~)/DH=~ for different values of the heterogeneity m m 1parameter s in the case that E~ = ~ (a) and E~ = ~e (b). Diffusion coefficients for a variety of gases on vycor glass, Linde silica, Carbolac, Spheron 6 carbon black and activated carbon, were fitted with average relative errors ranging between 9% and 46%, the highest errors corresponding to activated carbon in which microporosity may introduce new transport mechanisms. 3.2.3.
Percolation model
Following the ideas introduced by Ambegaokar et al. [58] to treat the problem of the hopping conductivity of electrons in amorphous semiconductors, Zgrablich and co-workers [38,39] developed a quite general model considering the surface diffusion of adsorbed gases as a percolation process [59,60]. The underlying idea can be better understood by visualizing the adsorptive energy surface E ( x , y ) for an heterogeneous surface ( ( x , y ) defines a position on that surface) as a mountainous landscape where depths are adsorptive sites. If the thermal energy of adsorbed molecules is considered as the "height of waters" in the mountainous landscape, then we could describe the situation at two different temperatures as in Fig. 10, where the adsorption energy surface is represented by its "contour" lines, or equipotential curves. At low temperature, Fig. 10(a), molecules are confined to disconnected "lakes" (shadowed areas) through which they can migrate by a hopping mechanism (of course, some jumps
400
E 0
Gas Phase
Ec J
Figure 11: Adsorptive energy variation along a direction x on the surface. Due to lateral paths, in the true adsorptive energy surface the minimum energy level E~j connecting sites i and j may be at the lower position shown. outside the shadowed regions may occur but they are infrequent) and the overall surface diffusion coefficient is very low. As the temperature increases, the "water" level becomes higher and the lakes grow wider until a thermal energy is reached for which the "water" percolates to form an "ocean" where low diffusivity regions are disconnected islands, Fig. 10(b). This state defines the critical energy level, Ec, for surface diffusion of adsorbed molecules and its value is determined by the overall adsorptive energy topography and not by a "local" value of the activation energy. The formulation of the percolation model is based on the following assumptions: (i) For each pair of sites "i" and "j" separated by a distance Rij, there exists an energy level Ei3 which is the minimum energy level connecting both sites, such that E~j = Ej~ (see Fig. 11). In general, E~j will be lower than the critical energy level Ec defined by the percolation threshold. (ii) For a molecule to jump from an occupied site "i" to an empty site "j" a distance Rij apart (jumps to sites other than nearest neighbors are allowed) it must make a transition to an activated state determined by E~j and the interactions with other adsorbed molecules. (iii) These interactions are described by a mean field energy W, for adsorbate - adsorbate, and W*, for activated particle-adsorbate interactions. (iv) A molecule in the activated state moves from site to site and may either be readsorbed at an empty site (with probability 1) or, if the site is occupied, have a probability Pd of being desorbed to the gas phase and 1 - Pd of continuing its random walk. If the probability for the activated particle of either being readsorbed or desorbed in travelling a distance dR is defined as dR~A, then )~ is a "mean free path" and will depend on the surface coverage ~.
401 Now, the number of molecules Fij migrating from i to j per unit time can be written in general as:
Fij = FoP+_P~exp [-Rij/)~(O)] P~j
(3.32)
where F0 is the characteristic vibration frequency of an adsorbed molecule, P+_ is the probability that site i is occupied (+) and site j empty ( - ) , P~ is the transition probability to the activated state for the molecule adsorbed at site i, exp [-R~j/$(O)] is the probability for the activated molecule to travel a distance R~j without being either readsorbed or desorbed and P~j is the probability for the activated molecule to be readsorbed at the empty site j (P~j is taken = 1, as usual). If a mean field approximation is used to take into account lateral interactions, then
P+_ - P__exp[fl(#i- E i - WO)] ~, (1 - O ) 2 e x p [ / 3 ( # i - E ~ - WO)I
(3.33)
where Ei is the adsorptive energy and #i the chemical potential at site i. Now, according to assumptions (i), (ii)and (iii),
P,o = ~xp{-Z [(E,j + W*O) - ( E , + WO)])
(3.34)
so that rij = F(1 - O)2exp[13(#i- W*0)] exp [-R~j//~(O)] exp(-flAEq)
(3.35)
where F = Foexp(-/3E---~), AEq = E~j- E---~and E~ is the mean energy of adsorptive sites. In a similar way: r~ = r(1 -
o)2ezp [~(m - w'o)] ezp [-R~j/,X(O)] ezp(-~AE~j)
(3.36)
At equilibrium, as #i = #j (and given that in all cases Eij = Eji) it turns out that Fij = F ji and there is no net current between i and j. However, when a surface concentration gradient is present, which can be expressed through a chemical potential gradient such that #j = # and tti = # + A#, then the net current Cq = Fij - F j i becomes:
r
- GijAO
(3.37)
where the "conductance" Gij is given by
a~j - rA(O)~xp{-[Rq/~(O) + /~XE~j]},
A(O) = (1 - O ) 2 e z p [ j 3 ( # - W*O)]
0(~)
0O
(3.38) (3.39)
The problem is now reduced to that of finding the conductance of a network of random resistors whose conductances Gij, given by Eq. (3.38), are composed by a slowly varying factor A(0), Eq. (3.39), depending on mean field values of coverage and chemical potential, and a stochastic factor exp [-(R~j/A + 3AE~j)]. An exact solution for such a problem is out of reach Until now, and some suitable approximation must be made. Of course, an
402
E
r
/ 4
v
R Figure 12" Representation of a "completely connected cornponent". approximation like EMA could be applied here, even though the greater generality of the present model (for example the distribution of the P~j is needed) may bring about some difficulties which were not present in the simple model discussed in the previous section. However, a very direct and powerful approximation leading to a very simple expression for D will be applied in this opportunity, say, to replace the total conductance of the medium by the critical value Gc given by the percolation threshold for conduction. Before the calculation of Gc is attempted, we give a brief discussion of why this is a suitable approximation. Different points of the three-dimensional space (R, E) are connected by resistors whose conductances G~j are distributed according to some probability distribution. If we choose resistors from that distribution in decreasing order of conductance values G1 > G2 > G3 > ..., and assign them randomly to pairs of points in the (R, E) space, then we will achieve a critical conductance Go, when a path crossing the entire region occupied by the system is formed, Fig. 12. At this moment the percolation threshold is reached and there will be conduction through the whole system. Now, if more resistors are added, new paths will be created but the total conductance will not increase appreciably because only small conductance resistors are available. Then the total conductance is approximately given by G~. Going now to the calculation of G~, we see that the condition for conduction, G~j > Go, can be expressed through Eq. (3.38) as: ..r
R~j/~(O) + ~AE~j < ln(Gc/FA) -1
(3.40) ..r
For a given site i, Eq. (3.40) defines a region in the three-dimensional space (R,E) within which conduction is possible. Then, for several sites we have a collection of partially overlapping conducting polyhedra in the (R, E) space, as shown in Fig. 13. The critical conductance will be reached when the conducting regions percolate in the continuum three-dimensional (R, E) space. The maximum spatial dimension for a typical conducting
403
-%
R Figure 13" Percolation three-dimensional space and conducting polyhedra. region is obtained by making AE~j = 0 (for example, a fiat adsorptive energy surface), in Eq. (3.37) and is
AR~
= ~l~(G~/rA) -~,
(3.41)
while, similarly, the maximum energetic dimension turns out to be the one corresponding to R U 0 -
-
1
AEm~: = ~In(Gc/FA) -1,
(3.42)
so that a typical volume of conducting polyhedra is given by ~ ~ (AR,~)2AEm~
(3.43)
Supposing a constant density of states po in the (/~, E) space, then the total conducting volume is p0~ and, as well known, the percolation threshold is reached when the total conducting volume is a fraction 6 (6 ~ 0.16 for 3 dimensions) of the total space, i.e.: p0~ = 6
(3.44)
From Eqs. (3.41) to (3.44) we obtain Gc as:
G~= rA(O)ezp{- [(/~(5/t00)1/3~-2/3]}
(3.45)
Now, the surface diffusion coefficient D (conductivity) will be related to the conductance through D = gG~, where g is a geometrical factor, so that we have
D = DoA(O)exp{-(136/po)l/3[A(O)] -2/3}
(3.46)
404 where Do = gF. We must still calculate the mean free path A(0). According to its definition in assumption (iv), and given that the main contribution to the diffusion coefficient comes from the critical conductivity, we may suppose that any activated molecule contributing to D is at the critical energy level Ec, so that the desorption probability at an occupied site is P~ - ~ z p
[-Z(AE~- W*0)]
(3.47)
where/kEg = E a - Ec, Ea being the boundary energy for the gas phase. Now, assuming that the activated molecule performs a random walk of step length A0 (notice that this is the first time that it is necessary to assume the existence of a regular array of sites), then
,~(t~) = )~o/P~(O)
(3.48)
where Pc(O) is the effective probability for the activated molecule to stop its walk (either by readsorbing at an empty site or by desorbing at an occupied site) and, according to (iv) and Eq. (3.47), is given by P~(O) = (~ - o) + O ~ p [ - Z ( ~ X E ~
-
w*0)]
(3.49)
With this, D can be finally expressed as-
D = DoA(O)exp{-(To/T)~/3[P~(O)] 2/3}
(3.50)
where we have introduced the characteristic temperature To as
To = ks poA~
(3.51)
It is important to investigate the significance of To. From the definition of po and for a constant adsorptive energy distribution between E - cr and E + a, where cr is the dispersion of adsorptive energies, we have po - 1/(2~r)~o2), so that To is proportional to cr through
To = (23/ks)a
(3.52)
hence, To is a measure of heterogeneity (ksTo ~ 0.32cr). In the limiting case To ~ 0 (a ~ 0), we obtain D = Do(1 - 0) 2ezp [/3(# - W'O)]
o(Z~) 00
(3.53)
which is the result obtained by Zhdanov [61] for an homogeneous surface. Actual calculations of D require that the isotherm equation be given so that the factor A(9) could be evaluated. Since a mean field approximation for the adsorbate has been made, the following isotherm equation for the mean coverage 0 can be used [62]:
0(#, T) = / f(E)OL(E, #, T)dE
(3.54)
405
o
}
5["
~'4
o'=1
~s 3 2
a=0.5
.
1 i
I
9
I
,
I
0 0.2 0.4 0.6
e
I
I
I
0.8
1
Figure 14: Effect of heterogeneity on surface diffusion coefficient for noninteracting molecules. where
OL(E,#, T) is the "local coverage" for a site with adsorptive energy E, given by
OL(E, #, T ) =
exp [ - # ( E + WO - #)] 1 + ~ p [ - # ( E + W O - ~,)]'
(3.55)
and f(E) is the adsorptive energy probability density (in our case a constant between Es - a and Es + cr and zero outside). Figure 14 shows the effect of heterogeneity on D(O)/D(O), while Figures 15 and 16 show the combined effects of heterogeneity and lateral interactions W and W*. All curves were calculated for E, = -2.5 kcal/mol and T = 300K. Heterogeneity values, a, are in kcal/mol. Finally, the model is tested by fitting experimental data from references [35,36]. Since experimental data are given for permeabilities/,, from Eqs. (3.7) and (3.50) we have: L = Io(1 -
O)2ezp(-nW*O)ezp{-(To/T)~/3[P,(O)] 2/3}
where Io =
gFppVmezp(##o)/r2VsTP and #o is given by the well known expression
#o=-kTln
\
h2 ]
]
kTq,,q,.
(3.56)
(3.57)
qv and qr being the internal vibrational and rotational partition functions. This is indeed a very simple equation for the permeability and has four fitting parameters: I0, To, AEd and W*. Two of them, i.e. To and W*, can be determined independently by fitting adsorption isotherms when these are available for the same gas-solid system in which permeabilities are measured. Figures 17 to 19 show the results for surface permeabilities of ethylene, 1,3-butadiene and n-butane on carbon Regal 660 [36], while Table 3 presents the parameter values.
406 [a]
L '
"
1.0 0"=1
0.8 0.6
o.,~ x,...~ ~ 00 0.2
lt
t
0
ti
!t
0.2
. L
0,~
I~"~'=--"P ~,
"
O.B
0.8
I-
"
[
I
I
. l
i
0.2
0
l
I
0.~
l
i
0.6
|
0.8
1
Figure 15" Effect of heterogeneity on surface diffusion coefficient for molecules with attractive interactions and activated complex with repulsive interactions (in kcal/mol)" ( a ) W = -1, W * = 0; (b) W = 0 , W * = 1.
2O
18 (O] 16 14
/
-
ff=l
(b)
/
"
0"=1
12-
c3 10 s
C
_
0
|
a
0.2
1
t
0,/,
L
l
0.6
|
I
0.8
l
t
E)
~
0.2
i
i
0./,
I
I
0.6
i
J
0.8
I
_
1
Figure 16: Effect of heterogeneity on surface diffusion coefficient for molecules with repulsive interactions and activated complex with attractive interactions (in kcal/mol); (a) W = 1, W* = 0; (b) w = 0, w ' = - ~ .
407
!: :
9
-
~8
0
.
-
278K 9248K 9225K
206K
~
9194K
I
I
I
I
I
I
I
I
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
Figure 17: Surface permeabilities for ethylene on carbon Regal 660. It can be seen that To is fairly constant for all data (which correspond to the same heterogeneous substrate) with a mean value of 168.4 and a standard deviation of 11.8(7%). This value corresponds to cr = 1.04kcal/mol for the dispersion of adsorptive energies, a very reasonable value given the experimentally observed [63] variation in isosteric heat of adsorption (Aqst ~ 1.13+0.3kcal/mol) from 0 ~ 0 to 0 = 0.5 calculated by estimating the slope near 0 = 0) at low coverage where the lateral interactions contribution is expected to be small. For A E d we have also a remarkably good behavior. In fact, A E d is practically constant for each gas-solid system and leads to values of the mean activation energy for diffusion, E~, = -qst - / k E g , which are between 0.56 and 0.59 of the desorption energy, a result widely supported by independent experimental evidence [49,64]. Finally, the values for W* are reasonable although their variation with temperature has no apparent explanation. In conclusion, the theoretical background on which the model is formulated and the encouraging results obtained by fitting experimental data makes it worth to be further investigated by analyzing a wider variety of well characterized gas-solid systems. We now turn over to the study of the effects of heterogeneity on surface diffusion for solids with simple and well characterized energy topography.
0
TRACER DIFFUSION ON SIMPLE TOPOGRAPHY OUS SURFACES
HETEROGENE-
In this section we study the tracer diffusion on model heterogeneous surfaces whose adsorptive energy topography is very simple. These surfaces probably have no close relation to real surfaces but their study is clarifying. We have chosen two opposite kinds
of
random
(RT)
408
9283K 9298K = 323K 9348K *403K
~16 ==
==
vl
~
e
~o 8
==
i
~
4 e..t.....-t---~-'~e
2
u~
0 0
i 0.1
i 0.2
i 0.3
9..... e ~
I I I 0.4 0.5 0.6
I 0.7
I 0.8
0
Figure 18" Surface permeabilities for 1,3-butadiene on carbon Regal 660.
~8
i7 E ~6
413K 9
-
9358K 9323K 9298K 9283K
_ i
,~4
i3
==
-- __T 1
..Ilium
0 0
.-
--
I
i
I
0.1
0.2
0.3
i
I
I
I
0.4 0.5 0.6 0.7
I 0.8
Figure 19: Surface permeabilities for nbutane on carbon Regal 660.
409 Table 3 Parameter values obtained by least-squares fit of Eq. (3.56) to experimental data of Ref. [36]. Here 1~ = 10sI0v/-M--T
[(molKg)l/2/s cm mmHg] G~ Ethylene
1,3-Butadiene
n-Butane
4.1.
Random
T
I~
-W*/kB
(K)
(cm2/s)
(K)
278 248 225 206 194 403 348 323 298 283 413 358 323 298 283
2.1 3.3 8.9 10.6 15.0 3.4 7.3 14.4 23.6 29.7 3.1 820 11.4 19.5 24.9
933 807 471 445 385 1160 785 491 371 420 904 820 684 594 556
/XEd/kB To
(K)
(K)
658 686 691 651 671 868 850 863 835 839 928 907 921 950 836
182 179 186 169 168 156 162 147 158 152 171 168 169 185 174
Traps
A random traps surface is characterized by the fact that all the saddle point (bond) energies for jumps from one site to another have the same value EB, while site adsorptive energies are randomly distributed according to some function Fs(Es), Fig. 20. This model surface presents the important symmetry property that the jumping probability is independent of the direction of the jump, just like in a non-correlated random walk and is the only one for which exact results have been obtained. One of the most important of these results [65] concerns the tracer diffusion exponent d~ of Eq. (2.8), which is found to be d~ = 2 with the only condition that the initial state must be one of thermal equilibirum (this means that the probability for the tracer to start its walk from a given site with adsorptive energy Esi must be proportional to e-~Esi). The mean square displacement of a tracer particle can also be obtained exactly by solving the master equation [9]. However we chose here to obtain it by using simple intuitive arguments. We start by assuming that the probability r for a tracer particle located at site "i" to jump over the energy barrier in a very short period of time dt is proportional to dt, i.e., r = dt/z~, where 1/z~ is the proportionality factor. Then, the probability 9~(t) for the particle to wait at site "i" during a time t satisfies the differential equation: ~i(t) - 9~(t - dt)(1 _ _dt) T~
(4.1)
410
K8
~-sj E.SI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 20" Adsorptive energy profile for a random traps surface. By integrating this equation, we get
~ ( t ) = e -t/~'
(4.2)
Then, the probability r of time (t, t + dt) is:
for a particle at site i to make its jump in the interval
= -dte - - tl~.' 7i
r
(4.3)
From this, the mean waiting time at a given site "i" can be calculated as
tr
< t >i=
= ~'i
(4.4)
Now, our tracer diffusion process on the random traps surface is just equivalent to the uncorrelated random walk described in Section 2.1.1, with the only difference that at each site "i" we have a waiting time ri. Thus, if we replace the number of steps n(t) of Eq. (2.4) by an effective mean number of steps t
=
(4.5)
we obtain for the mean square displacement (R2(t)> = a2t/(T~),
(4.6)
and for D* (through Eq. (2.10)) a 2
D~
__
"
(4.7)
411
Fs(Es) ] l/hE
AE s
=
$ i ..........
I
I
-E B
-Es
-~
-E
Eo
Figure 21" Uniform site distribution for the random traps surface. which is the exact solution for the random traps surface. We show now how (r~) can be calculated from a given distribution of adsorptive site energies. The simplest case is a uniform distribution of site energies, as shown in Fig. 21. If the jumpling process is an activated process, then the waiting time ~'i at a site i is
Ti
=
e3E~
eB(EB-E%)
=
(4.8)
where E~ is the activation energy for a jump from site i. Then 1 [Es+AEs/2 < Ti >-- A E s J-Es-AEs/2 m
e x p [ 3 ( E s - Es)] des
(4.9)
which gives D
e3E~
< Ti > = 2 3 A E s S i n h ( 3
/kE 2
s)
(4.10)
and, for D*, m
1 D* = -
_
3vE~
_
4 exp(3E~)sinh(37E~)
(4.11)
where we have introduced the dimensionless heterogeneity parameter "7 = AEs/2E~ (0 <_ 7 -< 1). The behavior of D* given by this equation is shown in Fig. 22. At very high temperatures, 3 -+ 0, D* reaches its maximum value 1/4. As temperature approaches zero, 3 ---, oc, D* vanishes asymptotically. If the surface is modified at fixed temperature T, in such a way as to increase E~ without changing 7, D* decreases. On the other hand, at fixed T and E~, D* decreases with increasing 7-
412
D
1/4
~_
/Y3 Y2 ~Ea
Figure 22: Tracer diffusion coefficient for a random traps surface. The values of the heterogeneity parameter are in increasing order, 71 < 3'2 < 3'3. In a similar way, D* can be obtained for a random traps surface with a truncated gaussian distribution for site energies (4.12)
Fs(Es) = 0;
otherwise
After some straightforward calculations, D* can be found to be given by
[Erf(b)- Evf(a)] exp(/3Es)ezp D* =
I_.-5-(/~ ~ + ~-)~
2d{Erf ["/7(b - ,8o"2 - Es)] - E r f t 2[ ~ c~,a
-/3~
l + ~ 2~2j -
~s)]
(4.13) }
where Erf(x) is the error function defined by
Erf(x)
2fo~exp(-u 2) du
= --~
(4.14)
It is instructing to discuss the Arrhenius behavior of D*. For simplicity we take the case of a uniform distribution of site energies, Eq. (4.11). In Fig. 23, InD* is represented versus ~Ea for different values of the heterogeneity parameter 3'- It can be immediately seen that the Arrhenius law is valid only for 3' = 0, i.e. an homogeneous surface. As 3' increases, the plot deviates more and more from a straight line until it reaches the boundary curve corresponding to 3' = 1. If we force D* to behave as exp(-~E~ff), where E~ff is an effective activation energy, then for the case of a uniform distribution for Es it can be easily obtained from Eq. (4.11) that 1
E~f] = E---~- -~ + 7E---~tgh-a(flT-E~)
(4.15)
413 n
13E a
.
In D
=0
t
!
7=1
Figure 23" Arrhenius plot of D* for a random traps surface. which is clearly temperature dependent. For 7 = 0 we have the expected result E,.:.:= Ea (Arrhenius behavior). The same limit is reached also for/3 ~ 0 (infinite temperature) and any 7- For 7 > 0 and low temperature,/3 ~ oo, the limit is m
XEs
(4.16)
2 i.e., as the temperature approaches zero the effective activation energy is the maximum available value. This result is reasonable since it is assumed that the tracer particle starts from the state of thermodynamical equilibrium which is coincident with the deepest trap at zero temperature. Another important fact emerging from Fig. 23 is that the Arrhenius plot for an heterogeneous traps surface is below that corresponding to the homogeneous surface. At first sight, this could be a little surprising because one is tempted to think that for the heterogeneous case there will be deeper traps but also shallower ones, as compared to the homogeneous case, and their effect could cancel. However, this is not what happens in reality, due to the fact that with the presence of very few deep traps the tracer particle will be retained by them for very long times, and this effect is stronger as the temperature decreases (/3 increases). This is a very important feature of this kind of energetic surface topography and we name it as the "trap feature". 4.2.
Random Barriers
A random barriers surface is characterized by the fact that all adsorptive site energies have the same value Es, while bond energies are randomly distributed according to some function FB (EB)~ Fig. 24. As we can immediately see, a fundamental property of the random traps surface is now lost, i.e. now the jumping probability will depend on the direction of the jump. In fact, for example in Fig. 24, the barrier to overcome to jump from site i to j is quite higher
414
Ell ..........
s
/
!
i
k .... X
Figure 24: Adsorptive energy profile for a random barriers surface. than that to jump from site i to k. In particular, this means that the jump correlation factor f of Eq. (2.18) will not be equal to 1 and will be at least temperature dependent. Another important difference with random traps is the possibility of having anomalous diffusion, i.e. d~ > 2. In fact anomalous diffusion for random barriers has been reported [5] in the case of barrier energy distributions of the power type, involving infinite values of activation energy, for example: r
- (SE06E~"(1+6)
(4.17)
with E0 _< E~ < oc and (5 > 0. Using this distribution, and assuming a one-dimensional geometry, the following temporal behavior for < R 2 > is found [66]"
< R2(t)>1/2or (ln t) ~
(4.18)
In general, the random barriers problem is more difficult to handle by analytical methods than the random traps problem, and approximate solutions exist only for the onedimensional case. For this reason if is more convenient to study the problem by means of Monte Carlo simulation [67,68]. For a fixed site energy Es, a truncated gaussian distribution was used for bond enegies, with mean value E--B,dispersion (r and limiting bond energy values E~ 'i'~ and E ~ ~x. Simulations were performed not only for a two-dimensional (2D) square lattice but also for three-dimensional simple cubic ( 3 D - SC) and face-centered cubic (30 - FCC) lattices. Results for In [D*(T)/D*(oo)] versus/gE~ are shown in Fig. (25), and compared to the homogeneous and traps cases. It can be observed that, at any fixed temperature,
D*(T) D'ic~)
traps
D*(T) D'(oo)
[ D*(T)] homo
(4.19)
< LD*(~176
and the ratio is higher for higher dimensions (or lattices with a higher coordination number). The reason for this is a very important feature of the random barriers topography, which we name "barrier feature", i.e. the tracer particle can "get around" a high barrier
415 o
5
lO
I
w /t
I~Ea
8
k-
E~ w
--,,,~',,. ~ ~mc
..J
\ HOMOGENEOUS~
,
-10
'k ~ " ~JPs
"~
Figure 25: Tracer diffusion coefficient for random barriers for a truncated gaussian distribution with E s - l O . 5 k c a l / m o l , E B = 7 k c a l / m o l , cr = 1 . 6 k c a l / m o l , E ~ ~'~ = 3 . 5 k c a l / m o l , E'~ ~x = lO.5kcal/mol.
Eeff
Ea
4 t
I
0.5
1200/1'
Figure 26: Effective activation energy for a random barriers surface with the same parameters as Fig. 25.
416
Figure 27: Trapping of tracer particle between high energy barriers at low temperature" flipflop effect. by choosing a jump in another direction with a lower activation energy. This evidently is more probable for lattices with a higher coordination number. The behavior of the effective activation energy of the Arrhenius equation is another difference w'ith respect to the traps case. Figure 26 shows E~ff versus 1/T as obtained by Monte Carlo simulation. It can be seen that the maximum activation energy corresponds now to the limit of high temperatures, while at low temperatures E~ff ~ 0. This can be understood by realizing that at low temperatures the tracer particle will be oscillating between sites connected by low energy barriers, see Fig. 27, while as the temperature increases it can jump over higher barriers. This "flip-flop" effect, which has been observed experimentally [4], is also responsible for the behavior of the jump correlation factor f for a random barriers surface. In the case of traps, f was equal to 1 for any temperature, due to the jump symmetry. Figure 28 shows f versus ~E~ obtained by Monte Carlo simulation for barrier surfaces with different degrees of heterogeneity. We see how f drops rapidly to zero (faster for surfaces with higher heterogeneity) as the temperature decreases. In fact, as T ~ 0 the tracer particle is trapped in sites like those of Fig. 27 in such a way that each jump is followed by the reversal jump, making the sum in Eq. (2.17) give -n/2 and then f ~ 0. This particular behavior is another important characteristic of the barrier feature. We proceed now to study more complex adsorptive energy topographies in the following Section, and we will see that the trap feature and the barrier feature are two components which will appear as contributions to the general behavior.
417
\
\
k
-,,
,
\\
x%
,
"..
c=0.5
0.5
\o=1
\ 9
\
\ o =1.6
0
"\
5
"\
"\
PEa
10
Figure 28: Behavior of the jump correlation factor as a function of ~E~ for random barrier surfaces with different degrees of heterogeneity. 5.
DIFFUSION APPROACH
ON CORRELATED HETEROGENEOUS SURFACES- AN TO THE DESCRIPTION OF GENERAL TOPOGRAPHY
The general description of energetic topography of an heterogeneous substrate is a very difficult and unsolved problem. As already stated in Section 2.6, the gas-solid interaction energy is in general a three- dimensional potential energy surface E ( z , y ) , and this, for an heterogeneous substrate, is a stochastic process depending on the two continuous variables, z and y. This means that the most complete mathematical description of such a surface would involve dealing with a doubly infinite continuous set of intercorrelated random variables, a formidable task! A more modest program would be the development of a formulation capable of describing the statistical behavior of a few "well chosen" points of the energy surface, in such a way that different energy topographies should result by changing the degree of statistical correlations between such points. Two models in the spirit of this picture have been developed, i.e. the Generalized Gaussian Model (GGM), [62,69,70], and the SiteBond Model (SBM), [71,72]. The GGM chooses as special points of the energy surface the deeper points, i.e. what are known as adsorptive sites, and establishes a correlation function between pairs of sites which decays with the distance between them. The SBM, instead, chooses as special points a site and its neighbor bonds (saddle points in the energy surface connecting two sites) establishing a correlation function between them. We shall use these models here to study the influence of energetic topography of a general heterogeneous surface on surface diffusion.
418 5.1.
Generalized Gaussian Model
The GGM was first introduced in 1975 [69] as an attempt to study the adsorption of gases on a generalized heterogeneous surface with correlations between pairs of adsorptive sites through a gas-solid virial expansion. The correlation function between adsorptive energies of two sites i and j separated a distance r was given as =<
-
-
>=
where a is a measure of the heterogeneity degree and ro is the "correlation length". According to this model, any two sites which are within a distance r0 are strongly correlated (the correlation decays with distance as a gaussian function of half width ro) in such a way that their adsorptive energies have a high probability of being nearly equal. For sites separated a distance greater than r0 the correlation is almost null. Two well known simple topographies, i.e. random sites and patchwise surfaces, are limiting cases of the GGM corresponding to ro = 0 and ro = cx~, respectively. Due to the difficulties in calculating higher gas-solid virial coefficients, the GGM was reformulated more recently in terms of a lattice-gas description for the adsorbed phase using mean field approximations to handle adsorbate-adsorbate interactions [62,70]. The resulting adsorption isotherm equation is expressed as:
(5.z) where r
r
is the bivariate gaussian probability distribution given by
= [(2r)2det(H)] -'/2
exp{--~l ~ [Es, - -Es)(H-1)ij(Es3-- ES)])
(5.3)
i,j-'l
H being the matrix whose elements are the Hij defined in Eq. (5.1), and 0 is the "local" coverage given by
----(01 + 02)/2
(5.4)
where 81 and 82 are the coverages of sites 1 and 2 and can be obtained through the system of coupled equations ~,[-n(E a +W0:+~00+~-,)] (5.5)
As indicated in Fig. 29, A0 is the mean field interaction with particles within the circle of radius r0, A is the mean field interaction with particles outside that circle and W is the interaction between particles adsorbed at sites 1 and 2. In the GGM only the statistical properties of pairs of adsorptive sites are considered. Bonds energies are determined by assuming that heterogeneity is really a random perturbation of a periodic potential, represented by a broken line in Fig. 30. This means that
419
o
9
9
9
AN
o
9
9
9
,
,
,
9
9
.
,
,
,
,
,
,,
,
,
,
,
,
.
.
.
.
,,
,
o
,
.
,~
~
9
.
.
o
,
9
9
9
---lal---
,
~
,
,
9
9
,
,
,
~
.
,
,
,
o
,
.
,
~
.
,
9
,
,
.
,
,
.
9
9
9
.
,
.
Figure 29: Different regions considered for adsorbate-adsorbate interactions in the GGM.
E
Eb E O
Q
Es X
Figure 30: Determination of bond energies in the GGM.
420 for the heterogeneous potential (full line) the perturbation of the bond energy should be proportional to the mean value of the perturbations of the energies of the two neighbor sites, [70], i.e. E--s - E~ = o~ [(Es - Es,)+ (-Es - Esj)]
(5.6)
This leads to the following expression for the activation energy for the jump from site i to site j:
E~j = E ~ + ( a - 1)(Es - Es,) + a(-Es- Esj)
(5.7)
The "structure" parameter a (0 < a < 1/2) determines partially the topography of the adsorptive energy surface, for example it is easy to see that a = 0 corresponds to a "traps" surface. The topography is more completely determined by a and the correlation length r0. Figures 31-33 show different topographies obtained by different combinations of these parameters. In Figs. 31(a), 32(a) and 33(a) we have random traps (to = 0, a = 0), correlated traps (r0 -~ 1, a = 0) and strongly correlated, or patchwise, traps (r0 ---* c~, a = 0), respectively. More general topographies arise in (b) and (c). We now go to the calculation of the chemical diffusion coefficient to discuss the effect of the adsorptive energy topography. 5.1.1.
I n f l u e n c e of a for r a n d o m surfaces (r0 = 0)
We first study the simple case of an uncorrelated, or random, surface characterized by null correlation length, r0 = 0, to observe the effect of the structure parameter a on D, [23]. We use here the general expression obtained for D for a lattice-gas description of the adsorbed phase, Eqs. (2.63), (2.61) and (2.50). In the limit ro ~ 0, [70], the isotherm equations (5.2) to (5.5) reduce to
=
/= 0
+
[Z('[Z(, -
Fs(Es)O(Es, O) dEs
(5.8) (5.9)
where z is the lattice coordination number and Es the adsorptive site energy distribution. From these equations the thermodynamic factor O3#/Oln-~ in Eq. (2.61) can be numerically calculated. Now, the activation energy at zero coverage, E~(0), of Eq. (2.63) is just E~j given by Eq. (5.7), so that F(~) = 4v~yf eZu (~-, P00,i exp [-/~(W~* + aEsl + aEs2 )]>
(5.10)
where u, H = uexp(-~E~ W~ is the interaction energy between the activated particle and the environment "i", and we have taken for simplicity the origin of the energy scale
e-~
~=
0
o
0
o~
o
o~
o
ee~.,~o
Oq
o
(1)
0~" O-
4~
o
tmL
~j
t~ oo
i-~o
o~ CT
v
423
Ca)
(b)
(c)
Figure 33: Same as Figure 31 for
ro
=
~.
424 such that E s = 0. Finally, through Eqs. (2.61) and (2.50), we get for the chemical diffusion coefficient"
D = Ueffa2ez"i)~#R;
(5.11)
where
R - exp [-/Y(2z - 2)W*0 ] i Fs(Es)exp(-l%~E8)[1 - 0(Es, 0)] des
(5.12)
where W* is the interaction energy between the activated particle and an adsorbed one. We immediately notice that the effect of W* on D(O) is given by the trivial factor exp [ - 3 ( 2 z - 2)W*0], so that in what follows we consider W * = 0. At very low coverage, 0 --+ 0 we can easily obtain the limit value D(0), which should also represent the tracer diffusion coefficient D*, as" L
.i
D(O) = u~fsa2[f Fs(Es)exp(-~aEs)dEs]2 f Fs(Es)exp(-~Es)dEs
(5.13)
It can be seen that since the adsorptive energy Es involves negative values, D(0) for a = 0 is smaller than that for a > 0 at any temperature (higher/3), in coincidence with the results obtained for the tracer diffusion coefficient for random traps surfaces. Several adsorptive site energy distributions were used in actual calculations of D(O). (i) For a uniform distribution 1
Fs(Es)=
7-~
0
; for - A < _ E s < _ A ;otherwise
(5.14)
the isotherm equation can be easily obtained as
ezp(~#) =
exp(zW-O) [exp(2/~O)- 1] exp(~A) - exp [/3(20- I)A]
(5.15)
The results for D(O) normalized to D(0) are shown in Fig. 34. (ii) For a gaussian energy distribution C
Fs(Es) = v~aezp(-E]/2a2),_
(5.16)
the energy values must be truncated at E,~i,~ and Ema~ in order to avoid unphysical situations and the normalization constant C determined such that
/EE'~~ Fs(Es)dEs = 1 rnin
The results for the Gaussian distribution are shown in Fig. 35.
(5.17)
425
/A -5
f
O
113 t o~= A/-r--5
'
rr-3
'
Zl~llT=3
'~10 2
0
o
-3
l 0
0.2
0.4
0.6
0.8
1
0
O
I
I
I
I
0.2
0.4
0.6
0.8
1
Figure 34: Diffusion coefficient for ro = 0 and a uniform distribution of site energies. (iii) For a log-normal energy distribution
(5 S)
Fs(Es)- TEsv/~exp
where Em and 7 are the median and dispersion, respectively. Results are given for the case of non-interacting molecules in Fig. 36. The coverage dependence of the chemical diffusion coefficient is seen to be much stronger for random traps surfaces (a = 0), Figs. 34(a), 35(a) and 36, than for surfaces with a > 0. This is explained as follows. At low coverage the deeper sites are preferentially occupied. For a = 0 these sites are also those corresponding to the highest activation energy for jumping. As coverage increases, particles from sites with lower activation energies contribute to migration and D increases. As soon as the surface differs from a traps surface, for example for a = 1/3, Figs. 34(5), 35(5) and 36, the energy of the destination site begins to influence the jump rate and the deepest sites do not necessarily correspond to those with the highest energy barriers for jumping. This effect leads to a much weaker increase of D with coverage for a > 0. It is interesting to observe that for a = 1/2 a maximum in D(8) occurs at 0 = 1/2 for the rectangular (not shown) and gaussian energy distributions, and D(8) is symmetric about that value. This is not true for a non-symmetric distribution like the log-normal. The absence of a maximum in D(0) seems to be another characteristics of the random traps surface. Let us now discuss briefly the effect of adsorbate-adsorbate interactions. Repulsive (attractive) interactions result in an increase (decrease) in the diffusion coefficient. This is explained by a decrease (increase) in the activation energy for diffusion due to a rise (fall) of the bottom of the potential energy of adsorbed particles. The effect is the same as in the case of homogeneous surfaces. On the whole, the coverage dependence of D depends m
m
426 e=o
(a) ioo
arI'--2
O'/T=I
ZsI J T = ~
=100
g
1
/~
o
|
0.1 0
0.5
1
c~/T---I
0.5
0,5
a=.I/3
a/T=2
1 /
e
1
o'/T--4
m
~o
0.1
,
0
0.5
1~176
I
o.[r=l
,~
1
!
0.5 w=lO. crfI'=2
1
0.5 8
J9 a/T--4
I
I
i
zE1/Z'=2
1
0,5
C3 1
0.1
0
0.5
1
0.5
1
8
Figure 35: Diffusion coefficient as a function of coverage for the Gaussian distribution of sites.
427
10 0 o~=I/3
Cb
/
0
0.2
0.4
0.6
0.8
1
0 Figure 36: Diffusion coefficient as a function of coverage for the log-normal energy distribution of sites with EmIT = 4 and r/T = 0.7. on the relative weight of heterogeneity and lateral interactions. If heterogeneity is rather considerable and lateral interactions are repulsive, the coverage dependence of D is very strong because both factors lead to an increase in diffusivity with increasing coverage (Fig. 34(a) for z~W = 3 or Fig. 35(a) f o r / 3 a = 4 and z/3W = 2). If heterogeneity is rather strong but lateral interactions are attractive, the coverage dependence of D can be weaker because the two effects compensate each other (Fig. 34 for z13W = - 3 and Figs. 35(a) and 35(5) for 3~ = 4 and zl3W = - 2 ) . Finally, if heterogeneity is weak and lateral interactions are attractive, D decreases with increasing coverage (Fig. 35 for/3a = 1 and z~W
5.1.2.
=
-2). C o m b i n e d effect of c~ a n d r0
We now study the most general case within the GGM, where the combined effects of the structure parameter ~ and the correlation length r0 are present. From Eqs. (2.61), (2.62), and (5.1) to (5.7), the chemical diffusion coefficient can be expressed as
- = u~/]a2 -0/3# D(O) ~ R(0)
(5.19)
where R(O) is given by
,
+
-
}
(1 - 0z)(1 - 02) dEs1 des2 Here, as before, we have taken E s = 0. Calculations representing D(O)/Do(O) where D0(0) is the Value of D(~) for ~ = c~ = r0 = 0, corresponding to T = 400K, 13a = 1
428
10 2
~,~ 0
(a)
(b)
(c)
/ / . .. /-" J / s d
.
7
1
0.5
!
!
0.5
0.5
0
Figure 37: Surface diffusion coefficient for a noninteracting adsorbate, W=0;-,ro=0;---,ro=l;--.,r0=2. (a) a = 0 ; (b) a = l / 4 ; (c) a= 1/2. and W* = 0 are shown in Figs. 37 and 38 for noninteracting and interacting adsorbates, respectively. In order to discuss properly the influence of the correlation length r0 on the diffusion coefficient it is convenient to use some results obtained by Monte Carlo simulation on a square lattice in the framework of the GGM [70]. Fig. 39 shows the behavior of the vacancy factor V, defined as the mean number of nearest-neighbor empty sites per occupied site, as a function of 8. As we can see, at intermediate coverages the vacancy factor decreases strongly for surfaces with a larger correlation length and for attractive lateral interactions. In Fig. 40 snapshots of the adsorbate taken during Monte Carlo simulation are shown for different correlation lengths, coverages and lateral interactions. W'e start by discussing the behavior of D(~) for a traps surface, a = 0, and for a noninteracting adsorbate, Fig. 37(a). As already pointed out in the previous section, for a random traps surface D(~) increases strongly with ~ due to the fact that the activation energy decreases as adsorbed molecules cover less energetic sites (shallower traps). However curves for r0 = 1,2 show a much faster increment for ~ > 0.2 reaching much higher values at ~ ~ 1. The cause of this behavior for correlated traps surfaces must be found in the "texture" of the adsorbate, or, more precisely, in the topology of the regions of empty sites, Fig. 40. In fact, it can be seen that at high coverages, for r0 = 0 migrating molecules must move in a very intricate region (white areas), while for r0 > 0 white areas form larger islands so that the net flux through a given line is clearly enhanced. The contrary effect due to the behavior of the vacancy factor is not so important in this case due to the fact that it is weaker for non-interacting adsorbates and that it vanishes for m
As a increases, Fig. 37(b), (c), the barrier to be jumped over depends not only on the energy of the starting site but also on the energy of the destination site in such a way
429
10
i
(a)
C3
i
(c) i
.-
/
"-.
I
I
I
0.5
0.5
0.5
1if'
\
o
Figure 38: Same as Figure 37 for an interacting adsorbate, 13W = - 0 . 5 .
V
I 0.8 0,6 0.4
nO 17
0 0 0
0
9
n
0 17
0,2
0
9
0
0
0
0
[]
DO
0,5
0
Figure 39" Monte Carlo results for the vacancy factor (mean number of nearest-neighbor empty sites per occupiedsite) as a function of mean coverage. Symbols" for ro = 0, o/3W = 0.5, [3flW = 1; for ro = 2, o/3W = 0.5, DflW = 1. The solid line represents the well-known behavior of a noninteracting_ adsorbate on a homogeneous surface, V = 1 - 0 .
430 that barriers are lowered for strongly adsorptive starting sites (low coverage) and raised for weaker starting sites (high coverage) giving rise to an increase (decrease) of D at low (high) coverages in addition to the general behavior described for a = 0.5. The symmetry of curves for a = 0.5, showing a maximum at 8 = 0.5, is a consequence of the symmetrical role played by the two sites participating in a jump in determining the activation energy, Eq. (5.7), and the assumed symmetry of the site energy distribution. A more complex situation arises in the case of an interacting adsorbate. W'e have represented the more interesting case of attractive interactions in Fig. 38. Here a qualitatively different behavior is produced by the correlation length for a given value of the structure parameter. The minimum clearly displayed by the curves in (a) and (b) for r0 > 0 at low 0 is due to the competing effects of the decreasing activation barriers as 0 increases and the strong decrease in the vacancy factor as r0 increases at low 0. At intermediate and high coverages D is higher for higher ro due to the effect of the adsorbate texture, which is more pronounced for an attractive adsorbate. At high coverage lateral interactions take over by strongly decreasing the diffusion coefficient, producing the maximum observed near the monolayer in (a) and (b) for r0 > 0. In (b), for r0 = 0, and in (c), for all values of r0, the influence of attractive lateral interactions competes with the decreasing of activation barriers even at low 0 producing a monotonous decrease of D with 0. Finally, it is interesting to study the behavior of the factor R(0), Eq. (5.20), which is closely related to the effective jump frequency F(8). Analytical calculations and comparison with Monte Carlo simulations were performed in the framework of the GGM for noninteracting particles, [73], and are shown in Fig. 41. The behavior of R(O) can be analyzed in a similar way as for D(0) by taking into account that two main factors contribute to R, i.e. the vacancy factor, through Po0,~, which decreases with coverage, and the activation energy factor, exp(-flE~J), which increases with coverage. We have seen through the GGM how surface diffusion is strongly affected by the adsorptive energy correlations (a way of controlling the energetic topography) through induced correlations on the activation energies for diffusion as well as through the adsorbate texture, or morphology of adsorbate clusters. 5.2.
Site-Bond Model
The SBM was first introduced in the context of describing the structure of porous media [74-76] and later developed for the description of adsorptive energy topography of heterogeneous surfaces [77-79]. The basic idea is to describe statistically both site and bond energies and give a correlation function between a site and its connected bonds through which different energy topographies can be generated. In what follows we change our sign convention for the adsorptive energy surface in such a way that site and bond energies Es and EB, are positive numbers measuring the depth of the gas-solid potential energy at those points, see Fig. 42. Statistically, site and bond energies are described by the probability density functions Fs(Es) and F s ( E s ) and the distributions functions
S(Es) = 0E s Fs(E) dE
B(Es) = /0E s Fs(E) dE
(5.20)
o
r~
o
o
r~
o
o
oo o
~,.~o
k
0=-0.65, W = 0 . 5 K c a l / m o l - ~,,,-I,
J
e=o.65, w = o
iJ
,.,~-*.
I ~ . ~ # l l _ ' ( ~
, ...~ :.~:
~":-~'.:.~.r-,::~'~; i ~ ' ~~, ' ~ -..-,,;:..:-.~ ~2 -! ..........
0=036, W = O
m
,-, u ~
8O.3, W = O
.,-"
i
"
;"
='~;:'
,~~ . ~.:- : ~ . .; . . .,;.,_x;~., ,-,
-
~
''C,
"),.~,
-_ ~ . . -
,.,~'. ,~.~,
;~,'a
..
~,.'~.
~
~.
"'"
~"~'ld~ ~
' " " "~ "
...'
~-
::' " , " . i
~,.~
.'.~
:
"~
~',
...
,~ ~ . ,
,,~
~- :~
..:~, :, .~..,,,~:, .', ~, ;p,,/, .~.<
....
~ . :~ ,...~,... ".-.._ .,--, . - '. , .:-:.,.,~-.!,,~ .,~.,~," ~" "~ ,~',-: } . ' ~- 91" , ~ . " ~ . , : ' ~ . . 9, , . . , , : . ~ : , ~.,...'~ ' . ' ~ : ~,, ~
.~:,,...~,-', - , , ~ - .< ~-,
'.,"~-.
.,-;:, .- ,~,.-,,,:,.-,~ ;. ,,,- , -&, . - ,
' ~-' ~-~-
~,
..:
m
JJ 0
o~
i===i
432 eo
0
.... .......
r~=O ro=1 r~--2
(a)
(b)
.-;,.. .* ,; . - .
"" / / O
9Monte Cado
rv,
J
5
bl s
9
I I
0"
"~
.;6
9 ~' .~"
"" ""
". I
"i,
";/
I
~.
'
~
.
',!I ,~ I ~. ~ I ,.
'i
.'~'
zE
,,
\
"~/
""
O~
-;*/r
."
.
li~ L r
9
o
9 ,~ " I " ,
I
I
~
t
.;r .7
t ! "
-,;
0.5
0.5
e
Figure 41" Average jump frequency versus coverage for T = 300K, Es = - 2 . 5 K c a l / m o l , ~ = 1 K c a l / m o l and different values of a. Curves represent the model results and points, the Monte Carlo simulation.
"ES
"
S
~Y
-E Ix.y)
Figure 42: A part of the adsorption energy surface showing sites (S) and bonds (S).
433 A fundamental principle must be satisfied, the "Construction Principle", which states that "for a given adsorptive site its energy must always be greater than, or at least equal to, the energy of any of its connecting bonds". For this it is necessary (though not sufficient) that
B(E) >_ S(E)
for every E
(5.21)
Now, statistical correlations between sites and bonds are introduced by assuming that the joint site-bond probability density, expressing the probability of finding a site of energy Es C (Es, Es + dEs) connected to a bond of energy EB C (EB, EB + dEB), is given by
F(Es, EB)dEsdEB = Fs(Es)FB(EB)r
(5.22)
EB)dEsdEB
where r EB) is a correlation function which will carry valuable information on the energetic structure of the surface. Of course, the correlation function r will be different for different methods of "constructing" the adsorptive energy surface. If bonds are assigned to sites in such a way as to get the maximum degree of randomness allowed by the construction principle, then r can be determined as [77]: { ~=~[-r s(~s) as(sB)
r
EB ) =
dS/(B-S)]
for EB _< Es for EB > Es
B(Es)-S(Es)
0
(5.23)
The site and bond distributions can be completely arbitrary as long as they fullfill the construction principle. However a particularly simple expression for r is obtained in the case of uniform distributions: 1/A 0
fors<_Es<_s+A
Fs(Es) =
FB(EB) =
1/A 0
forb<EB otherwise
(5.24)
otherwise
(5.25)
With this, we can easily obtain
r
EB) = exp[-7(Es, Es)f~/(1
-
f~)]/(1
-
(5.26)
f~)
where ft is the overlapping area of Fs and FB, given by f~ = (b + A - s)/A, and
I (Es - s)/(b+ A - s) 1 7(Es, EB) = (Es - EB)/(b + A - s)
(b+ A -
forEs_<s; forEB<s; forEs>s; forEB>s;
Es Es Es Es
<_b + A > b+ A <_b + A > b+ A
(5.27)
As we can see, the correlation function depends strongly on the overlap ft, which is then the principal parameter determining the energetic structure. In Fig. 43 we show
434
R,a--{
~a-~
i' @ 1 ~ ' , . . . ~ r "~ i
I
~
_a._Z
1 ~
_
_
~
tt~
ILLI
1 .........
J
,
.
.
Figure 43: Site-bond distributions and their corresponding energy profiles along a direction of the surfaces.
435 schematically different kinds of adsorptive energy topographies that can be easily generated in the framework of the SBM. When Fs and Fs do not overlap, ~ = 0 and r = 1, and we have a noncorrelated, or random, heterogeneous surface. For example, if FB is a ~-function we have a random traps surface and if Fs is a &function the result is a random barriers surfacg. For overlapping distribution we have a site-bond correlated surface and if fl increases approaching the limit value ~ = 1 then the surface approaches the limiting case of a patchwise topography. A general method to generate Monte Carlo simulations of an heterogeneous surface in the framework of the SBM has been presented in detail in Ref [79]. Fig. 44 shows the simulated surfaces for different values of ~. In the SBM, the topography parameter ~/can also be related to a correlation length parameter r0. In fact, it is found by Monte Carlo simulation [79] that the correlation function C~j(r) between the energies of two points (sites or bonds) i and j, separated a distance r, can be expressed to a good approximation as
(5.28)
=
where the correlation length is related to the overlap through: ro = ~ / / ( 1 - ~ t )
(5.29)
We notice that in the SBM the spatial decay of the correlation is exponential and not gaussian like in the GGM. Nevertheless, r0 has here the same meaning as in the GGM, and is an alternative measure of ~/. We shall now use de SBM to study surface diffusion on different kinds of surfaces. 5.2.1.
T r a c e r diffusion on c o r r e l a t e d t r a p s and b a r r i e r s s u r f a c e s
We start with simple and well determined topographies, like correlated traps and barriers surfaces, and study the diffusion of a tracer particle on them. Correlated traps (barriers) can be easily obtained in the SBM by first generating a surface with general site and bond frequency functions Fs and FB, with the desired overlap fi which will correspond to a correlation length r0, and then letting FB(Fs) reduce to a &function. The result will be a correlated traps (barriers) surface characterized by the correlation length r0. Notice that in the GGM it is easy to generate a correlated traps surface, with = 0 and r0 > 0, but it is not possible to generate a correlated barriers one, so that the two models are in some sense complementary to each other in order to describe a bigger variety of energy topographies. The behavior of the tracer diffusion D* on these surfaces can be easily simulated through the Monte Carlo method. Figs. 45 and 46 show the results for uniform distributions with different correlation lengths. The first result we observe is that D* for traps surfaces is not affected by the correlation length r0, behaving exactly as the analytical solution found for the random traps surface (broken line), the same is true for the jump correlation factor f. This can be understood on the basis of the fact that for any trap the jumping frequency and jump direction are independent of the depth of surrounding traps (this will not be true when other adsorbed particles are present). In the case of barriers, on the other hand, both D* and f are influenced by the correlation length. It is
436
/
",.-...,~,.~.~,
. t . . r~
t
.......... .................. ~
................................'
(b)
Figure 44: Adsorption energy surfaces obtained through the Site-Bond model for different overlapping degrees: (a), f / = 0.3; (b), f / = 0.5.
437
(c)
(o) Figure 44: Adsorption energy surfaces obtained through the Site-Bond model for different overlapping degrees: (c), ft = 0.7; (d), f / = 0.95.
438 TRAP MODEL ,,
,
,
,
,
-4
-8 13 " -12
v
,.J
ro=O ro=l to=2 Theory
-16
-20
Homogeneous
i
I
i
2
0
1.5
I
!
4
i
6
I
i
lO
8
12
Eact/RT
TRAP M O D E L
,
,
,
1.0
0.5
+ • [] 0 , 0
ro=O ro=l ro=2 i
0
I
2
i
I
i
4
I
6
i
I
8
i
10
Eact/RT
Figure 45: Tracer diffusion coefficient D* and jump correlation factor f for correlated traps surfaces.
439 BARRIER
MODEL
,
[]
-2
•
[]
1:3
v
r"
..J
~0
-6
~2 Homogeneous
-8
I
i
I
1
i
I
i
I
3
2
4 Eact/RT
BARRIER
1.0
MODEL
i
i
+o
x + []
X +
0.5
[] XEI -+
x []
0.0
,
0
E] X +
to=0
+
[]
ro=l
X +
ro=2 I
2
,
,
4
'
[]
[] []
'
6
"
'
8
17
P
10
12
Eact/RT
Figure 46: Tracer diffusion coefficient D* and jump correlation factor f for correlated barriers surfaces.
440 interesting to notice that if r0 is sufficiently large, D* can change from the characteristic barrier feature (D* is larger than that corresponding to an homogeneous surface) to a trap feature (D* smaller than that of an homogeneous surface). The explanation of this is that for large r0 barriers will group together according to their heights, forming patches in such a way that a group of low barriers may be surrounded by bigher barriers, and this region of the surface will actually behave like a super-site showing the trap feature. This particularity explains both the decrease of D* and the increase of f as r0 increases, since the flip-flop effect observed for a barriers surface, which is responsible for the strong fall in f as temperature decreases, is clearly reduced as r0 increases. Finally, it is also clear that both D* and f need to be analyzed in order to disclose the kind of energy topography characterizing the heterogeneous surface. 5.2.2.
Tracer diffusion on a general site-bond surface
When neither Fs nor FB are g-functions and their overlap fl is not null, we have a general site-bond surface characterized by a correlation length r0 corresponding to the given value of ft. Figure 47 shows results obtained by Monte Carlo simulation for the case of uniform distributions for Fs and FB. The behavior of D* and f can be analyzed by taking into account that both the trap feature and the barrier feature are now acting simultaneously. However, it is by now clear that the trap feature will be predominant in the behavior of D* while the barrier feature will determine that of f. Thus, there is a hope that in the future it will be possible to determine how much of each feature is present for a given heterogeneous surface by analyzing the behavior of D* and f obtained by independent experiments. This hope can be made possible by the rapid development of surface analysis techniques at the atomic scale. We end by discussing the hypothesis advanced by Limoge and Bocquet [80] on the basis of Monte Carlo simulations, according to which for a general heterogeneous surface there could be a compensation between the statistical distributions of sites and bonds in such a way that D* could behave just like for an homogeneous surface. We see, from Fig. 47(a) that such a compensation can exist only for noncorrelated surfaces, r0 = 0, and for low values of E~. This discrepancy can be due to the fact that Limoge and Bocquet model of overlapping sites and bonds distributions does not take into account the construction principle. 5.2.3.
Chemical diffusion coefficient for a general s i t e - b o n d surface
The chemical diffusion coefficient behavior for a general site-bond surface is similar to that predicted in the framework of the GGM. However, it is worth to discuss it in the framework of the SBM, at least for the simple case of noninteracting particles, because it will help to understand how the model works [78]. The general equations for the adsorption isotherm, Eqs. (5.2), (5.4) and (5.5), are still valid now but r be obtained in terms of Fs(Es), FB(EB)and r EB). By using the joint distribution of Es and EB, Eq. (5.23), it is easy to obtain
r
E& ) = Fs(Esl )Fs(E& )r
, E& ),
(5.30)
441
SITE-BOND
[]
~..
A
[]
+
A
b~ v
MODEL
.
X x []
A
-5
X-~ O
X []
X
§
[]
§
X
a
-t-
r
--I
+
x
-1 5
x
ro--0
-10
ro= 1
[]
ro-2
99 ,
Homogeneous , , , 2 4
0
ro=10
,
, 6
,
h
I
i
,.,
lO
12
Eact/RT
SITE-BOND 1.0
,
,
OX
MODEL ,
,
,
,
,
,
+ +
,
,
,
r =0
O
•
[]
(=1 rU=2
~
"Or=10 O
_A A
[]
A
[]
x
+
X
[] A A
-t-
X []
A
+ X
[]
A
A ,
0
+
x []
0.0
,
,
§
Ox
0.5
,
,
~-
[] •
_
,
,
~
2
,
[]
,
4
9
Bx
6
,
~x
8
,
-I-
h,
10
,
, +,
12
,
14
,
~
16
t,
18
Eact/RT
Figure 47: Tracer diffusion coefficient D* and jump correlation factor f for general correlated surfaces.
442 where
~(Es~, Es~)
=/min(Es~ ,Es2) r .,o
(5.3~)
des
With this, and remembering that now r0 = ~ / ( 1 - ~ ) , the isotherm equation and then the function # = #(8) and its derivative can be calculated. Now, as before, D(8)
~ z~,Ofl~ = a ~,e -o-~R(O)
(5.32)
where now R(0) = / / / [ 1 -
81(Esl)] [ 1 - 82(E&)] eZEBF3(Esl,EB, E&)des1 dEsdE&
(5.33)
and
F3(Esl, EB, E&) = Fs(Esl)Fs(E&)FB(EB)r
, Es)r
EB)
(5.34)
Fig 48 shows the behavior of D(8), normalized to D(0), for noninteracting particles on site-bond surfaces characterized by uniform distributions Fs and FB with different overlapping degrees. We see that a maximum accurs for 0 < 8 < 1 and that this maximum shifts to lower 8 values as the overlapping degree increases. This behavior can be understood by analyzing the effect of the correlation function r which is schematically represented in Fig. 49 for fl = 0.5. For any given Es, each of the bonds connected to it is distributed according to the function FBr EB). This function is represented in Fig. 49 for three particular values of Es, namely" 1) Es = s; 2) Es = s + A/4; 3) Es = b. As coverage increases, sites with lower binding energies are being occupied by adsorbed molecules. For a fixed fl, the mean energy of bonds connected to sites of a given energy is closer to the site energy when it is high (low coverage) and farther when it is low (high coverage), as long as Es > b + A. Therefore, for fl = 0, while the mean site energy is always decreasing with coverage, the mean bond energy is constant, and, as a result, the activation energy for migration continuously decreases with 8 and D('8)/D(O) presents no maximum. For fl > 0, on the contrary, the mean bond energy is constant as long as Es > b + A and then starts to decrease, compensating the decrease in Es. This produces a minimum in the activation energy for migration and then a maximum in D(-8)/D(O) which shifts to lower values of 8 as fl increases. Modifications in the behavior of D(8) due to lateral interactions follow the same pattern as discussed for the GGM. 6.
CONCLUSIONS AND OPEN PROBLEMS
Surface diffusion presents many still not well understood aspects and open problems, even for homogeneous surfaces, which have been widely reviewed [4,9,81]. Here we concentrate on those aspects which are strictly related to the surface energetic heterogeneity.
443
C~
....... I = 0 . 1 .
.
.
.
I=0.5 I= 0.7 I-->l
.
~2
"
(3
.
-
s
s
"v
i
0.5
0
1
0
Figure 48: Normalized surface diffusion coefficient for T = 300K, A = 2Kcal/mol and W = 0.
1
2
/
3
__/____ .
.
.
.
! I ! ! ! 0 ! ! ! !
____.I_ Figure 49: Schematic representation of the effect of the correlation function ~b for uniform energy distribution with ~t = 0 . 5 .
444 The general diffusion equations, developed in Section 2, are quite well established, though the validity of the Reed- Ehrlich equation for heterogeneous surfaces is not clear and should be further investigated through Monte Carlo simulation. This is an important aspect since the great majority of diffusion models are based on the Reed-Ehrlich picture. Surface diffusion in porous media is influenced by so many effects, and experimental determinations are so indirect, that an attempt to use refined models taking into account the energetic topography would be totally worthless. The simple phenomenological models discussed in Section 3 are representative of a great amount of effort dedicated to this problem and many more semiempirical equations have been developed trying to modify the Arrhenius law or by considering mechanistic models for the jumping molecules. In this respect, the percolation model is quite unique and it is based on clear and deep physical arguments leading to a comparatively simple final expression for D(~). The fitting of some experimental data are satisfactory and reasonable values of the parameters are obtained. The model seems to be promising, but a much wider comparison with experimental data would be necessary to make it reliable. It would also be useful to perform Monte Carlo simulations folowing closely the hypothesis of the model in order to determine its selfconsistency, and in particular to check the approximations made in obtaining the critical conductivity. The study of simple ideal topographies in Section 4 is instructive, the principal goal being that of understanding the two principal features present in any further analysis involving energetic topography, the "trap feature" and the "barrier feature". Traps and barriers surfaces are sufficiently simple limit topographies to encourage theoretical efforts in the field of statistical mechanics to obtain exact results. Two models, the GGM and the SBM, presented in Section 5, are first approximations toward a general statistical description of surface heterogeneity taking into account the energetic topography. It is not easy to say which of these models is more convenient. In the GGM three important parameters must be determined: or, a and r0. The SBM, on the other hand, depends on Fs, FB and the overlap f~. If uniform distributions are assumed for Fs and FB, then the model depends on two parameters: A and ft. On the other hand we have already pointed out that some kinds of topographies cannot be generated by one of the models and the other is necessary for it. In this sense both models are complementary. Principal questions to be answered in this field are: a) What is the relation between a given physical surface and the adsorptive energy surface? b) Which kinds of defects, impurities, etc., produce a given energetic topography? c) How to determine topography parameters (characterization) from the analysis of gas-solid experimental data? Monte Carlo or Molecular Dynamics, simulations will be of essential help to answer a) and b). With respect to c), tracer surface diffusion is a potentially useful tool for the characterization of the adsorptive energy topography since D* is quite sensitive to it and does not present the inconvenience of D(0). Here, heterogeneity and lateral interaction effects compete. However, it is still necessary that experimental techniques for surface
445 analysis at the atomic scale be perfected in such a way that both D* and the jump correlation factor f be measured. In fact we have learned that, for a general site-bond heterogeneous surface, the trap feature dominates in the behavior of D* versus ~E~, while the barrier feature dominates in the behavior of f versus 1/T. Finally, a still open problem of great practical importance is the development of a theory for surface diffusion of mixtures of gases on heterogeneous surfaces for both non porous and porous adsorbents.
ACKNOWLEDGEMENTS The present monograph is the result of many years of continuous efforts of my students and collaborators at the Surface Physical-Chemistry Group of the Universidad Nacional de San Luis, San Luis, Argentina. I am indebted to all of them for their enthusiasm, creativity, dedication and friendship. Special thanks are due to W. A. Steele, V. P. Zhdanov, J. L. Riccardo, R. J. Faccio and F. Bulnes for critically reading the manuscript.
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W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
451
C o m p u t e r S i m u l a t i o n of Surface Diffusion in A d s o r b e d P h a s e s William Steele Department of Chemistry, The Pennsylvania State University, 152 Davey Laboratory, University Park, PA 16802, U.S.A.
1
Introduction
Computer simulation is a now well-known technique for the evaluation of the thermodynamic properties of gases adsorbed on solid surfaces or in pores. The basic idea is to use the power of the computer to bypass the simplifying assumptions made in the past to pass from a molecular model of the adsorption system to the evaluation of its observable, macroscopic properties. In essence, one defines the solid adsorbent in terms of its interaction energies with gas molecules that come in contact with its surface. This model is completed by defining the interactions of the adsorbate molecules with each other. The simulation which can then be performed relies on one of two general algorithms: Monte Carlo or molecular dynamics [1, 2]. For both, tricks are needed to make such systems mimic the macroscopic adsorption of a gas on (or in) a solid: the potential energies of interaction must be modeled accurately, but not so accurately as to prevent the computer from evaluating them in a reasonable time; and periodic boundary and minimum image conditions must be applied to allow one to associate the properties of a few hundred molecules with those of a macroscopic fluid. These well-known ideas have been utilized in a very large number of problems explored using this technique. Features that are perhaps unique to adsorption are that the solid adsorbent is taken to be rigid so that one has a mechanical system of adsorbate particles with translational and rotational degrees of freedom moving in the external force field of the solid. Vibrational degrees of freedom for the adsorbed molecules are usually neglected, at least when the molecule is small- obviously, intramolecular motions are an important aspect of the simulations of long-chain adsorbed molecules. The molecular dynamics algorithm produces molecular trajectories for interacting molecules over periods of time ranging up to a nanosecond. This is accomplished by numerically integrating the classical equations of motion for each molecule of adsorbate to determine the time-dependent trajectories followed. Time-averages of quantities such as the average potential energy of the entire sample of adsorbate molecules can then, by the postulates of statistical mechanics, be equated to ensemble averages. Of more importance to the present discussion is the fact that the information about the time-dependent positions of the molecules in the system allow one to evaluate dynamical properties such as mean square displacements. It is well-known that these displacements are related to self-diffusion constants (or tracer diffusion constants, as they are sometimes called) in a straightforward but exact manner. An alternative calculation of the diffusion constant relies on the fact that molecular velocities are also known at each instant in time. This allows one to evaluate velocity autocorr~elation functions and, by integration of this function over time, to evaluate the self-diffusion coefficient in a calculation which is completely equivalent to the mettiod based on the long time limit of the slope of a plot of mean square displacement versus time. The formalism and the explicit equations for the self-diffusion constant
452
are written out in the chapter in this book by Zgrablich. In fact, other dynamical properties of a fluid such as the shear viscosity and/or the thermal conductivity can also be evaluated using suitable analogues to the mean square displacements or the velocity autocorrelation functions [2]. An important point is that all these quantities can be evaluated for fluids in geometries where the confining dimensions are of the order of nanometers. Furthermore, for inhomogeneous fluids, the dependence of the transport coefficients upon position within the fluid and upon the direction of the flux (of mass, momentum or energy, for diffusion, shear viscosity and thermal conduction, respectively) relative to the confining geometry can also be evaluated. It should also be emphasized here that these very detailed results can be generated by either molecular dynamics or by a suitably modified form of the Monte Carlo algorithm. Here, we will discuss some of the results that have been obtained over the past decade or so for diffusional motion in fluids on surfaces. In the application of the Monte Carlo algorithm to transport, dynamics are not followed per se, but the passage of the system through phase space is evaluated by making a lengthy series of small random displacements of the molecules. These trim displacements are accepted or rejected by a probability rule (hence, Monte Carlo) that has been shown rigorously to generate the statistical ensemble of interest. Many different types of ensemble can be produced (Grand Canonical, isobaric-isothermal, canonical, etc.) by appropriate modification of the probability rules that govern the random process. The tricks of periodic boundary conditions, rigid solid, etc. mentioned above for molecular dynamics are also utilized in Monte Carlo simulations. Since there is no time variable in this algorithm, one might think that evaluation of transport properties via Monte Carlo simulation is not possible. However, many workers have argued that this is unnecessarily restrictive. The "kinetic Monte Carlo" algorithm is based on a master equation with phenomenological rate constants [3]. In this way, molecules trace out trajectories in space and time that allow one to evaluate mean square displacements (in particular) and thus, a kind of diffusion coefficient. The problem is that the units of time remain undefined; nevertheless, many useful results have been obtained in this way [4]. In fact the kinetic Monte Carlo algorithm is indispensable for adsorbed phases that have been modeled as lattice gases. In this case, motion is due to instantaneous jumps between lattice sites and molecular dynamics is not applicable. This is a particularly appropriate model for the diffusive motion of strongly adsorbed (i.e., chemisorbed) species, especially on clean metallic surfaces - see for example, [5]. However, the lattice gas model is widely used for systems other than chemisorption - specifically, nearly all current models of heterogeneous surfaces involve the concept of adsorption on distributions of sites with varying adsorption energy. In this case, jump models plus kinetic Monte Carlo are employed to evaluate surface diffusion - the only added assumption needed is the specification of the energy barriers to jumps between neighboring sites. Simulation studies of such model systems are rather wide-spread [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Over the past decade or two, molecular dynamics simulations of transport in adsorbed fluids have been reported for a large number of model systems. We give here a brief and not exhaustive review of this work and subsequently discuss in detail simulations of self- (or tracer-) diffusion in thin films (one or two atomic layers at most) of gases adsorbed on model surfaces. Many of the molecular dynamics simulations of transport in confined fluids have been concerned with gases sorbed in pores. Initially, model pores with a simple geometry were studied. Two of the most popular are slit pores with planar parallel walls and straight-walled cylinders.
453
For such models, one must then specify the roughness of the pore walls. Perfectly smooth walls can be studied, but in more realistic systems, one attempts to include the atomic structure of the wall in a way that is simple enough to allow one to evaluate fluid-wall interactions reasonably efficiently. Within the category of transport of pore fluids, one should distinguish two rather different cases: 9 Pores that are essentially filled with adsorbed fluid [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32] 9 Pores that have submonolayer or at most, one or two strongly held layers of fluid on the pore walls (see below). In the first case, one is interested in the behavior of either diffusive or shear flow in the small-size limit where the nature of the boundary conditions has a significant effect on the hydrodynamics of flow, i.e., on the solutions to Fick's equation for diffusion or to the Navier-Stokes equation for viscous flow down a microscopic tube (Poiseuille flow) or between two planar walls (Couette flow). In the second case, the molecular motion in a thin layer on the wall tends to be rather similar to that for a fluid adsorbed on a free surface - that is, the presence of another wall at some distance from a very thin adsorbed film has a minimal influence on the dynamics of the molecules in the film except in the case of extreme constriction where the second wall is at a separation distance of a couple of atomic diameters. Well known examples of transport through constrictions are fluids in the pore systems of zeolites, [33, 34, 35, 36, 37, 38, 39] where the sizes of the openings to the cages inside the solid are of the order of a molecular diameter, thus producing the molecular sieve effect. As an example of the simulation of molecular motions of rare gas atoms, Figure 1 shows the trajectories followed by xenon atoms at 300 K in the pore system of a zeolite known as zeolite rho [33]. This crystal contains two interlaced non-connecting cubic lattices of cages with diameters _~ 12 ~t. The cages are connected by short tubes with diameters of "~ 3.6 )1. When xenon is brought into contact with this system, the atoms first are adsorbed quite strongly in the connecting tubes and then begin to fill the cages. Figure 1 shows three cages as the volumes within the hexagonal rings that actually define one end of the connecting tubes to the six neighbors of each cage. The small dots show the limited trajectories followed by atoms trapped in the tubes and vibrating on their sites at this point. All these sites are filled, but the periodic b o u n d a r y conditions cause some of them to be invisible (each atom of this kind is half on one face and half on the face on the opposite side, but is shown here as a single atom). If we focus our attention on the pair of cages vertically aligned in Fig. 1, one of which is a periodic image, we see t h a t the 6 atoms that are in each cage are translating across the inner wall of their cage in a random walk that is produced by collisions with neighboring xenon atoms. During the duration of this simulation, one atom leaves the tube that connects the central cage and its image above. It moves into the upper image cage, where it collides with the image atoms (not shown) and diffuses within the second cage. As it makes the change of cages, an atom from the cage below moves into the vacated tube. In this way, one sees that inter-cage diffusion controls the overall diffusive motion of the xenon sorbed in this pore system. Since only one such event has occurred in 37 pic0seconds, it is evident that a very long simulation will be needed to observe a sufficient number of inter-cage jumps to give a statistically satisfactory estimate of the jump rate in this system. A problem that frequently arises in this and other zeolite studies is that the rigid lattice model assumed in this work may give results for transport that are significantly
454
different from those expected if the atoms making up the solid lattice are allowed to undergo vibrational displacements. This problem is magnified in importance in porous systems that are "tight" - i.e., where the sorbate and the pore dimensions are sufficiently close to be at the edge of the molecular sieving that occurs when the gas atoms are too large to pass through the cage
F i g u r e 1 Computer generated trajectories for 16 xenon atoms in cages taken from the two independent networks of connected cages in zeolite rho. Also shown is a periodic image of one of the cages. Six of the atoms are tightly held in the openings to the cages (not all are shown) and the remaining 10 are evenly divided between the two cages. During the time of this simulation, one atom jumps from its position in the window into a neighboring image cage and is replaced in the window by a different atom from the cage. openings. If we now focus on the molecular dynamics simulations of self-diffusion of molecules confined to few-layer films on flat surfaces, one can again divide the problem into homogeneous and heterogeneous model adsorbents. In the homogeneous case, we begin with the fact that a very large effort has been devoted to experimental, theoretical and simulation studies of gases adsorbed on graphite. This work has included a number of simulation studies of diffusion on this surface together with a few experimental reports [40]. As is well-known, the surface of this solid can readily prepared so that it is nearly entirely made up of large-area perfect basal planes. Furthermore, the basal plane is believed to be remarkably smooth compared to ionic solids such as MgO or oxides such as Ti02. Thus, one expects very small barriers to translation across such a surface and diffusion that is very different from the standard descriptions of activated jumps over energy barriers with height equal to an activation energy Ea. It would appear that the best description of diffusive motion in monolayers on such surfaces might be based on
455 a two-dimensional gas approach, slightly modified to take account of the effects of the small variations in gas-solid energy as a molecule translates across the surface [41]. In fact, even for the rougher surfaces presented by adsorbents with atomic structure such as those presented by model amorphous or crystalline oxides, the barriers to translation of a molecule across the surface tend to be small enough that a description of diffusive motion in such systems in terms of jump motion should be carried out with caution. For the highly irregular gas-solid potential functions presented by heterogeneous surfaces, one has a wide range of "activation energy" in a complicated surface geometry that leads to trajectories that exhibit frequent changes in direction and which do not necessarily pass near the bottoms of the saddle points in the energy surfaces [42]. Many of these points will be illustrated by the simulation results that will be presented here. Another group of dynamical simulations involves the diffusive motion of isolated molecules over an adsorbing surface [43, 44]. Such systems, which can be studied experimentally by techniques such as field emission microscopy, are of course primarily probes of the gas-solid potential function, which for molecules with rotational degree of freedom and/or internal flexibility (such as short hydrocarbon chains) can be quite complex. It should be noted that molecular trajectories can be used to determine a variety of dynamical quantities, some of which are essentially
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F i g u r e 2 Number of atoms in a dense N2 monolayer on the graphite basal plane at 73.6 K as a function of time. Note that 1000 timesteps = 2.2 picosecond. The coverages, relative to commensurate, are 1.04, 1.10, 1.25, 1.35, 1.50, 1.75, 2.00 and 2.50 for the indexes on the panels increasing from 2 to 9. (Panel 1 corresponds to the commensurate layer - no interlayer transfers were observed for this case during the time of observation.
456
inaccessible to experiment. The rate of transfer of molecules in and out of a monolayer film can be monitored- for example, see Figure 2, which shows the time dependence of the number of molecules in several monolayer and submonolayer films of nitrogen adsorbed on the graphite basal plane at 73.6 K [45]. The fluctuations in the number of molecules in the first layer can be due either to transfer from (or to) the gas phase or from (or to) the second layer. Figure 2 indicates that the jumps occur rather infrequently on a molecular time-scale with a frequency on the order of a few per picosecond, depending upon coverage. The jumps themselves are completed very quickly on this time scale. Information of this kind is of interest to the determination of rates of adsorption and desorption, but there appears to be little use of simulations in this connection to date. Another type of dynamics that can be simulated is the rotational motion of polyatomic molecules in an adsorbed film. In this case, angular velocity and reorientational time correlation functions can be evaluated (and compared with experiment- see [40]). In this case, one must
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F i g u r e 3 Time-correlation functions for N2 on the graphite basal plane at low temperature. Part (a) shows the in-plane tcfs < cos(m$r > for m = 1 - 4 (top curve to bottom) for a commensurate monolayer at 18.8 K; part (b) shows the same functions for 41.4 K; and part (c) shows angular velocity time correlation functions for 41.4 K - the solid line is for out-of-plane tilting motion and the dashed line is for the in-plane reorientation.
457
realize that in-plane and out-of-plane molecular reorientation can be very different from each other due to the relatively large torque which will, for linear molecules, usually have the effect of constraining them to lie mostly parallel to the surface. In the case of in-plane rotation, the torques are due to the variations in gas-solid potential energy as a molecule moves parallel to the surface and to the torques arising from interactions with other adsorbate molecules in the same layer as the rotating species. Simulations of motion in the dense nitrogen monolayer [46, 47, 48, 49] gave the curves in Figure 3 which show in-plane libration at very low temperature (usually, a damped more-or-less harmonic oscillation as in the solid curve of Fig. 3c). At high temperatures, the in-plane molecular reorientation may show an approximation to rotational diffusion. In this case, theory gives exponential time-decays of the angular correlation functions gin(t)=< cos(m~r >, where ~ r the change in the in-plane angle of the molecular axis in time t and m is an integer. In fact, the solution of the two-dimensional rotational diffusion equation yields gin(l) = exp [-m2~t], where ~ is a rotational diffusion constant. In this limit, the angular velocity time-correlation function has been assumed to have a negligibly short life-time compared to the decay time of the orientational correlation function. Note that the in-plane rotational motion can be primarily diffusive even when the out-of-plane reorientation is a damped harmonic torsional oscillation. Part (a) of Figure 3 illustrates the in-plane orientational behavior for a fairly dense (15.7 .~l2 per molecule, or v/3 x v/3 commensurate) monolayer of N2 adsorbed on the graphite basal plane at a temperature below the in-plane orientational ordering transition which occurs for this system at ~_ 27 K and part (b) shows the behavior at a temperature above this transition. The almost constant time correlation functions (tcfs) of part (a) are indicative of small in-plane torsional oscillations, while the tcfs in part (b) correspond to in-plane motion which is more or less diffusional. Figures 4 and 5 show simulated orientational tcfs at 40.3 K for a N2 layer on graphite that is 5% denser than commensurate. In Fig. 4, rotational diffusion
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F i g u r e 4 In Cm(t)/m 2 for in-plane motion of N2 at 40.3 K on the graphite basal plane in a layer that is slightly compressed relative to commensurate.
458
theory is tested by plotting the in-plane orientational tcfs for for m = l to m = 4 on a scale that should give a single straight line for all m if the theory is obeyed [50]. Deviations from linearity at short time are due to the persistence of angular velocity correlations. It is probable that the
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F i g u r e 5 In 7>~(t)/l(l + 1) for out-of-plane reorientational motion in the system of Fig. 4. l = l corresponds to the lowest curve with the other curves in ascending order for l=2 - 4 and ~t(t) denotes a tcf of t h e / ' t h spherical harmonic function of the out-of-plane angle.
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F i g u r e 6 Reorientational tcfs for monolayer N2 on the basal plane of graphite. Temperatures are shown on the Figure. The nearly constant curve is for out-of-plane (tilting) motion, and the decaying curves are for in-plane reorientation.
459
differences shown in Fig. 4 between the observed and the predicted slopes at long time are due to the fact that reorientation is no longer strictly in-plane at this temperature and surface density, but has become three-dimensional. This idea is supported by the out-of-plane reorientational tcfs that are shown in Figure 5. The decays of these functions, which should be linear and identical for all l if the out-of-plane reorientation is diffusional, indicate torsional oscillation at short times and non-diffusional but significant exponential decays at long time. Figure 6 shows the in-plane orientational tcfs for molecules in the commensurate N2 monolayer on graphite as a function of temperature for the case m = l . Also shown is the out-of-plane reorientational tcf. Clearly, out-of-plane motion is minimal for all the temperatures considered, which range up to the bulk N2 boiling point, even though the in-plane motion is becoming ever more rapid as temperature increases. It should be noted here that .,N2 on graphite exhibits two-dimensional melting at a temperature that depends strongly upon coverage, but is close to 48 K for an isolated commensurate patch on the surface [51]. The conclusions to be drawn from the simulation studies of rotational motion in dense N2 monolayers on the graphite basal plane are that orientational ordering is almost complete at sufficiently low temperature, and that the in-plane reorientation is considerably less hindered than the out-of-plane at higher temperatures. In fact, the out-of-plane reorientational motion is essentially torsional oscillation that is induced by the strong gas-solid forces on a N~ molecule on the graphite surface (or more accurately, strong torques). The torques are much weaker in the in-plane direction because of the flatness of the basal plane. Thus, hindrance in that degree of freedom is due primarily to interactions with neighboring N2 molecules in the adsorbed film. Although simulations of orientational dynamics for other adsorbates on other surfaces are quite limited, it is not unreasonable to assume that this behavior of the N2/graphite system might be characteristic of small non-spherical molecules on relatively fiat surfaces. Furthermore, persistent velocity correlations can have a significant effect upon the relevant tcfs in the translation problem as well the rotational, as we will see in the following.
2
D i f f u s i o n on n e a r l y flat s u r f a c e s
An obvious but important feature of diffusional motion in an adsorbed film is that it is extremely anisotropic, with motion perpendicular to the surface tending to be mostly harmonic oscillation, interrupted by occasional desorption events a n d / o r collisional changes in the oscillatory motion. Thus, the three dimensional molecular motion in real physisorption systems must be treated as two distinctly different types. One expects that the mean square displacements (msds) < ~z2(t) > and < 5p2(t) > = < ~x2(t) + ~y~(t) > as well as the corresponding velocity tcfs, will behave quite differently. Here, z is the coordinate perpendicular to the surface and x, y (and p) are parallel to it. The brackets here denote an average over all (or over a specific subset - see below) of particles and time-origins and 5z2(t) is (z(t) - z(0)) 2, the square of the displacement in the z direction in time t. The tcf and the displacement are related by the equation [52]: < t~z2(t) > - 2
( t - r) < v'._(O)vz(,) > dr
(1)
Analogous equation hold for the x and y msds and their velocity tcfs. If the molecules that give rise to the statistical quantities in eq. 1 are unbounded in their z-displacements, one has the
460
the well known long-time limiting form: ~ m < ~z2(t) > = 2t t----* OO
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This argument shows that simulated msds must be quadratic functions of time initially, with a curvature that depends only upon the mean square velocity of a particle at temperature T with mass m, and not upon the density or forces acting in the adsorbed film. At the other limit of time, diffusive motion yields msds that are linear in time with slopes that give the diffusion 6
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461
constants I)z, T~z and I)y. The approach to linearity in the msds is governed by the decay time of the velocity tcf, as can be seen explicitly by supposing for the moment that Cz(t) = (kT/m)exp[-t/Tx], where 7"z is the correlation time for the tcf Cx(t) and is equal to ~-1 where ~z is the diffusion constant. An integration of eq. 1 for this case gives:
kT < &~(t) >= --~-~[t + T~(~xp(-t/T~)- 1)]
(7)
?72
This msd has the correct initial (quadratic) time dependence but becomes linear in t only for times much greater than the decay time of Cx(t). The approach to linearity given by eq. (7) is shown explicitly in Figure 7. Simulated translational tcfs for dense physisorbed films are far from exponential function of time, as will be shown shortly for mono- and bi-layer films of oxygen on the graphite basal plane. Nevertheless, there is a class of systems that might be expected to conform to this simple expression. The physical basis goes back to the kinetic theory of dilute three-dimensional gases. A molecule in such a gas undergoes a series of binary collisions with, on average, a frequency vcou. It is reasonable to assume that a molecular velocity is constant until a collision occurs and that the velocity vector of the molecule after a collision is completely uncorrelated with its previous value. Since the fraction of molecules which have not yet collided in a time interval t is just exp[-~,coU t], the decay of the velocity tcf is given by this expression. If one asks whether there is an analogous situation in adsorbed films, the answer is a qualified "yes" if one considers a dilute film at high temperature on a very flat surface. Although the out-of-plane motion can be essentially vibrational, the in-plane motion could be well approximated by a freely translating two-dimensional gas. In this case, one might begin with the simple two-dimensional version of the collisional expression for the in-plane diffusion coefficient T~ll which is: 1 v,I = ~
< c>
(8)
where < c > is the mean speed = (8kT/rcm) 1/2 and )~ is the mean free path in two-dimensions and approximately equals 1/2v/2ap, with a equal to a hard disk diameter and p equal to the density in mole/cm 2. Simulations of a system that might fit this simple picture have been reported by two groups [53, 54] who have studied methane at room temperature in slit pores with parallel walls made up of the graphite basal plane. However, the point of view taken was rather different in the two studies. In [54], the total flux of molecules down the pore and the associated msds were evaluated after considering carefully the boundary conditions that controlled the collisions of the gas phase molecules with the pore walls. (This was a non-trivial problem because the gas-wall interaction had been assumed to be that for a perfectly fiat wall, thus yielding specular reflection and a physically unlikely flow mechanism in the absence of extra assumptions for the boundary conditions.) In [53], the msds were evaluated separately for the molecules in the monolayer on the wall and for those molecules in the interior of the pore. The molecules that jumped between the two regions and those in the central volume were excluded from the calculation. In fact, the local density within the pore shown in Figure 8 indicates that nearly all the gas in these systems was in fact in the monolayer. In [62], the gas-wall interaction was taken to have the periodic variation with position along the wall that has been used in the most accurate representations of the energy and that had the
462
advantage of spoiling the specular nature of a gas-wall collisions. In addition, two other systems were considered in which the wall had been roughened by adding "sulfur" atoms amounting to 1/13 (in a v/7 x ~/7 lattice) and 1/7 (in a 2x2 lattice) of the wall atoms. The variation in minimum methane-pore wall energy as an atom translates parallel to the wall is small for the non-sulfided surface, amounting to -'~ 400 joule/mole, but is "~ 2100 joule/mole for the dilute sulfided case and _ 2900 joule/mole for the more concentrated example. Apparently, these variations were insufficient to affect the surface diffusion process very much. In spite of the differences in outlook of the studies in [53] and [54], the self-diffusion coefficients obtained were also numerically similar over most of the range of methane loading. Since only the motions of the molecules in the monolayer films were considered in [53], the displacements in this case are "surface diffusion". The variation of the in-plane T~ with surface coverage ranging up to the approximate monolayer of 8 x 10 -1~ mole/cm 2 is shown in Figure 9 and is compared with the curve calculated from the crude two-dimensional coUisional expression given in eq. 8. The agreement is as good as one could expect from such a simple treatment. Note that the rapid variation of T~ with coverage is ascribed to the decrease in the mean free path of the adsorbate 60
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F i g u r e 8 Local density plots for various surface coverages of methane at 300 K in a slit pore with sulfided graphite walls. The separation of the two peaks at the opposing walls is 7.6/1. molecules in the two-dimensional gas on the surface as the density of this gas increases. The question of what happens in the limit of very low density is left unanswered, although one anticipates that the surface roughness will be the controlling factor in determining 79 in this limit. In addition to the simulation at 300 K, the methane/graphite system was studied at the
463
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F i g u r e 9 Surface diffusivities for methane at 300 K in a slit pore with pure graphite walls, and with walls that contain model sulfur atoms in lattices with spacings that are v/7 • x/'7 or 2 • 2 relative to the underlying graphite lattice. Also shown is the self-diffusion constant evaluated for a crude version of the two-dimensional hard-disk gas.
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F i g u r e 10 Same as Figure 9, but at 150 K. Here, the dependence of the surface T) upon wall separation ZP in the slit pore is shown together with the dependence upon surface methane density. (The separation of the peak methane densities in Figure 8 corresponds to a pore wall separation of 14.8 ~t).
464
considerably lower temperature of 150 K. Such adsorbed films can still be treated as twodimensional gases rather than surface liquids, and one can see from Figure 10 that a) the results are not very sensitive to the spacing of the two walls that make up the slit pore; b) the rapid decrease of :D with increasing coverage is quite similar to that found at 300 K; c) the change of D with temperature at fixed coverage is not large, and is of the correct order of magnitude to fit the simple collisional kinetic theory result of a v/T dependence. We now consider surface diffusion in very thin films at much lower temperatures and considerably higher coverages than for the methane/room temperature case. In particular, simulations of oxygen adsorbed on the graphite basal plane at temperatures from 50 - 60 K in monolayer and bilayer films will be discussed. [55]. The oxygen was modeled as diatomic molecules made up of Lennard-Jones interaction sites separated by the bond length so that orientational changes were accounted for in the calculations. Molecular dynamics was utilized to evaluate thermodynamic properties as well as the velocity tcfs for the molecules in each layer separately - only the molecules which remained in their designated layer for the duration of the calculation were included. In principle, in-plane diffusion constants could be evaluated from the time-integrals of the tcfs. The first point to be noted is that the oxygen at low temperature forms an ordered monolayer with all molecules standing nearly perpendicular to the surface with a density corresponding to 9.3 )12 per molecule. At slightly below 60 K., the monolayer melts to an area of 12.5 ~2 per molecule, with a considerable loss in orientational and in-plane translational order. The in-plane velocity tcfs for the monolayer molecules in the monolayer and bilayer films are
....
55K
.......... 6 0 K 0.5 tcf
-0.5~0
I
2 time
3
(picosec)
4
F i g u r e 11 In-plane velocity tcfs for an oxygen monolayer on the graphite basal plane at three temperatures. plotted in Figures 11 and 12 for several temperatures in this region. It is evident that they are far from the exponential decays expected for the binary collision mechanism. Their initial decays are quadratic, as required by theory, and they all show a negative dip at times less than one picosecond. This dip is associated with a reversal of the initial direction of motion due
465
to collisions or equivalently, to the strong forces that tend to enclose the adsorbed molecules in "cages" under these conditions. Of course, in a calculation of the diffusion coefficient by integration, the positive contribution of the short-time decay of the velocity tcf will be partially canceled by the negative dip, producing a small D which is rendered more uncertain by being the resultant of relatively large positive and negative contributions. In fact, the areas under
I-
I
0
....
I
60K
.
~176
--0.
0
I
2.
time (picosec)
3
4
F i g u r e 12 Same as Figure 11, but for the molecules in the first layer of a bilayer film. the curves in Figs. 11 and 12 are all zero to within the considerable uncertainty in the long-time parts of the tcfs. The alternative route of evaluating the msds gives much better estimates of D for the in-plane motion. Such curves are shown in Figures 13 and 14. Evaluation of these slopes yield D of ~_ 0.08 ~2/picosec. at 65 K. for both systems (l~i2/picosec. = 10 -4 cm2/sec.). At 60 K., D ~ 0.07 and 0.03 ~12/picosec. for the monolayer and the first layer in a bilayer film respectively. It appears that the presence of a second layer over the monolayer can impede the diffusive motion of the molecules below; this effect is associated with an increase in the density of the monolayer when it is covered by a second layer [55]. In all cases considered here, the diffusion constants are much smaller than those obtained for the two-dimensional methane gas discussed above, where D is of the order of 10 ~12 per picosec, for surface density equivalent to roughly 1/2 monolayer. The N2/graphite system has also been extensively investigated [42, 56]. In addition to the low-temperature reorientational results discussed above, translational motion in the N2 monolayer has been simulated over a range of higher temperatures. For example, msds for molecules in a complete N2 monolayer are plotted in Figure 15. The curves for T<70 K are as expected for a solid film in which the molecules are vibrating around lattice points, but the diffusive curve obtained for 77 K indicates that melting has occurred, probably at a temperature very near to 77 K, consistent with other simulation data. The in-plane diffusion constant obtained from the slope of the curve at 77 K is 0.21 ~t2 per picosecond.
466 ~=~
=
.=
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time(picosecond) Figure 13 Mean square in-plane displacements in reduced units for the system of Figure 11. Reduced distance = distance i n / i divided by 2.46 ~.
|
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=
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3
3.5
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Figure 14 Mean square in-plane displacements in reduced units for the system of Figure 12.
467
Another system where diffusion on a flat surface has been simulated using molecular dynamics is that of benzene on graphite over the range 85 - 298 K [57]. We will discuss here some results obtained for the complete monolayers in this system. To begin, note that the simulations are in good agreement.with thermodynamic experiments but have yielded information concerning
0.3
T= 7 7 ~
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F i g u r e 15 Mean square in-plane displacements in nitrogen monolayers at several temperatures. Also shown is a single curve for the displacements perpendicular to the surface (denoted by < z 2 >) - it is essentially independent of T in this range). The same reduced units for distance are used here as in Figs. 13 and 14. molecular orientation and time correlation functions that have not yet been measured in detail. It is known that the monolayer films form two-dimensional solids at T < 140 K. At 85 K, these films are highly ordered, forming a v/7 • v/7 lattice on the basal plane substrate with all molecules lying nearly flat on the surface. As temperature rises, the adsorbed benzenes begin to reorient into on-edge (perpendicular) configurations. The plots of two-dimensional density as a function of distance from the graphite plane in Figure 16 show how the molecular centers move away from the surface as molecules change from flat to on-edge orientation, giving double-peaked density distributions with an on-edge fraction that increases as temperature increases.. Since the area occupied by a reoriented molecule is significantly less than that for one lying flat, there is more space available for surface self-diffusion. This is clearly shown in the slopes of the msd curves given in Figure 17 and in the resulting self-diffusion constants plotted versus temperature in Figure 18. One sees small, nearly temperature independent values for the two-dimensional solid and a rapid increase in D at temperatures greater than 150 K. It seems most likely that the increase is primarily due to the increase in the available space for diffusion with increasing
468 temperature even though the number of molecules in the monolayer is remaining nearly constant kinetic energy of the adsorbed film. Orientational tcfs were also evaluated for this system and provide support for this physical picture. For molecules such as benzene, one should consider two types of reorientation: around the molecular axis, which should be relatively free because of the high in-plane symmetry of the molecule; and reorientation of the axis, which describes the tilting motion mentioned above. Figures 19 and 20 show the tcfs for < c o s ~ ( t ) >, where r is the angle for reorientation either around the molecular axis (Figure 19) or of the axis (Figure 20). It is evident that motion around the axis is indeed much easier. More significant for the
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zCk) F i g u r e 16 Local densities for benzene monolayers on the graphite basal plane plotted as a function of distance from the surface z in ~t for a range of temperature. The peak at small z corresponds to those molecules lying fiat on the surface, and the second part of the split peak corresponds to molecules standing on edge.
469
20.00 18.00 16.00
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F i g u r e 17 Mean square displacements for benzene molecules in the monolayer on graphite are shown for various temperatures.
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0.25
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F i g u r e 18 In-plane self-diffusion constants for monolayer benzene obtained from the slopes of the lines in Figure 17 are plotted as a function of temperature.
470
present purposes, one can see that the tilting motion is nearly frozen at the lowest temperatures, but becomes quite extensive on a picosecond time-scale as T increases to 298 K. In comparing the :P values for monolayer benzene with those reported above for monolayer oxygen, one should first realize that the temperatures of the two systems are rather different, especially on a comparative scale that takes the two-dimensional critical temperatures as reference points. These are
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F i g u r e 19 Orientational tcfs for benzene on graphite at the same series of temperatures as in Figures 17 and 18. These curves illustrate the rapid reorientation of a vector fixed in the plane of the benzene molecule. i
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F i g u r e 20 Same as Figure 18, but here the vector is the molecular axis perpendicular to the molecular plane, so the motion characterized here is the tilting motion of the adsorbed benzene molecules.
471
approximately 205 K and 60 K for benzene and oxygen, respectively. Thus, much of the benzene data shown is supercritical and that for oxygen is subcritical. (Of course, the densities of the monolayers are roughly three times the critical density, so that neither system is actually near the critical region at any temperature.) In any case, over most of the temperature range, the benzene diffusion constants are much larger than those for the oxygen layers. This is probably due to the relative lack of space for diffusional motion in the oxygen case.
3
D i f f u s i o n o n s u r f a c e s w i t h s t e p s or g r o o v e s
In the previous section, we have been discussing diffusion on surfaces that are geometrically quite flat. Even in those cases where atomic structure plays a significant role in determining the gassolid potential energy surface, the roughness is only of the order of the atomic size and is widely distributed over the surface. We now take up an important class of adsorption systems that are characterized by strong barriers to translation in specific regions of an otherwise flat surface. Examples include model pores with sharply defined constrictions, a simple version of which has been studied by Demi and Nicholson [58, 59], who considered atoms in spherical pores connected by short, straight-walled narrow cylinders. We do not give a detailed discussion of this interesting study here on the grounds that the simulations of the flux of molecules down these tubes included both surface and volume flow and is consequently not clearly relevant to surface diffusion, which is the primary subject of the present review. Another type of heterogeneity is encountered when one has surfaces with atomic-scale steps or even with straight-walled grooves cut into the surface. There has been considerable interest in the diffusion of strongly bound gases on metallic stepped surface. Uebling and Gomer have studied diffusion by Monte Carlo simulation of a lattice gas on stepped surfaces with various assumptions concerning the interaction of the gas atoms at the steps [60, 61]. These and similar studies have been reviewed by Gomer [5]. Simulations of the surface diffusion of krypton on grooved and stepped surfaces at temperatures ranging from 90- 150 K (boiling point of bulk Kr = 120 K) [62, 63, 64, 65] will be discussed here. These simulations are not subject to the limitations of the Monte Carlo algorithm as applied to the lattice gas model, but the main features of this process are qualitatively the same as those found by Uebing and Gomer, when the temperatures and surface coverages are comparable. The nature of the diffusion on these surfaces is determined by the same features as for other adsorption systems: the gas-solid potential and its variations across the surface relative to kT plus, for finite coverage, the adsorbate-adsorbate interactions. Figure 21 shows a side view of a model stepped surface [62] with a nominal monolayer of Kr adsorbed on it at 110 K. The straight lines denote graphite planes separated by 3.4 ~i, and the terraces are 25.5 ~t wide and 44.3 ~1 deep (in the direction perpendicular to the plane of the paper). The trajectories of the adsorbed krypton atoms generated in an isokinetic molecular dynamics simulation lasting for 71 picoseconds are also shown. The thick black regions are overlapping trajectories for the atoms in the monolayer, and the irregular lines are trajectories for those few atoms that have left the monolayer at 110 K. This graphitic adsorbent shows strikingly large variations in the minimum gas-solid potential energy (i.e., the adsorption energy) as an adsorbed atom moves perpendicular to the steps, as is illustrated in Figure 22. Clearly, there is a very large barrier to free translation
472 present at the top of each step equal to 6.3 Kjoule/mole for the Kr/stepped graphite system. This is a consequence of the model used for the gas-solid interactions of this non-metallic substrate in which it is assumed that this energy is a pair-wise sum of the interactions of a Kr atom with sites in the solid that have been taken to be carbon atoms with interaction parameters adjusted to give good agreement between the calculated adsorption energy and the voluminous experimental data available for the Kr/graphite system. As a result, an atom sitting at the top edge of the step sees what amounts to only half a surface, so that its gas-solid interaction energy
_ ~:--
F i g u r e 21 Side view of the trajectories followed by 288 Kr atoms on a stepped surface over a period of 70 picoseconds at 110 K. The surface has a step height of 6.8 ~l, a terrace width of 25.5 ~1 and a dimension perpendicular to the page of 44.3 ~i. (Periodic boundary conditions are imposed in the two directions parallel to the surface.) The Kr density of 0.0636 a t o m / ~ 2 is that of the monolayer on the flat surface ([67], but a few atoms have been thermally promoted into the second layer. ,,6OO
i -,,t20(
S S ,.-~SG
!
0
51
Y (A)
,,
102
F i g u r e 22 The potential energy of an adsorbed Kr atom on the surface shown in Fig. 21 is shown as a function of position relative to the steps (Y-coordinate). The parameters and potentim model used were drawn from the analogous calculations for Kr on the infinite flat graphite surface [42].
473
is roughly half the value of-11.2 Kjoule/mole for an atom on the infinite flat surface. (Note that other models used for the interactions of gases with metallic surfaces can yield no barriers or even wells at the tops of steps rather than the barriers obtained here [66].) Another feature of this model of the surface is the set of deep but narrow potential wells for atoms at the bottoms of the steps adjacent to the step risers. This amounts to 2.6 Kjoule/mole for the model Kr/stepped graphite system, which is quite large compared to kT (= 0.9 Kjoule/mole at 110 K). The energy variations have a large effect on the diffusion and the local densities of krypton adsorbed on this surface. The local densities are shown in Figure 23 as a function of Y, the coordinate perpendicular to the steps, for two temperatures and two Kr surface coverages (1/2 and 1 monolayer, based on the known monolayer density of 0.0626 atom/A 2 for Kr on the fiat graphite surface [42]). It is evident that the strong variations in adsorption energy have induced one-dimensional order in these layers. Although a localized (in Y and in Z, the coordinate perpendicular to the surface) line of atoms forms in the deep wells, the ordering in the 50
.
50
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.
,
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o 25
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51
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102
0
0
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Y (A)
102
F i g u r e 23 The local density of the first layer Kr atoms on stepped graphite is shown as a function of the Y-coordinate defined in Figure 22. Densities are shown for the monolayer at 110 K (part (A)- see also Fig. 21) and 150 K (B), and for 144 adsorbed atoms (equal to 1/2 layer) at 110 K (C) and 150 K (D).
474
Y-direction actually extends from the bottoms of the steps out into the terrace, gradually decaying as the atom moves away from the step. The potential energy variations and the local densities of Kr on grooved graphitic surfaces [64, 65, 66] are much the same as for the stepped surface case, except that the symmetry of a rectangular groove is reflected in both the energies and the densities. This is illustrated in Figure 24 which shows the local density for Kr at 110 K. on a surface with a series of grooves of depth ~ 7 .~t, separation = 34 A (referred to as step width) and groove width = 68 .;t. Strong one-dimensional ordering is seen for those atoms confined in the grooves, even though the temperature is above that for freezing of the monolayer
0.300.25~- 0.20 ;
i
o.os
0.00 ~,
- 69 0 - 4 S - 3 0 - t S
0
IS
[
30
9
45
60
,r (1)
F i g u r e 24 Local density is plotted in the top part of the Figure as a function of Y (in ~) for monolayer Kr on a grooved graphitic surface at 110 K. The lower part of the Figure shows the trajectories of the adsorbed atoms (for 70 picoseconds), both as a side view and as a top view. The top view shows the localized fines of atoms adsorbed at the bottoms of the groove edges - this corresponds to the highest pair of peaks in the local density plot. The relatively vacant regions near the tops of the edges are visible in the top view of the trajectories and in the local density.
475
on the flat graphite surface. Weak ordering is also seen in the layer adsorbed on the steps between the grooves. Of course, the degree of ordering increases rapidly as the total surface coverage increases, as can be seen in the trajectory plots for the simulations of the systems with coverages of up to 2 1/2 nominal monolayers. As temperature decreases, the order also increases and indeed, for high coverage and low temperature, one observes two-dimensional solid-like order in each of the grooves [65]. We now turn to a discussion of the time-dependent properties of the atoms adsorbed on such surfaces. Although we will limit the actual results presented to those for a nominal monolayer, a number of general features should be mentioned at the outset. The first point is that the large barriers to motion in the Y-direction will cause surface diffusion to be quite anisotropic. Of course, in the simulation it is not difficult to evaluate mean square displacements in each of the three directions separately. At the temperatures and coverage of interest here, motion in the Z-direction is nearly pure vibration except for the occasional jumps at the steps or the groove walls. These jumps differ greatly from the small-step translation that occurs in the dense layers on the fiat regions of the surface. Motion on the flat regions is likely to be diffusive, but should be quite different in the X-direction, where there are no barriers associated with the surface structure, and in the Y-direction, where the atoms encounter both deep (trapping) wells and repulsive barriers. In fact, at long times, the mean square Y-displacement should approach an almost constant value that is determined by the width of the step or the groove- any small increase over this limit will be due to those atoms that jump to the adjacent step or groove. It is therefore necessary to evaluate X and Y displacements separately and in addition, the jump rates from one confined area to the next should be evaluated if one wishes to obtain the full description of motion on these surfaces. The rates of transfer of atoms by jumping from one region to another can be monitored rather easily by evaluating the number of atoms in a region (step or groove) as a function of time. Figure 25 shows this for a nominal monolayer at 110 K on the grooved surface of Fig. 24. The changes in the number of atoms on the steps given in this Figure determines the step--groove transfer rates. (In the equilibrium layer, the forward and the reverse rates determined by counting over a long time are, of course, equal.) The rate is equal to the total i
r
0 0
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,
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r I
Timesteps]l(~30
F i g u r e 25 Changes in the number of atoms on the step of a grooved surface at 110 K are shown as a function of time (1000 timesteps = 7.4 picoseconds). The system is monolayer Kr on a graphitic surface with a total step edge length of 88.6 ~l.
476
number of jumps divided by the observation time and by the total length of the groove edge, but can be only roughly estimated because of the rather small number of events observed in the time intervals of the simulations. Table I shows rates estimated in this way from data gathered for a 71 picosecond time interval for a total edge length of 88.6 A. The rates amount to 0.013, 0.018 and 0.018 for T = 90, 100,and 130 K., respectively (in units of atoms/~t/picosecond). Since the rates depend upon the density of atoms near the groove edges, the expected rate increase in going from 100 to 130 K appears to be hidden by a decrease in this density. Mean square displacements for atoms in the Kr monolayer were evaluated for those atoms on the steps, in the grooves but not at the step edges, and at the bottoms of the steps (denoted by "edge" atoms). The time dependences of the displacements in the X and Y directions are shown in Figs. 25 and 26 respectively. First, note that the X-displacements are not very sensitive
1.01
1
0.8
((~y)z)
i
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130 K
...-
0.6 0.4 0.2 0.5 1.0 time (picosec) i
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0.6
1
!.5 [ .=
90 K ==
.=
0.4 0.2
0.5
1.0
time (picosec)
1.5
F i g u r e 26 The time dependence of the mean square displacements of first layer Kr atoms on the grooved graphite surface are shown here for two temperatures. The curves given are for atoms inside the groove (excluding the edge site) - solid lines, on the step - dashed fine, and on the edge sites - dotted fine. The displacements here are in the direction parallel to the grooves.
477
to the initial location of the diffusing atoms, at least for the step and groove atoms. This is reflected in the values of the self-diffusion constant T)x that were calculated from the slopes of the linear portions of the X-displacement plots - see Table I. The Y-displacements differ greatly, depending upon starting position. The deep potential wells occupied by the edge atoms means that this atomic motion is primarily vibrational, and corresponds to a constant mean square displacement which is reached after a rather short time interval. (At 130 K, it appears that a few atoms escape from their initial positions at the bottom of the step). The repulsive potential barriers encountered during motion in the Y-direction reduce the displacements of atoms that are on the steps, relative to those in the grooves. The apparent diffusive motion of these atoms in the Y-direction is misleading: the linear dependence of the root mean square Y-displacements upon time that are shown in Figs. 25 and 26 is for displacements that are still quite small compared to the sizes of the confining boxes (34 and 68 Jl for a step and a groove, respectively). Thus, the slopes of these lines give :Dr listed in Table I that are only apparent, valid for small rms displacements, with true long-time values that are nearly zero. In fact, a reasonable approximation to this situation would be one-dimensional diffusion in a box with impermeable walls. The many solutions of the diffusion equation [68, 69] (or of its mathematical relatives, the Schroedinger equation [70] or the heat conductivity equation [71]) include those for this boundary condition so that comparisons of the simulated and the theoretical msds for motion in the Y-direction for large time intervals should give a good test of the model. |
I
I /
l.Oi 130K
/ i
~
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F > o-6t"
/. :;-"
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9
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90K
0.4
0.2
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0.5
t i m e (pir
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,
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F i g u r e 27 Same as Figure 26, except that the direction of motion here is perpendicular to the groove direction.
478 Table 1: Self-diffusion constants for monolayer Kr on a grooved graphite surface (units of ~!2 per picosec) Groove atoms 9O K 130 K Step Atoms 9O K 130 K Edgeatoms 90 K 130 K
Z)x
1)y
0.16 0.34
0.17 0.34
0.15 0.31
0.13 0.29
0.15 0.23
_~ 0 "" 0
Of course, surface diffusion is also dependent on the transfer of atoms between layers or into the vapor phase. In simulations, it is possible to keep track of the locations of all atoms at all times. Thus, the mean square displacements shown in Figs. 26 and 27 are actually for atoms that start in the first layer on steps, in grooves or at edges, and that stay there for the duration of the calculation. Any atoms that change layers or change regions have been excluded. Figure 28 shows a limited comparison of mean square displacements when the layer-changing atoms have or have not been included. It is evident that diffusive motion is enhanced by atoms that have been promoted. They move much more quickly in the very dilute second layers present in these nominal monolayer systems, as one might expect. The importance of this factor will depend very much on coverage, temperature and the magnitude of the adsorption energy. The I
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~ -
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4
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F i g u r e 28 Same as Figure 27, except that here, the displacements are compared for atoms that stay in layer one throughout (A) and for those atoms that start in layer one but can be promoted to higher layers during the time of the simulation (B). T = 140 K.
479
point to make here is that the simulation technique has the power to elucidate the various complications that arise in atomic diffusion on a realistic model surface. Results for diffusion on the stepped surfaces modeled are more limited, in part because the atomic motions on these surfaces are expected to be rather similar to those for the grooved surfaces. However, jump rates over the steps have been simulated [62] for several Kr coverages and temperatures. The data shown in Fig. 29 are for terraces of width 25.5 .~1. Coverages range from 1/2 to 3/2 layers and the jump rates are shown for three temperatures. At the lowest coverage, the rates are very small, indicative of jumps over the high barrier shown in Fig. 22 for atoms on the surface. At coverages of one and 1 1//2 layer, most of the jumps are due to atoms that are not on the surface but are in the second layer where the step-crossing barrier is much lower. Finally, the reason why the rate for 1 1//2 layers at 110 K differs from those at the same coverage but at 130 and 150 K is that the atoms must first break free of the solid-like structure that exists at 110 K but not at 130 or 150 K (for this coverage) before they can begin to translate to the adjacent step.
6oI 50 -
-'" J..." /'...."
0
ol
40
,.,.:.'.-"
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30
20
10
0 !
,
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144
288
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432
N u m b e r of atoms
F i g u r e 29 Jump rates for Kr on a stepped graphitic surface with terrace widths = 25.5 .~1 and total step length = 178 .~. Rates are shown for three coverages (1/2, 1, and 1 1//2 layers), with the simulated values connected by straight lines. The solid line is for 110 K, the dotted line, for 130 K, and the dashed line, for 150 K.
480
4
Conclusions
In this paper, surface motion that is diffusive and, in some cases, proceeds by jumps, has been simulated for models of varying levels of complexity. All cases considered have been representations of actual adsorption systems. Because of the emphasis on surface motion in the first couple of layers on the adsorbing solid, there is relatively little discussion of diffusion in porous solids, where a number of issues such as the connectivity of the pore system or the effect of variable pore dimensions, especially when they give rise to constrictions with sizes comparable to molecular diameters, complicate the problem of relating simulation to experiment. Even so, the work on the dynamics of fluids in confined geometries is just beginning. There are almost no molecular dynamics studies of the important case of atoms on heterogeneous surfaces where the heterogeneity extends over the entire surface. Also, relatively little effort has been put into the study of the effect of non-spherical shape upon surface mobility. Still, the results obtained so far provide the beginnings of a picture of the way in which molecules move when they are on or near to the surface of a solid. The values obtained for diffusion constants are consistent with those when the three-dimensional case is modified into two dimensions. For example, the results for the high temperature two-dimensional "gas" of adsorbed methane can be accounted for in terms of a simple twodimensional kinetic theory argument. Furthermore, the dense, two-dimensional "liquids" such as N2, 02 and benzene show self-diffusion constants that are reasonable consistent with the bulk liquid results [72, 73, 74]. Here one has the problem that the diffusion constants for both the twoand the three-dimensional fluids are quite sensitive to density, and it is not clear how one should estimate corresponding densities in two and three dimensions. For example, the experimental 7) for bulk liquid benzene at 300 K is 0.2 ~2/picosec, but Fig. 18 indicates that this value is reached at 200 K for the monolayer fluid. However, one should realize that 200 K is nearly equal to the estimated 2D critical temperature of 205 K for benzene on graphite [57], whereas 300 K is well before the 3D critical temperature. Therefore, a careful scaling of density and temperature would be required before a quantitative comparison of the 3D and 2D diffusivities can be made. Similarly, the self-diffusion constants are known for the bulk liquid nitrogen, both from computer simulation [75, 76] and experiment [77]. A value of 0.33 )i2/picosec for the saturated bulk liquid at its normal boiling point is comparable with that of 0.21 Jl 2/picosec obtained from the slope of the 77 K curve in Figure 15 (and 0.08 )i2/picosec for the oxygen monolayer at 65 K - see Figure 13). Of course, these simulations are not without problems. A point that is often overlooked in work of this kind is that the assumption that the the substrate is rigid, providing only a potential field for the fluid adsorbate means there is no energy interchange between adsorbent and adsorbate. Within this model, different molecular dynamics algorithms allow one to evaluate trajectories with several different constraints. Two of those most often used are: constant total energy (of all the atoms); and constant total kinetic energy. In the first case, motion of an atom that results in a large change of its adsorption energy causes the entire sample to "cool"; i.e., any increase in potential energy is associated with a decrease in kinetic energy. Since this seems to be unrealistic (and possibly important, in a simulation of atomic time-dependence), the second or isokinetic algorithm is preferred. (Both give the same thermodynamic properties but well-defined differences in the fluctuations of the thermodynamics.) Nevertheless, it remains to be proved
481
that conservation of kinetic energy in adsorption systems is actually a good representation of the actual situation. It should also be noted that the assumption of a rigid, inert adsorbent means that thermal equilibrium in the adsorbed phase is achieved via energy-exchanging collisions between the adsorbate molecules only. This can produce questionable dynamical results at low coverages where such collisions are infrequent. Furthermore, the errors that might be produced depend upon the time-scale of the observation. (Imagine a particle in the process of passing over a feature in the gas-solid potential that corresponds to a barrier to diffusion. Is it in thermal equilibrium at all points along the trajectory? If not, what are the consequences relative to, say, an analysis via transition state theory?) To investigate these questions would necessitate a much larger simulation in which the adsorbent atoms are allowed to take part in the dynamics, if only to the extent of (anharmonic) vibrations that allow energy interchange between solid and gas. Such calculations are costly in terms of computer time, but are nevertheless highly desirable.
Acknowledgements Support for this review and for many of the results reported here was provided by grant DMR 9022681 of the N.S.F. Division of Material Research. Many of the results discussed were obtained by Alexei Vernov, Mary J. Bojan and V. Bhethanabotla while at Penn State. Not all of this work has been previously published and their contributions are greatly appreciated. Finally, permission to republish many of the figures was kindly granted by the authors and initial publishers. These include: American Chemical Society: Figures 1 [33], 2 [45], 8 [53], 21,22,23 [62] and 25 [65]. Taylor and Francis: Figures 3 [47] and 4,5 [48]. Engineering Foundation: Figures 6, 15 [46]. Materials Research Society: Figures 9, 10 [53]. VCR Verlagsgesellschaft mbH: Figures 25, 26, 27, 29.29 [66].
482
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(~99~) 441. [25] J. J. Magda, M. Tirrell and H. T. Davis, J. Chem. Phys. 88 (1207) 1207. [26] J. Koplik, J. R. Banavar and J. F. Willemsen, Phys. Fluids A 1 (1989) 781. [27] G. Mo and F. Rosenberger, Phys. Rev. A 42 (1990) 4688. [28] M. Schoen, J. H. Cushman, D. J. Diestler and C. L. Rhykerd, Jr., J. Chem. Phys. 88 (1988) 1394. [29] D. J. Diestler, M. Schoen, A. W. Hertner and J. H. Cushman, J. Chem. Phys. 95 (1991) 5432. L. K. S. [30] M. Schoen, J. H. Cushman and D. J. Diestler, Mol. Phys. 81 (1994) 475. [31] W. Dong and H. Luo, Phys. aev. E 52 (1995) 801. [32] V. N. Burganos, J. Chem. Phys. 98 (1993) 2268. [33] A. A. Vernov, W. A. Steele and L. Abrams, J. Phys. Chem. 97 (1993) 7660 and references to earlier simulations given therein. [34] A. K. Jameson, C. J. Jameson and R. E. Gerald, II, J. Chem. Phys. 101 (1994) 1775. [35] S. Bandyopadhyay and S. Yashonath, J. Phys. Chem. 99 (1995)4286; S. Yashonath and P. Santikary, Mol. Phys. 78 (1993) 1. [36] Y. Nakazaki, N. Goto and T. Inui, J. Catalysis 136 (1992) 141. [37] R. L. June, A. T. Bell and D. N. Theodorou, J. Phys. Chem. 96 (1992) 1051. [38] G. Schrimpf, M. Schlenkrich, J. Brickman and P. Bopp, Phys. Chem. 96 (1992) 7404. [39] P. R. Van Tassel, S. A. Somers, H. T. Davis and A. A. McCormick, Chem. Eng. Sci. 49 (1994) 2979. [40] M. Bienfait, J. P. Coulomb and J. P. Palmari, Surf. Sci. 182 (1987) 557; M. Bienfait, in Dynamics of Molecular Crystals, ed. J. Lascombe, Elsevier, Amsterdam, 1987, p. 353. [41] D. L. Koch, J. Chem. Phys. 101 (1994) 4391. [42] W. A. Steele, Chem. Rev. 93 (1993) 2355. [43] K. Fichthorn, Adsorption 2 (1996) 77.
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485 [67] N. D. Shrimpton, M. W. Cole and W. A. Steele, in Surface Properties of Layered Materials, ed. G. Benedek Kluwer, Dordrecht, Netherlands, 1991. [68] M. P. Allen and A. J. Masters, Mol. Phys. 79 (1993) 435. [69] J. Crank, The Mathematics of Diffusion, Clarendon Press, Oxford, 1979. [70] H, Margenau and G. M. Murphy, The Mathematics of Physics and Chemistry, Second Edition, D. van Nostrand, New York, 1956, Chap. 7. [71] H. S. Carslaw and J. C. Jaeger, Conduction of Heat in Solids, Clarendon Press, Oxford, 1959. [72] A. F. Collings and I. L. McLaughlin, J. Chem. Phys. 73, 1980, 3390. [73] C. Hoheisel, Theoretical Treatment of Liquids and Liquid Mixtures, Elsevier, .New York, 1993, sec. 8.2. [74] H. J. V. Tirrell and K. R. Harris, Diffusion in Liquids, Butterworth's, London, 1984, sec. 7.3. [75] P. S. Y. Cheung and J. G. Powles, Mol. Phys. 30 (1975) 921; ibid, Mol. Phys. 32 (1976) 1383. [76] J. Barojas, D. Levesque and B. Quentrec, Phys. Rev. A 7 (1973) 1092. [77] K. Krynicki, E. J. Rahkamaa and J. G. Powles, Mol. Phys. 28 (1974) 853.
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W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
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Multicomponent diffusion in zeolites and multicomponent surface diffusion YuDong Chen a and Ralph T. "fang b aThe BOC Group, Inc., 100 Mountain Avenue, Murray Hill, NJ 07974 bDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109
Recent development of theoretical models and experimental results on multicomponent surface diffusion and diffusion in molecular sieves have been briefly summarized. The model development of multicomponent diffusion have been focused on four most attractive approaches which are: single-file approach, Maxwell-Stefan approach, irreversible thermodynamics approach and kinetic approach and with a more detailed model derivations on the last one. A step by step procedures for predicting multicomponent diffusion from single component information have been demonstrated in the examples. Besides, a direct comparison between these four models for co- and counter-diffusion cases are also presented. The single-file and irreversible thermodynamic models have the advantage of being easy to use and having no adjustable parameters. The only information required for this prediction is single component diffusivities. They can be used to predict multicomponent diffusion, especially, at low surface loadings. The kinetic model is capable of predicting multicomponent diffusion between Aij from 0 to 1. The Aij is indicative of the level of molecular interactions which can be directly obtained from single component information. Based on these comparisons, it is clear that the kinetic model is superior to the others.
1. I N T R O D U C T I O N Despite the fact that nearly all adsorption processes of industrial interest involve multiple adsorbates, studies on surface diffusion and diffusion in molecular sieves have been largely limited to single component systems. The literature on multicomponent diffusion in these systems is scanty. Habgood[1] studied the diffusion of mixtures of nitrogen and methane in 4A molecular sieves in 1958. He observed that nitrogen diffuses faster than methane and is preferentially adsorbed at the beginning. The preferentially adsorbed nitrogen is later displaced by methane, resulting in an overshoot phenomenon with a maximum in the amount of nitrogen adsorbed over time. Using chemical potential as the driving force for diffusion, Round et al.[2] presented a numerical solution to the equations describing the sorption of a binary mixture. Kokoszka[3] studied the rate of sorption of propane and butane mixtures from helium in
488 5A molecular sieves. He reported that the rates of sorption of a ternary system of propane and butane in helium are lower than the respective rates of sorption from pure components. Riekert[4] studied the rates of exchange between CO2 and C2H6 in hydrogen and sodium mordenites. He found that a countercurrent migration is possible in both zeolites, and observed a reduction in diffusivity of about an order of magnitude as compared with the single component sorption. Ma and Roux[5] studied rates of sorption of SOs and COs in sodium mordenite by using a volumetric method. They observed a large overshoot for the fast diffusing component COs. Later Ma and Lee[6] reported binary diffusion data on isobutane/n-butane and isobutane/1-butene in X zeolite and found that the binary diffusivities are smaller than those for pure components. The overshoot phenomenon which was caused by displacement was also observed. Ks and Billow[7] investigated the binary diffusion system of benzene/n-heptane in NaX zeolite and used a irreversible thermodynamic approach to derive a set of binary diffusion equations which has the same form as that given earlier by Habgood[1]. Those equations provided a satisfactory description of their binary experimental data. Ruthven and Kumar[8] reported binary diffusion data for methane and nitrogen mixture in 4A molecular sieve by using a chromatographic method. They found that their results are consistent with the hypothesis that each species in the mixture diffuses independently with the same intrinsic mobility as that for single component diffusion at the same temperature. This notion was confirmed by Kumer et al.[9] by using the same experimental technique. Palekar and Rajadhyaksha[10] studied binary sorption in channel type zeolites by a Monte Carlo method. From their uptake simulations, they concluded that the apparent diffusivity of the fast moving component decreases with an increase in the occupancy of the slower diffusing component and increases with the fast diffusing component. Yasuda et al.[ll] measured binary diffusion of CH4/He and CH4/Kr over 5A zeolite. They concluded that each component never diffuses independently in spite of the low equilibrium partial pressures. Both of the binary systems involve two intracrystalline diffusion processes with diffusivities different from those of the pure components. Also, a negative cross-term diffusivity was shown. Ks and Pfeiffer[12] extended a nuclear magnetic resonance (NMR) spectroscopy technique to investigate multicomponent diffusion in zeolites. Marutorsky and B/ilow[13] were able to evaluate the Fickian diffusion coefficient matrix in a microporous sorbent under constant pressure conditions. The method involved the solution of the reverse problem of calculating the matrix of diffusion coefficients based on experimental uptake profiles. In their study, a binary uptake experiment of the n-hexane/ammonia mixture in NaCaA zeolite at 200~ was conducted. The calculated results showed a negative cross-term diffusion coefficient for the co-diffusion case. Carlson and Dranoff[14] measured rates of adsorption and desorption of methane and ethane in 4A zeolites by a differential adsorption bed (DAB) technique. A large overshoot of the fast diffusion component methane was observed for the co-diffusion case. Also, they solved the binary Fickian diffusion equations analytically by assuming constant (i.e., concentration independent) main- and cross-term diffusivities. The diffusivities obtained from regression of their binary experiments showed that the cross diffusion coefficients are quite small in comparison with the main-term diffusivities, but they were significant
489
enough to improve the fit of the experimental data. Yasuda and Matsumoto[15] used the frequency response method to determine diffusivities of a binary N2/O2 mixture in 4A zeolite. They concluded that even at low occupancies, the binary interactions are important. Micke and Biilow[16,17] used Valterra integral equations to model the sorption kinetic processes of multicomponent mixtures under constant and variable concentration conditions. This sorbent was surrounded by the fluid bulk phase. The model was used to calculate the sorption kinetics in single-step arrangements. Krishna[18,19] used the Maxwell-Stefan equation to describe the multicomponent diffusion in zeolites. The vacant sites were treated as the (n+l)th component in the diffusing mixtures. The coefficients, Dij, which describe the facility for counter-exchange between the adsorbed species i and j were related to the coefficients 7)iv and Djv by using an empirical Vignes[20] formula which was correlated from liquid mixtures. Here :Div is the facility for diffusive exchange between species i and the vacant sites. This model can be used to calculate multicomponent diffusivities but it should not be considered as a predictive model. It is appropriate to quote below the discussion of Ks and Ruthven[21] on Krishna's model: "In principle the Krishna model does not imply any assumptions concerning the concentration dependence of the Stefan-Maxwell diffusivities. In a formal sense it may therefore be regarded as simply a transformation of one set of parameters (the phenomenological coefficients or the Fickian diffusivities) into another set of equivalent parameters (the Stefan-Maxwell diffusivities). The main advantage is the Stefan-Maxwell diffusivities are somewhat more amenable to a microdynamic interpretation than either the Fickian diffusivities or the phenomenological coefficients. However, the physical basis of the model can be questioned. The Stefan-Maxwell formulation is derived from momentum transfer arguments and it is not immediately obvious that the extension to an adsorbed phase in which, instead of considering the transfer of momentum to the adsorbent, the vacancies are treated as an additional component, is physically justified." Kraaijeveld and Wesselingh[22] reported that negative Maxwell-Stefan diffusion coefficients were possible as did Krishna[18,19]. Tsikoyiannis and Wei[23,24] used the stochastic theory of Markov processes to model diffusion and reaction in zeolites. The Markov theory was applied to a system where each site can be only occupied by one molecule. Those molecules migrate on the surface as a single file. This model can be also used to describe different site-site and transition-site interactions for different types of dependency of single and binary Fickian diffusivities on occupancy. A simple formula of binary diffusivities based on these processes was derived for predicting binary diffusion phenomenon. This formula has the same form as derived from the lattice-gas model of Sundaresan and Hall[25]. Qureshi and Wei[26,27] further extended the single-file diffusion model to describe multicomponent apparent diffusivities in ZSM-5 which is a channel-type zeolite. They compared this model with the benzene/toluene binary experimental results measured by using the Wicke-Kallenbach method. Satisfactory results were obtained. More recently, Nelson and Wei[28] developed
490 another single-file diffusion model by using the lattice-gas model[29] to simulate co- and counter-diffusion cases in self-diffusion. From the simulation results, they reached a simple binary diffusion formula which embodied a correlation factor. From their model, binary diffusivities can be predicted from single component information. However, the theory is thus far developed only for self-diffusion. Yang et a1.[32] derived a binary diffusion model based on irreversible thermodynamic approach which included the cross-coefficient L12. In their model, they introduced an interaction parameter w which takes into account the molecular interaction between two diffusing species. The value of w can be obtained from the interaction energy as will be discussed later. Thirteen sets of co- and counter-diffusion data for CO2/C2H6 in 4A zeolite which were measured by the DAB method were also reported and compared with the model. Recently, Chen and Yang[33] and Chen et a1.[34] derived a fully predictive multicomponent diffusion model based on kinetic and irreversible thermodynamic approaches. This model has considered interactions between the same and different molecules and use only the information from single component experimental data to predict multicomponent diffusivities. The predicted data by this model have been compared with binary experimental data from literature and their own experimental data[35,36] with fair agreements. This is the only simple explicit model available for predicting multicomponent diffusion that considers adsorbate interactions. The detail derivation of this model will be given in the following sections. Dahlke and Emig[37] reported results of Monte Carlo simulations of binary co-diffusion in cage type zeolites. In their simulation, the number of diffusing molecules in each cage were variable and the maximum loading of a cage can be two molecules. For the two apparent diffusivities, higher values were found when small amounts of the other component were present. The cross-term diffusivity, Dij, of the Fickian diffusion matrix reached the same order of magnitude as the main-term diffusivity, Dii, if the surface occupancy was high. Karge and Niessen[38] developed a technique, using Fourier transform infrared spectroscopy (FTIR), to measure single and multicomponent transient uptake. The experimental results of co- and counter-diffusion for benzene/ethylbenzene in ZSM-5 have been reported and were compared with two different models for the Fickian diffusivities matrix. Van den Broeke et a1.[39] used Monte Carlo simulations to study single and multicomponent diffusion in channel type zeolites and obtained a similar results as the single-file model. Andersson and Agren[40] developed a formalism based on previous work in the literature for implementation on the computer. Hu and Do[41,42] derived a multicomponent kinetic model based on different equilibrium isotherms. They used the ideal adsorption solution model[43] instead of the extended Langmuir model in the phenomenological equation to predict binary diffusion coefficient. Meanwhile, three combination of binary diffusion data of ethane, n-butane and n-pentane in activated carbon were reported by using the DAB experimental method[44]. Later, Do and Hu[45] used a heterogeneous extended Langmuir model proposed by Kapoor et a1.[46] to describe the multicomponent equilibrium of ethane and propane in activated carbon. This model computes the gas mixture equilibrium by using an extended Langmuir isotherm on a patch of surface and then integrates it over an uniform energy distribution. The predicted results are satisfactory.
491 2. L A T T I C E - G A S
SINGLE FILE APPROACH
The single-file theories are developed by Sundaresan and Hall[25] with quasichemical approach and by Wei and coworkers[23,26], and have been used successfully to interpret diffusion data in channel-type zeolites, specifically ZSM-5127]. The assumptions of this theory are as follows. The adsorbed molecules are found only on the sites. Each site can be occupied by one sorbate molecule. The diffusion of the adsorbed molecules may be viewed as a succession of discrete jumps from site to site; jumps to occupied sites are forbidden. Molecules move through the pores in single file, thus two molecules cannot cross each other moving in opposite directions in a pore. The migration of a molecule from one site to an adjacent unoccupied site through the pore connecting these sites is an activated process. Consider a lattice containing M sites. Let NA and NB be the respective numbers of molecules of A and B in the lattice. The number of unoccupied sites, No, is equal to M - NA -- Ns. Therefore, the lattice coverage 0 has the relation:
(i)
OA + OB + Oo = I
where Oi - Ni/M is the lattice sites covered by molecule i. In zeolite crystals, the flux of species i under isothermal conditions, Ji, can be expressed as
Ji = - L i V ( # i / R T )
(2)
where L{ denotes the phenomenological coefficient for species i and # is the chemical potential. The phenomenological coefficient, in general, depends on the sorbate composition as well as the lateral interaction energies between the sorbate molecules. In the absence of lateral interactions between adsorbates, it can be shown that, for a random walk in an unblocked lattice[47]
L, = ~-~-120,0og(6)
(3)
where kmi is the rate constant for the migration of species i in the lattice, I is the distance between adjacent sites, Z is the number of nearest neighboring sites for every site in the interior of the crystal and g(6) is the permeability of the lattice. The chemical potential #~ in Eq.2 is determined from
#i(OA, OB, T) = #~
+ RTln (O~o)
i = A,B
(4)
where p~ is the standard chemical potential of adsorbed species i molecules. It should he noted that, in the above derivation, it is assumed that there are no interactions between the adsorbed molecules and the adsorbed molecules are presented as a single phase. Based on these assumptions, the binary diffusivities are Dij = L~
O(,,/RT) 00j
i, j = A, B
(5)
492 Substitute Eqs.l,3 and 4 into Eq.5, one gets
o=(o
0
D~
OB
0A)
1 - OA
(6)
where
Di = ~ - 12g(5)
(7)
The Di is the single component diffusivity of species i and it is concentration independent. Equation 6 has been used extensively by Tsikoyiannis and Wei[23,24] and Qureshi and Wei[26,27] to predict the binary diffusivities from the constant single component diffusivities in order to compare the predicted data with their Monte Carlo simulation results. More recently, Eq.6 has been modified by Nelson and Wei[28]. They use molecular simulation employing Poisson-distributed event times described by Nelson et a1129] with two diffusing species A and B. In their derivation, components A and B are assumed to have identical diffusive and adsorptive properties. The apparent diffusivity of component i, D +, is defined as
Ji = -D+VOi
(8)
where Ji is the flux of component i between adjacent rows and VOi is the concentration gradient of component between the same two adjacent rows. From the simulation results at steady state, the following conclusions are reached For co-diffusion: DA+ = Ds+ = Do
(9)
OB
V0s = ~ VOA
(10)
For counter-diffusion: D + = D~ = Do (1 - OT) f (Or)
(11)
VOA = --VOB
(12)
where f(OT) is the correlation factor[30,31] for self-diffusion, it has the form
f (OT) =
1 + (cos r 1 + 2----OT (cos r
(13)
For a square lattice (cos C) = -0.36338023. In Eqs.9 and 11, Do is the single component diffusivity and OT = OA + OB. The binary diffusion equation can be written as JB
=-
D21 D22
V0s
493 Equating the fluxes in Eq.14 with the fluxes obtained from the co- and counter-diffusion simulation results, Eqs.9-12, and solving for the diffusivities Dij gives
D = D~ ( 1 - OBgOBg 1 -- oAgOAg )
(15)
where
9 (OT) = f (OT) +
1 - f(OT) OT
(16)
the factor 9(OT) can be calculated from Eq.13 and is approximately equal to 1.571 0.5710T. Equations 6 and 15 can be used to predict binary diffusion. These two equations have the advantage of easy to use and have no adjustable parameters. The only information needed for this binary prediction is the single component diffusivity data which can be obtained from regression of the single component uptake results.
3. MAXWELL-STEFAN (M-S) APPROACH This approach[18] treats the vacant sites as the (n + 1)th component in the diffusing mixture. Therefore, for surface diffusion in the (n + 1) component system which consists of n adsorbed species, the bulk fluid phase Maxwell-Stefan diffusion equation[48,49] is used for this system: 0k RT VP ~ =
/=1
OiNi - OiUj ~ OvNi ntDij ntDiv
i# j
(17)
where ~)ij denotes the M-S diffusivity describing the facility for counter-exchange between the adsorbed molecules i and j, •iv for exchange between molecule i and vacant sites, nt is the total surface concentration, and 04 is the fractional surface coverage of molecule i that follows n
O~ + ~ Oi = 1
(18)
i=1
In Eq.17, #i represents chemical potential of the adsorbed species i. For thermodynamic consistency, the chemical potential #i should obey the Gibbs-Duhem equation[43]" n+l
}2 o,v , = o
(19)
i=l
Therefore, only n of the equations in Eq.17 are independent. The chemical potential of species i can be expressed as
#i = #o + RTln (fi)
(20)
where fi is the fugacity of component i in the bulk fluid phase in equilibrium with the adsorbed mixture. Substituting Eq.20 into Eq.17, the driving force on the LHS of Eq.17 becomes Oi n dOj RT V # / = j=l~FiJ dz
(i = 1,2, . . . , n )
(21)
494 where
Fij = Oi 00---7-
(i,j = 1 , 2 , . . . , n )
(22)
Here [F] have been called thermodynamic factors. They can be calculated from equilibrium isotherms. For example, if one assumes the gas phase in equilibrium with the adsorbed species is an ideal gas mixture (fi = Pi), which implies no interactions between molecules, then the extended Langmuir isotherm can be applied and one gets
Oi r~j = ~s + O~
(i,j
= 1,2, "'" ,n)
(23)
where 8is is Kronecker delta (=1 if i = j, =0 if i # j). In order to make the M-S model predictive, Krishna used the empirical Vignes relation [20] which was obtained for diffusion in liquid mixtures:
Oj
O~
z~,s = [z~s~]~ + os [z~,~]o, + os
(24)
Furthermore, let N = J and from the Onsager reciprocal relation it follows that those counter-sorption M-S diffusion coefficients Dis and :Dsi are symmetric that is
~,s = z~j,
(25)
Therefore, from single component M-S diffusivities :Div and :Dj~, which can be obtained from regression of single component experimental data, one can calculate Dis and Dji values based on Eq.24. The multicomponent fluxes can then be obtained by [J] =
-n,[B]-'[r]VO
(26)
where the elements for the n-dimensional matrix of M-S diffusivities [B] are Oi
~
0j
Bq = ~ + j=x ~s Bij = -Oi ( ~ j
Divl )
4. I R R E V E R S I B L E
[i(j
# i) = 1, 2,... , n]
[i,j(i#j)=l,2,...,n] THERMODYNAMICS
(27)
(28)
APPROACH
For the theoretical description of multicomponent surface diffusion and diffusion in zeolites, a number of models based on irreversible thermodynamics with a Fickian diffusion coefficient matrix have been derived by using the Onsager formalism with a vanishing cross-coefficient L12[1,2,7,33]. "fang et a1.[32] included the cross-coefficient Lx2 in the description of binary diffusion in zeolites based on the Onsager formalism. The main concept of the derivations by using this approach is discussed below. The phenomenological expression for flux J is given by: n
Ji = - ~ LijV#j j=l
(29)
495 where the chemical potential # has been assumed as the driving force. If the gas phase behaves like an ideal gas mixture, and the chemical potentials of the adsorbed phase and the gas phase are equal under the equilibrium condition, then the chemical potential can be expressed by #, = #o + R T In Pi
(30)
where P~ is the partial pressure which is a function of the adsorbed amounts qi(i = 1 , 2 , . . . , n). Substitute Eq.30 into Eq.29, one gets n
Ji = - ~_~ Dij Vqi
(31)
j=l
where 0 In Pi N 0 In Pk Dij = R T Li, Oqj + y~ Lik k=l Oqj
(k r i)
(32)
Next, one may assume all cross-term phenomenological coefficients follow the Onsager relation and are equal to zero L# = Lji = 0
ir j
(33)
This assumption implies that all drag effects by other components are negligible. Therefore, by introducing the following relation[54] Di0 = R T L , qi
(34)
one can get multicomponent diffusivities from Eq.32 O ln Pi Dij = Dio qi Oqj
(35)
For the Langmuirian case, i.e., the multicomponent adsorption isotherms are expressed by the extended Langmuir equation: q, biPi
qi -"
(36)
n
1 i--1
By substituting Eq.36 into Eq.35, one can obtain the diffusion coefficients for multicomponent diffusion with no interactions between adsorbate molecules:
496 where qv = qs - ~2i~1 qi. The multicomponent diffusivities in Eqs.37 and 38 are predictable from single component diffusivities Dio; and are concentration dependent. It should be noted that Eq.36 is the same as the IAS theory when qsbi for all components are equal. This irreversible thermodynamic approach for multicomponent diffusion has been extended by Yang et a1.[32] by considering the non-zero cross-term phenomenological coefficients where they introduced an interaction parameter w which indicates the extent of interactions between the two diffusing species. For a binary diffusion system, they combined with the Onsager reciprocal relation and noting that Ll1 > 0 and L22 > 0,
Lx2 = L21 = w(Lx,L22) 1/2
(39)
where
1
(40)
Following the derivation procedure discussed above, for w = 0, the diffusivity formula Eqs.37 and 38 are obtained for the binary diffusion case when a binary Langmuir isotherm equation is applied to express the gas phase partial pressures. However, when the absolute value of w is between 0-1, one obtains
Dij = Dioqi 0 Oqj In Pi +w(qiqJ)~(Di~176 ,
Oqj
i,j = 1,2
(41)
where Pi is a function of ql and q2, and can be correlated by using binary equilibrium isotherm data. w is the interaction parameter for diffusing molecules. It can be positive or negative depending on whether attractive or repulsive molecular interactions are operative. The value of w must be pre-determined based on information on single component diffusion, and it should satisfy the following conditions 0~(0j) .... >0
Eij
>0
~
>0
w
)0
Following the constraints above, one possible relationship between w and Eij is: w = (1
-
e -Ei'/'T) OiOj
(42)
where Ei i is the interaction energy between two different diffusing species. It can be calculated from E/i and Ejj where the energies E/i and Ejj can, in principle, be obtained from single component diffusion data. Here the geometric-mean rule or mixing rule is needed to calculate the cross-term energy
Eij= ~/Eii Ejj
(43)
Equation 42 is an empirical correlation that has the correct limits. More recently, Chen, et a1.[34] have taken an entirely different thermodynamic approach by considering the rate processes involved. For the diffusion of a multicomponent system, the phenomenological flux equation for component i is
Ji = - L i V ~ i .
(44)
497 where i* denotes the activated molecule i. The underlying assumption for this equation is that all migration steps must involve the activated species. The interactions between the unlike molecules will be accounted for in the calculation of V#i., so no cross-term is needed in Eq.44. Assuming ideal behavior where the activity coefficient is equal to unity (it needs to be stressed here, however, that this model is not limited to this condition; an activity coefficient may be added to take into account the lateral interactions between molecules), the chemical potential gradient is expressed as V # i = R T V In Oi
(45)
where 0 is the surface coverage. The relationship between the phenomenological coefficient Li and the Fickian diffusion coefficient at zero surface concentration Di0 is already expressed by Eq.34. Therefore, the Hux equation can be recast to Ji = - D i o qiV in 0i.
(46)
where qi = qisOi, and qis is the saturated amount adsorbed. In order to relate 0i and 0i., one needs to understand the rate process. For diffusion in zeolite, the rate processes are[33]" Activation:
Mi k,)
(47)
Mi. + V
Deactivation (to a vacant site)" (48)
mi. + V k'r ~ Mi
Deactivation (to an occupied site)" (49)
Mi. + Mj k,,> Mj. + Mi
In the step indicated by Eq.49, the exchange of the activated molecule i* with the adsorbed molecule j causes the adsorbed molecule to become activated due to the exchange of energy while i becomes adsorbed. The rate of formation for the activated i* is given by OOi. '~ Ot = k~O~ - k~O~.O~ - y ~ k~jO~.Oj
(50)
j----1
where the first term on the RHS in Eq.50 is the rate of activation, the other terms are the rate of deactivation on a vacant site (ki~Oi.O~) and sites already occupied by j; here j could be equal to i. The deactivation terms include both forward and backward movements, Furthermore, the steady-state condition stipulates that 00i. =0 Ot
498 It follows then kiOi
kiv 1 - ~ (1 - Aij) Oj j=l where O~ = 1 - ~in__l Oi, and ki...,i _ sticking probability of molecule i on adsorbed molecule j -ei,,-eij)/RT (53) Aij = kiv sticking probability of molecule i on vacant site = e
In Eq.53. kij and ki,, are, respectively, the rate constants for an activated i" to land on and stick to an adsorbed molecule j or a vacant site. A further underlying assumption for this equation is that the transit time between sites is negligible relative to the residence time at either site (whether vacant or occupied). Substituting Eq.52 into Eq.46, one gets J = -[D]V~
(54)
where J and V~ are two vectors of n components for, respectively, flux and concentration gradient. The diffusivity matrix [D] is given by
Dii = D,o 1 +
(ln-A,,)O, 1 - ~(1
-
(55)
A,j)Oj
j=l
(i - Aij )Si
Dij = Dio
n D
~(1 j:l
j #{
(56)
- )~,j )oj
Procedures for calculating the values of A and application of the above equations will be shown in the next section. Based on Eq.35, which was derived from the phenomenological flux equation, Hu and Do[41,42] have modified it by using different equilibrium isotherm formula. In their models, they used single component Langmuir or Toth isotherms to formulate the single component diffusion equation and applied IAS (ideal adsorption solution) model to compute the multicomponent adsorption equilibrium. Since the IAS model can be used to predict multicomponent equilibrium adsorption data from single Langmuir or Toth models without requiring any additional information, this model can also be used to predict multicomponent diffusion behaviors of the adsorbed species from the information of single component diffusion. More recently, Hu and Do[45] further modified Eq.35 by using the heterogeneous extended Langmuir model proposed by Kapoor et a1.[46]. This model computes the gas mixture equilibrium by using an extended Langmuir isotherm on a patch of surface and then integrates it over a uniform energy distribution, From the information obtained by fitting
499 single component dynamic data, they were able to predict the multicomponent diffusivities.
5. K I N E T I C ) k P P R O A C H 5.1. Model
formulation
Diffusion in zeolite and on surface are activated processes in which the adsorbed molecule must be activated in order to migrate to an adjacent site. Migration is then the result of a hopping process of the activated molecule. Before the derivation for binary diffusion, some assumptions must be made. First, the pore spaces are small but an adsorbed molecule will not cause pore blocking. Second, unlimited multilayer adsorption is not allowed but the adsorbed molecules may grow as a cluster[50]. The second assumption is equivalent to stating the desorption of the adsorbed molecules A or B requires the same activation energy regardless whether they are activated directly from the sorbent surface or from the occupied site. Following the above assumptions, a binary mixture of A and B may undergo the following rate processes for molecule A located at lattice site at (x - ~), where x is the distance coordinate and 5 is the inter-site distance (each hop covers a distance of 5). A potential energy diagram for the diffusion process is given in Figure 1.
A*
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
x'--~2
x
x+~
Figure 1: Potential energy diagram for activated diffusion along distance coordinate x. 1. Activation (A)x_ ~
'
ka
) (A')x_~6
(57)
rl = ka~A,~_~
2. Deactivation (to vacant site) (A*)=_~ + (Y)=_~ r2 --
km)
(A)=_~
km~A',x-~ ,x-~
(58)
500 3. Forward Migration (to vacant site) (a*) x - 6 ~ + (V)x+~ ,k,%(a)~+,~r3 -- k m O A , , x _ _ ~o~, ~+~
(59)
4. Forward Migration (to site occupied by A) (A*)~_~ + (A)~+~ koo>(A 9A)~+~ r4 -- kaaOA.,x_~OA, x+ ~
(60)
5. Backward Migration (to site occupied by A) (a*)~_~ + (a)~_~ % ( a . Ak_, r5 -- kaaOA.,x_ ~ 0 A
(61)
6. Forward Migration (to site occupied by B)
(a*)~_~ + (B)~+~ k~ (B. A)~+~ r6 = kobOA.,~_~Os,~+ ~
(62)
7. Backward Migration (to site occupied by B)
(a*)x_~ + (B)~_~ k.b>(B. A)~_~ r7 = kobOa.,~_~Os,~_ ~
(63)
Similarly, for molecule B at lattice (x - ~) one can have the following steps: 1. Activation (B)~_~ kb> (B*)~_~
r~ = kbOs,~_~
(64)
2. Deactivation (to vacant site) (B*)~_~ + (V)~_~ --~ (B)~_~ r2 = k~OB.,~_~O~,~_ ~
(65)
3. Forward Migration (to vacant site)
(B')~_~ + (V)~+~ kn >(B)~+~ r3 = knOB.,~_~O,,,~+~
(66)
4. Forward Migration (to site occupied by B) (B')x_~ + (B)~_~ kbb,, (B 9B)~+~ r4 -- kbbOB,,x_~OB,x+ ~
(67)
501 5. Backward Migration (to site occupied by B)
(B'),~_~ + (B)x_~ % (B. B),~_~ rs = kbbOs.,~_~Os,~
~_~
(68)
6. Forward Migration (to site occupied by A) (B')~_~
+ (A).+,
~ kbo, , (A 9 B)~+,
r6
kba 0 B,, x- ~,0A z+ ,
=
(69)
7. Backward Migration (to site occupied by A) (B')~_~ + ( A ) . _ ~ ~
=
k~o,, (A. B)~_~
kbo0s.,~_~0A,~_~
(70)
One advantage of using these rate equations is that one does not need to have the knowledge of the actual events occurring during the molecule migration on the surface or pores. In the RHS of Eqs.60-63, for example, molecule A can either stick on sites occupied by molecule A or B, or switch between A or B (exchange energies); the rate equations are the same. As mentioned earlier, for diffusion in narrow channels and pores, blockage or partial blockage (by adsorbed A or B) may occur, then two additional rate processes (blocking by A and B) should be included. Such a blockage process has been attributed to be the cause for the decrease of diffusivity with increasing concentration, and has been treated previously[51] for the case of single-component diffusion. For simplicity, the blockage processes are not included in this treatment. In steps 60-63, the mobile molecule sticks to the adsorbed A or B by forming a bond. There is a possibility, however, that it does not stick. In that event, two possibilities arise (for one-dimensional diffusion): it continues the movement either forward or backward (to make multiple jumps). For simplicity, we assume that these two probabilities are equal and therefore the subsequent events do not contribute to the net flux. 5 The net rates of forward migration for molecules A and B located at lattice site ( x - 5) are. respectively: MA, x-~ -- kmOA" ,z-~'Ov, x+~' + kaaOA',z-~OA, x+~, + kabOa. ,x- ~OB,x+~'
(71)
' z - ~Os,~+ ~, + kb~Os ., ~_ ~0a,~+~ B , ~_ 5~ = k,O s , ,z-~~O~+~+kbbOs. ,
(72)
Following the same procedure, one may write for the net rates of backward migration for molecules A and B located at lattice site (z + MA ~+~) MA, z+ ~ = kmSa.,x+~Ov, z_ ~ + ka~SA.,z+~OA, z_ ~ + kabSA.,z+~SB,z_ ~
(73)
Ms~+ ~ = k , Os.,~+~O~ ~_~ + kbbOs . ~+~0s ~_~ + kb~Os. ~+~OA~_ ~
(74)
502 The net rates for formation of activated molecules, A* and B* at two lattice sites are:
Ot
'
~-
'
~-~
-~o~o~.~ (o~,~_~ + o~,~+~) (rs) oo~. ~
=
Ot
~o~ ~
-
'
~oo~. ~ (<,
, +
o~ ~+~) - ~o~. ~ (o~,~_~ + o~,~+~) '
~-~
~
-k~oe~..•
(o.~_~ + o.=+~) (76)
The steady-state theory stipulates that the net rates of formation of the activated species are zero. Hence, the concentrations for the activated species are k=OA'~'+~
(77)
o~. ~ = ~ (<.~_~ + o~.~+~) + ~oo (o.~_~ + o~.~+~) + ~o~ (o~.~_~ + o~.~+~)
o~. ~ = ~o (<,~_~ + o~,~+~)+ ~ (o~ ~_~ + o~,~+~) + ~o (o.~_~ + o~,~+~)
(78)
The net rates of migration of molecules A and B are
~A = MA,~_~ =
~
-
M~,~+~
(o~.~_~o~+~-o~..~<~_~)
+ ~oo ( o ~ . ~ _ ~ o . ~ + ~ - o ~ . ~ + ~ o . ~ _ ~ )
+ko~ (oA.. ~_~e,,,.+~ - Oa...+~eB, =_ ~)
~s
=
Ms,._~ - Us,.+~
-
k~ (o~,.~_~< . ~ + ~ - o~ .. ~+~o~ _ .~_~ ) + k~ (oB.~_ ~ oB .~+~ - oB. ~+~os, ~_ ~)
+k~o (o~.._~o.~+~ - o~. ~+~o.~_~)
(79)
(8o)
If the lattice parameter ~ is sufficiently small, one may approximate: OOA,.
OA,.+ ~ = OA,. + 2
0,~,~+~ = 0,,,~ +
2
Ox
Ox
(81)
(82)
At any lattice site OA,. + OB,~ + 0~,~ = 1
(83)
503 Moreover, the mass fluxes can be related to the rates of migration:
JA (A,)
=
JB (As)
= rB (V~)
JA J8
"A
= 5rA = 5rs
(84)
(85)
where V~ is the volume of the lattice sites, As is the cross section area of the sites and is the distance between lattice sites. Substitute Eqs.79-83 into Eqs.84 and 85, one gets: JA -- k~km 52
2
OA, xOOv'x __ Ov aOA,x
o~
,x a~
kmO,,,~+ kaaOA, z + kabOB,z
kbkn 52
Js = --~
OB, X ~ az
-- Ov,z OOB, Oz x
k,~O,,,~:+ kbbOs,~ + kb,~OA,x +
kakab -2 + .... 2 b kbkb~ 5~ 2
OA
OOB,x __ OB,x
,~ 0~
OOA x ,
,
a~
kmOv, z + kaaOA, z + kabOB,z
0.. oe~.~ _ OA. "
knOv,~+ kbbOB,z + kbaOA, x
(86)
(87)
Let ~_552 = -~--5 l,/a 2e e,,/RT DAO = 2
(88)
kb 52 = 2 52eCb/R T DBO = "~
(89)
where u is vibration frequency of the bond holding the molecule to the site and e is the effective energy of that bond, i.e. the difference in energy between the states corresponding respectively to adsorption at the ground vibrational level of the bond and to free mobility on the surface. We further define A as that in Eq.53 k~ )~AA --" k m
--
sticking probability on adsorbed A sticking probability on vacant site
(90)
The second equality in the above equation arises because ka~ and km are, respectively, the rate constants for an activated A* to land on and stick to an adsorbed A or to stick to a vacant site. A further reason for the equality is that the transit time between sites is negligible relative to the residence time at either site (vacant or occupied). Following the above derivation, one can have
AAA = e -(~~176
(91)
Similarly, kbb
(eb~--,bb)/nT .
(92)
kab = e_(e,.,_e.b)lR T
(93)
~ B B "- ~
AAB = -~
-" e -
504
ABA = ~kba = e- (ebv--eba)/RT
(94)
where e=~ and eb~ are the effective bond energies between molecules A and B, respectively, with the vacant site; e== is the effective bond energy between molecules A and A; e=b is the effective bond energy between molecules A and B. Since diffusion is concerned, these effective bond energies are the activation energies for diffusion. Substituting Eqs.88-94 into Eqs.86 and 87, one has
OA,= + 0,,,~ + AABOB,, ] OOA,,: JA = -- DAO Ov, x dr. AAAOA, z _~_ AABOB, xj OX
(1 - :~A,~) O~,~
1 oe~,~
-- DAO Ov,z q_ AAAOA, z + AABOB, xj
OX
(95)
(1 -- ABA) OB,~ ] OOA,z JB = -- DBO Ov,x + ABBOA,z + ABAOA,z COX [ OB'z + Ov'z + /~BAOA'z I OOB'x
(96)
In comparison with Fickian equations and dropping the subscript x, one has
JA =--DAA~
O0.._~B DAB Oqx
JB = --DBA~--:
DBB OZ
(97)
00B
(98)
The concentration-dependent Fickian diffusivities are
DAA = DAO
1 -
1 - (1
(1 -
-/~AA)OA 1 -
(1
AAB)OB
(99)
-- (1 - AAB)OB
- AAB)OA
]
DAB = DAO 1 -- (1 - ,~AA ) OA -- (1 - AAB) OB 1 - (1 - AAS)OB
]
DBA = DBo 1--(1--ABB)OB--(1--ABA)OA D B B = DBo
1 - (1 - ABA) OA ] 1 -- (1 -- ~BB) OB -- (1 -- ~SA) OA
(loo) (~ol) (~o2)
From pure-component diffusion, by following the same procedure, the concentration- dependent Fickian diffusivity is[52]:
DA 1 = DAO 1 -- (1 - )~AA)OA
(103)
505 which is the same result as that of Yang et a1.[53]. From pure-component diffusivity data the value of "~AA may be obtained by regression. Using Eq.91, the value for e~a can be calculated e~a = ear + R T
In AAA
(104)
where ear is the activation energy for diffusion of molecule A from a bare surface. For some systems, it can be estimated from the heat of adsorption at zero surface coverage. The same procedure is applied to pure-component B and the value of ebb may be obtained from the pure-component diffusion data. For the interaction energies between unlike molecules, A on B or B on A, that is, e~b or cb~, we use the following geometric-mean rule or "mixing rule"[55] (105)
Cab = ~ba = (5aaC.bb) 1/2
The geometric-mean rule, which has been derived by London for nonpolar molecules, has been used frequently and fruitfully in equations of state for gas mixtures and in theories of liquid solutions[55]. It has also been used in Monte Carlo simulations of mixed-gas adsorption[56,57]. Using this rule and the values of cab and cba from the rule, one may calculate the values for AAB and s directly from Eqs.93 and 94. By using the geometric-mean rule for interactions between unlike adsorbate molecules, one is able to obtain a first approximation for binary diffusion on surfaces and in zeolites. The geometric-mean rule is a good approximation for nonpolar molecules with comparable sizes, shapes and adsorption potentials. These restrictions may be relaxed by introducing an additional parameter, Z~b[55,ss,57] ~o~ = ~ o
)1/2
= (~oo~bb
(1 --~o~)
(106)
where ~b is a constant and is characteristic of the A - B interaction. As discussed by Prausnitz et a1.[55], ~b can be expressed in terms of molecular parameters from London's theory of dispersion forces. The values of ~ab are small compared to unity, usually within the range -0.1 to 0.1 for hydrocarbon mixtures. For binary diffusion, f~b may be considered as an empirical interaction parameters. The power of Eq.106 is that once the value of ~b is determined from one data point, this equation can be employed for all conditions for the given mixture A and B, e.g. all compositions and amounts adsorbed. To summarize the result of the theory, Eq.104 is first used to obtain the like-molecule interaction parameters "~AA (and ABs) from pure-component Fickian diffusivities and activation energy or heats of adsorption. The interaction energy for the unlike molecules, e~b (= cb:) is calculated by using the geometric-mean rule, Eq.105 or 106, which yields values for parameters '~AB and "~BA directly from Eqs.93 and 94. The binary Fickian diffusivities are then calculated by using Eqs.99-102. This is indeed a very simple calculation procedure. This theory has been extended to multicomponent mixtures without requiring the introduction of new principles or assumptions[34]. 5.2. L i m i t i n g cases For surface diffusion and diffusion in zeolites, the interaction parameter )~ takes a positive value, ranging from 0 to 1. When "~AA --" '~BB --" O, the binary values of )~AB and
506 ~, 2.5 o
-.~ e~
2.0 1.5 1.0 0.5 0.0 0.0
0.2
0.4
0.6
0.8
1.0
XA
Figure 2: Plot of binary Fickian diffusivities with surface concentration at fixed total surface loading OT ~- 0.7 (• OA + OB) and DAo/Dso = 10. The dashed lines are for Aij = 0 and solid lines for Aij = 0.2. The x axis XA = OA/(OA + On). )~BA a r e also zero. Consequently the binary Fickian diffusivities, expressed by Eqs.99-102, reduce to Eqs.37 and 38 which are the forms for ideal binary diffusion. In deriving Eqs.37 and 38, however, the chemical potential in the gas phase is assumed to be equal to the partial pressure and the Langmuir isotherm is also assumed. The result derived here by using kinetic approach is not subjected to these restrictions. In the other extreme case, when '~AA "- '~BB : 1, and the activation energy for component A and B on bare surface are equal, i.e. "~AB -" /~BA " - 1, the main-term diffusivities (Dii) in binary diffusion are independent of concentration, and the cross-term diffusivities (Di.i) are zero. Consequently the system is well-represented by single-component Fickian diffusion equations with constant (but concentration independent) diffusivities. It is seen that the origin of the concentration dependence lies in the difference between activation energy (e~v) and bond energy between adsorbate (e~). When e~v = e~, there is no difference between landing on a vacant site or occupied site; hence, there is no concentration dependence. 5.3. Parametric
behavior
The binary diffusivities Dij v s OA from Eqs.99-102 have been plotted in Figure 2 for a fixed total surface concentration 6T = 0.7(0T = OA + OB) for two special cases. In case 1, one assumes all interaction coefficients Aij are equal to zero, which results in ideal binary diffusivities (Eqs.37 and 38). In the other case, /~AA -- )~BB "-" 0.5 and e~. = ebb. By comparing these two cases, one finds that a small change of the A values can have significant effects on the diffusivities. If A equals to zero, the diffusivities are increased (component A) or decreased (component B) dramatically at high surface loadings, and eventually reach infinity after the surface is saturated. At high surface loadings, the molecular interactions are important. Therefore. it can cause errors by using Eqs.37 and 38 to calculate diffusivities under this conditions. Experimental data are generally available in the form of uptake rates, i.e. the cumulated amount of diffusion vs time. In order to calculate the uptake rates, solutions to the
507
0.8
I
I
I
r
Z,B
Z,A I
B
0.6
0.8
I
~
~.~.
~
~
0.0 0.5 1.0
I
r
0.6
., 0.o
J
J
J
,,"
0.0 0.0
0.2
A
0.2
,."
"
Single
.-
0.2 -, 9 / ."
,~, s
s
,...
s
~. -
,. -- " 0.5 - . - " ...- 1.0
~- 0.4
S
l
I
B -
t~
04-
'1
II
o
o f:a.,
I
. . . . ~. . . . . i . . . . . 0.4 0.6 0.8 1.0
0.0 0.5 1.0
Time z
Single
-,o
I",,,,~ 0.0 0.0 0.2
m
-
"
........ i i " 0.4 0.6 0.8
0.5~176 1.0 1.0
Time z
Figure 3" Uptake behavior for binary co-diffusion. Where DAo/DBo = 20 and dimensionless time r = D A o t / R 2. Cases I and II with initial clean particles, 8A0 = 0B0 = 0, subjected to a step increase at the surface to 8A = 0.05 and 0B = 0.8. AB = 0 in case I and /~A = 0 in case II. diffusion equations are needed. Assuming spherical particles, the binary Fickian diffusion equations are
OqA 1 0 Ot = r2 Or [r2 (DAA ~ r
Ot - r 2 0r
r2
DBA--
+DAB
_~rB)]
(107)
+ DBB'-~" r
where the binary Fickian diffusivities Dij are given by Eqs.99-102. These equations can be solved numerically with proper boundary and initial conditions, i.e.,
I.C.
t = 0
qA = qao, qB = qBO
B.C.
r-O
--=0 Or Or qA = qA~, qB = qBcr
r = R
~qA
CgqB
(109)
The calculated parametric results in terms of uptake rates are shown for two cases of codiffusion (fluxes of A and B in the same direction) and two cases of counterdiffusion (fluxes of A and B in opposite directions). In all cases, predictions based on assuming pure-component diffusion are also included for comparison. This comparison will show the importance of inclusion of the cross-term diffusivities. In the case of single component prediction, the cross-term diffusivities are assumed to be zero and Eq.103 is used for main-term diffusivities. The results for codiffusion are shown in Figure 3 and that for counterdiffusion Figure 4. One notable conclusion from these results is that for codiffusion, the diffusion of
508
0.51 o
~.o
0.4
~
.
.
9
.
.
.
.
.
.
.
.
.
.
0.5
.
00
~
~
0.4
1
~ II
l
i
i
Single . 9. . . . . . . . . . . . . . . . .
1.0 0.5
t-q
0.3
"
.'
0.3 " "
"-., ~... A
0.2 '
-
~'~
"t3"~"~ : ' :
D~ :.- :'.
1.0 0.5 0.0
-
o.o
A
~' B~ ~ ~~"-~---'"- 1.0 ' ~ 0.5
0.2
0.0
0.1 0.0 0.0
0.2
0.4
0.6
0.8
1.0
0.1 0.0 0.0
0.2
Time x
I
I
I
0.4
0.6
0.8
1.0
Time x
Figure 4" Uptake behavior for binary counter-diffusion. Where DAo/DBo = 20 and dimensionless time r = D A o t / R 2. Cases I and II with initial 8AO = 0 and 8so = 0.4, subjected to a step change at the surface to 0 A = 0.4 and 8B = 0.05. AB = 0 in case I and AA = 0 in case II. both components are increased (or facilitated) by the presence of the flux of the other component (Figure 3). While for counterdiffusion, the diffusion of both components are decreased (or hindered) by the other component (Figure 4). The other notable conclusion is that, for both codiffusion and counterdiffusion, the influence exerted by the other component is much stronger on the fast diffusing species; the slower component is much less affected by the fast component. From Figure 3, it is seen that varying A for the fast component has little effect on the diffusion of the slow component, while varying A for the slow component has a large effect on the fast component. Also shown in these figures are the "overshoot" phenomenon for the fast component, which has been discussed extensively in the literature and will not be further discussed here. It is also clearly shown in these results that the differences between single-component diffusion and binary diffusion formulations are large and large errors can result if the single-component equations are used for binary diffusion. However, for a dilute binary diffusion system, it is possible to predict the uptake rate of slow diffusion component by using single-component diffusion model. 5.4. E x a m p l e s
for p r e d i c t i n g m u l t i c o m p o n e n t
diffusion
In order to predict multicomponent diffusivities with this model, the concentration dependent single-component diffusivities, activation energies (or heats of adsorption) and the saturated amount adsorbed for each component are required. Example
1
Binary diffusion of 02N2 in Bergbau-Forschung carbon molecular sieve at 27~ were measured by using the DAB technique[35]. By fitting the 02 and N2 isotherm data with
509 the Langmuir equation q~bP q = l + bP
(110)
one can obtain the saturated amounts of adsorption q~ for each component. Therefore, the surface coverage 8(8 = q/qs) can be calculated. The heat of adsorption for each component can be calculated by using at least two sets of isotherm data at different temperatures
OP)
H~t
q= RT 2
(111)
where H st is the isosteric heat of adsorption. The single component diffusivities Do and the interaction parameters A can be obtained from concentration dependence of single component diffusion experiments. By least-square fit of the diffusivities D with surface coverage ~ from Eq.103, one can get Do and A values for each component. As a first approximation, one may assume that the diffusional activation energies for each component from the bare surface is equal to the heat of adsorption at zero surface coverage. Here, it is worth noting that the activation energy, for many cases, can be related to the heat of adsorption by an empirical correlation: H st
ev =
(112) m
where the empirical constant m is found to be integers varying between 1 to 3[59] and it can be less than 1 for zeolite[35]. In this example, the activation energy is assumed to be equal to the heat of adsorption. Therefore, from Eq.104, one can calculate ea~ which is the interaction energy between two like molecules. The interaction energy between two unlike molecules e~b can be calculated from Eq.105. All calculated results are listed in Table 1. These results will be used to predict binary O2N2 co- and counter-diffusion. In order to predict the binary uptake rates, one needs to solve diffusion equations, Eqs.107 and 108, with the binary diffusivities, Dij ( i , j = A,B), given by Eqs.99-102. In these diffusion equations, the value of qs for the binary mixture is calculated from[54] 1 --
XA =
qs
XB t
qsA
(113)
qsB
where x is the adsorbed phase mole fraction at equilibrium which may be determined from the extended Langmuir equation for mixtures: qsabA P] a qA = 1 + bAP] a + bBP~ s
(114)
with a similar expression for qB. za =
qA
(115)
qA + qB
Equations 107 and 108 are solved numerically with proper experimental boundary and initial conditions. The predicted co- and counter-diffusion results are shown in Figure 5
510 0.20
I
I
I
I
CD
I
N2 o
,~ 015
0"35t ~ O.3O II
'
*
*
~
,-. 025 o
020
0, 0.10
. . . . . . . . . . . . . . . . . . .
,,,,F
015
02
0.10
005
.9 ....
0.05 0.0;
0
t
I
t
I
10
20
30
40
50
Time (min)
0.0 ~ v 0
o:1
. . . . . . . . . . . . . . . . . . .
~
i
I
10
20
30
I
/
40
50
Time (min)
Figure 5: Co- and counter-diffusion of 02N2 in Bergbau-Forschung carbon molecular sieve at 27~ Symbols are experimental data. Curves are predictions using single- component diffusivities (dashed line) and theoretical binary diffusivities (solid line). Case I with initial ~O2 - - ~N2 - - 0, subjected to a step change at the surface to 8o2 = 0.105 and ~N2 = 0.333. Case II with initial 0o2 = 0.376 and ON2 = 0, subjected to a step change at the surface to 002 = 0.105 and ~S2 -- 0.328 (Data are listed in table 1). (cases I and II), with a comparison of the predicted binary diffusion results which assuming single component diffusion. The single component diffusion results are calculated by assuming that the cross-term diffusivities are zero and the main-term diffusivities follow Eq.103. Points on the figures are the experimental data. By comparing the predicted results from binary and single component model with the experimental results, the superiority of the binary model is clearly demonstrated, especially, (case I) for co-diffusion case.
Example
2
Binary diffusion of benzene/toluene in ZSM-5 zeolite at 65~ were measured by using the Wicke-Kallenbach method[27]. The heats of adsorption which used in this prediction were from Tsikoyiannis and Well24]. The calculated single component parameters are listed in Table 1. The predicted binary uptake rates for benzene/toluene are shown in Figure 6. By comparing the predicted results with their experimental data, the binary model is indeed satisfactory. Example
3
Binary diffusion of CHa/C2H6 in a carbon molecular sieve membrane at three different temperatures (24, 50 and 80~ were measured by using the Wicke-Kallenbach method[36]. This carbon molecular sieve membrane was prepared by pyrolysis of polyfurfuryl alcohol supported on a macroporous graphite substrate. The single component
511 ,
0.5[
,
,
,
,
i
0
0.2
F
.-"
-
0.1 0.
-
0
5
10
15 20
25
30
Time (min) Figure 6: Counter-diffusion of benzene/toluene in ZSM-5 zeolite at 65~ Symbols are experimental data. Curves are predictions using single-component diffusivities (dashed line) and theoretical binary diffusivities (solid line). isotherm data were fitted by the modified Langmuir equation q~bP n q = 1 + bP '~
(116)
The Do and A values were obtained by an integral analysis of the data on flux vs partial pressure. The flux J for gas diffusion through a membrane can be expressed in the Fickian form (117)
dq J = - D d--~
Substitute Eq.103 into Eq.ll7, and integrating from qH to qn, one gets j __. Do qs Ax(1 - A )
In
(118)
where subscripts H and L denote high and low concentrations on the two sides of the diffusion cell. For diffusion through the carbon molecular sieve layer, qL << qH. Therefore, Eq.ll8 can be further simplified by neglecting the amount adsorbed on the low concentration side and substituting Eq.ll6 into Eq.ll8, one gets J =
Do
Az
qs
(1 -
A)
ln[1-(1-A)
bPn
1 --!-
]
bP'~J
(119)
where P is the partial pressure of the diffusing gas on the high concentration side. By fitting single component J vs P data with Eq.ll9, the values for Do and A can be obtained. The activation energies can be calculated from the temperature dependence of diffusivities by using the Arrhenius equation Do = Uoe ,-,. -ev/RT
(120)
Table 1" Parameters from single component isotherm and diffusivity data. t,o
T(~ 27
65
24
Sorbent BergauForschung CMS
ZSM-5
CMS
Sorbate
Isotherm parameters Diffusivity q~(mmol/g)_ b(1/atm)' ' n .... Do(cm2/s)
A
El/
Reference
(kcal/mol)
02
1.87
0.12 \
6.46.10 TM
5 . 2 8 . 1 0 -2
5.59
N2
1.47
0.16
1.77-10 -2
1.29.10 -2
6.85
C6H6
6.65. I0-"
8.73.I0-'
13.20
CrHr
1.15.10 -l~
1.24.10 -1
12.80
CH4
1.15
0.37
1.24
1.70.10 -s
5 . 6 0 . 1 0 -2
2.51
C2H6
2.07
15.2
0.84
1.46.10 -9
0
2.97
Chen et al. 19941351
Qureshi and Wei 1990127] Chen and Yang 1994 [36]
Table 2: Binary diffusion of CH4/C2H6 in carbon molecular sieve at 24~ Mol fraction in
mol fraction out
Expt. flux (mol/cm2s)
Predicted flux from binary theory (mol/cm2s)
Predicted flux assuming single-comp. diff. (mol/cm2s)
CH4
C211s
CH4
C2H6
CH4
C2H6
CH4
C2H6
CH4
C2H~
0.611 0.138 0.093 0.465 0.349 0.218
0.021 0.580 0.430 0.317 0.625 0.125
2 . 7 0 . 1 0 -3 4 . 6 0 . 1 0 -4 2 . 8 6 . 1 0 -4 1.93.10 -3 1 . 8 2 . 1 0 -3 7 . 8 1 . 1 0 -4
0 1.68.10 -3 1.39.10 -3 7.22.10 -4 2.37.10 -a 3.23.10 -4
1.88.10 -s 3 . 1 8 . 1 0 -9 1.99.10 -9 1.36.10 -s 8 . 0 4 . 1 0 -9 5 . 4 9 . 1 0 -9
0 1.16.10 -s 9 . 6 0 . 1 0 -9 5 . 0 9 . 1 0 -9 1.04.10 -s 2 . 2 6 . 1 0 -9
1.75.10 -s 1.67.10 -9 1.19.10 -9 8 . 5 3 . 1 0 -9 4 . 7 8 . 1 0 -9 4 . 8 7 . 1 0 -9
5 . 6 3 . 1 0 -1~ 6 . 0 7 . 1 0 -9 5 . 3 6 . 1 0 -9 3 . 9 7 . 1 0 -9 5 . 8 5 . 1 0 -9 2 . 5 6 . 1 0 -9
1.59.10 -s 9.89.10-1~ 7 . 3 7 . 1 0 -l~ 5 . 6 6 . 1 0 -9 2 . 8 2 . 1 0 -9 3.67 910 -9
4 . 3 9 . 1 0 -1~ 5 . 9 4 . 1 0 -9 5 . 2 7 . 1 0 -9 3.58.10 -9 5 . 4 7 . 1 0 -9 2.42.10 -9
513 where ev is the activation energy and D; is the pre-exponential factor. The calculated single component data are listed in Table 1. The binary fluxes can be predicted by substituting diffusivities, Eqs.99-102, into the flux equation
Ji = -
Dij--~x
i = A, B
(121)
j"-I
Integrating the flux equation over qA by keeping the other component at a constant average qB over the membrane, one gets
Ji =
Axl ~
[---~,j (qjo~t - qj~)]
i = A, B
(122)
j--1
where qA
D---AA =
out
1 f DAA (qA, "qB) dqA qA out -- qA in q A i n
(123)
q B o~t _
DAB -_
1
qB out -- qB in
f
DAB (-qA, qB) dqB
(124)
qB in
Similarly, one gets -DBA and -'DBB. The subscripts in and out stand for conditions on the two sites of the membrane. The examples of predicted binary flux results at 24~ are listed in Table 2. Comparing the experimental fluxes with the predictions (Table 2), it is clear that the binary theory predictions were consistently superior than predictions ignoring the cross-term effects, i.e., assuming single-component diffusion.
Example 4 A comparison of the Chen-Yang model with other models is in order. The three other models are SFM (single-file model)J23,25,26], MSM (Maxwell-Stefan model)[18] and ITM (irreversible thermodynamics model)[1,2,7,32]. A direct comparison between these models for co- and counter-diffusion cases are shown in Figure 7 (cases I and II). For co-diffusion, the four models show only little differences in their initial uptake slopes at very short time. After this period, the Maxwell-Stefan model shows a more enhanced overshoot for the fast diffusing component. This additional overshoot is attributed to the drag effect by considering the counter-exchange coefficient 7912 in the M-S model. Due to the vanishing 7912 (which is equivalent to saying L12 = 0 in the phenomenological equation), the M-S model is eventually reduced to the irreversible thermodynamic model (Eqs.37 and 38) which is indicated by long dashed lines in Figure 7. After the maximum peaks, the single-file model gives a different behavior than the others; it takes much longer to reach equilibrium. This behavior can be understood from the basic assumption made in this model: At high surface concentrations, an activated molecule will have little chance to migrate to adjacent sites because of the limited vacant sites available. In the basic assumption, molecules moving to occupied sites is forbidden. Therefore, a molecule
514 0.6 r
I
i
I
I
I
~
0.5
t.,
o
0.4
=.
0.3
...... ......
~
CYM SFM ITM MSM
'
'
0.4 I-.....
=
0.3
'-
'cY
.... ITM 9. . . . . M S M l .................. .~..,
D
0.2
I
..,...,
....
~
,
~ " " 2"- " " " " " " " - "-
0.2 0.1 0.0 0
1
2
3
4
5
Time x
6
0.1-
""
0.0 0
1
................
2
3
4
5
6
Time 'z
Figure 7: Comparison of theoretical predictions of co- and counter-diffusion between SFM (Single-file Model), MSM (Maxwell-Stefan Model). ITM (Irreversible Thermodynamics Model) and CYM (Chen-Yang Model) (with ,~ij = 0.2). The uptake rate of mixture in a particle is specified by the diffusivity ratio DAo/DBo = 35 and dimensionless time r = D A o t / R 2. Case I with initial 0A0 = 0 and 0S0 = 0, subjected to a step change at the surface to 0A = 0.5 and 0B = 0.1. Case II with initial 0 A - - 0.5 and 0B = 0, subjected to a step change at the surface to 0 A --" 0 and 0B = 0.5. will take longer to migrate on the surface at high loadings. The Chen-Yang kinetic model has a wide range of predictability. As discussed in the previous section, when Aij = 0 (no interactions between molecules), the Chen-Yang model is essentially reduced to the irreversible thermodynamic model. However, when )~ = 1, the Chen-Yang model will predict two independent single component diffusion in a binary system. Here there are no cross-term diffusivities (Dij = O, i 5~ j) and the main-term diffusivities are concentration independent. In other words, the Chen-Yang model is capable of predicting binary diffusion between ~ij from 0 to 1. The Aij is indicative of the level of molecular interactions which can be obtained from single component information. For counter-diffusion, the Maxwell-Stefan model shows a large deviation from the other models. The drag force exerted between two counter diffusing molecules tends to reduce both migration rates. Therefore, the results of considering the drag effect in the system will cause a retardation of molecular movement. This explanation is consistent with the model calculation results. On the other hand, the single-fiIe model shows a slightly slower rate of uptake to reach equilibrium. This phenomenon can also be explained by the reason discussed above for the co-diffusion case. The experimental results for counter-diffusion of 02/N2 in Bergbau-Forschung CMS and CsHs/CrH8 in ZSM-5 zeolite with the predicted results from the Chen-Yang model have been shown in Figures 5 (case II) and 6 with fair agreements. Therefore. it is clear that the Chen-Yang model is superior to the others based on these comparisons.
515 0.6 / (:D ~D
,
,
,
i
0.5
-
O.4 0.3 0.2
0.1 00 0
._ 2
4
B 6
8
Time "c Figure 8: Example for ternary diffusion prediction by the Chen-Yang kinetic model. The parameters used in prediction are D A o / D B o = 2, D A o / D c o = 50, ~AA = 5.3-10 -2, )~BB = ,kCC = 1.3 910 -2, E~v = 5.6, Ebv = 6.5 and Ecv = 6.9 kcal/mol. Dimensionless time r = D A o t / R 2.
Example 5 An example for ternary diffusion predictions are shown in Figure 8. From this figure, both fast diffusing components A and B exhibit the overshoot phenomenon, with component A showing a sharper maximum since component A is the fastest. Component A competes better for the vacant sites at the initial stage and will then be quickly displaced by components B and C since Ebv and Ecv are larger than E~v. This figure demonstrates the capability of the Chen-Yang model for predicting multi-component diffusion with only single component information. Experimental data from the literature have been compared with theoretical predictions by the Chen-Yang model for co- and counter-diffusion shown in Figures 5 and 6. Considering the simplicity of the theory, the agreements between the theory and the experiments are indeed excellent. As mentioned, large errors can arise by using single-component diffusivities for binary diffusion systems. The results using single-component diffusivities are also shown in these figures and large errors are clearly seen. 5.5. C o n c l u d i n g remarks A general and simple binary diffusion model based on kinetic theory is developed for surface diffusion and diffusion in zeolites. Predictions by the theory compare well with the experimental data. To use the model, single component isotherms and concentrationdependent single-component diffusivities are needed. Moreover, the main-term diffusivities are always positive, and the cross-term diffusivities can be either positive or negative. Comparing the different models (SFM, MSM and ITM), the single-file and irreversible thermodynamic models have the advantage of being easy to use and having no adjustable parameters. It can be used to predict multicomponent diffusion, especially at low surface loadings with fair agreement. The Chen-Yang model, however, is superior to the others.
516 This model can be used to predict a wide range of multicomponent diffusion systems by taking into account of the interactions between the diffusing molecules. Furthermore, it can be easily extended to heterogeneous surfaces by considering surface energy distributions of e~v. The lateral interactions may be accounted for by using a non-unity activity coefficient in Eq.45.
6. L I S T O F S Y M B O L S
As b B D T~ Do
f~ f(er)
g(a) HSt J k l L M n
N P q q, r
R t T V
y~ Z
Z
cross section area of the lattice sites Langmuir constant Maxwell-Stefan diffusivity Fickian diffusivity Maxwell-Stefan diffusivity Fickian diffusivity at zero surface coverage fugacity of component i correlation factor given by Eq.13 permeability of the lattice isosteric heat of adsorption flux rate constant distance between adjacent sites phenomenological coefficient rate of migration or lattice sites Langmuir constant or number of components number of molecules pressure or partial pressure amount adsorbed saturated amount adsorbed rate or radial distance gas constant or particle radius time absolute temperature vacant site volume of the lattice sites coordinator number of nearest neighboring sites
517 Greek letters correction constant for mixing rule thermodynamic factor, Eq.22 F distance between two adjacent adsorption sites g effective bond energy activation energy for diffusion of A on site covered by B gab gay activation energy for diffusion of A on bare surface surface coverage or dimensionless surface concentration, (= q/qs) ratio of rate constants defined by Eq.90 or molecular interaction parameter chemical potential # bond vibration frequency I/ dimensionless time (= DAot/R 2) T interaction parameter defined by Eq.39 Superscript * a, b A, B i, j x 0
or Subscripts activated species species species species distance coordinate at q=O
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Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
519
E n e r g y a n d S t r u c t u r e H e t e r o g e n e i t i e s for t h e A d s o r p t i o n in Z e o l i t e s A.S.T. Chiang ~, C.K. Leeb, W. Rudzifiski ~, J. Narkiewicz-Michatek ~, P. Szabelski r "Department of Chemical Engineering, National Central University, Chung-Li, Taiwan ROC 32054 bDepartment of Environmental Engineering, Van-Nung Institute of Technology, Taiwan ROC 32054 CDepartment of Theoretical Chemistry, Maria Curie-Sktodowska University, 20-031 Lublin, Poland
1. I N T R O D U C T I O N It is well known that the real solid surfaces are heterogeneous in nature. The energetic heterogeneity of an amorphous solid surface has been intuitively associated with the geometrical structure distortions, generally observed in the solid surfaces. The surface geometric heterogeneity and energetic heterogeneity are thus two sides of the same coin
[1, 2]. That the surface of amorphous solid must be energetically heterogeneous was easy to understand. It was much more difficult to conceive, that a similar heterogeneity should exist on the surfaces of well-defined solids. The most important example of such solids is zeolite, which is a micro-porous but highly crystalline material. At the molecular scale, there exists a variety of local potential minima in the micropores of a perfect zeolite crystal. Chemical impurities and unavoidable dislocations and distortions of the zeolite framework structure may contribute significantly to the variation of these local potential minima. In addition, one should not forget a possible change of the zeolite host structure due to the admittance of guest molecules inside [3, 4]. Such a 'structure induced' phase change of adsorption energy is quite different from those due to the adsorbate-adsorbent interactions considered in the classical adsorption models. The geometrical non-ideality becomes a crucial factor when the size of admolecules is comparable to the size of the cavities or channels in the zeolite structure. Numerous experimental studies [5-8] have demonstrated that a different view should be applied to such systems, compared to the adsorption on a more open surface. Computer simulations have also provided much valuable information concerning the behavior of guest molecules in tight-fit pores [9-13]. Unlike the classical picture of a surface, the local potential minima in zeolites are arranged in a 3-dimensional way. Thus, the adsorption in zeolites can be treated as the filling of an energetically non-equivalent, 3-dimensional lattice of sites. This is particularly true in the case Of small molecules adsorbed in large cavities and channels of some zeolites.
520 If the energetic heterogeneity is properly considered, it may be possible to describe the adsorption in zeolites with some simple statistical models, as we will demonstrate it in the present review.
2. T H E O R I G I N OF HETEROGENEITY
ADSORPTION
ENERGY
An energetically homogeneous surface is, of course, a product of theoretical imagination. It is a mathematical surface, where every point on the surface is equivalent to the others even at a molecular level of resolution. A real surface, however, must be made up of atoms. Since atoms have finite sizes, a surface composed of atoms is never homogeneous in the molecular scale. The heterogeneous nature of non-crystalline solid surfaces can be traced back to their mode of formation [14]. Various mechanisms of formation may lead to a heterogeneous distribution of surface atoms, functional groups, and mostly important, geometrical roughness (stacking arrangement of atoms). It has been a topic of much recent interest that the geometrical roughness of most solid surfaces exhibits a fractal characteristics [15-19]. For practical purposes, the surface energy heterogeneity can be defined by the potential experienced by a guest molecule at different locations on the surface. To some extent, this can presently be accomplished with an Atomic-Force Microscope on a flat surface. However, adsorption measurement is still the best way to define the surface energy heterogeneity. Several different sources of energy heterogeneity should be considered for the adsorption of molecules on solids. First of all, admolecules in different configurations and orientations on the surface always interact with the surface atoms differently, even if these atoms are aligned in a perfect order on a flat mathematical plane. The second source of adsorption energy heterogeneity is attributed to the geometric nature of the solid surface. In the simplest picture, an admolecule is affected by a different number of surface atoms at different locations when the surface atoms are placed on a curved mathematical surface. However, when the pore size of a microporous adsorbent is comparable to that of guest molecules, then one has to consider the system at a resolution of molecular level. Under such resolution, it is no longer possible to define a surface curvature when discrete atoms are visible. The third possible source of adsorption energy heterogeneity is the relaxation of solid structure under the influence of adsorbed molecules. This effect has not yet been discussed in most adsorption studies. One usually assumes that the solid is an inert material with a fixed structure. This, for instance, is certainly not the case for the adsorption on ion-exchange resins, for instance, which may swell upon the admittance of guest molecules. Even for solids with a more rigid structure, there may still occur similar relaxations of the solid structure. This effect is small and restricted in most cases, since the admolecules have translational degree of freedom sufficient to respond to the interactions. However, if a micropore is so small that only one admolecule can fit in, this admolecule will be limited in its translational freedom. To respond to the interactions, the chemical bonds between surface atoms and those within the admolecule may be affected. Consequently, the relaxation of solid structure may have to be accounted for when dealing with microporous solids.
521 From the above discussion, we may conclude that two levels of energy heterogeneity must be considered when modelling an adsorption system. The first level of heterogeneity is mainly due to the geometric factor. A guest molecule at different locations on the surface may experience much different interactions. In the extreme case, the surface can be considered as either a random or an ordered lattice of non-equivalent adsorption sites. Molecules adsorbed on different sites should, in principle, interact in a somewhat different way. The most difficult problem in modelling a surface with such a picture in mind is to determine the proximity and relative locations of different sites. This is especially important when the adsorbate-adsorbate interactions have to be considered. The two extreme cases, a patchwise and a random distribution of non-equivalent sites, have been considered in the past to derive adsorption isotherms [1]. Here we will present another model, where different sites are assigned a position at a regular lattice with fixed coordination numbers. Another level of energy heterogeneity to be modelled is the effect of the configurational freedom of admolecules. In most cases, the change of adsorption energy due to this effect is not as large as that coming from the solid geometrical heterogeneity. Nevertheless, the configurational freedom introduces a dispersion of adsorption energy even for the same adsorption site. Thus, the heterogeneous site models, either with a random or an ordered spatial distribution of sites, have to be modified. A dispersion of adsorption energy must be associated to each type of sites. With these explanations, the following discussions will be restricted to zeolites only. The pore structure of zeolites can be broadly classified into three groups. Many zeolites of industrial interest possess a pore structure consisting of three dimensionally arranged cavities. These cavities are usually connected by windows having a smaller size. More than one molecule can usually be accommodated in each cavity. Due to the restricted size of the window that connects neighboring cavities, the interaction of molecules adsorbed in neighboring cavities is largely screened. Unless an adsorbed molecule causes a large distortion of the zeolite structure, one can usually assume that the adsorption in different cavities proceeds independently [20]. However, there are cases where the inter-cavity transitions have to be invoked to explain certain observed characteristics [21]. Besides the cavity and window type of pore structure, many zeolites exhibit parallel pores of one-dimensional molecular size channels. Mordenite, ZSM-11 and A1PO4 - 5 are among them. The third important type of pore structure is that of MFI zeolite. There are two intersecting channel systems, a straight and a zig-zag one, forming a three dimensional pore structure. Different models are therefore needed to describe the adsorption in different types of pore structure. There are two force fields acting on a guest molecule enclosed in a zeolite host structure. The force field created by the zeolite structure comes mostly from the larger oxygen atoms and from the electrostatic force field of the associated cations. Due to the crystalline nature of zeolite, the force field created by the framework atoms must possess some symmetrical properties. The second force field acting on the admolecules is that created by the admolecules themselves. This force field changes in the course of adsorption. Zeolites are crystalline materials, and presumably each unit cell is equivalent to the other. They are the perfect candidates to apply statistical thermodynamic analysis. Before discussing specific models for zeolite adsorption, we shall first review the general statistical thermodynamicapproaches to the adsorption in microporous solids, and particularly in
522 zeolites. Much of the conclusions will be applied directly to different types of zeolites.
3. S T A T I S T I C A L
THEORY
OF ADSORPTION
IN ZEOLITES
The canonical partition function for a system of N~ molecules adsorbed in a zeolite can be written as 1 Q'(Na, T,V,) = Q~(T, V,)~-~.t{qt(T)}N~Z'(N~, T, Vs) ,
(3.1)
where Vs is the volume of the zeolite, T is the system temperature, qt(T) is the partition function for the translational degree of freedom, Zs is the configuration integral for N~ molecules in this volume [22]. Z'(N., T, V.) = f exp{-~[ E r i
9i, r
+ E Cmt~(r i
Na inter " ~ - E E (I) (ri, till, r rj, @j, Cj)]} YI dridlItidr i <j i=l
(3.2)
In Eqn (3.2), ri is the Cartesian coordinate of the mass center of the molecules, @i is the Eulerian angle specifying the orientation of the admolecules, r is related to the internal degree of freedom of the molecules. The zeolite-adsorbate interaction potential, Cz, is a function of the position, orientation and conformation of each adsorbate molecule. The intramolecule potential energy (I~intra(r , on the other hand, includes the energies that come from bond rotation, bending, stretching etc. The adsorbate-adsorbate interaction potential, (I)inter is assumed to be pairwise. Three body interactions can of course be included if necessary. The function ZS(Na, T, Vs) has a dimension of volume to the Na-th power. The translational partition function qt, on the other hand, has a dimension of inverse of volume. If the zeolite lattice is not affected by the intrusion of sorbate molecules, Qz(T,V~) will be independent of N~. For the time being, we assume this to be true. With this assumption, the grand partition function of the guest-host system takes the form T,
- q (w, Vs)
where A = e x p ( - ~ ) (N~), is given by
=
T,
(a.a)
and 13 = 1/kT. The average number of adsorbed molecules in V~,
Y~ ~.,{Aqt}N~Z~(N~,T, Vs) 0 In --s Na=0 (Na) = A 0------~-- ~ N~.,{Aqt}N~,Zs(Na, T, V,) N~=O
(3.4)
Likewise, the canonical partition function of N molecules in the gas phase can be expressed as t {qt(W) }N Z~ (N, T, V) QG(N, T, V) = ~.t
(3.5)
523 where ZG(N, T, V) = exp
/
-~[
/
~intr~(r + E E r 9
~i, r rj, ~j, ~j)] I I drid~idr
i < j
(3.6)
i=1
The pressure of the gas phase can be expressed in terms of the grand partition function as follows 1 p = ~ In Z G(#, T, V)
(3.7)
If we assume that the gas phase is ideal, then we have zIG(N,T,V) = {ZIG(1, T, V)} N, and the pressure is given by the simpler expression P=
qtAZIC(l' T'V) ~V
(3.8)
In the limit of Henry's law region, where A approaches zero and the gas phase is ideal, we have lim(Na) = A "qt" Z'(1,T,V~)= p"
,k~0
V, flZ~I:~-~VS)--~
(3.9)
Knowing that both Z~(1, T, V~)/V~ and zIG(l, T, V)/V are independent of the corresponZ'(1,T,V,)/V, is just the Henry's constant K'(T), or the initial ding volume, the term Vs/~zm0,T,V)/V slope of a (Na) vs. p plot. Furthermore, the well known Clausius-Clapeyron equation can be proved as 01nK'
=
1
_ _ ((r
_ (r
= (r
(r
(3.10)
+ kW
where (r is the ensemble average of both the zeolite-sorbate potential and the intra-molecule potential. Putting AqtZS(1, T, Vs) = K'p into the isotherm equation (3.4) we have E N---*-{K'P}N~ ZS(N~'T'V0 Na! [Zs(1,T,Vs)]~ (N~> = N~=o 1 Zs.(Na,T,Vsl E K~7~,.{K'P}N~'[z'({:T,V,)F"
(3.11)
Na=O
For the adsorption of non-rotating rigid molecules, the expression for the configuration integral Zs can be replaced by a simpler form N~
Zr(Na, T, Vs) = / exp{-~[ E
CZ(ri) + E E r i
i < j
rj)]} H dri
(3.12)
i=l
The adsorption of non-rotating rigid molecules often is taken as a first approximation in many theoretical treatments on adsorption. The more general situation can then be
524 built on the simple case by separating the configuration integral into that of the mass center (as a rigid non-rotating molecule) and the integral over other degrees of freedom. Z~(N~, T, Vs) = / exp[-fl Z
(I)intra(r i
"
Zr(Na' T, V~; @i, r
1 I d@idr
(3.13)
i
Let us assume that the Eulerian angles @i and internal degrees of freedom r can all be lumped by its effect to the host-guest potential. We then write ZS(N~, T, V~) = f Zr(N~, T, V~; e)-f(e)de
(3.14)
and (N,.) = f (Na) r (e)-g(e)de
(3.15)
where Zr(Na, T, Vs; e) is the configuration integral of a rigid molecule having an average host-guest potential of e, and (N~)r (e) is the average adsorption amount for the same rigid molecule. The f(e) and g(e) are two related but unknown distribution functions. In many discussions of heterogeneous surfaces, the energy heterogeneity has been stressed as a property of the solid. In the case of zeolite, the possibility of crystalline faults and foreign atoms may indeed introduce a distribution of adsorption energy. However, as we have demonstrated, energy heterogeneity may also come from the averaging over internal degrees of freedom, and therefore would be adsorbate dependent. This part of energy heterogeneity persists even in the case of a perfect zeolite crystal, and should not be disregarded when discussing adsorption data.
4. F I N I T E L A T T I C E M O D E L F O R C A G E T Y P E
ZEOLITES
The adsorption of gases in microporous zeolite has been applied in many industrial separation processes [23, 24]. Zeolites A, X and Y are among the most widely employed materials for such purposes. For example, both A and X zeolites have been used in the separation of air [25-27]. The Isosieve process [28] that separates small normal and branched paraffines in zeolite A is one of the most important adsorptive processes in petrochemical industry. The X and Y zeolites are also employed in the PARAX and SORBEX [29, 30] types of processes to separate the isomers of xylene and other akylbenzenes [31, 32]. The micropore voids in the above mentioned A, X and Y zeolites consist of three dimensionally arranged cages connected by windows. An upper limit can be assumed for the number of molecules that can be packed into each cage. The interaction of framework atoms and small admolecules may create distinct potential minima. Adsorbed molecules are confined in these local potential minima during the course of adsorption. Each local potential minimum can, thus, be considered as a discrete adsorption site. Van Tassel et al. [33, 34] proposed to consider the adsorption of small molecules in cage type zeolites as localized at nodes of a polyhedron. This picture was supported by their own molecular simulation studies and by many others [35-39].
525 Presumably, the maximum number of admolecules m, that a cage can accommodate, is the same as the number of local potential minima, or adsorption sites in the cage. If these sites are located at the nodes of a polyhedron, they may be considered as energetically identical. However, if the cage size is very large compared to that of the admolecule, one may have to include a second polyhedron of sites inside the first shell. For example, it has been observed in the simulation of Razmus and Hall [37] that oxygen and argon are initially adsorbed at the sites near the cage wall of A zeolite. At higher loadings, when most of the near wall sites are filled, the admolecules are forced to adsorb on the sites which are closer to the cage center. With the above picture in mind, a grand partition function can be written for a single zeolite cage. The zeolite-adsorbate interaction @Z(r) is assumed to concentrate in the vicinity of m identical local potential minima. Since these m local potential minima (sites) are defined by the zeolite-sorbate interaction @Z(r) alone, and are therefore independent of the inter-molecular force, we can write [33]
ZS(1, T) =/ca e-ZCz(r)dr~ m -
Vsite.
e -z
(4.1)
ge
where
f CZ(r)e-ZV"(r)dr (4.2)
((i)z) = site f e-#V'(r)dr
site
is the average zeolite-sorbate potential over an adsorption sitel and Vsite is the volume of that adsorption site.
4.1. A d s o r p t i o n of n o n - i n t e r a c t i n g hard c o r e m o l e c u l e s We will now consider the adsorption of rigid non-rotating molecules on m identical sites in the zeolite cage. The adsorbate-adsorbate interaction r is a function of the sorbate coordinates ri. We further assume that each site can only accommodate one admolecule. That means (I)inter(ri, rj) =
{oc ifi=j 0 otherwise
(4.1.1)
The configuration integral (3.2) for each cage can be simplified to ZS(n,T) _ 1 n, -~fr
=
exp{-#[E i
ml {ZS(1 T)/m}" n ! ( m - n)!
Cz (r~) +
E E r i<j
rj)]} 1-I dri i
(4.1.2)
The factor of m l / ( m - n ) l comes from the requirement that two admolecules do not occupy the same site. There are m sites when integrating over rl, but only ( m - 1) unoccupied sites when integrating over r2.
526 Putting the configuration integral ZS(n, T) into equation (3.11), we obtain a Langmuir-like isotherm [40]
m { ( 7y~ ) (%p)n} E n(n) = n=O 12
m{( )
E
n=O
~
7/
(K',)"
x --~
}
(4.1.3)
/
This isotherm is different from the general statistical model in that, only m molecules can be adsorbed.
4.2. Interacting elastic molecules The binomial factor in the above isotherm equation appeared because of the assumptions of localized adsorption and the hard core repulsion. The assumption of non-overlapping localized adsorption may be relaxed to some extent if one assumes an elastic molecular interaction. The binomial factor can also be modified if the volume of adsorption site is large enough to accommodate more than one admolecule. Conversely, a molecule may be slightly larger than the available volume of an adsorption site. In such a case, not only is the overlapping of admolecules impossible but the occupancy of the neighboring site may also be affected. The first few admolecules will avoid a proximate occupancy whenever possible. They will be forced to pack closely only under large external forces (chemical potential). For the adsorption of interacting elastic point molecules, the expression for the configuration integral of n sorbates in a unit cage takes the following form
Z'(n,T)_ 1 f~e exp{--fl[E (I)z(ri) + E E (i)inter(r,,9Fj)]) 1-I dri ., .-~ i i<j i = {Z~(I' T)/m}= " E
e-t~#iF"
(4.2.1)
Cn where the summation is running over all possible configurations of n identical adsorbate molecules on m sites. The n! term at the LHS, originally related to the permutation of n identical molecules, has now been absorbed into the configurational summation in the RHS. Let us define a new function 9/n as
Cn
_ i.,., Z'(n, T) e ~#'J = n!-{Z~(1 T)/m}"
(4.2.2)
to represent the configuration partition function with respect to all the possible arrangements of n admolecules on m sites. We have also assumed that admolecules, even if they interact with one another, always go to one of the m sites defined by the zeofite-adsorbate potential ~z. This will be true if the zeolite-adsorbate interaction is indeed concentrated at local minima.
527 With these assumptions, a general expression can now be written for the grand partition function of a m-site cage. From equations (3.3) and (3.11) we have m {1 (_~)n ~s(tt, T ) = Q~(T). E ~.
n=0
m ZS(n,T ) } [Z,(l:y)-]--m] n = Q~.(T). E
{ (_~)n
n=0
an
} (4.2.3)
and thus
~
= n=Om
n.
(e)n "~n} (4.2.4)
E {( Ktp)n } ]m "~'~n n--0
For hard core molecules, ~n reduces to the binomial factor in the Langmuir isotherm (4.1.3). As mentioned previously, there may be cases where an inner shell of adsorption sites has to be considered. If there are mz local potential minima with an average potential ( ~ / a n d m2 local potential minima with a higher average potential ( ~ / , we may write .-, "- = E nl = 0
(Kip) nl-
e-ar CI
(K;p) n2.
TM
n2 = 0
e-~*ir ''r.
e--,J Or176
(4.2.5)
C2
This is nothing but a two-patch model frequently employed in the analysis of adsorption on a heterogeneous surface. It is generally accepted that a mean-field approximation can be used to simplify the cross-patch interaction ~c~oss. -ij The derivation is in many instances similar to that for the adsorption of a mixture, but we will postpone the discussion concerning this problem to a later time.
4.3. Locating the adsorption sites in zeolite cavity If one accepts the assumption that adsorption sites in a zeolite cavity are arranged as the vertices of a polyhedron, then the number of sites and its arrangement can be determined as follows. First, there is a topological limitation on the possible vertices in a polyhedron. A list of possible site numbers can thus be generated. Furthermore, the symmetrical nature of zeolite structure poses some geometrical requirements on the site arrangement. For a chosen zeolite, those polyhedrons fail to meet the particular symmetry requirement. Afterwards, there should only be a handful of eligible polyhedrons site structures. The capacity of the zeolite cavity can then be estimated by the cage to molecular volume ratio. Then, the liquid density of the adsorbate can be used to estimate its molar volume. The largest polyhedron with fewer vertices than the cage capacity can be taken as the most probable site arrangement of the first shell. Any extra capacity can be considered as the filling of an inner shell of sites. The distances between adsorption sites are then calculated, by considering the geometry and assumed sizes of the admolecule. With these distances, sorbate-sorbate interactions can be then estimated. A more accurate determination of the site structure, as well as of the interactions between admolecules at different sites, should be achieved by molecular modelling. The calculation can be done with a canonical ensemble, which is less time consuming than that
528 using the grand canonical ensemble, usually required for the evaluation of adsorption isotherm. When the site structure and the inter-molecular potential at different locations have been obtained, the problem of modelling the adsorption in cage type zeolites has been reduced to finding the configuration partition function ftn for molecules arranged on these sites. In other words, the site structure and the inter-molecular interactions serve to define the configuration partition function f~n needed in equation (4.2.4).
4.4. Effects of i n t e r - m o l e c u l a r i n t e r a c t i o n s The cavities in A, X and Y type zeolites are only about 10 to 15 angstroms in size. Thus inside the cavity, admolecules are never very far away from one another. Therefore, the nearest neighbor interaction may be either repulsive or attractive. If the van der Waals diameter of an admolecule is slightly larger than the nearest neighbor distance between sites, a repulsive force will exist. This is similar to the exclusion volume effect considered by Ruthven [20]. His formulation may be translated to our notation as follows
~R--(V/~--n)n (n~).
(4.4.1)
where v is the cavity volume and /3 is the effectivemolecular volume of sorbate. The
capacity of the cavity is given by m < v/~. Davis [41], on the other hand, proposed a similar model with an extra factor to account for the possible van der Waals attraction force en. His model can also be converted to an expression of f~n as follows f~D = (m + 1 -- n)ne -~n/mkT
(4.4.2)
In the recent work of Van Tassel et al. [33], the attraction force was calculated by a numerical summation of assumed nearest neighbor attractions over all configurations of n admolecules on m sites. For a larger molecule such as Xe in NaA, this assumed attractive interaction was turned off when all the nearest neighbors of a site are occupied. Thus an exclusion volume effect has been implicitly accounted for, at least partially. However, instead of adjusting the transition from attractive to repulsive interaction to model the system better, they have stepped backward to include an overall volume reduction factor as Ruthven and Davis did. As a consequence, the physical meaning of their model became somewhat confusing. Finally, they still have to keep a few adjustable parameters to fit the experimental isotherm. In our notation, the final expression which they arrived at can be written as follows f~v= { m - m t a n h [b(n - m/2)] } n 1 -- tanh [b(1 - m/2)] Z
e_r (4.4.3)
C
where the parameter b is an adjustable parameter, assigned to be 0.35 for the case of Xenon in zeolite NaA. For this case, they have used m = 12 and a nearest neighbor interaction between Xe molecules em = 211. k taken directly from the Lennard-Jones parameters. These various models discussed above, are different ways to model the configuration partition function fin of n admolecules on m sites under the influence of molecular interactions. Without interactions, the partition function ftn reduces to a binomial factor.
529 Since attractive interaction generally dominates at a small value of n, ~n should be larger than the corresponding binomial factor. On the other hand, as n approaches m, repulsive interactions become more important, and ~n will be smaller than the binomial factor at large n. In any case, equation (4.2.4) should be able to fit the adsorption isotherm of any finite site system if one considers ~n as fitting parameters. However, using m parameters to fit an isotherm seems redundant. A model with fewer parameters to describe the arrangement of admolecules on m vertices polyhedron under inter-molecular forces will be useful. If we consider a pair of neighboring admolecules as forming a bond, then the arrangement of admolecules can be classified as forming some single bonds, double bonds and triple bonds etc. If one associates the interaction energies to the forming of these pseudo-bonds, one can then write the summation in ~n over the number of different pseudo-bonds. Let us consider the sites located at the m vertices of a u-valence polyhedron. Let Pn,~ be the probability that an occupied site has x occupied neighbors when randomly assigning n molecules to these vertices. This probability can be derived exactly for any polyhedron
[42]
We further assign a bond energy x-g,~ to each x-bond. The configuration partition function f~,~ can now be written as follows ~ n = ( mP) ~n ~ ' e - ~ / k 'T n
(4.4.5)
~--0
Since ~0 = 0, we are left with u parameters g~ to describe the configuration partition function fin. Furthermore, as more admolecules are clustering together, the bond will be weaker if it is attractive, and stronger if it is repulsive. Therefore, we expect to have ~ + ~ > ~,, for n >_ i.
So far we have considered that all sites in the cage are identical, and treated the energy heterogeneity of packing molecules into the zeolite cage as the result of molecular interactions. However, another possibility to model the evolution of the adsorption system is to treat the sites as energetically heterogeneous without invoking explicitly the inter-molecular forces. This will be the approach taken in the following sections. However, before we do that, let us first consider the heat of adsorption related to the exclusion volume effect observed in a finite cage. 4.5.
H e a t of a d s o r p t i o n effects related to exclusion v o l u m e
interactions It is well known that heat effects accompanying adsorption at gas/solid interfaces are much more sensitive to the nature of an investigated adsorption system than adsorption isotherms. Experimental adsorption isotherms can, frequently, be fitted almost equally well by isotherm equations corresponding to different adsorption models. On the contrary, a simultaneous fit of accompanying heats of adsorption is usually difficult. Therefore the
530 studies of related heat effects create more chance for a proper description of an adsorption system. The "excluded volume interaction" model has been known for a long time, but its applicability to an adsorption system was not verified by the studies of the predicted heat effects. An extensive study of that kind was done first by Rudzinski et al. [43] Below we present their results. If only the reduction in the free volume of the cage is considered, one may write, as a rough approximation {Zs(1, T)}n
=
1 -- n
(4.5.1)
where ~ is the effective molecular volume of the sorbate which is given, approximately, by the van der Waals constant "b" and the small v is used for one cavity volume. This leads to the isotherm equation proposed by Ruthven [20, 44] n-
K'p + (K'p) 2 (1 - 2~) 2 + . . - + 1 + K'p +
1 ~(K'p) 2 (1
-
2~) 2
(re_l), (K'p) ~ (1 -
1 + - - . + ~.,(K'p) m (1 -
m~)
(4.5.2)
TM
where m < ~. This equation has been shown to provide a useful representation of the isotherms for several non-polar sorbates in various cationic forms of zeolite A [45-47]. It is somewhat more logical to write the Henry constant also as the product of an ideal Henry constant for a point molecule (K*) and a free volume reduction factor [20]
(4.5.3)
K ' = K* ( 1 - ~ ) This leads to a slightly modified form of isotherm equation (4.5.2) K'p + (K'p) 2 n-"
~_-U~-]
[1-25l 2
1 + K'p + 89
+"" +
(m-l)' (K'p)m
[1-m~l TM 1-~J
2 [~-2~] 2
L~---~~J + " " + ~.
(4.5.4)
m
x-(
Eqns (4.5.2) and (4.5.4) have been quite frequently used to correlate the experimental adsorption isotherms in adsorption in zeolites. It was, however, intriguing that the experimental heats of adsorption were not interpreted in terms of the appropriate theoretical expression corresponding to Eqn (4.5.2) or (4.5.4). (One of the few exceptions is Figure 4.1 in the monograph by Ruthven [20]). The equation for the isosteric heat of adsorption Q~, corresponding to Eqns (4.5.2) and (4.5.4) developed by using the relation Qst = - k k-~--], for the case of Eqn (4.5.2), takes the form [43]
=
[
]
QH(1 - n ) + i:2 ~ (g'P)i'10-1)! QH (1 - i -~v)i+ i ( 1 - i ~ ) '-1" (1 - ~)Q(V) ( i - n) Qst --
Ill
(1 - n ) + ~
i=2
(K'p)i-1 (1 --i~) (i-~)!
i ( i - n)
(4.5.5)
531 where QH = k dlnK' d-(1)T-)
Q(V) _
-k
-
d(fl/v)
(4.5.6)
(1 - ~ ) d ( 1 / T )
whereas in the case of the slightly more accurate Eqn (4.5.4), we arrive at the following expression m
Q(v) (1 - n ) + E (K'P)~-~ i=2 (i-1)!
Qst -- Q* +
( 1-i~ ~ i
~, 1-~- ,]
1-i
1-~
In (1 -- n) + E (K'p)i-I / ' l - i ~ i i=2 (i-i): kl_-~v ] ( i - n)
)]
(i-n)
} (4.5.7)
where
k
O(Zlv)
Q(V) = kd_~_in ( 1 - vfl__)= _ _ ~ 9 1 - z__ 0(l/T) v
(4.5.8)
Now let us remark that QH being the heat of adsorption at the zero-surface coverage (in Henry's limit), is, in the present improved model the following sum QH = Q. + Q(V)
Q* = k d In K* d~
(4.529)
where Q* is the non configurational contribution coming from the gas-solid interactions, and the contribution Q(V) due to changing molecular volume of the adsorbed molecules 9 While fitting Eqn (4.5.2) to his experimental isotherm for N2 adsorbed in the zeolite 5A, Ruthven found the parameters collected here in Table 1 [48]. Table 1 The values of parameters found by Ruthven [48] by fitting his isotherm to the experimental isotherm of nitrogen adsorption on the molecular sieve 5A. T 205.5 218.0 232.0 252.0 255.0 273.0 298.0
K' (molecule/cavity.torr) 0.062 0.034 0.015 0.007 0.008 0.0037 0.0016
!
fl(A 3)
10 10 10 10 10 10 10
77 77 77 77 77 77 77
532 Miller et al. [49] repeated these measurements in the same temperature range and applying the Ruthven's Eqn (4.5.4) found somewhat different best-fit values of the parameters K', and ~, which are collected in Table 2. Table 2
The values of the parameters K' and ~ obtained by Miller et al. [49] for nitrogen adsorption in molecular sieve hA. The values of QH and of Q(V) calculated by us from the data presented in this table were 16.91 kJ/mole and 0.166 kJ/mole respectively. T
K' (molecule/cavity. torr) 32.50 10.96 1.40
203.15 233.15 297.15
~
~()t 3)
14 12 10
55.4 64.6 77.6
We accepted the values of the parameters collected in Table 2 to calculate the parameters01n K '/ 0 y1, and 0~~/0y1 appearing in Eqns (4.5.5) and (4.5.7). Using these parameters we calculated the theoretical heats of adsorption from Eqns (4.5.5) and (4.5.7). Figure 1 shows the comparison of the theoretical heats of adsorption with the pseudoexperimental values found by Miller et al. [49] by taking appropriate temperature derivative of their three experimental isotherms measured at three different temperatures. (The details of that procedure were not reported). --o
35.0. 30.0,
25.0
~
20.0
"
....~
.
.
.
15.0:.t I0.0
. . . . . . . . .
0.0
I . . . . . . . . .
2.0
I
. . . . . . . . .
I
....
4.0 6.0 molecule/cavity
Figure 1: Prediction of the isoteric heat of adsorption at 233.15 K; the strongly dashed line is based on the Ruthven's Eqn (4.5.4), whereas the slightly dashed line is obtained from Eqn (4.5.2). The heat parameters were calculated from the data by Miller et al. [49]: QH = 16.91 kJ/mole, Q(v) = 0.166 kJ/mole. The circles denote the (pseudo) experimental points obtainedby Miller et al.
533
120.0. 80.0 40.0 0.0
.
-40.0! -80.0
iii llllll|
0.0
llllllllU|InllUlllU|
2.0
4.0
I l l lUUllU| l U U l l l l U U '
6.0
8.0
10.0
molecule/cavity
Figure 2: The model isosteric heats of adsorption curves calculated from Eqn (4.5.7). The parameters K' and ~ are equal to 10.96 (molecule/cavity.bar) and 12 respectively, QH = 31.85 k J/mole, whereas the parameter Q(V) was for the curves from the top to the bottom equal to: 0.181, 0.091,-0.091,-0.181 kJ/mole. y
32.0 0
"'':"
-~
".,
28.0 24.0 20.0 16.0 12.0
......... , ........ ,,,, ....... , ......... , .........
0.0
2.0
4.0 6.0 8.0 10.0 molecule/cavity
Figure 3: The model isosteric heats of adsorption curves calculated from Eqn (4.5.7), and shown as a function of z_. The parameter K' is equal to 10.96 (molecule/cavity-bar), Q(V) = -0.181 kJ/mole, QH = 31.85 kJ/mole. The ~ values for the curves from the top to the bottom are equal to: 15, 12, 5, 4. v
534 One can see that the theoretical heats of adsorption do not even predict the observed trend in the experimental data. That means that the excluded volume interactions assumed when developing Eqn (4.5.4) is not the main factor governing the adsorption of N2 inside the molecular sieve 5A. Figures 2-5 present some model calculations based on Eqn (4.5.5). The theoretical heats of adsorption presented there are not commonly found in experiment [43]. That means, that the concept of the excluded volume interactions cannot be longer considered as a generally promising one while developing realistic- yet reasonably simple equations for adsorption in zeolites. That conclusion could be drawn only by considering the behaviour of the related heats of adsorption which constitue a much more rigorous criterion for the validity of a certain theoretical approach. Provided even that the excluded volume interactions might be dominant in some systems, they should be calculated more precisely. That problem has been known for a long time in the theories of gas adsorption on planar solid surfaces [45]. In the case of adsorption of hard discs, the Scaled Particle Theory shows that the maximum dense hexagonal packing of hard discs leaves still about forty percent of the surface unoccupied. A similar situation will exist in three dimensional systems. Thus, even a crude consideration leads to the conclusion that the "imperfection" factor [ 1--~]1-~ should be represented better by the following one ~
1 -
0.5
(4.5.10/
That obvious conclusion explains the apparently paradoxical relations between the values for Oxygen, Argon, and Nitrogen, reported in Table 4 of the work by Miller et al. [49]. The larger 02 molecule is characterized by ~ values roughly one half of those found for the smaller N2 molecule. As we have already shown, the adsorption of nitrogen in the zeolite 5A cannot be described by equation (4.5.4). But then, in view of the constant heat of adsorption of 02 and Ar adsorbed in the zeolite 5A, there are good reasons to believe that Ruthven's equation (4.5.4) might be applicable now. The ~-values reported by Miller et al. [49] were obtained by fitting Ruthven's equation to the experimental adsorption isotherms. However, according to our equation (4.5.10), these were, in fact, r/~ values estimated in that way. In other words, the true B values for 02 and Ar molecules are roughly twice as large. And then, they are comparable to ~ values of N2 molecule. Also, the maximum number of the 02 and Ar molecules that can be accomodated by one cavity is not the paradoxical value 22-26, but about 10, which is closer to the maximum values observed on the experimental adsorption isotherms. And now, a next important observation is to be made. Namely, the estimated in that way fl-values of nitrogen are comparable to those estimated from the molar volumes of liquid nitrogen. This, on the other hand, would advocate for treating the nitrogen adsorption as a volume filling process. That apparent contradiction might be explained as follows.
535
40.0 20.0 0 "~ ~ 0.0 -
: :
I t
-20.0
\ \
, !
-:=
I I
-40.0 I
-60.0
t
-80.0
i ii
ii
i iii
0.0
| iiiiii1'1
4.0
ii
iiiiii~11|
8.0
ii
iiii111
12.0
16.0
molecule/cavity Figure 4" The model isosteric heats of adsorption curves calculated from Eqn (4.5.7), shown for slightly higher K' parameter than that given by Ruthven, K' = 33.12 (molecule/cavity.bar), Q(V) = -0.181 kJ/mole, QsHt = 31.85 kJ/mole. The ~ values for the curves, from the top to the bottom were: 15, 10, 4.
0
40.0
20.0 CY
-
: -
"
~
\
~
0.0
\
',
\
: -
i t
-20.0--
'
\ \
-40.0-
-60.0
.........
0.0
, .........
4.0
,,,,
8.0
......
12.0
molecule/cavity Figure 5: The model isosteric heats of adsorption curves calculated from Eqn (4.5.7) shown as a function of ~, for slightly lower K' parameter than that given by Ruthven, K' = 4.482 (molecule/cavity.bar), Q(V) = -0.181 kJ/mole, QH = 31.85 kJ/mole. The ~ values for the curves, from the top to the bottom were: 15, 10, 4.
536 For the chemical (polar) character of N2 molecule, its gas-solid interaction potential will exhibit a stronger variation inside a cavity than those of 02 and Ar molecules. That strong variation of the gas-solid potential of N2 molecules moving from one point to another inside the zeolite cavity of 5A will probably be the main factor governing the adsorption of N2. The adsorbed N2 molecule will spend most of its time around the local minima of that gas-solid potential which we call - - the adsorption sites. So, this is a localized-like adsorption on the adsorption sites created by an induced zeolite energetic heterogeneity. The first adsorbed molecule occupies the deepest gas-cavity potential minimum. The second molecule entering the zeolite cavity is adsorbed on the most favourable place created by the zeolite cavity with the "built on" first adsorbed molecule. So, this is the kind of gradually induced adsorption potential. Until adsorption runs in an ideally "stepwise" fashion, one cannot distinguish between the dispersion of gas-solid potential minima, and that of the induced adsorption minima. This may explain a remarkable success of Miller et al. [49] applying the Langmuir equation along with the concept of energetic heterogeneity of adsorption sites, to describe the adsorption in zeolites. We consider it in the next section.
4.6. Application of Langmuir model along with the concept of site adsorption energy distribution There is still another possible way to deal with adsorption in zeolites. This way is applicable to all types of zeolites, provided that the zeolite wall-adsorbate molecule potential map exhibits sharp local minima. In such systems a molecule intruding zeolite will be "trapped" in these local minima, and spend there most of the time. In other words, we have to deal with localized adsorption. While considering thermodynamic limit for the number m of these local minima, and neglecting interactions between two molecules occupying neighbouring sites, one arrives at the Langmuir equation describing the fractional coverage of adsorption sites. Thus, the adsorption on a heterogeneous surface is considered in terms of localized adsorption. The different adsorption sites are simply the different local minima in the gas-solid potential function, which "trap" the adsorbed molecules. These different local minima are identified by the value of the adsorption potential at these minima, taken with the reverse sign, and called "the adsorption energy" ~. The fractional coverage of the adsorption sites characterized by a certain value of the adsorption energy, 0(p, T, e), is usually represented by the Langmuir isotherm 0(e,p,T) =
Kpexp {k-~} 1 +Kpexp{~}
(4.6.1)
Then, it is assumed that the experimentally measured "total" adsorption isotherm 0t(p, T), is represented by the following average [1, 2] 0t(p, T) = / 0(e, p, T)x(e)de f~
(4.6.2)
537 where X(e) is the differential distribution of the number of the adsorption sites among the corresponding values of e, normalized to unity, i.e.
f X(e)de = 1
(4.6.3)
ft
and fl is the physical domain of e, for a given adsorbent-adsorbate system. The integration in Eqn (4.6.2) can be performed easily by using the Rudzinski--Jagiello approach [1] 0t(p, T) = - E
~~l(kT)l '~ [ 01X ]
1=0
(4.6.4) e-ec(p,T)
where
X(e) = f X(e)de
(4.6.5)
Cl's are the temperature-dependent coefficients tlet
~(1 +d et) t 2
C1 =
(4.6.6)
kT
where el and em are the lowest and the maximum adsorption energies, and the function ec(p, T) is found from the condition 0e2 ) ~=~c = 0
(4.6.7)
When T ~ 0, all the terms under the sum in Eqn (4.6.4) vanish except for the first leading one. It is also true when the variance of X(e) is considerably larger than that of the derivative (~~ ) . Then 8t(p,T) = -X(ec)
(4.6.8)
The features of the adsorption model are coded now in the function ec(p, T). When 0(e,p,T) is the Langmuir equation (4.6.1), the condition (4.6.2) is fulfilled when 1 Then 0(~ = ~ ) = ~. er = - k T l n K p
(4.6.9)
Let x(e) be the following gaussian-like adsorption energy distribution
=
[1 + exp { ~ _ } ] 2
(4.6.10)
538
=
centered at e X(e). Then
e~ the heterogeneity parameter c being proportional to the variance of
__ [1
(4.6.11)
so, the adsorption isotherm St(p, T) reads kT
St(p, T ) =
[Kp exp {~T}] 7"kT 1 + [Kpexp{ ~~
(4.6.12)
The numerical model calculations [1] showed that for the function X(e)in Eqn (4.6.10), Eqn (4.6.8) constitues a good approximation to St(p, T), until kT < 0.9. Eqn (4.6.12) c is just the well-known Sips', (called also Langmuir-Freundlich), isotherm which is used frequently in the theories of the dynamical gas separation by adsorption processes, where zeolites are so commonly used as adsorbents. Miller et al. [49] applied successfully Eqn (4.6.12) to correlate their experimental data for N2 adsorption in the molecular sieve 5A. Table 3 collects the best-fit values obtained by Miller et al. by fitting Eqn (4.6.12) to their experimental data. Table 3
The values of the parameters found by Miller et al. [49] by fitting the Langmuir-Freundlich isotherm (4.6.12) to their experimental isotherms of nitrogen adsorption in the molecular sieve 5A. '
~T
T(K)
K exp ~ (molecule/cavity-bar)
Z-
203.15 233.15 297.15
1.053 0.617 0.120
0.549 0.653 0.826
Of course it seems natural to assume that the adsorption energy distribution x(e) may not be a symmetrical function in general. Rozwadowski and co-workers [50] applied successfully the following function to represent zeolite heterogeneity for adsorption of small molecules in a variety of zeolites X(e)=r(e-el)r-1
{
[e-
(Elr exp - 'E
r} (4.6.13)
Here E is the variance of x(e), el is the minimum value of e, and the parameter r governs the symmetry of X(e). Thus, x(e) in Eqn (4.6.13) is a gaussian-like function, pretty symmetrical for r = 3, right hand widened for r < 3, and a left hand widened for r > 3. For the function X(e) given in Eqn (4.6.13), 8t takes the following form
8t(p,T)=exp{-[~-In-~] r}
(4.6.14)
539 s lnp ~ = - I n K - k--T
(4.6.15)
which is simply the well-known Dubinin-Astakhov isotherm equation. The interactions between the adsorbed molecules can be easily taken into account by replacing the Langmuir isotherm by its more general f o r m - the Bragg-Williams isotherm. And, because different adsorption sites are located in the same cavity, the random topography model is to be accepted. Then, instead of ec given in Eqn (4.6.9), the following more general expression for ec(p, T ) i s to be used [1] (4.6.16)
er = - k T ln(Kp) - wOt
where the interaction parameter a; is the product of the number of the nearest neighbours adsorption sites and of the interaction energy between two molecules adsorbed on two neighbouring adsorption sites. The generalized Langmuir-Freundlich isotherm takes then the following form kT -F" kT
"
1 + [Kpexp {e0+,o0,kT}] 7In the case of the Dubinin-Astakhov isotherm, its generalized form taking into account the interactions between adsorbed molecules reads 0t(p,T) = exp
-
In P
got
(4.6.18)
Equation (4.6.14) was first used to correlate the experimental isotherms of adsorption in activated carbons. While fitting the isotherm to the experimental data p0 was traditionally taken as the saturated vapour pressure of adsorbate at a temperature T, or its extrapolated value for supercritical temperatures. In the case of adsorption in activated carbons there is some justification for such a choice of pO, which is not valid any longer in the case of adsorption in zeolites. Here p0 is to be treated simply as a best-fit parameter. A convenient way to correlate the experimental data by using Eqn (4.6.18) is to use it in its logarithmic form In 0t = In g
= -
In
p
(4.6.19)
kTM
where Nt is the amount adsorbed, expressed in certain units, and M is the maximum adsorbed amount expressed in the same units. By chosing suitably the parameters: M, p0, w, r, one should get a linear plot of In N_x vs " the variable r[lnpO _ k _ r~-]' M p
"
Usually the term ~ N, within the square bracket is a correction term compared to in po. Thus, in a first step one can find approximate values of p0, r, M, by making p
the plot In ~ v s. [ln P-2-]r linear. That first step should be done for the region of small adsorbate pressures,~ whererJthe correction term is expected to be small. In a next step the
540 region of higher adsorbate pressures is to be taken into analysis too, and by using the previously estimated values of the parameters M, p0 r, one has to make the plot in ~M p - k-~ M
vs
linear in the whole region of pressures.
Similarly, in the case of the generalized Langmuir-Freundlich isotherm (4.6.17), the following linear representation is convenient for fitting the experimental adsorption isotherms In
Nt/M _ k._T_TIn K ~ + ~kT In p + w - - -Nt 1 -- 1Nt/M c c c M
(4.6.20)
where K~ - Kexp ~
(4.6.21)
Here only two parameters: M and ~ are to be found by computer, while making linear Nt/M VS. - ~ [ln p + ~y~-j. w Nt ] In a first step one can try to make linear the plot the plot In 1-Nt/M Nt/M
In I:N,/M vs. In p. Although for r = 3, the function (4.6.13) will look pretty symmetric, its behaviour is still different from that observed in the function (4.6.10). It means, even if r = 3, the Dubinin-Astakhov isotherm will not simulate exactly the behaviour of the Sips' (Langmuir-Freundlich) isotherm, and vice versa. As for r = 3, the largest differences between the functions (4.6.13) and (4.6.10) will be observed in the region of low adsorption energies, i.e. the largest differences between Dubinin-Astakhov and Langmuir-Freundlich isotherms will be observed in the region of high surface coverages. This can be seen best in the behaviour of the related heat of adsorption curves. This is because the heats of adsorption are much more sensitive to the nature of an adsorption system than adsorption isotherms. For the Langmuir-Freundlich isotherm (4.6.17), the related equation for the heat of adsorption reads Qst = Q O + w0t + c In
1 - 0t Ot
(4.6.22)
where Qs~
=
kdlnK ~ ~ d~-
d y1
+
e0
(4.6.23)
Figure 6 shows the comparison between the pseudoexperimental heats of adsorption of N2 in the zeolite 5A found by Miller et al. [49], and the theoretical heat of adsorption calculated from Eqn (4.6.22) by using the parameters collected in Table 3. Figure 6 would advocate strongly for treating the variations in the gas-solid potential function as the main factor governing the adsorption of N2 in the molecular sieve 5A.
541
..
_a
35.0 30.0
25.0 20.0~
"-<_'
15.01 10.0
9 . . . . .
~
,,, ...... , ...... , , , , . . . . . . . . . , .... 0.0 2.0 4.0 6.0
molecule/cavity Figure 6: The comparison between the pseudoexperimental heats of adsorption of N2 in the zeolite 5A determined by Miller et al. [49] (eee), and the theoretical heat of adsorption (--) calculated from Eqns (4.6.22) and (4.6.23) by using the parameters collected in Table 3. The derivative 0(1/'r) 0InK~ was determined from the values K~ presented in Table 3, so the Qs~ defined in Eqn (4.6.23) established in this way is 16.68 kJ/mole. In the case of the generalized Dubinin-Astakhov isotherm (4.6.18), Qst is given by
Qst
+ 0t +E [ln
r
4024
where the non-configurational contribution Qlst is now given by
Qlst = - k d In p~ _- k d in1K + el d~
dy
(4.6.25)
However, one serious disadvantage of using both Langmuir-Freundlich, or Dubinin-Astakhov equation is that they do not reduce to Henry's law when p ---, 0. In the next section, we show how the concept of the "induced heterogeneity" may lead to still another "statistical theory" of adsorption in zeolites, predicting Henry's region to appear when p ~ 0.
4.7. An "induced heterogeneity" m o d e l for l o c a l i z e d - l i k e adsorption in cage type zeolites Our concept of "induced heterogeneity" followed from the observations of the temperature dependence of the parameters M, K ~ kT/c or M, p0, kT/E, r found by computer while fittinig Eqn (4.6.20) or (4.6.19) to the experimental adsorption isotherms. Table 4 collects the values of the parameters M, KO, kT/c obtained when we fitted Eqn (4.6.20) to the adsorption isotherms of nitrogen in the zeolite 10X, reported by Nolan et al. [51]. Figure 7 shows that linear fitting for the three investigated temperatures: 172.04 K, 227.6 K, and 273.15 K.
542 Table 4
The values of the parameters M, K ~ kT/c found by fitting Eqn (4.5.20) to the isotherms of nitrogen adsorption in the zeolite 10X, reported by Nolan et al. [51]. After checking a number of reasonable error functions, it was found, that only the following ERRORLIN error function had always a minimum ERRORLIN = ~
Temperature (K) 172.04 227.60 273.15
M (cm a, STP/g) 162.92 143.19 41.70
1 (theori - expi)2 9 (expl - expl)2
K~ (mmHg) -1 7.7.10 -4 1.0.10 -4 3.8.10 -4
kT/c
ERRORLIN
0.467 0.600 0.874
7.9.10 -4 8.1.10 -4 1.1-10 -a
,
,
Looking at the Table 4, one can see that the estimated number (amount) of adsorption sites M decreases with temperature. Next the estimated kT/c values do not increase linearly with temperature. This observation advocates strongly for treating these parameters as not related solely to a temperature independent gas/solid potential function varying from one point to another within the zeolite cage.
1.0
-
-1.0 -'9q
-2.0 -3.0 -4.0
-5.0 -6.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 In(p) [mm Hg] Nt/M
Figure 7: The agreement between the theoretical ( - - ) linear functions In x-N,/M vs. In p, calculated from Eqn (4.6.20) by using the parameters collected in Table 4, with the experimental ones for the three different temperatures: 172.04 K (***), 227.6 K (ooo), Nt/M and 273.15 K (AAA). The " e x perimental ~ functions In 1-Nt/M were calculated by using M-values collected in Table 4.
543 With rising temperature, less strongly adsorbing sites will not be capable of "trapping" adsorbate molecules and keeping them localized. The admolecules being in a mobile state will contribute less to the total adsorption effect because their average potential energy will be smaller than in the case when they residue at the local minima of the gas/solid potential function. When the cavity is large and adsorbate molecules are small, the first adsorbed molecules go into the temperature independent gas/solid potential minima. The next adsorbed molecules may go into local potential minima induced by the combined effect of gas-solid and gas-gas interactions with already adsorbed molecules. This picture has led Rudzinski and co-workers [52] to the concept of localized adsorption in a 3-dimensional lattice of adsorption sites "induced" inside a zeolite cage. Below, we are going to present the theoretical approach proposed recently by Rudzinski et al. [52] Theoretical studies of the filling of lattices composed of different lattice sites show, that there are two conditions under which Eqn (4.6.8) is applicable: 1. When the dispersion of gas-solid interactions is small, Eqn (4.6.8) applies only at low temperatures. 2. Eqn (4.6.8) is applicable also at moderate and higher temperatures if the dispersion of the gas-solid interactions is large. Eqn (4.6.8) is equivalent to the assumption that the filling of adsorption sites proceeds in an ideally stepwise fashion in the sequence toward decreasing adsorption energies. In a process of a perfectly gradual filling of the induced adsorption sites, at a certain pressure p, the adsorption sites are being filled, the adsorption energy of which is equal to ec, defined in Eqn (4.6.9). This is because the situation is fully analogous to the perfectly stepwise (gradual) filling of a lattice of non-induced heterogeneous sites, described by the step function 0c(e, p, T) { 0 1
O=(e, P, T)
for c < e'=(p,T) for, > ,'r T)
(4.7.1)
Then from the variational principle stating that 8r p, T) should approximate best Langmuir isotherm, one arrives at the definition of de = er defined in Eqn (4.6.9). The overall adsorption isotherm/gt is then given by the condition oo
St(p, T) = ] x(,)d, =
(4.7.2)
I v
~r
To be correct one has to replace +oo by a finite maximum value of e, era- In the case of strongly heterogeneous surfaces em is usually large, so replacing em by +c~ does not affect much 0t(p, T). And then for physically reasonable functions X(e), like these in Eqn (4.6.10) or Eqn (4.6.13), A'(+oo) = 0, i.e., we arrive again at Eqn (4.6.8). Thus, when the adsorption process is perfectly gradual (stepwise), we have the one-to--one dependence between ~ and p, and a one-to-one dependence between e~ and 0t, the particular form of which depends on the form of X(e). Let us assume for the moment, that
544 w = 0 i.e. neglect the interactions between the adsorbed molecules. Then, for the function X(e) defined in Eqn (4.6.10), the corresponding Eqn (4.6.11) yields
1 -0t
er - eo = c In 0----~
(4.7.3)
As we are neglecting for a moment attractive interactions between the adsorbed molecules, we compare Eqn (4.7.3) with the Eqn (4.6.22) for the heat of adsorption in which w = 0. It means, that in the process of the gradual filling Qst changes with the surface coverage in the same way as er Thus, at every step of adsorption process, (at every surface coverage), the observed heat of adsorption will be equal to the adsorption energy of the sites which are being filled at this coverage, within a constant independent of surface coverage. Of course, the process of filling will never be perfectly gradual, and at high temperatures, in particular. Nevertheless, accepting this approximation makes it possible to draw interesting conclusions about the change of the energy of the gradually induced sites, from the behaviour of the heat of adsorption. As our adsorption system is composed of many subsystems, (cavities), being only in thermal and material contact, the one-to-one dependence between er and 0t must exist also at a level of one cavity. The experimentally monitored one-to-one dependence (4.7.3) represents a certain statistical interrelation averaged over all the cavities in the system. To a certain approximation we may assume this statistical interrelation to be close to the local one, existing at a one cavity level. So, let ei denote the adsorption energy of the adsorption site induced when the ( j - 1) molecules are already adsorbed. Then er should be the function ec (~), averaged over all cavities. Thus, to a good approximation, we can accept the following relation cj - c o =
cln
1-• • m
(4.7.4)
m
Assuming a perfectly gradual filling of the induced adsorption sites, we assume implicitly that their number is equal to the number of the adsorbed molecules. So, the configurational degeneracy factor in Zs will be equal to unity, and we can write
(4.7.5)
ZS(s,m,T) = exp { ~ (ei - e~ } j=~ kT Thus ASQ~(s, v, T) = p*(K~ s exp
{s
~
In
1
}
(4.7.6)
j=l where QS(s, v, T) is the canonical partition function for s molecules in one cage, and where K ~ = K' exp
~
(4.7.7)
545 We replace now symbol n by s to denote the number of molecules in one cavity in order to emphasize that instead of mobile adsorption we consider now localized adsorption on s sites. Now it is the time to realize that the relation (4.7.5) corresponds to the assumption that ~ E ( - o c , +co), because X(C)in Eqn (4.6.10)is defined in this interval. However, for obvious physical reasons there must exist a maximum value of ~ on a heterogeneous surface and inside zeolite cavity (channel), Cm, and a minimum value cl. The assumption that ~ varies in the interval ( - o c , +oc) is accepted frequently in the theories of adsorption on heterogeneous surfaces for the purpose of mathematical convenience. Assuming such non-physical energy limits does not have a serious negative impact on the behaviour of the calculated theoretical isotherm until we do not consider the adsorption at very small and very high surface coverages, i.e. when p ~ 0 o r Ot - + 1. The most striking consequence of assuming that Cm = +OC is that the Langmuir-Freundlich or the Dubinin-Astakhov isotherm do not reduce to Henry's isotherm when p ---, 0. In the case of the corresponding theoretical heats of adsorption (4.6.22), (4.6.24) the assumption that ~,~ = +oc results into infinite values of Qst, when ~t --"> 0 (and to - o c when Ot --'+ ])" Meanwhile, the experimental adsorption isotherms in zeolites have the Henry region as a rule, and experimentally measured heats of adsorption become constant in that region. This suggests the following modification to be made in Eqn (4.7.6) ,kSQS(s,m, T) = p~KH(K~ ~-1 exp
~
in ~
j=2
j/m
for s < m -
1
(4.7.8)
where K H is the Henry's constant KS=
K'exp ~
(4.7.9)
which can be easily determined experimentally. In the case of the adsorption energy distribution (4.6.10) some further modification is to be made, to eliminate the situation that Zs = 0 for s = j = m. That situation is due to the fact that el = - o c is assumed in the function (4.6.10). For s = m, AmQ~ should be written as follows )~mQm(m,m, T) = pmKH(K~
~-~ In ~1 -j/m}j/m
exp
(4.7.10)
k j=2
where K TM is another temperature-dependent constant, which should be found by fitting the isotherm equation to the experimental data. However, as it can be deduced from Eqn (4.7.10), the values of ~SQS will decrease quickly with s, so, one can neglect safely the difference between zero and the true value )~mQm. Thus, Eqn (4.7.8) can formally be written in the form c_s kT
j/m /
(4.7.11)
546 Now, let us consider the case when, in the region of mediate surface coverages, the experimental adsorption isotherm can be correlated better by the Dubinin-Astakhov equation _ C0 r }
0t = exp { - ( e r
E
)
(4.7.12)
or 1
so, we assume now that 1_
ej = e~ + E In
(4.7.14)
Then ASQS(s' m' T) = p*KH(K~
exp
E k-Tj=2
In
;
(4.7.15)
So, finally, we arrive at the following equations for the adsorption isotherm. For the gaussian-like dispersion (4.6.10) of the adsorption energy of the induced adsorption sites we have m
E psKH(K~ s-is s=l
?(
1--j/m~
=2
j/m ]
r
n=
r
(4.7.16)
(1--j/m
1 + E psKH(K~ ~-1 fi s=1 =2 \ j/~
whereas for the adsorption energy dispersion (4.6.13) we obtain E p~KS(K0)~-ls exp n=
Ej:~
s--1
1 + s=lEpsKH(K~ ~-x exp
(4.7.17) k~j=~
The applicability of this new approach is demonstrated by considering again nitrogen adsorption in the zeolite 10X, studied by Nolan et al. [51] In view of the good correlation of these data, obtained by using the linear form of the Langmuir-Freundlich equation (4.6.20), we decided to verify that new theoretical approach using now Eqn (4.7.16). As the volume of this zeolite cages is comparable with the volume of 5A zeolite cages, we assumed that m = 10, according to our discussion in the section 4.5 of this review. The n value in Eqns (4.7.16) and (4.7.17) can, thus, be expressed as Nt/(M/10).
547 Theoretically, while fitting Eqn (4.7.16) to the experimental adsorption isotherms, M should be treated as a best-fit parameter, together with K H, K ~ and kT/c. It is, however, well known that the "adsorption capacity" is estimated best from the adsorption data measured at higher surface coverages. But there, Langmuir-Freundlich isotherm may be applicable, because the existence of Henry's law is not the main point here. So, we took M values listed in Table 4. The other parameters K H, K ~ and kT/c were found by computer by accepting the following ERROR function
1 1 (theor_i-expi) 2 ERROR = ~
]- k
expi
i=l
The parameters K s, K ~ and kT/c found in that way are collected in Table 5. Figure 8 shows the agreement between experimental adsorption isotherms of nitrogen adsorbed in the zeolite 10X with the theoretical ones, calculated from Eqn (4.7.16) by using the parameters collected in Table 5. Table 5
The parameters found by computer when fitting Eqn (4.7.16) to the experimental isotherms of nitrogen adsorption in the zeolite 10X, reported by Nolan et al. [51]. T
KH
K~
kT/c
ERROR
172.04 227.60 273.15
3.023-10 -1 1.176.10-2 7.219.10 -3
1.194-10-3 2.084.10 -4 4.877.10 -4
0.478 0.850 1.000
6.125-10 -s 8.289.10 -4 1.015-10 -4
10.0 .
8.o_
6.0~
T=172.04 K T 2276 K 73. 5K
4.0 2.0 0.0
0
400
860
1200' 1600 2000 p [ramHg]
Figure 8: Comparison of the theoretical isotherms (--) calculated from Eqn (4.7.16) by using the parameters collected in Table 5, with the experimental isotherms of nitrogen adsorption in 10X, measured by Nolan et al. [51] at the three temperatures: 172.04 K, 227.6 K and 273.15 K.
548
5.0 ~4.4 ~'~ .~. 3.8 3"2t N 2.6 2.0] , ~ { x ~ tga=0.38 I
0.0
I
1.6
I
I
l
I
3.2
I
I
4.8
I
8.0
6.4
ln(p) [ram Hgl Figure 9: The ln Nt vs. In p plot for the data on nitrogen adsorption in 10X at 172.04 K, reported by Nolan et al. [51].
3.0 r~
2.0 1.0 0.0-1.0-2.0
a~ tg a = 1.0 0.0
I
1.6
3.2
I
I
4.8
I
I
I
6.4
8.0
ln(p) [ramHgl Figure 10: The In Nt vs. In p plot for the data on nitrogen adsorption in 10X at 273.15 K, reported by Nolan et al. [51]. Eqns (4.7.16) and (4.7.17) predict existence of Henry's limit at sufficiently low surface coverages (zeolite loadings). It means that the tangent of the double logarithmic plot In Nt vs. In p should approach unity at a sufficiently low adsorbed amount. Figures 9 and 10 show that plot for the two temperatures: 172.04 K and 273.15 K, respectively. Looking at Figure 9 one can see a typical Freundlich linear plot at the lowest surface coverages, the tangent of which is equal to 0.38. Then, the lowest surface coverages (zeolite loadings) studied at the temperature 273.15 K are lower by two magnitude orders. At such low loadings the double logarithmic plot has a slope equal to unity, indicating thus the existence of Henry's region.
549
5. H E T E R O G E N E I T Y ZEOLITES
EFFECTS
IN ADSORPTION
IN MFI
MFI type zeolite is one of the most versatile and valuable zeolites in modern hydrocarbon processing. A wide range of applications including catalytic and adsorptive processes has been proposed based on the size and configuration of its pore structure. In particular, the adsorption of aromatics in MFI zeolite aroused tremendous research interest in the recent years. The general understanding is that there are several energetically non-equivalent locations in this zeolite for the adsorption of aromatics. Due to the comparable size of the pore channels and of the adsorbate molecules, a strong and position sensitive potential field is experienced by the aromatic molecules. This adsorption system may be successfully modelled by a regular lattice of energetically different sites. Such a model is the simplest case where both the energy heterogeneity and the structure heterogeneity (packing arrangement) are involved. The adsorption of aromatic compounds in ZSM-5, and its aluminum-deficient structural analog silicalite, has been extensively studied in the recent years. Various experimental methods including isotherm measurements [53-58], isosteric heat [59, 60] or calorimetry [7, 61, 62], gas chromatography [63, 64], detailed X-ray or neutron powder diffraction [4, 65-72], or NMR [73, 74] have been used. The results reveal some unusual adsorption characteristics of the MFI/aromatic systems, associated with the site heterogeneity and the possibility of a combined zeolite-adsorbate phase transition. This unusual behavior is the consequence of both the peculiar framework structure of MFI zeolites, and of the strong interactions induced by the tight fit situation. At least three different locations for adsorption have been suggested by considering the topology of the MFI framework. Site heterogeneity in MFI (silicalite) was first reported in the calorimetric studies of Thamm [7, 61]. The adsorption heat exhibited a strange jump at intermediate loading for benzene, ethylbenzene, toluene as well as for some alkanes. This could only be explained in terms of a rapid change in the state of the admolecules. A phase transition in the ZSM-5/p-xylene system was confirmed for sure [6, 75]. Talu et al. [5, 76] outlined the phase boundaries of ZSM-5/benzene and ZSM-5/p-xylene systems with isotherms measured at different temperatures, but the appearance of a phase transition in the benzene/silicalite system is still an open question. Using a deuterium NMR, Portsmouth and Gladden [74] proved that the adsorbed benzene molecules were indeed under different environments before and after the step on the adsorption isotherm. More recently, the powder diffraction studies of Mentzen et al. [65-68, 71] of the MFI/aromatics systems revealed the presence of several types of zeolite/admolecule complexes. In many cases [4, 68, 71, 72], the location of the admolecule was identified by comparing the model simulation with the refined diffraction data. According to Mentzen [71], there exist at least three types of polymorphic modifications of the MFI framework, depending on the nature and the amount of admolecules; a monoclinic (with P21/n. 1-1 symmetry), a first orthorhombic (Pnma) and a second orthorhombic phase (P21212a). They have assigned the name MONO, ORTHO and PARA for these structures respectively. ZSM-5 loaded with fewer than 4 benzene molecules/u.c. is in the MONO form. The case of toluene and ethylbenzene is the same. On the other hand, ZSM-5/n. p-xylene, (the short hand for ZSM-5 zeolite loaded with n p-xylene molecules per unit cell), is in the ORTHO form if n < 4 and ZSM-5/8-p-xylene is in the
550 PARA form. Apart from the difference in symmetry, there is also a slight variation of the lattice constants among the three polymorphic structures. A variety of theoretical approaches, based either on the computer simulation [10, 11, 77-82] or using the methods of statistical thermodynamics [83-86], have been proposed to model these adsorption systems. Thus, Thamm [7] assumed that this might be adsorption of dimers, or a cooperative redistribution of adsorbed molecules, whereas Stach et al. [58] saw it as a pore filling with the energy and entropy of different sites determined by the Monte Carlo simulations. Talu and co-workers [5, 76] launched a two-patch model of collective mobile adsorption with the surface phase transition. They argue that in spite of the channel structure of zeolite, the adsorption of aromatics in ZSM-5 cannot be treated in terms of one-dimensional systems. The idea to treat the whole adsorption system as a three-dimensional cooperative system has also been accepted by Chiang and co-workers [86]. They used a lattice gas model, with three kinds of adsorption sites and showed equivalence of their lattice gas model to an Ising model. The authors postulated next that the sharp increase in the adsorption isotherms is due to the phase transition predicted by this model. While considering the p-xylene adsorption in ZSM-5 Pan and Mersmann [85] launched a model of localized adsorption on two independent subsystems of sites, corresponding to the channel intersection and zigzag channels. Adsorption in the channel intersections was described by the Langmuir isotherm, while the adsorption in the zigzag channels was modelled by the isotherm corresponding to the quasi-chemical approximation for cooperative adsorption. The interactions between the molecules in the neighbouring zigzag channels were assumed to be mediated by structural relaxations in the zeolite itself, that occur when p-xylene is adsorbed at high loadings in ZSM-5 type zeolites. Pan and Mersmann proposed also a construction of the hysteresis loop for p-xylene on the basis of their model. That construction of the hysteresis loop of p-xylene has, next, been questioned by Snurr et al. [82] who launched another model of adsorption in ZSM-5 zeolites. They proposed that the phase change from the ORTHO to PARA form, which occurs when more than four molecules of p-xylene per unit cell are adsorbed, may also take place in the case of benzene adsorption. Snurr et al. accepted the three-site model launched by Chiang and co-workers, but assumed that the phase change in the zeolite structure causes the free energies of adsorption to change, on the three kinds of sites. In their model the sharp increase in the adsorption isotherms is essentially due to the change of the zeolite structure. Thus, in spite of the cooperative character of their model, the sharp increase in the adsorption isotherm is due to a cooperative redistribution of adsorbed molecules and not to a phase transition in the phase of the adsorbed molecules. The theoretical isotherms calculated by means of their lattice model were compared with GCMC molecular simulations but were not fitted to the experimental adsorption isotherms. Although calculations were performed for four temperatures, the corresponding heats of adsorption were not calculated. Heats of adsorption were estimated by these authors only by using the GCMC simulations for the changing zeolite structure. As well known, the behavior of isosteric heats of adsorption is a more stronger test for a theory than the behavior of adsorption isotherms. It should, however, be emphasized that none of the above discussed models can predict appearance of two steps which have been observed experimentally on the isotherm of benzene adsorption in silicalite at higher temperatures.
551 Next, theoretical studies of aromatics adsorption in ZSM-5 focus, almost exclusively, on the comparison between the calculated and experimental adsorption isotherms. Although the experimental data on the coverage (loading) dependence of the isosteric heats of aromatics adsorption in ZSM-5 were reported as long as a decade ago, they were largely ignored in the theoretical studies. A reliable theory of adsorption should lead to a simultaneous good fit of the experimental adsorption isotherms, and of the accompanying heats of adsorption.
5.1. T h r e e
site lattice gas model
To include the true geometric arrangement of the adsorption sites, we have proposed a lattice model with three different kinds of sites; namely the I (intersection), Z (zigzag channel) and S (straight channel) sites. They are shown in Figure 11.
lO-ring
Rlliptical
.[0.5x*o.57 .m) ,
2.006
b
nm
_ j
"
Z-L_; t
Circular lO-ring
(0.54 nm)
•
,i.T
~
S
S ,I
1"2
T
T
g.J
11
r7
TI
9
S 9 I s
g.J
7
T!
9
S r7 L.J
T] 9
S
S
S
S _
9
q
"1"i_
_
S Figure 11: (A) The channel structure of silicalite. (B) The schematic model of the three-site lattice structure accepted by Lee and Chiang in their theoretical considerations. Channel dimensions shown in this figure are those reported by Flanigen et al. [87]. In literature some different dimensions of channels in ZSM-5 can be also found [88]. Namely, the straight channels are nearly circular with dimensions 0.54 x 0.56 nm, while the sinusoidal channels are elliptical with dimensions 0.51 x 0.55 nm.
552 None of the phenomenological models mentioned above took the observed structure change of the zeolite framework into account. By the atom-atom grand Canonical Monte Carlo (GCMC) or molecular dynamics (MD) simulations, such a framework change may be reflected in the model. The atom-atom simulations are, however, time consuming and require access to a super computer or a dedicated workstation. The situation is even worse for the tight-fit system such as aromatics in ZSM-5 (Li and Talu [11], Snurr et al. [81]) The lattice model, on the other hand, is less computationally intensive but requires a large number of energy parameters to be determined by data fitting. To circumvent this difficulty, Snurr et al. [82] proposed recently a hierarchical atom/lattice strategy, in which the detailed atom-atom simulations are used to obtain the required parameters for the lattice model. They tested this approach by considering the benzene/silicalite system. Two sets of parameters were obtained corresponding to the ORTHO and the PARA symmetries respectively. An additional empirical parameter, which still had to be fitted, was included to account for the free energy change upon the transformation of a framework. In our previous study [86], we proposed to model the adsorption of aromatics in silicalite as a lattice gas with three types of sites, located at the straight channel, the zig-zag channel and the channel intersection respectively. In order to obtain an exact solution for that model, we considered only an attractive interaction J~ for the occupied nearest neighbor Z-I pairs. The adsorption on neighboring S and I sites was assumed to be mutually exclusive due to size restriction. One could have included more interactions between the adsorbed molecules. However, in such a case, only a mean field solution could be obtained. For example, we could include attractive interactions J~z, Jzi, J~s, Jii for the occupied S-Z, Z-I, S-S and I-I pairs, and a very large repulsive interaction Jsi as a way to account for the mutual exclusion assumption of the occupied S-I pairs.
5.2 M e a n field s o l u t i o n s For a system with N sites of each type, the canonical partition function of the three site lattice gas can be written as E(#,N,T; J~,J~,Jsz, Jzi, Ji~,e~,ez, ei)=
E
E
E
exp[-~H]
(5.2.1)
ns--0,1 nz--0,1 hi--0,1
where - ~ H = J~ ~ n s2 + J~ ~] n~ + Jsz ~] nsn~ + Jzi ~ nzni + Ji~ ~ nin~ +(es + In A) E ns + (ez § In A) E
nz + (el + In A) E
ni
(5.2.2)
Above es, Q, ei are the adsorption energies on sites S, Z, I, and the activity expk-~T is written as A. The summation is over all possible occupancies, with ni -- 1(0) denoting site I being occupied (unoccupied). This canonical partition function can be evaluated by a mean-field approximation. First we write the Helmholtz free energy as a function of H F = E - TS = Tr(pH) + kW-Tr(p In p)
(5.2.3)
553 where the entropy S = - k . Tr (p In p) and p is the density matrix. By the mean-field approximation we assume
P = H Pi
(5.2.4)
i
where the product is over all sites. We choose the pi's that minimize F subject to Wr(p,) = 1.
Following this procedure, we obtain from equation (5.2.3) BF
N = J~0~2 + Jn0~ + 4Js~0s0~ + 2Jzi0z0i + 2Jsi0s0i
+(e, + In A)Os+ (~ + In A)0, + (ei + In A)0~- [Tr(p~ In p~) + Tr(p, In p,) + Tr(p~ in pi)](5.2.5) where 0j is the fraction of j sites that are occupied, defined as 1 8j = ~ < E
nj > = < nj > = Tr(pjnj).
(5.2.6)
One then finds the ps, pz, pi that minimize F subject to Tr(p~) = Tr(p~) the Lagrange's multiplier method. The final result reads as 0s = Tr(p~n~)=
e, = T r ( p , ~ , ) =
Oi = Tr(pini)=
1 1 + -~exp [-(2J~,0s + 4J~0~ + 2J,i0i + Ca)] 1
1 + }exp [-(4J,,0, + 1 + 89
2JziOi
@s
1 [-(2Jii0i + 2J~i0z + 2Jsi0~ + ei)]
= Wr(pi) =
1, with
(5.2.7)
(5.2.8) (5.2.9)
For the special case with J~ = J~ = Jss = 0, a complete equivalence of the lattice gas with an Ising model exists. Having all energy parameters o's and J's, one can solve these equations for any A value and obtain the ffs. The pressure of the system is related to the chemical potential by the relation p = ~ - e x p ( - # ~ where #o is a reference chemical potential. Lee et al. [86] used their three site lattice gas model to fit the experimental isotherms of benzene and p-xylene adsorbed in silicalite. To arrive at the phase transition in the adsorbed phase they were forced to assume certain values of the parameters es, Q, ei and some interrelations of the gas-solid and gas-gas parameters to be fulfilled. However, the calculated isosteric heat of adsorption could not be seen as reproducing well the qualitative features of the experimental heats of adsorption reported by Thamm [61]. The estimated energy of adsorption es was larger than el, which would mean that the straight channel sites S are filled first. Meanwhile, the reported atomistic simulations [11, 77, 78, 80, 81] suggest that channel intersections I are filled first. There are some experimental findings [89, 90] that seem to support such a view. As the reported simulations already provide a solid support for the three-site model, we have decided to investigate further possibilities of arriving at a consistent theoretical
554 description explaining all the experimental findings. Looking for possible revisions and improvements of the three-site model, we rejected the concept that this is only the phase change in the zeolite structure which might be the source of the step on adsorption isotherms. Firstly, because in the case of benzene it is only a hypothesis at present. Secondly, because that hypothesis itself does not lead to predict the two steps on the adsorption isotherm of benzene which have been observed at higher loadings and temperatures. Further, looking to the computer simulations reported by Snurr et al. [82], one can see, that the zeolite phase change should not affect much the adsorbate-adsorbate interactions. As far as the solid-adsorbate interactions are concerned these simulations suggest that this zeolite phase change could affect strongly the adsorption on Z sites, but should not affect the adsorption on I and S sites much. Thus, we decided to see first, which agreement between theory and experiment could be obtained by neglecting the changes in the adsorption parameters which might be caused by the possible phase transition in the zeolite structure. Finally, we focused our attention on the recent discovery reported by Mentzen et al. [69]. They have found, that two equally probable orientations of benzene molecules adsorbed in the channel intersection are possible, and that these orientations are characterized by different values of the adsorption energy e. Thus, we can assume, that these forms denoted herefrom by I1 and I2 compete for occupying channel intersections. In such a case the equation system (5.2.7)-(5.2.9) takes the following form Aexp{(E~+ j~. a,jSj)/kT} 8, =
(5.2.10)
9 =
l + A e x p { ( E z + j~. wzj)/~j)/kT)
=
(5.2.12)
and
0i2 =
(5.2.13)
555 where Ej = ej + kT In fj, fj is the molecular partition function of the admolecules occupying the sites of type j, and wij is the interaction energy of a molecule adsorbed on the site i with the molecules adsorbed on the nearest sites j. Let us remark that the parameters Ej's in the above equations are related to the nonconfigurational part of the free energy of adsorption per molecule. The overall adsorption isotherm 0 obtained by solving the equation system (5.2.10)(5.2.13) is then given by 0 = g1 Z 0j J
" i2,s,z j = 11,
(5.214)
Theoretically, such an isotherm equation with the appropriate interaction parameters wij, might show a critical point at a temperature Tr such that
( )
:0
and a non-physical loop at still lower temperatures. Then, one would have to perform the Maxwell construction to arrive at the physical isotherm, represented by a straight line fulfilling the conditions f0~ # (0)d0 /~M --~
0G _ 0L
and
#M = # (0G) = # (0L)
(5.2.16)
where 0G and 0L are the surface coverages corresponding to the beginning and the end of the phase transition, respectively. The theoretical isotherms based on the concept of a phase transition in the adsorbed phase will predict a vertical increase in the experimental adsorption isotherm at a certain pressure defined in Eqn (5.2.16). Looking more carefully to the behavior of the reported experimental isotherms, one can see indeed a sharp increase which, however, is never perfectly vertical. It seems, that this fact has not received enough attention. Then, within the range of the surface concentrations corresponding to the phase transition region, the predicted heat of adsorption is independent of the coverage (loading), and is given by Q = 0G - 0L
f01~ Q (0)dO
(5.2.17)
Let us remark that such constant heats of adsorption have not been observed at higher loadings by the adsorbed aromatics, where the phase changes were believed to occur. This must rise serious doubts as to whether the sharp increase(s) in the adsorption isotherms of aromatics are due to phase changes in the adsorbed phase. Now, let us investigate the behavior of the heat of adsorption of aromatics predicted by our equations (5.2.10)-(5.2.13). For that purpose, we rewrite Eqns (5.2.10)-(5.2.13) as follows
E~ + Ew~jOj F~= #~- ~ =ln~0~ _ kT 1 - 0~
j kT
-ln~=0
(5.2.18)
556 E~ + E ~zjOj J - In A - 0 kT
F~ = #~ - # = In ~ 0 z _ kT 1 - 0, Fia
=
#ix
-- #
__
kT
In
1 --
0i~ 0il
_
- - 0i2
(5 9 19) '
Ei, + ~ wi,jOj j - In A = 0 kT
(5.2.90)
Ei~ + ~ COi2j0j Fi2
-
#i: -- ~ __
kT
In
0i~
1 - 0i~ - 0i2
--
J
kT
--
In A = 0
(5.2.21)
Let Qj ({0m}) denote the molar differential heat of adsorption on the site of type j, at a certain set of the average surface coverages {0m} (m = s,z, il,i2). It is given by Qj ({0m})=-k~0-~ (#JkT#){0,,) Thus Qj's take the following explicit forms
Q, ({0m})_. QO + ~ a~,j0j
(5.2.23)
Qz ({Ore})= Q~ + )-" ~'~jOj
(5.2.24)
J
J Qil ( { 0 m }) --
Qi~ + ~
~'ilj0il
(,5.9.25)
o.,,i2j 0j
(5.2.26)
J Qi~ ( { 0m} ) = Qi~ + ~ J where d ( Ej +tz~ o
Qi = k
)
dT1
(5.2.27)
and #~ in Eqn (5.2.27) is the standard chemical potential of adsorbate molecules in the gaseous phase. An incremental increase in ~, dp, will result in an incremental increase of 0, dO, represented by ( 00j '~ dO = Z . \~-~,] d#
(5.2.28)
J
That incremental increase will be accompanied by a heat effect dQ dQ-
Z
QJ (00J
(5.2.29)
557 Thus, the overall (measured) differential heat of adsorption Q will be given by
Q=
~Q5
\0u)
(5.2.30)
The derivatives \ 0u ] can be evaluated from the equation system (5.2.18)-(5.2.21). It can be done as follows. Let Gj denote Fj multiplied by kT. Thus 0Gj = _1 + Z
0Gj 00m = 0
0~
00 m 0~
m
(5.2.31)
The derivatives \(oo 0u ) are found by solving this system of four linear equations. Let G~ denote the derivative
Gj = \OOm] The solution of the equation system (5.2.31) reads 00 m
=Dm
0~
D
(5.2.33)
where D=
G:
G:
G~ ~ a~ ~
(5.2.34)
and Dm is obtained from D by replacing the m-th column of the determinant D by the column os constants. From Eqns (5.2.30) and (5.2.33) one will be able to evaluate the heat of adsorption as a function of coverage. The isosteric heat of adsorption of benzene in silicalite, reported by Thamm [61] (Germany) was taken for analysis, along with the adsorption isotherm measured in Chiang's Laboratory (Taiwan). This fact has to be taken into account while considering the obtained agreement between theory and experiment. The experimental isotherms of benzene in ZSM-5 measured by Chiang and co-workers [8] are shown in Figure 12. In the case of benzene adsorption two steps are observed: the first is at around 4 M/u.c. and the second one at around 6 M/u.c. The sharp steps in the isotherms of aromatics adsorbed in ZSM-5 silicalite at the loadings above 4 M/u.c. have been observed by many authors [59, 60, 62, 75, 76]. It seems, however, that we were first to report on the two steps on the experimental adsorption isotherm of benzene at higher temperatures. At the same time no hysteresis was found in our benzene adsorption isotherms. Such hysteresis was reported earlier by Thamm [7], using much smaller crystals than these
558 used in Chiang's experiment. It seems, therefore, that the observed hysteresis was due to desorption from the intraparticle pores. While considering the interaction parameters win, (m,n = il,i2,s,z), we have carried out numerical best-fit calculations, seeking for a minimum number of these parameters that would allow for a reasonably good (simultaneous) fit of experimental adsorption isotherms and heats of adsorption.
.....
8.0
e"
::3
,..
6.0
---r :3
4.0
(D Q.
0
(D 0
E
2.0 0.0 I 0.0
'
i 1.0
'
I
'
2.0
I
II
4.0
3.0
log p r e s s u r e
(Pa)
Figure 12: Experimental isotherms of benzene adsorption in ZSM-5 at 273 K (, 9e), 283 S (ooo), 293 S ([::]DO) and 303 K (AAA), measured by Chiang and co-workers [8]. The solid lines were drawn by hand to emphasize the trends in the experimental data.
Table 6 The values of parameters found by fitting simulataneously the experimental isotherms and heats of adsorption of benzene in silicahte, measured at 303 K, by our equations (5.2.10)-(5.2.13) and (5.2.30). The Henry constant Kj is defined as exp{(Ej + p~ Kil (Pa -a ) 8.54 7-10 -2 ~sz
Ki2 (Pa -1 ) 3.873-10 -3.. ~si2
Ks (Pa -1 ) 2.605.10 .3 ~zi2
K= (Pa -z ) 3.643.10 -2 ~sil
w=z (kJ / mol) 7.60 ~ziz
(kJ/mol) -2.75
(kJ/mol) 21.00
(kJ/mol) -30.30
(kJ/mol) -I1.00
(kJ/mol) -I4.00
(kJ/mo~)
(kJ/mol)
(kJ/mol)
(kO/mol)
(kJ/mol)
0.00
56.00
105.00
69.00
70.00
~iziz
qi~
Q~
qo
,,
QO
559 Table 6 collects the values of the parameters found by computer, while fitting simultaneously an experimental adsorption isotherm and the corresponding isosteric heat of benzene adsorption. Before we comment on the values of the parameters found by computer, we will analyze first the behavior of our adsorption system predicted by our equations for that particular set of parameters. Figure 13 shows graphically the agreement between the experimental adsorption isotherm of benzene in ZSM-5 and the theoretical one calculated by using the parameters collected in Table 6. In Figure 14 the comparison between the experimental and theoretical heats of adsorption is presented. Apparently, the number of the parameters is large. If, however, one looks to the complicated shape of the benzene isotherm and of the related heat of adsorption curve, and realizes that we fit them simultaneously by using the same set of parameters, one can see a thin margin for an arbitrary choice of these parameters. As a matter of fact, we have performed numerous model calculations which showed, that the calculated data are very sensitive to a particular choice of these parameters. In Figure 15, in addition to the overall theoretical adsorption isotherm, also the contributions are shown from the adsorption on various adsorption sites. One can see that neither the total theoretical isotherm, nor its composite isotherms on a particular kind of sites, (in a particular configuration), show loops which could be associated with phase transitions. The sharp, but not exactly vertical, changes in the adsorption isotherm of benzene are due to rapid changes in the occupation of various adsorption sites. We called it already - the cooperative redistribution of adsorbed molecules. Our theoretical calculations confirm, thus, the long-shared feeling by many scientists, that the adsorption of aromatics in silicalite is governed by a delicate balance between the adsorbate-solid, and adsorbate-adsorbate interactions. The reason why in Figure 15 we used z(s) and s(z) to denote the occupancy of Z and S sites is following: From a purely theoretical point of view, it is impossible to judge which of the calculated solid lines means the occupancy of Z sites, or S sites alternatively. This is because of the symmetry of Eqns (5.2.10)and (5.2.11). Eqn (5.2.11) can be obtained from Eqn (5.2.10) by replacing the index s by z, and vice versa. The discrimination between the calculated contributions must be made on a rational physical basis. For us that basis are the theoretical and experimental findings reported by Mentzen et al. [69]. They argue that at the highest loadings of silicalite, the adsorbed benzene molecules form one-dimensional polymer-like structures in the straight channels. The formation of such one-dimensional polymers means full occupation of both I and S sites at the highest possible loading. Looking to Figure 15 we can deduce that such an occupation could really exist, provided that the solid line s(z) means the occupancy of sites S rather than of the Z sites. The symbol z(s) has a similar meaning. If, however, we interchange the interpretation of these theoretical isotherms, our conclusion that I and Z sites are fully covered at the highest loadings, will agree with the conclusion drawn by Snurr et al. [81, 82]. Looking to Figure 15 one can see, that at a loading of about 4 molecules/u.c, a sudden increase of the z(s) and s(z) forms takes place, and a sudden disappearance of the form il. This takes place even in the absence of the phase transition in the zeolite structure assumed so far. We can imagine, however, a situation when this sudden redistribution induces the silicalite phase transition which, in turn, promotes further this sudden rearrangement.
560
_ r
=
6.0-
_.r
4.0-
ll...,
| cL
/"
8.0-
0 .,...
0
E 2.0"
0.0
I
'
I
0.0
'
I
2.0 4.0 log pressure (Pa)
Figure 13" The comparison of the experimental isotherm of benzene adsorption in silicalite ( 9 9 .) measured by Chiang at 303 K, with the theoretical one, ( ~ ) , calculated from Eqns (5.2.10)-(5.2.13) by using the parameters collected in Table 6.
0
80.0
8 60.0 -~
.'/
-
o 'v /
'B t~
.
I.,
"0
Lo
/'.
v
o e-
.2 ~- 40.0 0 t~
"-
'
0.0
I
'
I
'
I
'
I
2.0 4.0 6.0 8.0 molecules per unit cell
Figure 14: The comparison of the experimental heat of adsorption of benzene molecules adsorbed in silicalite (o 9 o), reported by Thamm [61], and the theoretical ones ( ~ ) , calculated by us, using the parameters collected in Table 6.
561
8.0 6.0 a._.
(D
r
(D
4.0
t
0
"~
E
2.0 0.0
I
0
~
I
1000 2000
~
I
3 0 0 0 4000
I
5OO0 60O0
pressure (Pa)
Figure 15: The occupation by benzene molecules of various adsorption sites in silicalite, calculated from Eqns (5.2.10)-(5.2.13), by using the parameters collected in Table 6. The line rising sharply at small adsorbate pressures is the occupancy of I sites by benzene molecules being in a certain state denoted here by il, whereas the solid line rising sharply at the highest adsorbate pressures is the occupancy of I sites by benzene molecules being in a state denoted here by i2. The lines denoted by z(s) and s(z) are the occupancies of the sites Z and S, or vice versa. The black circles (. 9o) are the experimental data measured by Chiang.
~__, 8C).0-
~o
|
9
o 9
On
0
O0
9 Oo O
9
o40"0-
J w
"-
0.0 I
0.0
/
n
i
l
2.0
i
l
/
i
~
i l l l i i l l i l l
4.0 6.0 8.0 molecules per unit cell
Figure 16: The contributions Qcj'S to the total isosteric heat of adsorption of benzene in silicalite, calculated by us using the parameters collected in Table 6. These contributions are denoted in the same way as the contributions to the total adsorption isotherm from various sites, shown in Figure 15. The black circles (o 9 o) are the experimental data reported by Thamm.
562 Such a view could be supported by the values of the parameters found by computer, and collected in Table 6. One striking property observed there is the high value of the interaction (attraction) parameter w,z for benzene adsorption. This would suggest a high positive cooperativity of adsorption of molecules adsorbed on Z sites. That means, the total energy of the molecules adsorbed on Z sites grows rapidly with the number of molecules adsorbed on these sites. However, the calculations show, that adsorption on Z sites starts rapidly only when the surface loading exceeds 4 M/u.c, i.e., when the phase transition in the zeolite structure can take place. One may, therefore, assume, that this unusual positive cooperativity simulates, in fact, another factor leading to such a sudden increase of adsorption on Z sites. This may well be a sudden increase of the adsorption energy Ez, induced by the zeolite phase transition. In case of the argument that for benzene the phase change in the silicalite phase is only a hypothesis at present, we might offer another explanation following the arguments by Pan and Mersmann [85]. They believe that a strong attraction exists between two molecules on the nearest Z sites, transmitted through the solid phase. Finally, a certain compromise between the views expressed by Pan and Mersmann, and those launched by Snurr et al. [82] also seems to be possible. As well known, the behavior of theoretically predicted isosteric heats of adsorption is a much stronger test for the theory than the behaviour of theoretical adsorption isotherms. This is because the behavior of the experimental heats of adsorption is much more sensitive to the nature of an experimental adsorption system. Thus, we believe, that a special attention should be given to the agreement between theoretical and experimental heats of adsorption. Figure 16 shows the contributions to the total isosteric heat of adsorption of benzene, from the heats of adsorption Qcj generated by the adsorption on various adsorption sites.
QJ [o_h \0~)
Qcj - ~ f~,~
(5.2.35)
The additional index "c" in Qcj means that this is the "contribution" to the total heat of adsorption, from the molecules occupying sites j. The relatively less successful fit of the experimental data by the theoretical heats of adsorption is probably due to neglecting another physical factor. These are the chemical and geometrical defects in the silicalite structure. Zeolites are usually viewed as very regular crystallographic structures. The common existence of various structure defects has been known for a long time but has not received enough attention so far. Indeed, such defects should not affect strongly adsorption of small molecules in large cavities and channels. If, however, the dimensions of cavities and channels become comparable to the values of Van der Waals interaction parameters, even small changes in the zeolite local dimensions may result into considerable changes in the gas-solid interactions. This will cause the appearance of a new level of surface heterogeneity. Not only the features of the sites S, Z, I are different. The local imperfections in the silicalite structure will introduce an additional dispersion of the nonconfigurational free energies, even for the same kind of adsorption sites.
563 This was Thamm [7] himself who first emphasized that the strongly decreasing heats of aromatics adsorption at small surface coverages must be due to structure imperfections. Similar views were expressed by Talu et al. [76]. This can be seen in Figure 14. Our model assuming that all the sites of the same type have identical adsorption features is not able to reproduce w~ll the strong decrease in the initial part of the heat of adsorption curve. From the existing literature on the adsorption on heterogeneous surfaces [1], it is known, that this decrease could be reproduced by assuming a certain dispersion of Ej values on various sites j. Because of the local distortions of the zeolite structure, Ej may vary when going form one to another site of the same type j. Then, it seems reasonable to assume that these local distortions have a random nature. In such a case, the external force field acting on an adsorbed molecule, and created by its interactions with other molecules adsorbed nearby, should be a function of the average occupancy of sites of all types. Let xj(Ej) denote the differential distribution of the number of sites j having adsorption energy Ej, among the j-type sites, normalized to unity. Traditionally, the surface energetic heterogeneity was viewed as the dispersion of e-values, (adsorption potential values at the local gas-solid minima), not affecting fj. Here we must consider the dispersion of Ej rather, because the local distortions may affect also the local movement of the adsorbed molecules, i.e., the molecular partition function of these molecules. This is because the dimensions of the channels of ZSM-5 and of the adsorbed aromatics are comparable. Then, it seems natural to assume that the local distortions of the zeolite structure will result in a gaussian-like dispersion of adsorption energies for all types of sites. If we assume that the dispersion of E i is represented by the gaussian-like function (4.6.10), the expression for the average surface coverage of sites of j - th type given by the integral (4.6.2) will take the following form
o~t =
(5.2.36)
[ {( )~exp
Ozt
--"
E~ + E a,jOjt
)
/kT (5.2.37)
J
~ 1+ [exp { (Eo + ~
jkT}l
~i 1
+
[ {( A exp
) }1"
E~12 + y~ wi2jOit / k T J
(5.2.38)
564
~i2t --
l+[Aexp{(E~ wiljOjt)/kT}]'r"+[Aexp{(E~
"},i2
(5.2.39) where, to a first approximation, -yj < 1 m a y be identified with kT/cj, and E ~ is t h e most probable value of Ej. Now, let us investigate the related behavior of the heat of adsorption. To t h a t purpose, we rewrite Eqns (5.2.36)-(5.2.39) as follows
Fs=#s-#=--I kT
ln~0St _ % 1 - 0st
E~ + E ~,joj~ J kT
-lnA=0
(5.2.40)
-InA-0
(5.2.41)
E~ + E ~jej, F~=tt~-# =--i I n ~ & t _ kT
%
J
1 - 0~t
kT
Fil - #il # _ 1 In 0ilt E~ '~ Et'dilJOJt - -J kT % 1 -- Oil t -- Oi2t kT
- lnA = 0
(5.2.42)
E9 + ~ ~)i2jOjt ~2 J - In A = 0 kT
(5.2.43)
and F i ~ -- # i ~ - t t kT
0i~t _ - ~1 In % 1 - 0i~t - 0i~t
T h e molar differential heat of adsorption Qj ({0=t }) on the site of t y p e j, at a certain set of the average surface coverages {Omt }, takes then the following explicit form
Qs ({e=~}) = QO+ r
~ 0st
+ y~ ~jej~
(5.2.44)
J
0zt Qz ({emt}) -- Qz~ "+- ~'z In 1 - ezt
+ ~ ~jej,
(.5.2.45)
J
Qi,, ({Omt}) = Qi~ + (i, In
Qi2 ({o~t}) - Qi~ + ffi2In
•ilt 1 - Oilt -
Oi2t-~- E j
~i2t 1 - 0il t - 0i2 t
Wilj0jt
+ Z ~ej, J
(5.2.46)
(5.2.47)
565 where
(~.~+.~ o
Qj = k
d
kT
(5.2.48)
"J
(5.2.49)
dT1
and Cj = - k
Inserting Eqns (5.2.44)-(5.2.47) into Eqn (5.2.30) allows to evaluate the heat of adsorption as a function of the total surface coverage (loading of zeolite channels). Table 7 collects the values of the parameters found by computer when fitting simultaneously the experimental isotherm and heat of adsorption of benzene by our equations (5.2.36)-(5.2.39) and (5.2.44)-(5.2.47). Table 7 The values of the parameters found by fitting simulataneously the experimental isotherms and heats of adsorption of benzene in silicalite, measured at 303 K, by our equations (5.2.36)-(5.2.39) and (5.2.44)-(5.2.47). The Henry constant Kj is defined as exp{(E ~ + #~ Kil Pa -1 4.53.10 -2 Wsz (kJ/mol) -19.80
Q~: kJ/mol 57.5
Ki2 Ks Pa -1 Pa -1 2.95.10 -3 6.23.10 -3 wsi2 Wzi2 (kJ/mol)(kJ/mol) 24.90 -5.41
QO kJ/mol 97.0
qo kJ/mol 97.0
Kz Pa -~ 1.33.10 -2
wilil kJ/mol 3.00
Wzz kJ/mol 10.00
"Yil
~'i2
%
wsi~ kJ/mol -17.13 7z
0.99
0.86
0.93
0.90
qo kJ/mol 55.0
C~ Ci: kJ/mol kJ/mol kJ/mol -2.72 -0.60 -2.92
w~l kJ/mol -12.40
kJ/mol -2.80
Figures 17 and 18 show the comparison of the experimental adsorption isotherm and heat of adsorption with the theoretical ones, calculated by using the parameters collected in Table 7. While comparing Figures 13 and 17, one can see, that taking into account that additional level of heterogeneity due to the dispersion of Ej's values, improves the agreement between the theoretical and experimental adsorption isotherms, and especially in the region of low adsorbate pressures. Much better agreement is also observed between the experimental and theoretical heat of benzene adsorption. The rapid decrease in the heat of adsorption at small surface coverages is better reproduced, as well as the two local minima observed at the surface coverages of about 4 and 6.5 molecules/u.c. Trying to understand which is the adsorption mechanism behind that improvement, we have displayed in Figures 19 and 20 the contributions to the adsorption isotherm and heat of adsorption from the benzene molecules adsorbed on various sites and in various configurations.
566
8.0r :3
,..
6.0"
et u~ 0
4.0-
=,=,=
0
E
2.0-
0.0
TM
~
,
0.0
I
'
I
2.0 4.0 log pressure (Pa)
Figure 17" The comparison of the experimental isotherm of benzene adsorption in silicalite (e 9.), measured by Chiang at 303 K, with the theoretical one, ( ~ ) , calculated from Eqns (5.2.36)-(5.2.39) by using the parameters collected in Table 7.
A
80.0
7o.o\ ~
6o.o I
=o
5o.0
.=_
40.0"~' 0.0
. - . - ..... -,,~../
'
-'I -"I"~-
, , , ,' , , ', 2.0 4.0 6.0 8.0 molecules per unit cell
Figure 18: The comparison of the experimental heat of adsorption of benzene molecules adsorbed in silicalite (eoe), reported by Thamm, and the theoretical ones ( ~ ) , calculated by us, using the parameters collected in Table 7.
567
8.0
9
8.0
f" ~
o
~
9 9
2o! 0.0-
111~0
0
2OOO 3OOO pressure (Pa)
Figure 19: The occupation by benzene molecules of various adsorption sites in silicalite, calculated from Eqns (5.2.36)-(5.2.39), by using the parameters collected in Table 7. The solid line rising sharply at small adsorbate pressures is the occupancy of I sites by the benzene molecules being in the state il, whereas the solid line rising sharply at the highest adsorbate pressures is the occupancy of I sites by the benzene molecules being in the state i2. The solid fines denoted by z(s) and s(z) are the occupancies of the sites Z and S, or vice versa. The black circles (. 9e) are the experimental data measured by Chiang.
..•
J "1
IPdP ee
~n
',d
oo
qb'%~e
9
9o
9 ,
9
g 4o.o ~
"-~
0.0
0.0
2.0
4.0 6.0 8.0 molecules per unit cell
Figure 20: The contributions Qcj to the total isosteric heat of adsorption of benzene in silicalite, calculated by us using the parameters collected in Table 7. The black circles (o 9 9 are the experimental data reported by Thamm.
568 Looking at Figure 19 one can see that at the loadings smaller than 4 molecules/u.c., benzene molecules fill mainly the channel intersections. At the coverages above 4 molecules/u.c, we observe a sharp increase of adsorption in the sinusoidal channels, accompanied by a decrease and reorientation of molecules occupying the channel intersections. At still higher loadings (above 6.5 molecules/u.c.) a second redistribution of adsorbed molecules takes place. At the maximum loading of about 8 molecules/u.c, all straight channels and channel intersections are filled by benzene molecules. These redistributions of adsorbed molecules are responsible for the two steps observed on the adsorption isotherm of benzene. From the comparison of Figures 15 and 19, it follows, that both our models predict the same location of adsorbed molecules at low and at the highest adsorbate loadings. The difference occurs at the loadings between 4 and 8 molecules/u.c.. The model accounting for the energetic heterogeneity of the adsorption sites of the same type predicts, that in this coverage region all zig-zag ( or stright) channels and most of the channel intersections are occupied, whereas the model neglecting this additional level of heterogeneity predicts the occupation of zig-zag and straight channels (see Figure 15). From Figure 20, one can deduce that the contributions to the total isostric heat of adsorption coming from the forms Z, S and I in the orientation denoted by i2 decrease at low loadings. Such a decrease of the heat of adsorption curve is usually attributed to the adsorption on an energetically heterogeneous surface. The heterogeneity parameters (kT/cj) for these forms listed in Table 7 are less than unity and the temperature derivatives r defined in Eqn (5.2.49) are equal to -cj, indicating thus that c5 are temperature independent. So, this is the dispersion of the free energy of adsorption of benzene molecules on these sites that is responsible for the rapid decrease of the heat of adsorption at very low loadings. On the other hand, the heterogeneity parameter obtained for the form il is very close to unity and the derivative (il is not equal to -cil. This would indicate that the adsorption of benzene molecules in the channel intersections at low loadings, i.e. in the state of benzene molecules denoted here by il, is not sensitive to imperfections in the zeolite structure.
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[62] H. Stach, H. Thamm, J. Janchen, K. Fiedler, W. Schirmer, in Proceedings of the 6th International Conference on Zeolites (Eds. A. Bisio and D.H. Olson) Butterworths, London, 1984, p. 225.
[63] H. Lechert, W. Schweitzer, ibid, p. 210.
[64] V.R. Choudhary, K.R. Srinivasan, Chem. Eng. Sci., 42 (1987) 382. [65] B.F. Mentzen, F. Vigne-Maeder, Mat. Res. Bull. 22 (1987) 309. [66] B.F. Mentzen, F. Bosselet, Mat. Res. Bull., 23 (1988) 227. [67] B.F. Mentzen, Comp. Rendu. Acad. Sci. Paris 307(II) (1988) 559. [68] B.F. Mentzen, in Zeolites and Catalysts, Sorbents and Detergent Builders, (Karger, H.G., Weitkamp, J., Eds.) Elsevier, Amsterdam, 1989, p.477. [69] M. Sacerdote, F. Bosselet, B.F. Mentzen, Mat. Res. Bull., 25 (1990) 593. [70] M. Sacerdote, F. Bosselet, B.F. Mentzen, Comp.Rendu. Acad. Sci. (Paris), 312(II) (1991) 1513. [71] B.F. Mentzen, Mat. Res. Bull., 27 (1992) 831. [72] M. Sacerdote, B.F. Mentzen, Mat. Res. Bull., 28 (1993) 767. [73] F. Lefebvre, M. Sacerdote, B.F. Mentzen, C.R. Acad. Sci. (Paris), 316(II) (1993) 1549. [74] R.L. Portsmouth, L.F. Gladden, in 4th International Conference on Fundamentals of Adsorption, Kyoto, Japan 1992. [75] D.H. Olson, G.T. Kokotalio, S.L. Lawton, W.M. Meier, J. Phys. Chem., 85 (1981) 2238. [76] O. Talu, C.J. Guo, D.T. Hayhurst, J. Phys. Chem., 93 (1989) 7294. [77] B. Grauert~ K. Fiedler, Adsorp. Sci. Technol., 6 (1989) 191.
572
[78] K.P. Schroder, J. Sauer, Z. Phys. Chemie (Leipzig), 271 (1990) 289.
[79] F. Vigne-Maeder, H. Jobic, Chem. Phys. Lett., 169 (1990) 31.
[80] O. Talu, Molecular Simulation, 8 (1991) 119. [81] R.Q. Snurr, A.T. Bell, D.N. Theodorou, J. Phys. Chem., 97 (1993) 13742.
[82] R.Q. Snurr, A.T. Bell, D.N. Theodorou, J. Phys. Chem., 98 (1994) 5111. [83] H. Stach, R. Wendt, K. Fiedler, B. Grauert, J. Janchen, H. Spindler, in Characterization of Porous Solids (K.K. Unger et al., Eds.) Elsevier, Amsterdam, 1988, p. 109. [84] P.T. Reischman, K.D. Schmitt, D.H. Olson, J. Phys. Chem., 92 (1988) 5165. [85] D. Pan, A. Mersmann, in Characterization of Porous Solids II (F. Rodriguez-Reinoso et al., Eds.) Elsevier, Amsterdam, 1991, p. 519. [86] C.K. Lee, A.S.T. Chiang, F.Y. Wu, AIChE J., 38 (1992) 128. [87] E.M. Flanigen, J.M. Bennett, R.W. Grose, J.P. Cohen, R.L. Patten, R.M. Kirchner, J.V. Smith, Nature, 271 (1978) 512. [88] W.M. Meier, D.H. Olson, Atlas of Zeolite Structure, Juris Druck und Verlag, Zurich, 1978. [89] H. van Koningsveld, F. Tuinstra, H. van Bekkum, J.C. Jansen, Acta Cryst., B45 (1989) 423. [90] C.A. Fyfe, Y. Feng, H. Grondey, G.T. Kokotailo, J. Chem. Soc., Chem. Commun., (1990) 1224.
W. Rudzifi.ski,W.A. Steele and G. Zgrablich(Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces
Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
573
Static a n d d y n a m i c s t u d i e s of the energetic surface h e t e r o g e n e i t y of clay m i n e r a l s F. Villi4ras, L. J. Michot, J. M. Cases, I. Berend, F. Bardot, M. Francois, G. G4rard and J. Yvon Laboratoire Environnement et Min6ralurgie. ENSG et URA 235 du CNRS. BP 40.54 501 Vandoeuvre les Nancy cedex. France
INTRODUCTION Clays are important constituents of soils. They are finely divided materials: the size of elementary crystallites does not exceed some micrometers. Because of these low dimensions, clay minerals develop large specific surface areas. This is why, clays generally carry, together with iron and aluminium oxy-hydroxides, the largest part of the specific surface area of soils and, as a consequence, they largely control the reactivity of the solid soil fraction. Furthemore, because of their high specific surface areas, clay minerals are reservoirs of water in soils as, in contrary to water trapped in the soil porosity, water adsorbed at the surface of clays is slowly released from the surface. This keeps acceptable moisture levels for plants in hot and dry conditions. Clay minerals feature variable exchange capacity for organic and inorganic cations. These properties play an important role in heavy metal cations abstraction. The exchange capacity can be limited to external surfaces for minerals such as kaolinite and illite. It can be also extend to the interlamellar space when interlamellar cations can be hydrated; it is the case of swelling clay minerals such as montmorillonite. Finally, other mineral species such as sepiolite and palygorskite present large adsorption capacities because of their structural microporosity. These natural properties of clay minerals render them attractive for industrial applications. They have long been used as fillers in paper, paint, polyphased materials, cosmetic industries... Clays are also used to control waste deposits. They play an important role in the design of geochemical barriers: clay formations ensure low permeabilities preventing pollutants migration and can trap heavy metals as well. On the contrary, the presence of swelling clays can be very detrimental in civil engineering applications as their swelling behavior can affect the stability of buildings. Swelling and microporous clays are also used in the pharmaceutical industry where their water retention properties make them suitable as antacids. Microporous clay species are also used for their retention properties, for instance in cat litter, or as catalytic supports.
574 Therefore, it is extremely important to carefully study the surface properties of clays in order to understand the different mechanisms involved in natural conditions (hydration, pollutants adsorption and release...) or to improve the surface properties of the minerals used in industrial processes. As it is the case for all crystalline materials, structural defects can be observed at the surface of clay minerals such as dislocations and steps. Because of their natural origin, clay minerals are also chemically very heterogeneous solids. The cristallochemical composition can feature local variations inside a same particle. It is then necessary to study precisely the nature of such variations and their effect on hydration, dehydration and swelling properties as well as to determine the energetic heterogeneity of surfaces in order to obtain reliable informations about textural properties of clay minerals. For the reasons mentioned above, the present review will start with a summary of clay minerals cristallochemical properties in order to provide the reader with the major keys governing their surface heterogeneity. Then, specific techniques developed in our laboratory for analyzing surface heterogeneity of clay minerals will be described. Finally, different examples will be examined including non swelling minerals such as kaolinite, muscovite and talc, fibrous and microporous minerals such as sepiolite and palygorskite. Finally, the particular case of water adsorption on minerals such as montmorillonite will be discussed. In this case, the mecanism of adsorption is driven by the access of water inside the interlamellar space; then these minerals swell in presence of water yielding structural heterogeneities depending on the nature of the interlamellar cations.
1. ORIGIN OF SURFACE HETEROGENEITY OF CLAY MINERALS Clay minerals are hydrous layered silicates and belong to the phyllosilicates family. Elementary crystallites are generally platy and sometime fibrous. This specific habitus is due to the crystallochemical properties of clay minerals (comprehensive reviews on crystal chemistry of phyllosilicates can be found elsewhere, for instance in ref. I and 2). 1.1 Structure of phyllosilicates Phylosilicates are made of the arrangement of successive sheets of octahedra and tetrahedra which are named tetrahedral and octahedral sheets. The composition of the tetrahedral sheet is generally relatively simple. The basic pattern is the SiO4 4- tetrahedra. Interconnection of silicium by oxygens defines the tetrahedral network. In the case of silica, this network is tridimensional as every oxygen anion is connected to two silicium cations. In the case of clay minerals, the network is two dimensional as only three of the four corner (basal oxygens) of one tetrahedron are linked to other tetrahedra while all the free corners (apical oxygens) are pointing in the same direction perpendicular to the tetrahedral sheet. A plan view of the tetrahedral network shows that tetrahedra connections feature hexagonal rings (Figure 1). Silicium cations can be
575
_
_
_
y
X ~,,
l
Figure 1" Idealized plan view of the tetrahedral sheet of phyllosilicates. The filled circles represents the silicium cations located at the center of each tetrahedron. substituted by trivalent cations. The most important one is A13+ as its size is very close to that of Si4+. The charge difference between A13+ and Si4+ generates, in the tetrahedral sheet, a localized charge defect which must be compensated by other cations. The substitution level for four tetrahedra ranges between 0 and 2. Small quantities of other cations such as Fe 3+ can be found in substitution for silicium. The apical corners form part of the adjacent octahedral sheet. Individual octahedra are linked together by sharing octahedral edges and to the tetrahedral sheet by sharing apical oxygens and unshared hydroxyls. These hydroxyls lies at the center of the tetrahedral hexagonal ring and can be substituted by fluor. The octahedral cations are mainly Fe 2+, Fe 3+, Mg 2§ and A13+. Other elements such as Li, Ti, V, Cr, Mn, Co, Ni, Cu and Zn can occur in some species. If all octahedra contain a cation at their center, the sheet is said to be trioctahedral. If two centers are occupied and one is vacant, the sheet is said to be dioctahedral. In trioctahedral minerals, OH groups vibrates in a direction a p p r o x i m a t e l y perpendiculary to the sheet whereas, in dioctahedral minerals, the OH is tilted in the direction of vacant octahedral site [3-4]. The organization of octahedral and tetrahedral sheets defines different types of layers (Figure 2). The classification of phyllosilicates including clay minerals and micas is presented table 1. 1:1 layered silicates are made of one octahedral sheet and one tetrahedral sheet. In this structure, the unshared plan of the octahedral sheet consists of hydroxyls unstead of oxygens. 2:1 layers links one octahedral sheet between two tetrahedral ones. If, because of cation substitutions, the layer is not electrostatically neutral, the excess layer charge is neutralized by various interlayer materials such as individual or hydrated cations or hydroxide octahedral sheets. Smectites and vermiculites are swelling minerals. Sepiolite and palygorskite are 2:1 minerals made of an arrangement of small 2:1 units generating small structural micropores.
576 e, ~ : ~ : : : : : : . :
.....
I Serpentine kaolin
d=7.1 -7.3A
Talc Pyrophyllite
Mica and Brittle Mica
d = 9.1 - 9.4 A
d = 9.6- 10.6 A
0
Oxygen
@ hydroxyl water molecule ~t Smectite Vermiculite d = 14.4- 15.6 A
Interlayer cation
Chlorite
d = 14.0- 14.4 A
Figure 2: View of major non modullated hydrous phyllosilicates groups
1.2 Textural and energetic superficial dependence From the above description, the reader can immediately perceive the heterogeneous nature of clay minerals. In addition to natural substitutions and defects, the different faces of clay minerals have different surface composition and therefore, different energetic properties. For instance, kaolinite with its 1:1 organization presents two distinct basal compositions. The tetrahedral surface is made of oxygens while the octahedral surface is made of hydroxyls. Lateral surfaces are made of A1-OH and Si-OH groups. In the case of 2:1 minerals, both basal surfaces have the same composition. However, talc and pyrophyllite which are not substituted present oxygen surfaces while smectites, vermiculites and micas present in addition to oxygens, monovalent or divalent cations at their basal surfaces in order to neutralize the charge defect of the 2:1 layer. Chlorite is a special case as the interlayer is made of an octahedral hydroxide layer. Then, as for 1:1 minerals, the 2:1 surface is made of oxygens while the other, the hydroxide surface, is made of hydroxyls. In fact, energetic distribution is more complicated as basal and lateral surfaces have their own energy distribution because of crystal defects, distribution of compensating cations, heterogeneity of cationic substitution and charge defects
577 Table 1. : Classification of phyllosilicates (From ref. [2]). Only few examples are given, x refers to an O10(OH)2 formula unit for smectite, vermiculite, mica and brittle mica. (t) : trioctahedral, (d): dioctahedral. group and exemples sub-groups interlayer charge (x) per Layer formula unit type Chrysotile, antigorite Serpentines (t) Antigoriteempty !I kaolin Kaolinite, dickite Kaolins (d) 1:1 or H20 ,x=0 Talc, wiliemseite Talcs (t) empty i TalcPyrophyllite Pyrophyllites (d) pyrophyllite i x=0 Saponite, hectorite Trioctahedral hydrated ! Smectite Beidellite, Dioctahedral exchangeable x = 0,2 - 0,6 montmorillonite cations Di and trioctahedral Di and trioctahedral 2:1 Vermiculite hydrated vermiculites vermiculites exchangeable cations Phlogopite, Biotite Trioctahedral micas Micas s.s. anhydrous Muscovite, illite Dioctahedral micas x = 0,5 - 1,0 cations Clinotite Brittle micas Trioctahedral micas anhydrous Margarite Dioctahedral micas x = 2,0 cations Trioctahedral chlorites Clinochlore,chamoisite Hydroxide Chlorites Dioctahedral chlorites Donbassite layer x variable (2:1:1) Cookeite, sudoite Di-trioctahedral chlorites Sepiolite, loughlinite 2:1 SepioliteSepiolites Palygorskite inverted Palygorskite H20 palygorskite ribbons .
.
.
.
.
i
r e p a r t i t i o n . These h e t e r o g e n e i t i e s r e s u l t f r o m the c o m p l e x m e c h a n i s m s of crystallization and e v o l u t i o n of clay minerals. The distribution and location of heterogeneities are c o m m o n l y studied using different spectroscopic m e t h o d s such as X ray diffraction and absorption, UV-visible-infrared spectroscopy, solid state NMR ... [5-16]. The heterogeneity of clay minerals can also be studied by using the so-called "molecular probes" m e t h o d s based on the detailed study of the adsorption of one or more adsorbate at the surface. In that case the surface of clay minerals m u s t be considered as being formed of patchwise-like domains (basal and lateral surfaces), each d o m a i n bearing a r a n d o m energetic distribution of adsorption sites: i--n e t = ~ xi.eit i=l
(1)
with
eit = J ei (e).%i (e).de f~
(2)
578 where 0t is the total adsorption isotherm, 0it the adsorption isotherms on the different faces of the mineral, Xi is its contribution to 0t, 0i(a) a "local" theoretical adsorption isotherm and Xi(a) the dispersion of the adsorption energy a on the i th domain.
2. EXPERIMENTAL M E T H O D S FOR HETEROGENEITY OF PHYLLOSILICATES
ANALYZING
THE
SURFACE
Clay minerals are naturally heterogenous materials as they are generally associated to other mineral species. Then, separation processes must be used in order to get products as pure as possible. For instance, in the case of swelling materials, the swelling properties, and then the water retention properties, depend on the size and charge of the interlayer exchangeable cation [2]. In order to control the swelling behavior of these minerals, it is necessary to understand the mechanisms of water sorption and swelling for homoionic minerals. In order to obtain a good description of the geometric properties of clay minerals, a set of suitable experimental methods must be used. If the nature of the different species are generally well known, their respective extension must be determined accurately. Such results can be obtained by coupling different complementary experimental techniques with modern modelisation methods. In this chapter a short description of the experimental techniques and modelisation methods employed in our laboratory will be given. Most of the experimental techniques described here have been developed by Prof. Rouquerol, Grillet and Davy in the Centre de Thermochimie et de Microcalorim6trie (Marseille, France). They were further adapted for studying the energetic and geometric heterogeneities of clay minerals. A special X-ray diffraction device has also been designed with the collaboration of Mr. Lhote and Uriot (Centre de Recherche en P6trographie et G6ochimie, Vandoeuvre les Nancy, France) in order to study the structural heterogeneity induced during the swelling of smectites. 2.1 Constant Rate Thermal Analysis Before any adsorption study, the reference state of the surface must be properly characterized. This means that the outgassing conditions, i.e. the v a c u u m - t e m p e r a t u r e couple, must be defined precisely, especially for microporous and swelling minerals. These fundamental informations are obtained by using the "Constant Rate Thermal Analysis" (CRTA) also called reciprocal evolved-gas-detection thermal analysis [17-20]. In conventional thermal analysis, a physical property of a substance is measured as a function of temperature that is controlled by a constant heating rate. For clay minerals, this procedure usaUy results in a partial overlap of successive dehydration steps. This problem is overcome by the use of CRTA. In this method, the heating rate of the sample is controlled by the sample as the rate of desorbed gas is kept constant (or controlled) over the entire temperature range of the experiment. This system operates in the reverse way of conventional thermal analysis because the
579 measured temperatures are dependent of the properties of the sample. The rate of desorption, or gas flow, is controlled by keeping constant (at a chosen value) the pressure drop through a restriction leading to the pump (Figure 3). This rate can be controlled at any value low enough to ensure a satisfactory elimination of the t e m p e r a t u r e and pressure gradients within the sample. When a family of molecules desorbs from the surface, the system remains at constant temperature until the desorption is finished. Then, temperature increases again until another desorption or decomposition is found. For a constant rate of water vapor loss, the temperature vs time data may be immediately converted into temperature vs mass loss data if the total weight loss is measured at the end of the experiment. R o u q u e r o l and coworkers have extensively d i s c u s s e d the multiple advantages of this method : the technique allows to detect the desorption of very small quantities; the determination of equilibrium temperature is more accurate than in conventional thermal analysis. Furthermore, if needed, the experiments can be directly carried out with the sample cell used for gas adsorption experiments. The most recent apparatus are now e q u i p p e d with a mass spectrometer which yields information about the nature of the desorbed gases. A typical curve is displayed figure 4. It was obtained for a microporous mineral, sepiolite, under a residual pressure of 4 Pa [21]. This curve shows four dehydration and dehydroxylation steps. The first one, region I, occurs at 25~ and corresponds to the desorption of zeolitic water adsorbed in the micropores. Regions II and III correspond to the expulsion in two steps (154 and 428~ of water bound to Mg atoms on the edge of the sheet. The last region IV is due to the dehydroxylation of the mineral which occurs at 725~
I. . . . . . . .
Mass Spectrometer
--
Pressure Gauge
]
P
,=
Leak ,
(~
,
Vacuum
Furnace .
.
.
Figure 3: Schematic representation of CRTA
.
.
.
.
.
.
=
constant
580
_
r~
.:~"
10-
15
I
,-u
'
t,,}/,,~
0
'
II I 200
'
'
III ! '
400
,
,
,
IIV
,,
~ 1 ,
,
600
'
800
I
1000
Temperature ~ Figure 4: Controlled rate thermal anlysis of sepiolite [21]. In the case of Wyoming sodium montmorillonite CRTA experiment has shown that after dehydration at 25~ under a residual pressure of 4 Pa, the water content is 0.97 molecule per exchangeable cation (Na +) while at 100~ there is still 0.87 H20 per Na [22].
2.2 Methods based on the evaluation of energetic heterogeneity In order to get information about microporosity distribution of microporous minerals and lateral to basal surface areas ratios of platy minerals, methods taking into consideration the energetic differences of these surfaces are used. The first one is the low temperature adsorption microcalorimetry (LTAM) that allows to measure the isosteric heats of adsorption as a function of surface coverage. The second one is the low pressure quasi equilibrium volumetry (LPQEV) that allows to m e a s u r e continuously and precisely a d s o r p t i o n isotherms in the submonolayer region.
2.2.1 Low temperature adsorption microcalorimetry In this method, adsorption isotherms are performed in a microcalorimeter [23]. It associates quasi equilibrium adsorption volumetry [24] and isothermal low temperature microcalorimetry. The heat flow and quasi equilibrium pressure are then recorded simultaneously as a function of the amount of gas introduced into the system. Adsorption isotherm and the derivative enthalpy of adsorption versus adsorbed quantities are easily derived. Low temperature adsorption experiments are performed at CTM (Marseille) in collaboration with Y. Gillet. A typical curve obtained by this method is shown figure 5 in the case of argon adsorption on a kaolirute [25]. The derivative heat of adsorption decreases rapidly
581 15
14113 -12
1 ~0
,
0
,
,
I
0.2
,
,
,
I
0.4
,
0.6
0.8
1
1.2
1.4
Surface Coverage Figure 5: Isosteric heat of adsorption vs surface coverage obtained by Ar LTAM on FU7 kaolinite [25]. in the low coverage region and then remains nearly constant up to the monolayer region. The first part was assigned to lateral faces, which are very heterogeneous and the most energetic faces, and the constant part to basal faces of the mineral, which are more homogeneous and less energetic. In the case of microporous samples, this method is preferred to conventional calculations derived from experiments performed at different temperatures as the structure of the gas adsorbed in the micropores might be different at different temperatures.
2.2.2 Low pressure quasi equilibrium volumetry It was showed by Michot et al. [26-27] that using low pressure quasi equilibrium volumetry proposed by Grillet et al. [28] and Rouquerol et al. [24] the resolution of adsorption isotherms can be enhanced in the low relative pressure domain, i. e. when the first layer of gas is adsorbed. Then, this method equipped with pressure sensors that work at low pressures allow to study in satisfactory conditions the surface heterogeneity of solids.
2.2.2.1 Experimental procedure The experimental procedure has been discussed by Rouquerol et al. [24] and Michot et al. [26-27]. The sheme of the most recent apparatus of our laboratory [29] is presented figure 6. A slow, constant and continuous flow of the adsorbate is introduced into the adsorption system through a microleak. The flowrate is constant, at least up to the BET domain, and can be adjusted by the pressure imposed before the leak. If the introduction rate is low enough, the measured pressures can be considered as quasi equilibrium pressures. Then, from the
582 recording of the quasi equilibrium pressure (in the range of 10 -3, 3.104 Pa)as a function of time, the adsorption isotherm is derived. The quasi equilibrium state can be tested by comparing adsorption isotherms performed at different flow rates with a same sample. Two high accuracy Datametric differential pressure gauges are used for pressure measurements: 1) 0-1.3x102 Pa and 2) 0-1.3x105 Pa (590 type Barocel Pressure Sensors). For each pressure gauge, four ranges of reading: xl, xl0, xl00, xl000 are available, providing 0-10 volts signals. The minimal sensitivities are 1.3x10 -3 and 1.3x10 -1 Pa for gauges 1) and 2), respectively. Pressure accuracy is 0.05% P + 0.01% of reading range. A dynamic vacuum of 10-7 Pa is ensured on the reference side by the use of a turbomolecular vacuum pump. An accurate constant level of liquid nitrogen is ensured using a home made electronic controlled device [29-31]. The frequency of pressure recording is adjusted after each measurement to ensure between 100 and 200 experimental values per unit of ln(P/Po). Thus, 2000 to 3000 experimental points are collected for relative pressures lower than 0.15. The potentiality of this experimental device is illustrated in figure 7 in the case of an activated carbon. The expansion of the isotherm using a logarithmic scale reveals inflection points. Due to the large quantity of experimental data points, the derivative of the adsorbed quantity as a function of the logarithm of relative pressure can be calculated accurately. As shown figure 7b, the derivative of the adsorption isotherm is much more sensitive as it features different peaks that can be simulated using theoretical derivative adsorption isotherms. Then, using a quasi equilibrium procedure, one can consider the adsorbate as a probe scanning directely the full spectrum of energetic heterogeneity of the surface.
r ...... ! ! ! 9
'c~ ,..om~,uLer '~
! ! ! I
By pass Vacuum control gauge PrimaR vacuum pump
]
~
Gas input Microleak
Turbomolecular vacuum pump
Sample Figure 6: Schematic representation of the quasi-equilibrium gas adsorption apparatus.
583 100
500
a)
b)
375
'~' 75
~t~ 250
-' 50
~
125
0 -16
i
-12
-8 -4 ln(P/Po)
I
0
25
0 -16
|
-12
-8 -4 ln(P/Po)
|
0
Figure 7: Argon a d s o r p t i o n on CX activated carbon (CECA). a) isotherm vs ln(P/Po), b) derivative of adsorption vs ln(P/Po).
2.2.2.2 the Derivative Isotherm Simulation method The example presented Figure 7 shows that different adsorption domains are present on the surface of the solid. In the case of non microporous clay minerals, at least two d o m a i n s corresponding to basal and lateral surfaces should be observed. The total adsorption isotherm is then defined as the sum of a limited n u m b e r of a d s o r p t i o n isotherms (eq. 1). The next step is to determine the different 0i i s o t h e r m s and their contribution to the total isotherm. These isotherms are different if adsorption occurs in micropores or on external surface areas; they have to take into account the energetic distribution of each domain (eq. 2). In the first method proposed to simulate experimental derivative isotherms, each domain, i.e. lateral and basal surfaces, was considered homogeneous [29]. The local isotherms 0i are the Langmuir/Temkin-Bragg-Williams and BET/Hill adsorption isotherms on h o m o g e n e o u s surfaces for one layer and multilayer adsorption respectively. The derivatives of L a n g m u i r / T e m k i n - B r a g g - W i l l i a m s are s h o w n figure 8 for different values of lateral interactions. Analytical expressions of the derivatives are:
e=
C'eae'P/P~ 1+ C.e ae.P / Po
de = e.(1- e) din(P/Po) 1- a@.(1- e)
(3) (4)
584 '
'
'
'
'
I
' '
' ' '
I '
' ' '
l
1
'
'
'
'
'
I '
'
'
'
'
I
t
co=0kT co = 1.5 kT co= 3kT o
m
0.8
0.6
0.4
0.2
0
t
i
-15
i
I
|
,
-12
-9
~
,
-6
"" ~'f --I
-3
=
I
0
In(PIPe) Figure 8: Theoretical derivative of Temkin adsorption isotherms vs l n ( P / P o ) w i t h C=1000 and various values of co calculated from eq. 4 (co = 0: L a n g m u i r isotherm).
d2e = e.(1-e).(1-2e) [dln(P / Po)] 2 [1- ae.(1- e)] 3
(5)
w h e r e P is the m e a s u r e d pressure, Po the condensation pressure of the bulk phase, C is the energetic constant describing the n o r m a l adsorbent-adsorbate interaction and a a constant describing the adsorbate-adsorbate interaction : a = c0/kT (6) w h e r e coo is the average force field exerted on one adsorbed molecule interacting with nearest neighbor adsorbed molecules at surface coverage 0, k the Boltzmann constant and T the absolute temperature. The condition of a m a x i m u m is derived from equation 5: 0*=0.5. Then, from equation 4, one finds:
dO 1
dln(P / Po) p=p.
=
1
(7)
4- a
and from equation 3: P~
c = p"~
-a/2
(8)
585 The same method can be applied in the case of multilayer adsorption (BET isotherm) taking into account the possibility of lateral interactions (extension of Hill [30]) :
0[1 -
C'eae'p / Po P / Po ].[1 + (C.e ae - 1).P / Po]
(9)
In this case, the derivatives are more complicated and computed methods can be used to determine C and a from the position of a maximum [29]. In this method, lateral interactions are introduced as best fit parameters : their role is to adjust the shape of the theoretical derivative to the experimental one. The simulation are performed without any automatic calculation as the operator chooses directely the adsorption models (one layer isotherms for space limited adsorption and multilayer isotherms for external surface areas) and adjusts the position of the maxima and the intensity of lateral interactions by try and errors until the total simulated derivative isotherm matches the experimental one. Then, the derivative isosteric heat of adsorption can be calculated using the energetic parameters and the monolayer capacity of each domain. The comparison of the calculated curve with experimental ones measured by LTAM shows that the agreement is not perfect but that the general features are conserved [29]. The observed discrepancies could be attributed to the fact that patches are not homogeneous and a random (or patchwise) distribution of energy must be taken into account on basal and lateral surfaces. Experimental results show that lateral interactions are rarely nil and values ranging between 1 and 3 k T , and 0 and 3kT are often found for argon and nitrogen, respectively. Recent tests show that these lateral parameters can be used as estimates of the energetic heterogeneity on the different domains. Indeed, there is a close relationship between lateral interaction and the width of a gaussian distribution of normal adsorption energies: O(P / P0)= yOl(C,c0i,P / P0).Gauss(ln(Ci),Ai / kT).dln(C) f~
(10)
where In(Ci) is the first order m o m e n t u m and Ai/kT the standard deviation of the gaussian distribution. Computer simulations demonstrate that the isotherm defined eq. 10 is an isotherm which can be modelled by the same equation as for Oi but with Cf> Ci and a)f < c0i and this for monolayer or multilayer isotherms and for patchwise or random distribution (Figure 9). Figure 10 gives the relationship between r and Ai/kT for roi = 2 and for random and patchwise distributions. This property suggests that, in the DIS method, lateral interactions with negative intensities can be used and correspond to very heterogeneous surfaces. Another consequence is that the higher c0i, the more homogeneous the surface.
586 0.6 a ) 0.5
Patch
~
_
~kT=l
b)
Rand~kT=l
- ........
~/kT
= 0
~
, ,
9 , , ,
, i
~0.4
~
,
0
_
=0.3
ii
0.2
/ I
I I
,'/:
0.1 0.0
,/; ,
i
-13
]
-9
I
i
i
[
-4
,
,
":',k, / ,
,
i
-4 0 ln(P/Po) In(P/Po) Figure 9: Calculated theoretical derivative isotherms using eq. 10 for patchwise and random energy distributions, a) adsorption limited to one layer; b) rnultilayer adsorption. 2.C-
9
-13
-9
0 9
0
0
9
9
0
o
9
1 . 5 -
Patch
o
Random
0
0 0
1.0-
0 0
0.5O O
0.s 0.0
,
,
,,
,
I
0.5
s
,,
~
,
I
,
1.0
,
,
I
1.5
I
,
I
|
|
2.0
Ai / k T Figure 10: relationship between (of and Ai/kT for patchwise and random distribution. The calculated values are the same for multilayer and monolayer initial isotherms.
587 However, the local isotherms discussed above correspond to symmetrical distributions of the normal interactions. In some cases, especially for microporous solids, non symmetrical distributions are needed to obtain a satisfactory representation of the experimental derivative isotherms. In a recent improvement, the generalization of the Dubinin-Asthakov isotherm was used [33]. This isotherm which can be derived from the Rudzinski-Jagiello approach [34-35] writes:
I i/ ll
_ kTln 0it(P / P0) = e
(11)
where Ei is the variance of Zi(e)and ri a parameter governing the shape of the distribution function. It is a gaussian like function widened on the low energy side for ri<3 and widened on the high energy side for ri>3. Its features are presented figure 11. For ri=2, equation 11 becomes the Dubinin-Raduskhevich isotherm. In practice, in application of the Dubinin-Asthakov equation, Pi 0 is commonly identified with saturated vapour pressure P0- If this approximation may be justified in some cases, Pi 0 can generaly not be identified with P0 and must be treated as a best fit parameter which represents the pressure at which the largest micropores of the family are filled. The mathematical relations between El, ri and Pi0 can be easily derived from the expression of the first and second derivatives of 0.25
"~'~~, 0.15
I,'"
--
\',,
ll
0.1
"~ 0.05
//
_
/
-
~
4
J-
/'
I
0 -15
-12
-9
-6 ln(P/Po)
-3
0
Figure 11: Derivatives of Dubinin-Asthakov isotherms using Pi 0 = P0, Ei = 5 kT, co - 0 and different values of ri.
588 equation 11. In practice, ri and Pi 0 are fixed and from the position of a maximum, the adsorption capacity and Ei are calculated, ri and Pi 0 are adjusted until the simulated curve matches the experimental curve. A multilayer extension of Equation 11 has been also proposed in order to simulate peaks which correspond to adsorption on external surfaces [33]. In conclusion, the concept of derivative of adsorption can be used to get quick and reliable results on energetic heterogeneity which can then be used to derive geometric properties of phyllosilicates and other finely divided solids [7, 29-30, 33, 33-40].
2.3 Water vapor adsorption In the case of clay minerals, water adsorption is particularly important. Water vapor adsorption is studied using quasi equilibrium gravimetry of adsorption and i m m e r s i o n microcalorimetry measurements. In the case of swelling minerals, additional informations are obtained by using a special X Ray Diffraction apparatus which allows to measure the size increase of the interlayer upon water adsorption.
2.3.1 Quasi equilibrium adsorption/desorption gravimetry As water vapor is not an ideal gas, its adsorption cannot be studied by the classical volumetric techniques. It is therefore necessary to measure directly the adsorbed quantities by the use of gravimetric methods. The experimental apparatus is based on that described by Rouquerol and Davy [41] and was presented by Poirier et al. [42] (Figure 12). It is built around a Setaram MTB 10-8 symmetrical microbalance sensitive down to the ~tg. Pressures are measured with a Pirani gauge for the 0.01-10 Pa range and a Texas fused silica Bourdon tube automatic gauge for the 1-65 000 Pa range. The adsorption isotherms (i.e mass of H 2 0 adsorbed at 303K vs quasi equilibrium pressure) are recorded on an X-Y recorder connected to the mass signal (Y-axis) and to the pressure signal (X-axis). Prior to each experiment, the samples are outgassed in situ for 16h under a residual pressure of 0.1Pa. Water vapor is supplied to the adsorption cell from a source kept at 41~ at a slow flow rate through a Granville-Phillips leak valve to ensure quasi equilibrium conditions at all time. The vapor supply is immersed in a air thermostat in which the balance and the pressure gauges are encased. The sample cell is immersed in a liquid thermostat and the temperature difference between the air thermostat and the liquid thermostat is always higher than 10~ in order to ensure that the sample is the "cold point" of the system. Desorption isotherms are obtained by connecting the balance and the vacuum pumps to the leak valve. Adsorption-desorption isotherms are obtained for 0.005 < P / P o < 0.98. The sensitivity is limited to samples with total surface areas higher than 0.5 m 2. The vapor flow rate can be adjusted during the experiment by changing the setting of the leak valve.
589
Vacuum ~ (outgassing) Pressure Gauge
1~11 j ///AN~ ,,,
Balance I
~
Vacuum (desorption) Watervapor supply
Reference Analyzed sample sample Figure 12: S c h e m a t i c representation adsorption/desorption apparatus.
of
the
water
vapor
2.3.2 Water immersion microcalorimetry The adsorption of water onto a solid can be studied on an energetical point of view by measuring the heat signal obtained when a solid is immersed in water. Hydration can be studied step by step by immersing solids previously covered with a known quantity of adsorbate. Depending on the relative pressure of precoverage, water will adsorb on the surface according to the energy distribution of surface sites. Thus, the heat of immersion will decrease when precoverage pressure increases as most energetic sites are first screened by adsorption (Figure 13). For a given value of precoverage relative pressure which is generally greater than 0.75, the organization of the outermost adsorbed layers is not influenced anymore by the surface energy of the solid. Then, these layers have the same organization as bulk liquid water and the measured enthalpies of immersion are constant. This value is proportional to the surface area of the solid as shown by equation 12: OTIv. Qw = S (?lv - T-~--). Cos 0
(12)
where Qw is the measured heat of immersion at the asymptote, S the specific surface area of the sample, Tlv the liquid-vapour surface tension of water and 0 the contact angle at the solid-liquid-vapour interface. This method was first developed by Harkins and Jura [44]. In the case of hydrophilic solids (cos 0 = 1), the specific surface a r e a s a r e easily determined as Qw is measured and ?Iv and 0?lv/OT known. The determined surfaces are called absolute surfaces as the method does not require any assumption on the cross sectional area of the adsorbate molecule. In the case of non porous solids, the obtained values are close to the surfaces determined by gas adsorption. In the case of micro and mesoporous solids, the specific surface areas derived from
590 500
E
400
o
o~
E E
300
om
o J
r,z,,1
200
A
100
. . . .
~
I
I
~
~
~
~
I
2
t
~
~
~
I
3
I
A
I
I
J
4
I
!
i
|
5
Surface coverage
Figure 13: Enthalpy of immersion of a kaolinite as a function of the statistical number of preadsorbed water layers [43]. immersion experiments are the external surface areas as the porosity is already filled at these relative pressures. Using these concepts, it was possible to show that the surface field of most solids does not influence the structure of water further than three layers i.e., roughly 10A [43, 44-46]. In practice, a glass bulb (with brittle end) containing a known amount of solid is outgassed and put under a certain pressure of adsorbate [47]. The pressure is controlled by using a thermostated bath. After equilibrium, the bulb is sealed and introduced into the experimental cell (half-filled with the immersion liquid) of a differential scanning microcalorimeter. The brittle end is then broken by depressing a glass rod to which the bulb is attached, the liquid adsorbate enters the cell and wets the solid. The resulting heat flow is then recorded as a function of time. The integration of this curve is proportional to the total heat exchanged. The standardization is carried out by joule effect and the parasite effects (breaking of the brittle end, heat of vaporization) are determined by immersing empty bulbs. Some difficulties are encountered if the solid is partly soluble in the immersion liquid. In order to avoid thermal effects or surface modifications due to dissolution, it is necessary to immerse it in a liquid which has been preequilibrated with an aliquot of the solid. In the case of ionic exchangers, the solution must be equilibrated with the ions exchanged by the solid.
591 2.3.3 X ray diffraction
In the case of swelling clay minerals, the size of the structural unit changes significantly with the hydration conditions. As these compounds are crystalline, their microscopic swelling can be studied by isothermal X-ray diffraction under controlled pressure. This can be achieved using an experimental setup, such as in figure 14, which results from a collaboration with Mr. Uriot and Lhote (CRPG). Oriented clay films containing kaolinite (non swelling) used as a reference are placed in the diffraction chamber (F). They can be outgassed at the desired temperature using the vacuum group (A+B). A given water relative pressure can then be applied to the sample by changing the temperature of the water source (D), the temperature of the diffraction chamber being regulated at 30~ by water circulation (C) in the double wall of the chamber. The X-ray diffraction data are then obtained from the Co Ks radiation on art Intel CPS 120 curved detector (G). Data are collected simultaneously over 60 degrees (20) during 20 to 40 minutes and processed using a multichannel Varrox analyzer (2048 channels for 60 degrees). The kinetics of the swelling phenomenon can be studied by recording Xray diffraction patterns at different times. XRD powder patterns can then be compared with simulated ones. The software used was developed in the Laboratory of Crystallography of Orl6arts, France, using the approach proposed by Drits and Sakharov [48], Drits and Tchoubar [49] and adapted by Besson and Kerm [50] for the study of the intensity diffracted along the 00 rod in reciprocal space from disordered phyllosihcates. The input data file includes (1) the abundances and positions of the atoms in the different clay sheets (2:1 layer structure) and of water molecules in the irtterlamellar space (Data proposed by Pezerat and Mering and by Drits and adapted from Ben Brahim et al. have been used [51-56]); (2) The basal spacings corresponding to the different hydration states (zero, one, two or three layers of water). (3) The proportions of the different hydrated states and the probabilities of succession of two kinds of layer. (4) The distribution of the number of clay layers per quasi-crystals (tactoids). The detailed simulation procedure can be found elsewhere [22, 57-59].
3. ENERGETIC HETEROGENEITY OF NON POROUS MINERALS 3.1 Determination of basal and lateral surfaces of clay minerals
Previous LTAM studies have shown that the most reliable results can be obtained using argon as an adsorbate as it does not have any permanent or inducible polarization [25]. As previously claimed (w 2.2.1., [25]), basal surfaces are assumed to be relatively homogeneous whereas lateral surfaces are assumed to be strongly heterogeneous. In addition, lateral surfaces are expected to share more energetic interaction with gases than basal surfaces. Then, the sharp decrease of differential enthalpy of adsorption can be assigned to lateral faces and the quasi horizontal branch to basal faces of phyUosilicates (Figure 5).
592
T
C A
I
V
1
Figure 14: Upper part: Water pressure and temperature controlled X-ray diffraction device. A: primary vacuum pump. B: turbomolecular pump. C: thermal regulator. D: spring of water vapour. E: diffractometer. F: Sample. Lower part: In situ X-ray diffraction device. G: diffraction chamber. H: bent detector. I: X-ray generator. J: Water pressure input. K: thin window (15~'n aluminum film). L: micrometric slits. M: Thermocouple.
593 By applying the DIS method to low pressure derivative adsorption isotherms these two types of surfaces can be distinguished [29]. Figure 15 displays the adsorption derivative obtained in the case of a kaolinite sample. This derivative can be simulated using four theoretical multilayer derivative isotherms. As with LTAM, the less energetic domain is assigned to adsorption on the basal faces and the three most energetic isotherms are assigned to adsorption on the lateral faces. This result was further validated through the comparison of calculated and experimental derivative heats of adsorption [29]. In the case of non porous phyllosilicates, argon derivative isotherms generally exhibit the same features. Some differences can be observed at high energy due to the presence of high energy adsorption sites. For instance, it could be demonstrated that high energetic adsorption sites observed in the case of talc correspond to the adsorption of argon onto octahedral OH located at the center of the hexagonal cavities of the basal faces [38]. A special section will be devoted to the particular behavior of talc. In the case of chlorite minerals, high energetic sites are also observed (Figure 16). The shape in the high energy part of the derivative isotherm is very similar to that of the derivative when adsorption occurs on basal surfaces. This suggests that the observed high energetic sites are located on the basal surfaces. Their exact physical meaning remains unknown. The DIS procedure yields more quantitative information about basal surfaces. First, the position of the maximum is quite constant in the case of clay minerals; around - 4kT. This result is relatively surprising as basal surfaces have different
1.0 0.8
Experimental Recalculated _- ........ Local derivative isotherms
f~ ./,",~ J' '~
f;
O a.
a. 0.6 r"
\
.t
i!
V
m
"O
~0.4 "D m
0.2 0.0 -15
-10
In(P/Po)
-5
0
Figure 15: Experimental and simulated Ar adsorption derivative isotherms at 77K on GB3 well crystallized kaolinite.
594
0.20
t
.........
Experimental Local derivative isotherms
0
,,-0.15 n_ c
"~ m
> 0.05 "0
0.00 -15
....... :::ii ............ ""-.-..
.."'
T
r
t
-10
L
-5
'
'
'
0
I n ( P / P o )
Figure 16: Derivative of argon adsorption on chlorite (Talc de Luzenac Sa) at 77K. Magnification of the high energy part shows the similitude between high energy domains and adsorption on basal surfaces. chemical compositions (Table 2). The isotherms obtained on kaolinite raise some questions. Indeed, its two basal surfaces are chemically different (Si-O-Si and A1OH) [7, 29]; however, only one theoretical basal isotherm is involved in the decomposition as in the case of talc that exhibits only one type of basal surface (SiO-Si) [38]. If the position of the maximum is relatively constant, the shapes of the derivatives are highly variable as evidenced by the wide distribution of lateral interactions (Table 2). As discussed in section 2.2.2.2, this reveals large variation in the variance of the energetic distributions. This property is illustrated in the case of kaolinites coming from Charentes orebodies, France [7]. The plot of lateral
Table 2: Some characteristics values obtained on phyllosilicates from high resolution quasi equilibrium volumetry of argon adsorption. Mineral Basal surfaqes Lateral surfaces -ln(P/Po) w/kT -ln(P/Po)l -ln(P/Po)2 Kaolinite 4.1 -4.5 0-1.5 7.6- 8.4 9.9- 12,6 Talc 4.1 1.5 6.8 - 7.1 Chlorite Mg 3.15, 4.3 2.7, 0.7 7.1 Muscovite 4.8 1.5 6.9 10, 11 Biotite 4.9 1.8 6.6 9, 11
595 interactions as a function of basal surface areas (Figure 17) shows linear inverse relationships between these two parameters. The samples can be discriminated into two families: the second one corresponds to a special orebody of Charentes province where the samples exhibit anomalously high surface areas for kaolinite minerals. The "decrease of c0/kT with respect to basal surface area is lower than the decrease observed for the other samples (family one). It can be noticed that in both families, lateral surface areas are relatively constant, around 2.5 and 7 m 2 / g for family one and two, respectively. These results can be related to crystallographical properties of kaolinites. Indeed, due to petrogenetic considerations, it is well known that the extend of basal faces is lower for well crystallized kaolinites than for poorly crystallized ones. This can be related to iron content in kaolinite which poisons the crystal growth. Then, the relationship between apparent lateral interactions and the extend of basal faces show that these crystallographical properties are also observed at the surface of the mineral. Lateral surface areas are generally decomposed using two local derivative isotherms. The maximum of the less energetic one is located b e t w e e n - 7 . 6 and -8 kT for kaolinites [7], at -7 kT for talc [38], chlorite and muscovite and at-9 kT for biotite. In all cases, the lateral interaction parameter is equal to zero, indicating the highly heterogeneous nature of lateral faces. Due to experimental limitations 1.80 0
1.60
(1) ,,~ ,..~ 1.40 0
"~
1.20
.oo
.~.,-
0.80 0.60 0.4(
,
I
10
. . . . .
I
20
,
,
,
,
I
30
,
,
,
,
I
40
,
,
,
,
50
Basal Surface Area (m2/g) Figure 17: Relationship between basal surface areas and their energetic heterogeneity expressed as lateral interactions between adsorbed molecules. (1) Charentes orebody except of (2) which corresponds to a special orebody of Charente Bassin.
596 at very low relative pressures, the behavior of the most energetic isotherm can not be interpreted safely. In the case of kaolinite of Charentes, the amount of lateral surface areas derived from the DIS procedure was used to calculate the theoretical cationic exchange capacity (CEC) which is borne by the lateral faces for this mineral [7, 25]. A linear relationship is observed between calculated and experimental CEC values (Figure 18). The slope of 0.92 obtained in the regression indicates that lateral surfaces could be underestimated by 8%. In conclusion, at the moment the DIS method seems to yield the most accurate determination of the flakiness ratio of platy materials. The lateral interactions used to simulate adsorption on basal surfaces can be used as an index of surface homogeneity. Such treatment can not be applied to nitrogen adsorption as previously evidenced by LTAM experiments on kaolinite minerals [25] due to the polarizability (quadrupolar momentum) of nitrogen molecules in the presence of protons of OH groups at mineral surfaces. Experimental evidences of such interactions were given by infrared experiments of Frohmsdorff et al. [60]. In the case of kaolinite minerals (Figure 19), the energetic distribution derived from nitrogen adsorption onto basal and lateral surfaces is not fully understood at the present time. However, the polarizability of nitrogen can be advantageously used as it can give information about electropositive groups of the surface as discussed in the next section.
12
m
9
10
9
r.,j r,j 9 m
-
9
O
9
r
m
r
r,j 0 0
2
4
6
8
10
12
14
Experimental CEC (meq/lOOg) Figure 18" Relationship between experimental and corrected cationic exchange capacities of Charentes kaolinites. Experimental values have been corrected of exchange capacity due to octahedral isomorphic substitution of A13+ by Fe2+ [7].
597
0.8 0.7
Experimental Recalculated ......... Local derivative isotherms
i(~
/
/
[!
~
l
I
~//
A0.6 O n_
a. 0.5 c "O
0.4 0.3
> 0.2 "O
0.1 0.0
........
-16
.---~_:t-~-~--~.-_-.:-~:--r":_'~'_:::~..... " : : :
-12
-8 In(P/Po)
. . . . . . J .... ~:::',
-4
Figure 19" Experimental and simulated N2 adsorption derivative isotherms at 77K on GB3 well crystallized kaolinite.
3.2 Evidence of basal high energy sites of muscovite Muscovite is a mica mineral. It is a 2:1 phyllosilicate with a tetrahedral charge defect due to the substitution of aluminium for silicium. This charge defect is compensated by potassium cations located between 2:1 layers. K + ions are located above half of the hexagonal cavities. On the basis of crystallographic data the surface area of a hexagonal cavity is 24 ~2, then K + density on basal faces is one per 48 ~2 on basal faces. As mentioned above, basal and lateral surface areas can be d e t e r m i n e d from the DIS method applied to argon adsorption isotherm (Figure 20a). Nitrogen adsorption isotherms (Figure 20b) are clearly different as the derivative exhibits two high energy sites and lateral and basal surfaces can not be distinguished (manuscript in preparation). The high energy site distribution depends on the outgassing temperature. This means that water adsorbed on high energy sites is not totally desorbed at classical outgassing temperatures used for non porous samples. By applying the DIS method to the nitrogen isotherm, the adsorbed quantities on both energetic sites can be measured. The total adsorbed amount can then be compared to the number of surface hexagonal cavities deduced from the basal surface area determined by argon derivative isotherms. The results (Table 3) show that the amount of nitrogen adsorbed on high energy sites corresponds exactly to half the number of hexagonal cavities. It can then be concluded that these high energetic sites are located on the basal surfaces either on exchangeable
598 0.6
........
I .........
I ........
0.6
a) 0.5
0.5
0.4
0.4
e,,
-
c
-"
0.3
-"
0.3
"0
1).1
"~
0.1
0
'
-15
-10
-5
ln(P/Po)
,,
0
0
#
,-i ~ r~_,.,~
-15
K
-10
,,
~ EL,--,"i't_
"-
_
Jr,,
I-_r-.~"=F r , ,
-5
In(P/Po)
,,
,
0
Figure 20: Muscovite, experimental derivative of adsorption (straight line) and decomposition results (doted lines), a) argon at 77K and b) nitrogen at 77K. Table 3: Description of surface properties of muscovite and biotite derived from simulation of ex ~erimental derivative of adsorption of argon and nitrogen at 77K Argon Nitrogen Adsorbed quantities on high basal surface area: 6.8 m 2/g Muscovite energy sites: 0.405 cm3/g STP Lateral surface area: 1.9 m2/g number of sites: 1.36 1019/g hexagonal basal sites: 2.83 1019/g Adsorbed quantities on high basal surface area: 3.9 m 2/g Biotite energy sites: 0.209 crn3/g STP Lateral surface area: 1.0 m2/g number of sites: 5.6 1018/ g hexagonal basal sites: 1.63 1019/g cations or above hexagonal cavities non occupied by K +. Experiments are in progress to clarify this assignment. Such high energy sites have already been observed on several samples such as apatite (calcium phosphate, in this case, the high energy sites correspond to superficial P-OH groups [39-40]), synthetic saponite (2:1 clay mineral with tetrahedral charge defect, in this case high energy sites are not assigned at the moment), synthetic silica (Si-OH groups) and chlorites (in this case, the high energetic sites are located on basal surface but their exact location is unclear at present time). The case of talc will be discussed in the next section. However, it seems that this phenomenon is not always observed. For instance, biotite, a trioctahedral mica with the same tetrahedral substitution as muscovite, features some high energy adsorption sites wich can not be easily assigned to the same basal sites as muscovite (Figure 21, Table 3).
599 0.35 0.3 A
o Q. a,.
V n
C
"0 "0 "0
. . . . .
I ....
J"
I
'
'
"
'
'
I
. . . . .
'i
. . . . .
.
L
"
0.25 0.2 0.15
/
.
0.1 0.05
.
/// tI
0
,
-15
f% "
jJ/ ~
'2 !
/
i
,
~
,
-12
,
~
I
-9
,
,
,
,
~1
,
-6
In(P/Po)
!
I
'
'
!
-3
,
,
,
=
,
0
Figure 21- Biotite, derivative of argon (straight line) and nitrogen adsorption (doted line) at 77K.
3.3 The microscopic hydrophilicity of talc Talc is a trioctahedral phyllosilicate with no layer charge which appears hydrophobic with experimental water contact angle around 80 ~ [61-64]. However, immersion microcalorimetry measurements show that outgassed talc can not be considered as truly hydrophobic [27, 38, 65-67]. The immersion enthalpy value is more than tripled for outgassing temperature of 25~ and 400~ indicating that the wettability of talc increases with outgassing temperature (Figure 22). Concomittantly, adsorption-desorption isotherms of water vapor are influenced by outgassing temperature (Figure 23). Their shape also suggests a hydrophilic behaviour. Furthermore, for high outgassing temperature all the adsorbed water cannot be removed even by pumping the samples under a vacuum of 0.1 Pa at 30~ As the crystallochemical properties are not affected at temperature below 800~ [30], it can be stated that the changes of water affinity toward the talc surface must be due to surface modifications. These surface modifications were studied using argon and nitrogen as probes (Figure 24) [38]. Derivative adsorption isotherms show that high energy sites appear when the outgassing temperature is increased. This is especially the case with nitrogen. The use of the DIS method shows that basal (=12 m2/g) and lateral (---4 m2/g) surface areas can be evaluated by argon and nitrogen. The adsorbed quantity on high energy sites measured with nitrogen is maximal (1,7 cm3/g) for outgassing at 250~ Close values are obtained with argon.
600 16
12
"E 8 4
0 0
0.2
0.4
0.6
0.8
1
P/Po
Figure 22: Immersion entha]py of talc in water as a function of water v a p o r
precoverage and outgassing temperature.
1.5
1.5 / ' ' , 1 , , , i , , , i , , , i , , ,
: 30~ o
E E
1
m "0 m
0
0
0.5
0.5
0
,
0
,
J
I
,
0.2
,
,
I
~
,
,
I
. . . .
I
x
~
,
0
0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 P/Po P/Po Figure 23: Water vapor adsorption isotherms at 303K on talc outgassed at 30 and 250~
601 1.5
'
'
'
"i
'
' ' '
I
'
'
'
I
'
'
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9
'
Ar 2 5 ~
l
.
i
1.5 I Ar 2 5 0 ~
i
m
,
,
'
,
.
'
,
|
,
_c
.E
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,,
"~ 0.5
0s
i
'-'
"o
1D
0
6
1.5
- 12
"
'
'
i
-8 In(P/Po) '
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-4
'
I
o
0
'
'
-16
1.5
'
,
'
-12
'
'
I
,
-8 -4 In(P/Po) '
'
'
I
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0
'
N 2 250~
N 2 25~
!'"
A
o o. 13.
,
1 _c -o
v
=.,= 1D
t
"o 0.5 ~> "o
9 "~ 0.5
t~
o
1D
.,
6
- 12
-8
In(P/Po)
-4
0
0
.::.. -16
.... .:...: -12
....
-8 In(P/Po)
....iiii.i -4
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Figure 24- Experimental derivative of argon and nitrogen adsorption at 77K (straight line) and decomposition results (doted lines).on talc outgassed at 25 and 250~ The octahedral layer of talc is trioctahedral and almost purely magnesian. Consequently, the octahedral hydroxyls point directly toward the surface inside the hexagonal cavity formed by the arrangement of the silica tetrahedra of the tetrahedral layer. These OH groups could then exhibit a particular reactivity toward polar molecules as evidenced by computer simulation of water adsorption [68]. From crystallographical parameters, it appears that each hexagon of the tetrahedral layer has an area of 25 ]k2. Then, the theoretical quantity of nitrogen adsorbed on OH groups can be calculated by taking into account the basal
602 surface area of 12 m2/g and an adsorption ratio of one nitrogen per hydroxyl group. The obtained value is 1.79 cm3/g which is very close to the experimental value (1.72). Therefore, the high reactivity of OH groups proposed by Skipper et al. [68] is experimentally confirmed in the case of nitrogen. The increased wettability with outgassing temperature can be assigned to outgassing of adsorbed molecules screening OH groups. Controlled rate thermal analysis coupled to mass spectrometric analysis confirmed that numerous surfaces species including water, carbon dioxide, nitrogen and organic molecules which seem to be long chain amines and nitriles are outgassed between 150 and 400~ (Figure 25). As a consequence, water adsorbs first on liberated OH groups and screens the high energy sites. The surface is then hydrophobic and water adsorption occurs only by cluster growth through hydrogen bonding with the first adsorbed molecules. If structural OH groups are replaced by fluor ions, talc is totally hydrophobic and no water adsorbs on its surface [38]. Another interesting consequence of the determination of basal and lateral surface areas is that they can be used in the Harkins and Jura formula to calculate water contact angle on the basal surface of talc. Because of talc hydrophobicity, the external Harkins and Jura surface can not be derived from enthalpy immersion after water precoverage at P/Po greater than 0.85 as this method assumes'a water contact angle equal to zero. Indeed, the obtained value is 4.2 m2/g and is very different from the values obtained by argon or nitrogen adsorption (16 m2/g). As 2 10 "11
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603 the basal and lateral surface areas have been measured, it is possible to calculate the water contact angle with basal surfaces if one assumes that lateral surfaces are totally wetted. This calculation leads to a contact angle of around 86 ~ which is close to the macroscopic experimental value [38]. 4. P O R O U S M I N E R A L S : SEPIOLITE
AND PALYGORSKITE Sepiolite and palygorskite are natural microporous clay minerals. Their microporosity originates from structural properties as they are made of ribbons of 2:1 talc-like layers organized in quincunxes. The linked ribbons form a 2:1 layer continuous along the x direction but of limited lateral extension along the y axis creating channels or structural micropores of 13.4A x 6.7A and 13.7A x 6.4A for sepiolite and palygorskite, respectively [69-70]. Sepiolite and palygorskite exhibit a fibrous habit and the association of fibers produces an interfiber microporosity [69, 71]. The external surfaces consists predominantly of {011} crystal corrugated faces [72-73]. Most studies on sepiolite and palygorskite textural properties have been based on adsorption isotherms obtained from conventional volumetric adsorption apparatus, typically using N2 [74-84]. Such techniques are unable to reveal information at relative pressure lower than 0.05. Hence, the usual method used to detect and study microporosity cannot be used to reveal the two types of microporosity. From the use of high resolution transmission electronic microscopy, well controlled thermal treatment and outgassing procedures, argon and nitrogen low temperature adsorption microcalorimetry coupled to quasi equilibrium volumetry, carbon dioxide quasi equilibrium volumetry, water vapor adsorption gravimetry and immersion microcalorimetry in water [21, 85], it has been possible to distinguish between the structural or intrafiber microscopy (c~ Figure 26), the interfiber microscopy (15 Figure 26) and the external surface of the fibers (7 Figure 26). The dehydration steps must first be characterized precisely. This was achieved using CRTA experiments (Figures 4 and 27). The case of sepiolite was described part 2.1. In the case of palygorskite, the dehydration behavior is close to that observed for sepiolite but, the dehydration of the first half of crystallized water occurs at lower temperature, around 100~ and the second dehydratation is immediately followed by the dehydroxylation of the 2:1 layers between 250 and 420~ For both samples, the structure folds after the dehydration of the first half of crystallized water i.e. above 350 and 120 ~ for sepiolite and palygorskite, respectively. After the folding the accessibility of structural micropores disappears. The study of the textural properties of these samples as a function of outgassing temperature can give valuable information about their geometric heterogeneity. The majors results can be obtained by Ar and N2 LTAM (Figure 28). Indeed, the derivative enthalpy of adsorption remains constant during the filling of the microporosity as it is the case for zeolites. When the samples are outgassed at temperatures higher than folding temperatures, this part of the isotherms disappears. The horizontal branch of the derivative enthalpy of adsorption can then be assigned to structural micropores.
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The direct distinction between filling of interfiber micropores and adsorption on external surfaces from LTAM experiments is less easy. However, using Harkins and Jura's method, the external surface area of the fibers can be measured without any assumption on the cross sectional area of water molecules. The obtained results were 125 and 63 m 2 / g for sepiolite and palygorskite, respectively. The adsorbed quantities in interfiber micropores can then be derived if the adsorbed volumes in intrafiber micropores and on external surfaces are subtracted from the monolayer capacity. More recently [29,31], it has
606 been shown that the derivative isotherm summation can be used to distinguish between structural micropores, interfiber micropores and external surface area (Figure 29). The evolution of the textural properties of sepiolite with outgassing temperature shows that the accessibility of structural micropores decreases between 200 and 250~ and disappears at 350~ The external surface area decreases from 125 to 48 m 2 / g when the structure collapses. In the case of palygorskite, it was observed that the folding was dependent on the couple vacuum-temperature. Furthermore, water vapor adsorption showed that the folding was reversible for outgassing temperatures below 225~ which is the temperature of folding of sepiolite. In contrary to sepiolite, the external surface area of palygorskite decreases only slightly from 65 to 54 m 2/g when the structure folds. The geometric microporosity detected by nitrogen and argon adsorption can be compared to the theoretical volume taking into account the density of the bulk phase, i.e. 0.808 and 1.427 for nitrogen and argon respectively [86]. The filling of the micropores was only partial as 20% of the total microporosity was available to nitrogen (= 15% for argon) for outgassing temperatures lower than 100~ However, adsorption of carbon dioxide at 293K using a quasi-equilibrium volumetric technique shows that the adsorbed volumes in micropores, as calculated by the Dubinin method, correspond to 100% of the theoretical volume
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607 for sepiolite [26] and palygorskite [85]. This feature of CO2 adsorption was recently confirmed in the case of All3 pillared saponites. Therefore, CO2 quasi-equilibrium adsorption volumetry appears as the most reliable method for total microporosity assessment [87].
5. THE SPECIAL BEHAVIOR OF SWELLING CLAY MINERALS The adsorption of water on swelling clay minerals is a very complicated phenomenon. Swelling clay minerals are 2:1 clay minerals with octahedral charge defects. In this case, the charge is not localized at the surface of the layer and the cohesion between layers, ensured by the interlayer cations, is weaker than in the case of clay minerals with tetrahedral charge defects. Due to the delocalization of the layer charge, the interlayer cations can share high interactions with water molecules which can enter between 2:1 layers. This interlayer cation hydration phenomenon is considered as being the driving force for swelling structure. The interlayer space increases with adsorbed water, creating structural heterogeneities. Then, the surface accessible to water changes constantly from 20-60 m2.g -1 in the dry state to = 800 m2.g -1 in the fully hydrated state. The swelling properties depend also on the nature of the interlayer cations:. mixtures of interlayer cations are generally observed in natural samples. This represent a source of heterogeneity in addition to crystal defects, faces distribution, and layer charge distribution. In the case of montmorillonite which is dioctahedral, the complexity of the phenomena involved makes it necessary to simplify the system by studying homoionic samples. The mechanisms of water adsorption and the influence of the nature of the interlayer cation can then be studied. Nine homoionic samples were prepared by ionic exchange starting from the homoionic sodium form [22, 57-59]. In the present review we have chosen to present the case of two monovalent cations, sodium and cesium and two divalent ones, calcium and barium. In order to approach such complicated phenomena various experimental techniques have to be used. The initial state or "dry" state, i.e. the outgassing conditions which were used in all adsorption experiments, was first carefully defined by CRTA (see paragraph 2.1.). The size of clay tactoids (i.e. the number of clay layers per quasicrystal) in the initial dry state and in the fully hydrated state was obtained from nitrogen adsorption-desorption volumetry at 77K and immersion microcalorimetry respectively. Once the initial and final states are well defined, the mechanisms of water adsorption can be approached by coupling water adsorption/desorption gravimetry and X-ray diffraction studies under controlled water vapor relative pressure. Subsequent modelling was used to characterize the various hydrated states obtained with increasing water vapor relative pressure.
608 5.1. Characterization of the dry state 5.1.1. CRTA Analyses. Figure 30 shows the CRTA curves corresponding to the four different homoionic montmorillonites. Three regions can be defined on these curves. Region I, between room temperature and 100~ corresponds to the desorption of physically adsorbed water. Region II (100~ 500~ corresponds mainly to the expulsion of water supposedly bound to exchangable cations whereas region III (above 500~ corresponds to the dehydroxylation of the structure. In the central region, the water content decreases in the order Ca2+> Na + > Ba2+> Cs + following the decreasing solvation energy of the cations. In the initial state chosen for the hydration studies (outgassing at 100~ there is still some water associated to the exchangable cations. This quantity varies between 4.6 and 1.7 molecules per cation in the case of Ca-montmorillonite and Cs-montmorillonite, respectively. 5.1.2. Analysis of the size of the tactoids. The "dry" state can be further characterized by nitrogen adsorption-desorption isotherms (Figure 31). Depending on the interlayer cation, the quantity of nitrogen adsorbed is very different as revealed by the BET surface areas displayed in Table 4. The high surface area of Cs-montmorillonite is due to the size of the interlayer cation which allows nitrogen molecules to enter the interlayer space. Each curve exhibits a H3 hysteresis loop [88] in desorption characterizing the
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rate
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609 presence of slit-shaped pores. The size of Cs also explains the low pressure hysteresis observed in this case. The low pressure hysteresis observed in the case of Ca-montmorillonite and to a lesser degree in the case of Na-montmorillonite has a different origin. It results from the modification of the arrangement of the clay layers in quasi-crystals (or tactoids) brought by the adsorption of nitrogen at high relative pressure. This was confirmed by carrying out successive adsorptiondesorption cycles [59]. The average number of clay layers per quasicrystal was calculated from the nitrogen adsorption data assuming that the plates (square parallelipipeds = 3000A by side and a multiple of the d001 in height) are perfectly stacked as in a deck of cards (Table 4). In the case of Cs-montmorillonite the contribution of the microporosity was first subtracted. The results show strong differences depending on the nature of the cation as Ca-montmorillonite has an average of 69 layers per tactoid whereas Cs montmorillonite is formed of quasicrystals of around 11 layers. 5.2. Characterization of the hydrated state. As the surface area of the materials changes upon water adsorption, it is necessary to define precisely the state of the quasicrystals after immersion in water. This can be achieved through immersion microcalorimetry experiments. The results displayed in Figure 32 reveal that the immersion enthalpy at zero coverage increases with the hydration energy of the exchangable cation : Cs + < N a + < Ba 2+ < Ca 2+. In each case, the asymptotic behavior is observed for precoverage relative pressures higher than = 0.8. Using Harkins and Jura's treatment, the external surface area of the wet samples was then derived and the number of clay layers per hydrated tactoid was subsequentely obtained in the same way as in the case of nitrogen adsorption (Table 4). The differences are less pronounced than in the dry state with 8 layers per quasicrystal in the case of monovalent montmorillonites and 12-14 layers for the divalent ones. The breaking of the tactoids into smaller units must then be taken into account for studying water adsorption.
Table 4: Specific surface areas and number of layer per tactoid deduced from BET surface area,.(dry state) and Harkins and Jura surface area (wet state). .Surf.a.ce area m 2 / g Number of layer per tactoid N2 BET Harkins and Jura Dry state Wet state Na 42 84 33 8 Cs 130 86 11 8 Ca 17 63 69 12 Ba 25 56 32 14 ,
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612 5.3 Study of water adsorption mechanisms. 5.3.1. Water adsorption-desorption isotherms. Water adsorption isotherms corresponding to the four homoionic montmorillonites are displayed in Figure 33. The shape of the isotherm is strongly influenced by the nature of the interlayer cation. The desorption branch closes in the case of monovalent cations in contrary to the case of divalent cations where all the adsorbed water is not desorbed at low relative pressure. The shape of the isotherms suggests that water molecules enter the interlayer space for very low relative pressures in the case of Ca- and Ba-montmorillonite whereas in Csor Na-montmorillonite it seems that water enters the interlayer space for a given value of the relative pressure. These curves can not be interpreted safely without any data concerning the texture of the quasicrystals after adsorption and the evolution of the interlayer distance with water relative pressure which can be obtained using the X-ray diffraction system described in Figure 14. 5.3.2. X-ray diffraction studies under controlled water vapor pressure. The evolution of the d(001) with water relative pressure is presented in Figure 34. All the points were taken after at least 4 hours equilibrium time as the swelling kinetics of monovalent montmorillonites are rather slow for high relative pressures [58]. The absence of defined steps in adsorption shows the presence of interstratified states, i.e. heterogeneous distribution of the hydrated states, in the whole range of water relative pressure. The desorption branches generally exhibit more pronounced steps revealing a tendency towards more homogeneous hydrated states. Figure 35 presents the proportions of the different hydrated states obtained for the four montmorillonites in adsorption. Basal spacings of the models were set taking into account the harmonics of the quasi-homogeneous states or, in the absence of such states, results from the litterature [55-57, 89-93]. The mean numbers (M) of layers per stack were determined in order to obtain a good superimposition of the positions of the experimental and calculated reflexions. Generally these values agree with those obtained using the width of the d(001) reflexion and Scherrer's formula except in the case of Csmontmorillonite which present symmetrical (hexagonal cavities face to face) and non symmetrical (hexagonal cavities shifted) stacking types noted 0L sym and 0L non Sym in figure 35. In order to explain the broadening of the peaks d(002) and d(004) and the high value of the position of the peak d(002), the different states (0L sym, 0L non sym and 1L) were assumed to be separated (demixed) for relative pressure values between 0.15 and 0.50 (Figure 35). In the case of Camontmorillonite at high relative pressure, homogeneous series of values of basal spacing are observed that do not correspond to one layer-two layer hydrate state interstratifications. The patterns were then simulated using a set of two values of basal spacing corresponding to sub-states 2a and 2b according to the study of Suquet and Pezerat [94].
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o~
616 In adsorption, the one layer hydrate appears at very low relative pressure for both divalent cations and for Cesium. In contrary, in the case of sodium it starts developping only for a relative pressure of around 0.25. The two layers hydrate appears at relative pressure values of 0.2, 0.4, and 0.6 for Ca-, Ba- and Namontmorillonite, respectively. It never develops for Cs-montmorillonite. For m e d i u m relative p r e s s u r e s (around 0.5) all m o n t m o r i l l o n i t e s exhibit interstratified states. Quasi-homogeneous two layer hydrates are present from relative pressure values of 0.7 and 0.9 for Ca and Na-montmorillonite, respectively whereas Ba-montmorillonite exhibits a two layers-three layers interstratified state at high relative pressure. Cs-montmorillonite tends towards a quasi-homogeneous one layer hydrate. In any case real homogenous hydrates are never observed. All the results obtained give some insight about the way water adsorbs on these four montmorillonites. In the first hydration stage Na, Ca and Ba-montmorillonite exhibit a splitting of the initial dried quasicrystals into smaller ones. This is not the case of Csm o n t m o r i l l o n i t e where the size of the tactoids appears u n c h a n g e d upon hydration. The repartition of water between internal and external surfaces can be derived assuming that the adsorption on the external surfaces of the clay layers can be represented using the reference isotherms obtained on non porous solids by Hagymassy et al. [95]. The amount of water in the internal surfaces can then be obtained for each relative pressure by subtracting to the water adsorption isotherm presented in Figure 32 the amount of external water and by adding the amount of residual water in the dry state determined from CRTA experiments (Figure 36). These curves reveal that for Ca and Ba-montmorillonite water starts entering the interlayer space for the lowest relative pressures investigated. This is not the case for Cs-montmorillonite where water starts adsorbing in the interlayer region for relative pressures higher than 0.05. The case of Na-montmorillonite is peculiar as the water starts entering the interlayer space to form a predominant one layer hydrate, once a monolayer is formed on the external surface. The same situation appears for the formation of the two layer hydrate which starts once a bilayer is formed on the external surfaces of the tactoids. It then seems that in the case of N a - m o n t m o r i l l o n i t e the bidimensional pressure is a factor governing the hydration as well as the hydration energy of the interlayer cation. Once the distinction between water adsorbed on the external or internal surfaces of the clay is clarified, it is possible to study the degree of filling of the interlayer space by combining data obtained from x-ray experiments and water adsorption data. The maximal amount of water in the interlayer region can be calculated using the following assumptions. 9 The internal specific surface area is given by Sint = 801.3 -(Sext- Sext lat) where Sext is the total external specific surface area and Sext lat the lateral external specific surface area. 9 Models of the one-layer hydrate and two-layer hydrate proposed by Ben Brahim et al. [55] for Na-beidellite are valid for homoionic montmorillonites. Cross sectional areas of 7.8 ~2 (c~1) and 8.7A 2 (c~2) are assumed for the water
617 molecule in the one layer-hydrate and two or three-layer hydrate, respectively. The amount of water Qmi, in mmol.g -1, adsorbed in the interlayer space as a monolayer (i=1), bilayer (i=2) and third layer (i=3) is given by the relation:
i.Sint Qmi = 2c~i.Na 9 Then for given abundances of relative proportions Wi of each type of layer, the maximal adsorbed amount in the interlayer region is given by: Qint max = W0*Qinit + Wl*Qml + W2*Qm2 + W3*Qm3. The filling of the interlayer space can then be calculated from the ratio (Qint / Qint max)- It is presented in Figure 37 for the adsorption of water on the four homoionic montmorillonites as a function of the % of interlamellar swelling defined as the sum of Wi balanced by i. It corresponds to the solvation of the exchangeable cations and to the filling of the remaining interlamellar space. The filling lies between 40 and 70% for the monovalent cations and is higher around 80-90% for divalent cations. This latter value is close to what is observed in the case of beidellite. In the case of Na-montmorilonite, the filling decreases upon the transition from a dominant one layer hydrate to the two layer hydrate. The variation of the filling reveals the complexity of the structure of the interlamellar water that should be studied using spectroscopic techniques. This study on homoionic montmorillonites shows the complexity of the phenomena involved in the adsorption of water on such swelling materials that are strongly heterogenous. More work is needed before the chemical heterogeneity of the nature of the interlayer cations in natural soil clays can be taken into account.
GENERAL CONCLUSIONS Clay minerals are typical examples of heterogeneous adsorbents showing both surface geometric and energy distributions. The heterogeneity of clays is governed by the geochemical crystallisation conditions generating a strong relationship between the structure, shape and chemical surface properties of these solids. Such complexity obliges to develop m o d e r n experimental techniques and modelling methods for studying solid surfaces. In this way, the high resolution low pressure quasiequilibrium adsorption technique, first developped to characterize geometrical heterogeneity of clay minerals, looks very powerful and promising for studying their energetic heterogeneity. Furthermore, the possibility to detect very high energy surface sites opens new investigation fields as those sites are always involved in interfacial interactions.
618 20
20
Na
C$
15
15_ A i
0;3
|
~0 O
O
E ~0E
10
5 1
0
0
0
0.2
0.4 P/P
0.6
0.8
o
'
,I,
,I,,,!,
0.2
0.4 0.6 PIP
0 .....
!,,,
External water I Internal water
0.8
1
~ 0.8
1
o
II
20
20
Ca ~
15 A
0
E u
~0
i
!1o U
5 s
i-.;-;-;'i-i 0
15
0.2
I ,,,,,,, 0.4 P/P
0.6 o
.s
I, ,,~ 0.8
5
;-;-;
0 1
0
.... ,
"i-;, -i-i .~, 0.2 0.4 0.6 P/P 0
Figure 36: Water distribution on external surfaces and in internal surfaces of Na, Cs, Ca a n d Ba montmorillonites as a function of relative w a t e r v a p o r pressure.
619 120 9
9
o Na A Cs
Ca Ba
100
o
80 0
&
9
A
60 o
40
20
0
i
0
I
100 % of inteflamellar swelling
i
200
Figure 37: Percentage of water filling as a function of the degree of swelling of Na, Cs, Ca and Ba montmorillonites.
The case of swelling clay minerals is of great applied and fundamental interest as water adsorption generates structural heterogeneities which render the interactions mechanisms rather complex. Here again, the d e v e l o p m e n t of adapted techniques such as X-ray diffraction under controlled water vapor pressure is required for a clearer understanding of the behavior of these minerals. This review also shows that it is important to combine different experimental approaches to analyze properly the surface heterogenities of these solids in order to understand their behavior in natural or industrial conditions. The succesful results obtained in the case of talc, microporous and swelling clay species validate the developped methods which are currently applied to other solid surfaces.
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621 30. F. Villi6ras, Etude des modifications des propri6t6s du talc et de la chlorite par traitement thermique, Th~se doctorat INPL, Nancy, France, 1993. 31. F. Vflli6ras, L. J. Michot, F. Didier, G. G6rard, submitted to Langmuir (1995). 32. T. L. Hill, Statistical mechanics, McGraw-Hill, New York, 1956. 33. F. Villi6ras, L. J. Michot, J. M. Cases, M. Frangois, W. Rudzinski, submitted to Langmuir (1995). 34. W. Rudzinski and D.H. Everett (eds.)The Adsorption of Gases on Heterogeneous Surfaces, Academic Press, 1992. 35. W. Rudzinski, K. Nieszporek, J.M. Cases, L.J. Michot, F. Villi6ras, Langmuir (1995) In press. 36. J. Y. Bottero, M. Arnaud, F. Villi6ras, P De Donato., M. Frangois, J. Colloid. Int. Sci., 159 (1993) 45. 37. J. Y. Bottero, A. Manceau, F. Villi6ras, D. Tchoubar, Langmuir, 10 (1994) 316. 38. L. J. Michot, F. Villi6ras, M. Frangois, J. Yvon, R. Le Dred, J.M. Cases, Langmuir, 10 (1994) 3765. 39. E. Bernardy, J.M. Cases, M. Frangois, F. Villi6ras, L.J. Michot, A. Wilmes , third symposium on Characterization Of pOrous Solids (COPS III). Abstract book (1993) 122. 40. E. Bernardy, M6canismes d'action d'agents de couplage : syst6me PEThydroxyapatite-arylphosphonate, Th6se de Doctorat de I'INPL, Nancy, France, 1995. 41. J. Rouquerol and L. Davy, Thermodynamica Acta, 24 (1978) 391. 42. J. E. Poirier, M. Francois, J. M. Cases and F. Rouquerol, in Fundamentals of Adsorption, T. Athanasios and T. Laiapis (eds.), A.I.C.H.E., New York, 1987, 473. 43. J. M. Cases and M. Frangois, Agronomie, 2 (1982) 931. 44. S. Partika, F. Rouquerol, J. Rouquerol, J. Colloid Interface Sci., 68 (1979) 21. 44. W. D. Harkins and G. Jura, J. Amer. Chem. Soc., 66 (1944) 1362. 45. J. J. Fripiat, J. M. Cases, M. Frangois, M. Letellier, J. Colloid Interface Sci., 89 (1982) 378. 46. J.J. Fripiat, J.M. Cases, M. Frangois, M. Letellier, J.F. Delon, J. Rouquerol, In Studies in surface science and catalysis, Vol. 10. Proc. of the international symposium on adsorption at the gaz-solid and liquiq-solid interfaces, J. Rouquerol and K.S.W. Sing (eds.), Elsevier Scientific, Amsterdam, (1982), 449. 47. M. Laffite et J. Rouquerol, Bull. Soc. Chim. Fr. (1970) 3335. 48. V.A. Drits and B.A. Sakharov (eds.), X-ray Structure Analysis of Interstratified Minerals, Nauka, Moscow, 1976 (in russian). 49. V.A. Drits and C. Tchoubar (eds.) X-ray diffraction by disordered lameUar structure. Teory and applications to microdivided silicates and carbons. Springer-Verlag Pub., Berlin, Heidelberg, 1990. 50. A.G. Kerm, Etude et Caract~risafion des Premiers Stades d'Hytratation d'une Nontronite. Th~se d'UniversitG Orl6ans, France (1988). 51. H. Pezerat and J. Mering, C.R. Acad. Sci. Paris, 265D (1967) 529. 52. V.A. Drits, in Crystallochemistry of Minerals and Geological Problems, Kossovskaya, A.G. (ed.), Nauka, Novosibirsk, (1975) 35 (in Russian).
622 53. J. Ben Brahim, Contribution a l'~tude des syst~mes eau argile par diffraction des rayons-X. Structure des couches ins6r6es et mode d'empilement de feuillets dans les hydrates homog~nes ~ une et deux couches d'eau de la beidellite Na. Thesis Doctorates Sciences, Orl6ans University, France, 1985. 54. J. Ben Brahim, G. Besson, C. Tchoubar, Journ. Appl. Cryst., 17 (1984) 179. 55. J. Ben Brahim, G. Besson, C. Tchoubar, 5th Meeting of the European Clay Groups, Prague, Konta J. (ed.), Univerzita Karlova, Praha, (1985), 65. 56. J. Ben Brahim, N. Armagan, G. Besson, C. Tchoubar, Clay Min., 21 (1986) 111. 57. I. Berend, Les m6canismes d'hydratation de montmorillonites homoioniques pour des pressions relatives inf6rieures a 0,95. Th~se, Institut Polytechnique de Lorraine, Nancy, France, 1991. 58. I. Berend, J.M. Cases, M. Francois, J.P. Uriot, L.J. Michot, A. Masion, F. Thomas, Clays and Clay Minerals, (1995) In press. 59. I. Berend, J.M. Cases, M. Francois, J.P. Uriot, L.J. Michot, F. Thomas, Clays and Clay Minerals, (1996) In press. 60. C. G. C. Frohnsdorff and G. L. Kington, Trans. Faraday Soc., 55 (1959) 1173. 61. M. E. Schrader and S. J. Yariv, J. Colloid Interface Sci., 136 (1990) 85. 62. R. F. Giese, C. J. Van Oss, J. Norris, P. M. Constanzo, in Proc. Int. Clay Conf., Strasbourg 1989, V.C. Farmer and Y. Tardy, (eds.), Sci. Geol. Mem., 86 (II) (1990) 33. 63. R. F. Giese, P. M. Constanzo, C. J. Van Oss, Phys. Chem. Miner., 17 (1991) 611. 64. J. Norris, R. F. Giese, P. M. Costanzo, C. J. Van Oss, Clay Miner., 28 (1993) 1. 65. J. Yvon, E16ments sur les propri6t6s cristallochimiques, morphologiques et superficielles des min6raux constitutifs des gisements de talc, Th~se de Doctorates Sciences Physiques, INPL, Nancy, France, 1984. 66. L. Michot, J. Yvon, J.M. Cases, J.L. Zimmermann, R. Baeza, C. R. Acad. Sci. Paris, S~rie II, 310 (1990) 1063. 67. L.J. Michot, J. Yvon, J.M. Cases, In Advances in Measurement and Control of Colloidal Processes, N. De Jaeger and R.A. Williams (eds), ButterworthHeinemann, (1991) 233. 68. N. T. Skipper, K. Refson, J. D. C. McConnell, Clay Miner., 24 (1989) 411. 69. M. Rautureau and C. Tchoubar, Clays Clay Miner., 24 (1976) 43. 70. M. Rautureau, C. Clinard, A. Misfud, S. Caill6re, in Proc. 104~me Cong. Nat. des Soci6t6s Savantes, Bordeaux, S6ries Sciences, 3 (1979) 199. 71. M. Rautureau and A. Misfud, Clay Miner., 12 (1977) 309. 72. P. Fenoll Hach-ali and J. L. Martin Vivaldi, An. R. Soc. Esp. Fis. Quim., 64B (1969) 77. 3. J. L. Martin Vivaldi and P. FenoU Hach-ali, in Differrential Thermal Analysis, Vol. 1, Fundamental Aspects, R. C. Macenzie ed., Academic Press, London, (1969) 553. 4. R. M. Barrer and N. Mackenzie, J. Phys. Chem., 58 (1954) 560. 75. K. P. Moiler and M. Kolterman, Z. Anorg. Allg. Chem., 41 (1965) 36. 76. A. J. Dandy, J. Phys. Chem., 72 (1968) 334. 77. A. J. Dandy, J. Chem. Soc. A, (1971) 2383. 78. A. J. Dandy and M. S. Nadiye-Tabbiruka, Clays Clay Miner., 23 (1975) 428. 79. J. F. Delon and J. M. Cases, J. Chimie Physique, 4 (1970) 662.
623 80. T. Fernandez Alvarez, In Compte-rendu de la Reunion Hispano-Belga de minerales de la Arcilla, J. M. Serratosa (ed.), Consejo Superior de Investigaciones Cientificas, Madrid, (1970), 202. 81. T. Fernandez Alvarez, Clay Miner., 13 (1978) 325. 82. A. Jimenez-Lopez, D. de Lopez-Gonzales, A. Ramirez-S~ienz, F. RodriguezReinoso, C. Valenzuela-Colahorro, L. Zurita-Herrera, Clay Miner., 13 (1978) 375. in Proc. Int. Clay Conf. Oxford 1978, M. M. 83. C. Serna and G. E. Van Sr Mortland and V. C. Farmer (eds.), Elsevier, Amsterdam, 1979,197. 4, B. F. Jones and E. Galan, in Hydrous phyllosilicates, S.W. Bailey (ed.), Review in Mineralogy Mineral. Soc. Amer., Washington, D.C., 19 (1988) 628. 85. J. M. Cases, Y. Grillet, M. Francois, L. Michot, F. Villi6ras, J. Yvon, Clays Clay Miner., 39 (1991) 191. 86. L'Air Liquide (ed.), Gas encyclopedia, Elsevier, Amsterdam, 1976. 87. L. Bergaoui, J. F. Lambert, M. A. Vicente-Rodriguez, L. J Michot., F. ViUi6ras, Langmuir, 11 (1995) 2849. 88. K. S. W. Sing, Pure and Applied Chem., 54 (1982) 2201. 89. H. Suquet, C. De la CaUe, H. Pezerat, C.R. Acad. Sci. Paris, 284D (1977) 1489. 90. U. Del Pennino, E. Mazzega, S. Valeri, A. Alietti, M.F. Brigatti, L. Poppi, J. Colloid Interf. Sci., 84 (1981) 301. 91. M.W. Kamel, Etude de l'Imbibition, du Gonflement et du D6ss~chement de quelques Argiles, Th~se Universit6 Toulouse, France (1981). 92. E.C. Ormerod and A.C.D. Newman, Clay Miner., 18 (1983) 289. 93. T. Iwasaki and T. Watanabe, Clays and Clay Minerals, 36 (1988) 73. 94. H. Suquet and H. Pezerat, Clays Clay Miner., 35 (1987) 353. 95. J. Hagymassy, S. Brunauer, R.S. Mikhail, J. Colloid Interf. Sci., 29 (1969) 485.
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w. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
625
Multilayer Adsorption as a Tool to Investigate the Fractal Nature of Porous Adsorbents Peter Pfeifer and Kuang-Yu Liu Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211, U.S.A.*
This chapter surveys the use of physical adsorption, from a monolayer upward, as an experimental method to study the fractal surface structure found in many porous and irregular adsorbents. The fractal structure leads to power laws of the Frenkel-Halsey-Hill (FHH) type for the adsorption isotherm, with exponents depending on the fractal dimension of the surface and on whether the dominant force is the substrate potential (van der Waals wetting, low coverage) or the film-vapor surface tension (capillary wetting, high coverage). We derive the power laws from a unifying framework which treats the two forces as competing effects and automatically identifies well-defined coexistence lines in the pressure-dimension diagram between the submonolayer regime, the van der Waals wetting regime, and the capillary wetting regime. We compute the resulting phase diagram for several adsorbate/adsorbent pairs, predicting which of the two power laws will be observed in what pressure range for a given surface geometry and adsorbate. A detailed comparison of the adsorption isotherm on a fractal surface with that in a single pore exhibits many parallels and differences between the two, which we also discuss in terms of t-plots and comparison plots. The aim of the presentation is to provide a simple, but complete set of guidelines for the interpretation of experimental adsorption isotherms, with a minimum number of parameters, in a thermodynamically and geometrically consistent way. A variety of recent experimental studies using multilayer adsorption for fractal analysis are reviewed as illustrations. The examples include some important test cases and range from metal films to carbon blacks, activated carbons, carbon fibers, pyrogenic silicas, silica xerogels and aerogels, porous glasses, and cements.
1. I N T R O D U C T I O N Much of our understanding of how solid surfaces interact with their surroundings, physically or chemically, depends on quantitative models for their structure. Until quite recently, most of these models have been based on Euclidean geometry, such as planar surfaces, straight-line step edges, cylindrical or slit-shaped pores, etc. However, many systems of practical importance (colloidal aggregates, adsorbents, catalyst supports, electrodes, hightemperature superconductors, reinforced materials, and other micro-engineered or naturally porous solids) have a complex structure which cannot be adequately described in such terms. * Research supported in part by the Petroleum Research Fund, administered by the American Chemical Society, Grant No. 28052-ACS
626 Typical of these complex geometries is that they exhibit structural features (departures from a planar surface) over a whole range of length scales----often several decadesmrather than features of one characteristic size only. Often the coexistence of features at different length scales leads to surface geometries where iteratively small pores (or other features) are subpores of larger pores. Such nested pore hierarchies render the concept of individual pores, cylindrical or otherwise, as underlying the conventional notion of pore-size distribution, meaningless. Similarly, other Euclidean descriptors, such as terrace-width distribution or surface height fluctuation, are inapplicable. This creates an eminent need for models in which structural features span a whole range of length scales, i.e., that treat surface irregularities as recurrent and nested, rather than isolated, entities. The simplest such model is that of a fractal surface [ 1-11 ]. A fractal surface has the s a m e structural features (Fig. 1) at length scales between s (inner cutoff) and s (outer cutoff) and is characterized by the fractal dimension D, 2 < D < 3, describing the irregular surface geometry in terms of its space-filling ability in the interval [gmin, s ]- At the low end, D = 2, the surface is planar, at length scales between s and gmax. At the high end, D = 3, the surface is maximally convoluted and fills a volume, at length scales between s and gmaxAt intermediate values, 2 < D < 3, the surface interpolates in a natural way between a plane and a volume. These basic properties have made fractal geometry, since its first application to surface problems [12-18], a highly appealing and successful new tool to study a wide range of phenomena associated with complex and disordered surfaces [4, 5, 7-11, 19-35]. The appeal comes from the fact that (i) the complexity is captured by a single number, the fractal dimension (for phenomena involving connectivity properties of the surface, also the spectral dimension is important [36]); (ii) the fractal dimension identifies the recurrence of the same features at different levels of resolution as a hidden symmetry (self similarity or self affinity) in an otherwise irregular structure; (iii) the resolution analysis automatically focuses on a range of features and is indifferent to whether small features are subfeatures of larger features or not; (iv) the metric properties of a fractal surfacemsuch as the number of pixels required to digitize the surface at a prescribed level of resolution, the number of surface sites present within a prescribed radius from a given site, or the number of surface sites as a function of the surface diameter--are as simple as for a planar surface (power laws with D-dependent exponents); (v) the metric properties, combined with the irregular structure of the surface, generate a wealth of explicitly D-dependent properties (Table 1.A);
Figure 1. Two fractal surfaces (cross sections), with small features being replicas of large features and small pores being subpores of large pores. The two cross sections have the same fractal dimension by construction.
627 (vi) a given value of D can be realized in many different ways (Fig. 1), giving the properties in (iv, v) a high degree of universality and making the description in terms of D stripped of all redundancy. The practical success comes from the fact that (vii) many of the random processes that produce complex surfaces (polymerization, aggregation, vapor deposition, electrolytic deposition, phase separation, drying, leaching, decomposition, corrosion, fracture, laser ablation) give rise to fractal structures by fairly well-understood mechanisms [ 1-5, 7, 8, 11, 22, 31, 34, 35]; and s and the ratio (viii) when a surface has features at length scales between s s is not very large, one may always approximate the features of different size as being in lowest order the same (least-biased guess), which makes the fractal model a good approximation even if the surface is not genuinely fractal; (ix) the D-dependent properties in (iv, v) generate explicit relations, usually in the form of power laws, between a wide variety of experimentally measurable quantities, offering many experimental methods of fractal surface analysis (structural analysis, Table 1.B) and predictions of how the structure controls the performance of the surface in physical and chemical processes (structure-function relations, Table 1.C). Common to all methods of fractal analysis is that the surface is subjected to the interaction with probes ("yardsticks") of different size. Depending on the method, the probes may be molecules of different size, electromagnetic waves diffracted at different angles, energy transfer from a donor molecule to acceptor molecules at different distances, liquid menisci with variable radius of curvature, films of variable thickness, molecules diffusing to the surface starting from different locations in the pore space, etc. In multilayer adsorption, the subject of this chapter, the probes are films which vary both in thickness and radius of curvature. Multilayer adsorption plays a special role among the methods listed in Table 1. First of all, in many applications one is interested in the morphology of the surface starting at atomic length scales, i.e., one would like to know whether the surface is fractal in an interval [gmin, gmax] of the order of hundreds of .&ngstroms with ~min of the order of a few ,~,ngstroms and s (nanostructured surfaces). Films of adsorbed N 2, Ar, Kr, or other inert gases can easily span this range and offer a structural resolution down to atomic length scales by virtue of the atomic size of the adsorbate particles. Second, no matter how convoluted and porous the surface is, gas diffusion through pore space and complete wetting of the surface by the adsorbate guarantee that the film probes the entire surface. Multilayer adsorption is therefore an important tool whenever the surface is too tortuous for methods like scanning tunneling microscopy, atomic force microscopy, or reflectometry to be applicable. The only other methods which compete with multilayer adsorption, in terms of length scales and applicability, are molecular tiling, small-angle X-ray or neutron scattering, electronic energy transfer, and preadsorbed films (Table 1). Combined, these other methods have provided some of the most extensively investigated case studies of fractal surfaces [43, 80, 81 ]. But they are quite time-consuming or require instrumentation that is not readily available in most laboratories. Thus, an important rationale for investigating fractal surfaces by multilayer adsorption is the wide availability of gas adsorption instruments and the ease with which adsorption isotherms can be measured up to quite high relative pressures. The first experimental study of a fractal surface by multilayer adsorption was published in 1989 [44] and has been followed by numerous investigations since. The purpose of this
628 Table 1 Geometric quantities controlled by the fractal dimension, experimental methods of fractal analysis, and applications controlled by the fractal dimension. References A. Geometric quantities: Pore-size distribution Chord-length distribution Porosity Density-density correlation function Height-height correlation function Fourier transform of surface cross sections Dimension of cross sections and projections of the surface
[14,20,21] [19,32] [19,32] [8,23] [8,11,31] [17] [ 1, 6, 30]
B. Experimental methods: Molecular tiling and monolayer capacity Small-angle X-ray and neutron scattering Electronic energy transfer Kelvin porosimetry Thermoporosimetry Hg porosimetry Surface area of preadsorbed films Scanning tunneling microscopy X-ray reflectivity NMR spin relaxation Multilayer adsorption
[12-16,29] [21,29,37] [29,38] [29,39] [40, 41] [42] [43] [44-46] [47, 48] [29,49] this chapter
C. Applications: Surface-diffusion controlled reactions Pore-diffusion controlled reactions Catalysis Dissolution and combustion Chromatography Electrochemical impedance Debye-Hfickel screening Electrical conductivity Hydrodynamic flow Magnetic phase transitions 4He phase transitions Vibrations Thermal conductivity Scattering and absorption of light Compaction Sintering
[20,50] [18,20,51-55] [ 18, 20, 25, 51-55]
[55-57] [58] [53, 54, 59-61 ] [62-64] [19,28,32,33] [19,28,32,33] [65-67] [21, 68, 69] [70-73] [73] [74-76] [77] [78, 79]
629 chapter is to give an account of these developments and to describe the various aspects which have placed multilayers on fractal surfaces at the crossroads of several different areas of surface science. One of the distinguishing features is that an adsorbed multilayer is not a "premanufactured" probe which is brought to the surface and interacts with it like a rigid object, but is a "self-assembled" probe formed by complicated substrate-adsorbate and adsorbate-adsorbate interactions (repulsive at short range and attractive at long range). These interactions result in a layer that is controlled by a strong interplay between energetic and geometric factors when the surface is irregular. At temperatures above the triple point and below the critical point of the adsorptive, i.e., at which gas and bulk liquid of the adsorptive coexist, the adsorbed multilayer is a liquid. So our first task will be to find an adequate description of the energy and thermodynamics of a liquid film, in equilibrium with its own vapor, on an arbitrarily shaped solid (Sect. 2). The description we shall use models the film as homogeneous liquid with sharp liquid-gas interface. The substrate-adsorbate interaction enters through the substrate potential, dependent on the surface geometry, and the adsorbate-adsorbate interaction enters through the liquid-gas surface tension. This describes the two interactions with a minimum number of parameters and provides a framework to calculate adsorption isotherms in various approximations. The key is that equilibrium shape of the film-gas interface is determined by a variational principle (minimization of the grand potential). In Sect. 3, we apply this variational principle to derive the adsorption isotherm for multilayers on fractal, self-similar surfaces. The genetic form of the isotherm is N or [_In(p/p0)] -(3-D)/3 N 0r [_In(p/p0)] -(3-D)
for low p, for p ---) P0,
(1 a) (lb)
where N is the number of adsorbed particles adsorbed at gas pressure p, and P0 is the coexistence pressure for gas and bulk liquid. These are the power laws that most experimental studies have used to infer fractal surface properties from multilayer data. The power law (1 a), called van der Waals wetting regime, is characteristic of multilayers in which the substrate potential is the dominant interaction. The power law (lb), called capillary wetting regime, results when the liquid-gas surface tension is the dominant interaction. Together, the two regimes provide a unified description of the transition from substrate-controlled adsorption to capillary condensation. The description includes the form of the crossover from (la) to (lb), explicit expressions for the prefactors in (1), the connection between gas pressure and length scales probed by the film, and refinements of (1) when the length scales probed approach the inner or outer cutoff of the fractal regime. Section 4 analyzes the transition from van der Waals to capillary wetting as a function of the fractal dimension. For every D value, there is a pressure which defines this transition in a natural way. Similarly, there is a pressure which defines the transition from submonolayer to multilayer adsorption. The resulting transition lines divide the D-p plane into a phase diagram with three distinct regions: submonolayer adsorption, van der Waals wetting, and capillary wetting. For example, when D decreases, capillary wetting is restricted to progressively higher pressures and is completely absent at D = 2. By explicitly predicting in what pressure intervals the power laws (1) occur for a given solid and adsorbate, the phase diagram offers important consistency tests for the interpretation of experimental data of the form (1). Armed with these theoretical prerequisites, we review experimental results in Sect. 5. The examples illustrate the wide applicability of the power laws (1) and amplify earlier accounts of the pervasiveness of fractal surfaces at nanoscales [29]. Our main focus, however, is on
630 studies in which the surface structure inferred from (1) has been tested for internal and external consistency. Internal consistency includes meaningful values for D, groin, and grnax, consistency with the phase diagram, etc. External consistency means agreement with results from other experimental analyses (fractal or otherwise). These tests form an important experimental confirmation of the adequacy of the liquid-film framework used in Sect. 2 to describe multilayers adsorbed on arbitrarily shaped solids. Indeed, the ultimate goal is to develop a general framework from which it will be possible infer structural properties of solids with arbitrarily shaped surfaces (fractal or not), without geometric model assumptions. This is the inverse problem of the program that wishes to predict the isotherm for an arbitrary given surface geometry. The common view is that both the direct problem and the inverse problem are intractably difficult. We therefore would like to point out that, in Sect. 3.6, we will present a solution of both the direct and the inverse problem. The solution will, of course, not be free of approximations; but the approximation will be one in the variational determination of the equilibrium film, not in the surface geometry. In this sense, this chapter may be viewed as fractal illustration of a much more general framework.
2. LIQUID-GAS E Q U I L I B R I U M ON NONPLANAR SURFACES When analyzing the 3-phase equilibrium between a liquid, its vapor, and a solid, we first must determine whether the liquid completely wets the solid or not. If the surface of the solid is planar, the macroscopic condition for complete wetting is that the contact angle between the liquid and the solid, 0, be zero (Fig. 2). Partial wetting corresponds to 0 < 0 < r~. The contact angle is determined by Young's equation, (2)
COS 0 = (O'sg -- O'sl)/(Ylg,
where the o's are the solid-gas, solid-liquid, and liquid-gas surface tension. Thermodynamic equilibrium ensures that the right-hand side of (2) always lies between -1 and 1 [82]. Thus complete wetting is characterized by the condition Csg- Cysl= c]g. Microscopically, complete vs. incomplete wetting can be distinguished by considering the high-pressure behavior of the adsorption isotherm: If N --->o0 for p --->P0, one has complete wetting; if N approaches a finite limit for p --->P0 (i.e., if there is a discontinuous jump to N = oo at p = P0), wetting is incomplete [83-85]. From this criterion and a large body of adsorption data, one concludes that nitrogen and other inert adsorptives, at their normal boiling temperature, completely wet most solids. Consequently, we assume the liquid to be completely wetting in the sequel.
0=0
0
0=n
Figure 2. Macroscopic contact angle of a completely wetting (0 = 0), partially wetting (0 < 0 < ~), and completely nonwetting (0 = ~) liquid on a planar surface. In the partially wetting case, a microscopic layer of liquid covers the surface outside the macroscopic drop.
631 Under these circumstances, the 3-phase equilibrium reduces to a 2-phase equilibrium, namely that between the liquid film, which completely covers the surface and can have any thickness, and the gas phase. A convenient starting point to treat this problem is to construct the grand potential of the film, f~, as a function of the chemical potential It. The potential f2, sometimes also called Landau potential, is the Legendre transform of the Helmholtz free energy F with respect to the particle number N in the film (see, e.g., [86]),
f~ = F - rtN,
(3)
and is natural whenever the system (here the film) exchanges particles with a reservoir (here the gas phase). Equilibrium then requires that ~t be equal to the chemical potential of the reservoir and that ~ , for given It, be a minimum. As reference state for the film, we take N particles of bulk liquid at chemical potential kt0. This turns (3) into
(4) (5)
ZKQ = zKF-AItN, Ait = It - Ito = kT In(p/p0),
where AF is the difference in free energy between N particles in the f'dm and N particles in bulk liquid. The last part of (5) equates It and ]ao to the chemical potential of the coexisting gas phase, at pressure p and P0, for the film and bulk liquid, respectively. The treatment of the gas as ideal gas, k being Boltzmann's constant and T the temperature, is for simplicity. The chosen reference state of the film eliminates the self-interaction of the film (transferring it to the gas pressure P0 instead), i.e., makes AF contain only the interaction of the liquid withthe solid and the gas. To complete the construction of the grand potential, we let the surface S of the solid have arbitrary shape and treat the film as a homogeneous liquid (continuum) with sharp liquid-gas interface I. The grand potential then becomes a function of I and Ait, given by Aft[I, AIx] = n [
. , f (s,i)
= I[nr
(U(x) - Ait)d3x + ~ [_ d2x -'1
(U(xll, x• - Ait)dx• + (~ ~/1 + IVI(x,)I 2 ]d2xll
(general)
(6a)
(no overhangs). (6b)
"1S(xj[)
We first discuss the general case, (6a). The function U(x) is the potential energy of an adsorbate particle at position x in the f'tlm, due to the interaction with the solid, relative to that of bulk liquid; it is referred to as the substrate potential. The integration domain f(S, I) is the set of points enclosed between the surface S and the interface I (Fig. 3). The prefactor n is the number density of the film, taken to be equal to the value for bulk liquid (incompressible film). Thus, the first term represents the solid-liquid interaction, obtained by summing the substrateadsorbate energies of all the particles in the film. The second term is the t e r m - A ~ N in (4). The last term, in which c is the liquid-gas surface tension and the integral denotes the area of the interface I, represents the liquid-gas interaction, i.e., the work needed to create a liquid-gas interface of area ~I d2x (free energy of the interface). We drop the liquid-gas subscript in a because no other surface tensions remain in the problem, and take (~ to be equal to the value for bulk liquid. Equation (6b) describes the special case where the surface has no overhangs, i.e., where S and I can be parametrized as height above some reference plane, at position xll in the reference
632
gas
I(x I) i
"
~
S(x u)
solid xll
Figure 3. A liquid film configuration with liquid-gas interface I, adsorbed on a surface S. Shown is the special case in which the surface has no overhangs.
plane (Fig. 3). In this case, the position x is specified by (xll, x.u) where x• is the coordinate perpendicular to the plane, and the square root expresses the surface element of I in terms of the gradient of I. The substrate potential U(x) is the interaction energy between the solid and a particle at position x outside the solid, minus the corresponding energy if the solid is replaced by bulk liquid. The reference to bulk liquid in U(x) comes from the fact that the free energy of the film was chosen relative to bulk liquid. It makes U(x) coincide with the substrate potential in the Frenkel-Halsey-Hill (FHH) theory of multilayer adsorption [87-90]. In good approximation, U(x) may be taken as U(x) = - e~/[dist(x, S)] 3
(7)
where a is a positive constant independent of S, and dist(x, S) denotes the distance between the point x and the surface S (shortest distance between x and any point on S). When the surface is planar, the potential (7) reduces to the familiar inverse distance cubed law for the long-range van der Waals attraction [87]. The constant ~ describes the strength of the attraction and is known from dielectric properties of the solid and adsorptive for a variety of substrate-adsorbate pairs [88-90]. The repulsive interaction at short distances, neglected in (7), may be added to (7) to make the integral in (6) finite, but makes negligible contributions to the multilayer adsorption situation at issue. The expression (6) for A,Q, often referred to as effective-interface or capillary-wave Hamiltonian, may be interpreted and used in several ways. The different interpretations differ in how they interpret the interface I. While we will use exclusively the interpretation as equilibrium interface in later sections, it is useful to compare it with other conceptual frameworks. The comparison identifies the conditions under which the equilibrium interpretation is justified and provides corrections when these conditions are not satisfied.
2.1 Interpretation as Effective Interface If one starts from a description of the liquid and gas in terms of a spatially variable fluid density, determined by the pair interaction between the fluid particles (density-functional theory), there is no sharp liquid-gas interface and the density representing the liquid may vary
633 as a function of position. Nevertheless, one may, in this description, evaluate the grand potential for a density ("trial density") that is equal to the density of bulk liquid on one side of some dividing surface I and equal to the gas density on the other side. The density drop at I identifies I as sharp, effective liquid-gas interface. The resulting grand potential, as a function of I, can be expanded in terms of derivatives of I (Taylor expansion around a planar interface). This gradient expansion has been carried out by Napi6rkowski and Dietrich [91 ]. The leadingorder term gives the expression (6b). The surface tension ~, in this framework, is a function of the pair interaction and the density of the liquid and gas (see also [82, 92, 93]). Higher-order terms in the expansion, which involve the curvature of I and represent bending energies of the interface, can be accounted for by letting ~ become curvature-dependent (see also [82, 92]).
2.2 Interpretation as Fluctuating Interface For fixed substrate potential and fixed parameters n and cy, one may interpret (6) as the energy of a microscopic film configuration, specified by I, in the grand canonical ensemble. This interpretation allows each I as possible interface, occurring in the film with relative probability e-&O[l,A~ t]/(kT). It views the I's as thermal excitations, or thermally fluctuating interface, of the film. The particle number N in the film then is the average of the particle number in the individual configurations, given in terms of the grand canonical partition function by 0 l n ( ~ I e_Z~ti, aM/(kT)). N = kT ~)(A~)
(8)
The sum over all I in Eq. (8) is a functional integral if I is taken in the continuum representation stipulated in (6), a sum over lattice walks if I is discretized on a lattice, or a sum over wave numbers if I in (6b) is decomposed into Fourier components (capillary waves). Equation (8), together with the expression (5) for the chemical potential, is the adsorption isotherm. It takes into account that the thermal motion of particles in the film creates thermal fluctuations of the interface I, making the interface effectively diffuse at T > 0. The importance of such departures from a sharp interface can be estimated by analyzing the width of the interface, w(T, A~t), in the special case where the surface S is planar. The width in this case is defined as standard deviation, thermally and spatially averaged, of the film height relative to S: (9)
w(T, A~) = ff (([I(xll) - (I(xll))Xll]2)i)xll .
Here (---)x.. is the average over all points in the reference plane, chosen as S, and (---)I denotes II the grand canomcal average, (f(I)) I = ( Z I f(I) e -af~[I' AI't]/(kT))/(Z I e -Af~[I' Alx]/(kT)).
(10)
The width is zero at zero temperature, w(0, AB) = 0, corresponding to a sharp interface, and increases with increasing temperature because the fluctuations increase. The attractive substrate potential, on the other hand, tends to reduce the fluctuations. Therefore, at fixed temperature, the width increases with increasing A~t because the effect of the substrate potential diminishes as the film grows in thickness. Upon adding to (6b) a term that accounts for the gravitational potential, which dominates over the substrate potential when AIx --> 0, this leads to the bound
w(T,A~) < w(T,O) =
lkT ~
In Apg a2 +1
1
(11)
634 where Ap is the difference in mass density between bulk liquid and gas, g is the gravitational acceleration, and ao is the thickness of a monolayer. The right-hand side of (11) is the width, due to the thermal excitation of capillary waves, of a free liquid-gas interface (no substrate) in the approximation where the gradient VI(xll) in (6b) is small [92-95]. When T approaches the liquid-gas critical temperature T c, the quantities cy and Ap go to zero and w(T, 0) diverges, in agreement with the fact that the liquid-gas fluctuations become macroscopic at T c. At temperatures not close to T c, the width w(T, 0) is very small, however, typically between ao and 2ao [94]. It follows that the width for finite film thickness is even smaller. Measurements of the width w(T, A~t) by X-ray scattering confirm this [96, 97].
2.3 Interpretation as Equilibrium Interface The foregoing analysis leads to the following conclusions if the film is sufficiently thick and the temperature is well below T c. (i) Departures of the film density from the density of bulk liquid are negligible because they occur at the surface and at the interface at most. (ii) Bending energies of the interface are negligible because the curvature of the interface decreases with increasing film thickness (Fig. 3). (iii) Thermal fluctuations are negligible since the width of the interface is of the order of a molecular diameter only. Under these conditions, the energy of a microscopic film configuration specified by I is well described by (6) and the number of particles in the film is well approximated by neglecting all thermal excitations in (8). This amounts to evaluating (8) in the limit T ~ 0 (keeping n and ff fixed), which gives N = - D(A~t)
A~2[Imi n, AlX] = n fff
(S,Imin)
d3x,
(12)
where Imin is the interface that minimizes z~,Q[I, A~t] for given A~t (ground-state interface). The second part follows from (6). The zero-temperature limit leading to (12) does not imply that the actual physical temperature goes to zero; it is simply a device to suppress the configurations in (8) that correspond to thermal fluctuations. In fact, the interface Imi n depends on the physical temperature through the temperature dependence of AFt, n, and o in (5) and (6). The interface Imin is the equilibrium interface, viewed as microscopic ground-state configuration in the grand canonical ensemble. Alternatively, A~[I, All.] as given by (6) may be viewed as grand potential of a macroscopic, thermally averaged trial configuration I of the film. In this case, the grand potential must be a minimum at equilibrium by the thermodynamic variational principle. This again singles out the equilibrium interface as the interface, Imi n, that minimizes z~[I, A~t] for given A~t. It again leads to (12) for the number of particles in the film, now by the inverse Legendre transform of the grand potential, or directly from N = n • film volume. A third way of constructing the equilibrium interface starts from the Helmholtz free energy, zkF[I], of a macroscopic trial configuration I of the film. The expression for zkF[I] is the righthand side of (6) without the chemical-potential term. At equilibrium and fixed particle number, the Helmholtz free energy is a minimum. Hence the equilibrium interface is the interface that minimizes ZkF[I] subject to the constraint that the number of particles in the trial configuration be equal to N. This yields the equilibrium interface Imin as a function of N. The chemical potential of the film, Ag, is obtained by differentiating the minimized Helmholtz free energy with respect to N. When the minimization of ZkF[I] is carried out using the Lagrange method, the Lagrange parameter for the constraint is AFt and the unconstrained quantity to be minimized is the grand potential zS~Q[I,A~]. Thus, the two procedures of minimizing the grand potential at fixed A~t and minimizing the Helmholtz free energy at fixed N are completely equivalent (Table 2).
635 Table 2 Variational principles for the grand potential and Helmholtz free energy, with Ag given by (5). Grand potential
Helmholtz free energy
Functional
A.Q[I, Ag] = AF[I] - Ag n ~f(s, I) d3x
AF[I] = n ~f(s, I) V(x)d3x + cr Si d2x
Interface Imin
Minimize A.Q[I, Ag] with respect to I, for fixed Ag
Minimize AF[I] with respect to I, subject to n ~f(s, I) d3x = N
Imin is function of
Chemical potential At.t
Number of particles, N
Adsorption isotherm
N =
A[.t = d-~ AF[Imin(N)]
Explicit formula
N = n If(S, imin(Ag)) d3x
Solve (*) for N as function of At.t
Strategy
Put N particles, N arbitrary, close to the surface (makes the potential energy and film area low for large N) while keeping the film volume small (makes the Ag-term small)
Put N particles, N given, close to the surface (makes the potential energy low) while keeping the film area small
d
d(Ag)
A~[Imin(A~), A[.t]
(*)
Equation (12) reduces the computation of the adsorption isotherm to the minimization of (6) with respect to I. The Euler equation for the minimizing interface Imin is n [ U ( x ) - Ag] +
o(
1 + 1 )=0 Ri(x) R2(x)
n[U(xll, Imin(xll)) - A[.t] - oV-
Vlmin(Xil)
= 0
for all x on Imin (general)
(13a)
(no overhangs)
(13b)
41 + IVImln(Xll)l2 from Eq. (6a) and (6b), respectively [87, 95, 98-105]. Here Rl(x) and R2(x) are the principal radii of curvature of I at position x, taken to be positive if the tangent circle lies on the liquid side of the interface, and negative if it lies on the gas side. The boundary conditions necessary if the interface is not closed are described in [ 101]. The surface geometry S enters through the substrate potential U(x), Eq. (7). Equation (13) is a nonlinear partial differential equation for the equilibrium interface. In the absence of the substrate potential, it reduces to the Kelvin equation. In the presence of the substrate potential, it is nonlinear both via the substrate potential and the curvature term, as seen in (13b). For general surface geometry, it has no simple solution. It may have several solutions (capillary instabilities, metastable states, unstable states [87, 95, 98, 106]), in which case the minimizing solution must be determined by additional evaluation of (6). Thus, an exact calculation of the adsorption isotherm (12b) requires considerable numerical effort in general and does not lead to results from which the geometry of S is easily reconstructed (interpretation of
636 experimental isotherms). In Sect. 3, we will present an approximate calculation of the isotherm which provides a simple, general one-to-one correspondence between experimental data and geometric information about S. Before we turn to that treatment, however, it is useful to recall two examples in which (13) can be solved exactly.
2.4 Van der Waals Wetting and Capillary Wetting The examples serve to illustrate the fundamental role of the substrate potential, even in situations where nU(x) is small compared to the curvature term in (13) (capillary condensation in large pores), in which case the folk wisdom is that the substrate potential can be neglected. At the same time, they illustrate the basic two adsorption mechanisms which compete with each other whenever the surface is nonplanar and which, for a fractal surface, will separate the adsorption isotherm into two distinct regimes. Example 1. The first example is that of a planar surface. Choosing the reference plane equal to S and substituting (7) into (13b), one verifies that the function Irnin(Xll ) - (-IX/A~I,) 1/3 solves (13b) and yields [ IX /1/3 N = nAl-~--~]
(14)
upon substitution into (12), where A is the area of S. This, together with Ag = kT In(p/p0), is the classical FHH isotherm on a planar surface [87]. It depends only on the substrate potential and is the paradigm of van der Waals wetting. It is independent of the surface tension because the last term in (6) is the same for all interfaces parallel to S. Example 2. The simplest case of a nonplanar surface is a spherical pore of radius R. Assuming the minimizing interface to be spherical and concentric with the pore, at distance z from the pore wall (0 < z < R), one finds from (7) and (13a) that z, the film thickness, must satisfy g(z)-A~t = 0 IX 2~ g(z) := - - - v 9 n(m z) z"
(15) (16)
For low A~, this equation has two solutions, 0 < z 1 < Z2 < R, with asymptotic behavior [ tX /1/3 z 1 -- ~-~--~] ,
2~ z 2 -- R + nAg
(17)
as Al,t -~ -,,o (Fig. 4). The expression for z 2 represents the Kelvin solution, i.e., the solution
0
z1 I l
ZXlac -J . . . .
Ag
z2 ........
i f
I
R Z i I
I
I
I
I
I
I
I
I
I
I
I
I
, I I I I
Figure 4. Graphical determination of the solutions z 1 and z 2 of Eq. (15) [schematic].
637 of (16) when z is so large that the substrate potential can be neglected. However, Z 2 cannot be the equilibrium solution because it would give the unphysical result that the pore is full at zero pressure. A simple calculation and Fig. 4 show that the grand potentials of the interfaces 11,12, 13 corresponding to z = z 1, z 2, R (full pore) satisfy A~[I 1, Ag] - A.Q[I2, Ag] = 4rm "Jz2 [Ag - g(z)l(R- z)2dz < 0,
(18a)
z1
A~[I 1, Agl - A.Q[I3, Ag] = 4rtn I R [Ag - g(z)](R- z)2dz,
(18b)
z1
i.e., 12 is unstable and the equilibrium state is 11 or 13 depending on whether the integral (18b) is negative (low Ag; metastable I3) or positive (high Ag; metastable I1). When Ag reaches the value Al.tc := max0 Ag c, in which case 13 is the equilibrium state. This gives the adsorption isotherm (partly metastable) N = (4rff3)n[R 3 - (R - zl) 3] N = (4/ff3)nR 3 Agc -- g(zr
Z1 = Zc "=
[ 4/ 1
-2__~_~ 1 + nR
~ 8(Y L ~
+
3~n 2(5R2
-
]
2R/-~ 3om8(5- 1
for Ag _< Ag c, for Ag > Ag c,
(19a) (19b)
for R ---) oo,
(20)
for Ag = Ag c.
(21)
For low Ag, one may use (17) to evaluate (19a), which in the limit of large R leads back to the FHH isotherm, Eq. (14). So adsorption at low Al.t is dominated by the substrate potential and, in a large pore, is indistinguishable from adsorption on a planar surface. As Ag increases, however, the film thickness grows faster than on a planar surface because the decrease of the energy (5~I d2x' resulting from a small interface radius, outweighs the increase of the particlenumber t e r m - n A g If(S,I) d3x" At Ag = Ag c, the enhancement of adsorption due to surface tension leads to spontaneous pore filling, resulting in a jump of the isotherm (Fig. 5). The jump describes capillary condensation, corresponding to a first-order phase transition ("thin-f'dm/fullpore" coexistence), in a highly idealized setting. The film thickness jumps from z c [Eq. (21)] to R. During desorption, the pore remains full at all Ag because 13 never turns unstable. Thus neither the adsorption nor desorption isotherm jumps at the point Ag c' < Ag e at which the integral (18b) vanishes (coexistence of 11 and I 3 as equilibrium states, "Maxwell construction"). The example illustrates that capillary condensation is controlled by surface tension, but that a thermodynamically consistent construction of the equilibrium interface is not possible without taking the substrate potential into account. This point has already been emphasized by Broekhoff and Linsen [ 107], and the example here has in fact been analyzed along similar, somewhat less explicit, lines before [ 108]. The example will serve as a useful comparison frame to appreciate similarities and differences in adsorption on a fractal surface. On a fractal surface, the adsorption regime controlled by surface tension will no longer exhibit any jump because the surface will have a hierarchy of pores and voids of many sizes. Instead, above some characteristic value Agc of Ag, one will have a continuous sequence of first-order transitions in each of which the "next" larger void in the hierarchy fills by capillary condensation. This turns the first-order transition at Al.t = Ag e in a single pore into what resem-
638
A~t
Al.t A~tc Figure 5. Film shape and adsorption isotherm in Examples 1 and 2 (schematic).
bles a second-order transition at A~t = Al.tc on a fractal surface. The change from a single pore to a fractal surface spreads the event of capillary condensation from a single chemical potential, Al.tc, to a whole range of chemical potentials, A~t > A~te. Since capillary condensation is often equated to an isolated jump in the isotherm, we refer to the regime Al.t > A~tc on a fractal surface as capillary wetting instead. Accordingly, we refer to the transition at A~tc as transition from van der Waals wetting to capillary wetting. This transition is worked out in the next section.
3. ADSORPTION ISOTHERM ON FRACTAL SURFACES The variational principle for the grand potential provides a powerful tool to construct approximations of the isotherm (12) when the Euler equation (13) has no simple solution, as is the case for a general fractal surface. We note that such approximations may be more desirable than the exact isotherm: The two fractal surfaces shown in Fig. 1 give isotherms that certainly differ in their exact details, but in practice will be indistinguishable from each other. So, an approximation well may capture this indistinguishability, while the exact isotherm does not. One chooses a family of trial interfaces and minimizes the grand potential with respect to this family. If the interfaces are sufficiently flexible, the one with the lowest grand potential should be a good approximation of the exact equilibrium interface. A simple and natural choice are equidistant interfaces I z [36, 43-45, 81,101,109, 110], defined as the locus of all points x outside the solid whose distance from the surface, dist(x, S), equals z (Fig. 6). The distance z is the variational parameter (0 < z < oo), making the trial family a one-parameter family. In comparison, the exact equilibrium interface may be regarded as member of an inf'mite-parameter family. The equidistant interfaces form a natural trial family for two reasons. First, they are equipotential surfaces of the substrate potential (7) because the latter depends only on dist(x, S). This makes them automatically solutions of the Euler equation (13) in the absence of surface tension. Second, they offer a convenient characterization of fractal surfaces. There are several, essentially equivalent ways of measuring the space-filling ability of a fractal surface [36]. The most precise one, well-defined for any surface S, is the function V(z) := If(S, Iz) d3x'
(22)
639 which is the volume of all points outside the solid whose distance from the surface is less than or equal to z. The derivative dV/dz equals the area of the interface I z, by the equidistance property of I z. We shall refer to f(S, I z) as film of thickness z, and to dV/dz as the corresponding film area. Equivalenfly, dV/dz may be interpreted as the surface area of the solid measured by tiling the surface with spheres of diameter z [36]. A self-similar surface with fractal dimension D, inner cutoff gmin, and outer cutoff gmax then is characterized by the power law dV dz
(23)
oc z 2-D
for gmin < z < s It describes how the film area on a fractal surface decreases with increasing film thickness by filling progressively larger pores and voids (D > 2), and remains constant on a planar surface (D = 2). The film area dV/dz plays a special role among different ways of assessing fractality because it depends only on the structure of the surface at length scale z, whereas the film volume V(z) and other interrogator functions depend cumulatively on all structure below z, including the regime below the inner cutoff, which may lead to departures from a [43]. If the fractal regime starts at the smallest pure power law for V(z) as z approaches s resolvable scale, i.e., at z = ao where a0 is the thickness of a monolayer (diameter of an adsorbate particle) as in (11), then (23) can be integrated and expressed entirely in terms of a0 and the number of particles in a monolayer, N m. The result, in its simplest form, is V(z) = Nma~ (z/ao) 3-D
(2 < D < 3),
(24a)
V(z) = Nma~ [1 + ln(z/ao)]
(D = 3, nonuniform space filling),
(24b)
V(z) = Nma~
(D = 3, uniform space filling),
(24c)
for ao < z < s Here the unspecified prefactor in (23) and the integration constant have been calibrated by the condition V(z)/V(%) -- (z/ao) 3-D or V(z)/V(a o) -- ln(z/a o) for large z/a o (scale invariance), and by equating the monolayer volume V(a 0) to Nma~. Other calibrations yield similar results [43, 110].
89 liquid
Figure 6. Equidistant interface, I z, at distance z from a self-similar fractal surface.
640
The two ways in which a three-dimensional surface can fill space, nonuniformly or uniformly, are discussed in [36, 43, 81, 111 ]. In the nonuniform case, the surface visits only certain regions in a compact manner and therefore, by leaving other regions empty, contains pores of many sizes. Examples for such surfaces, satisfying (23) with D = 3 and a nonzero prefactor, are certain silica and alumina xerogels [12, 39, 80, 81, 111 ]. A uniformly space-filling surface, by contrast, contains pores of one size only. A good example is a zeolite with channel width w and comparable wall thickness, which is uniformly space-filling at length scales above w. In this case, the volume V(z) trivially remains constant for all z > ao, if we choose a0 = w. This illustrates (24b) and the mechanism by which the prefactor in (23) may be zero. The expressions (24) show that the film volume grows, with increasing film thickness, increasingly slowly as the fractal dimension increases from two to three. They quantify that on an increasingly convoluted surface there is less and less space for a film to grow. The slow growth on a high-dimensional surface may seem to contradict the intuition that a highly irregular surface should have a large surface area and hence should be able to support a large film volume. There is no contradiction, however: In (24), we compare surfaces with variable D and fixed number of surface sites (adsorption sites), N m. In the surface-area consideration, one compares surfaces with variable D and fixed diameter L (largest distance between two points on the surface). Thus, the two opposed conclusions are due to different comparison frames. To switch from one frame to the other, one simply uses the relation Nm =
(L/gmax)3(gmax/ao)D,
(25)
in which the first factor counts how many fractal, identical "pieces" the surface consists of and the second factor counts the number of surface sites on each piece. For example, if the surface is fractal over the entire range of length scales between a0 and L ( g m a x = L), then N m = (L/ao) D and substitution into (24) shows that the film volume indeed increases with increasing D, for fixed L and' z. Similar differences in the performance at constant N m and at constant L, as a function of fractal surface dimension, exist also for diffusion-controlled reactions [20, 52-54], electrochemical response [53, 54, 59, 61 ], and other applications. This completes our analysis of the equidistant interface I z and the associated film volume V(z), in which we have treated the thickness z as a variable that can take arbitrary values. The variational determination of the equilibrium value for z and the construction of the isotherm is now easy: Substitution of (7) and (22) into the grand potential, Eq. (6a), gives A.Q[I z, Al.t] = n rjz [_tx(z,)_3 _ Akt]dV(z') + ~dV(z)/dz, a0
(26)
where we have used that dV(z') is the volume of a layer of thickness dz' at distance z' from the surface (equidistance property of Iz,), and have set the lower integration limit equal to the monolayer thickness to ensure a finite integral. Minimization with respect to z yields the algebraic equation (replacing the Euler equation (13)) n[--txz - 3 - Al.t]dV(z)/dz + t~dV2(z)/dz 2 = 0
(general surface)
(27)
n[-o~z -3 - A~t] - ( D - 2)~z -1 = 0
(fractal surface )
(28)
for the equilibrium value of z. The use of (24) in arriving at (28) is limited to the cases (24a, b), of course. In the case (24c), there is no isotherm to construct and we shall disregard it in the
641 sequel. The left-hand side of (28) increases with increasing z, so that the equation has a unique positive solution z for every value of Ag. The equation is equivalent to a cubic equation for z, which can be solved exactly. But the basic features of the solution can be deduced directly from the low- and high-Ag regime in (28): For zig ---) ..oo, the surface-tension term can be neglected, while for zig ~ 0, the substrate-potential term can be neglected. This gives z - ( ~__~)1/3
(Ag --+ _._~),
(29a)
z ~ - (D - 2).._.____~ nag
(Ag ~ 0).
(29b)
-
The crossover between the two regimes occurs at the chemical potential and film thickness, Ag c and zc, at which the asymptotes (29a, b) intersect, i.e., at
A~I,c 1= - 0~-1/2 [(D zc :=
2)6/n] 3/2,
(30)
(D - 2)6 "
(31)
Substitution of the equilibrium film thickness z as a function of Ag into N = nV(z) [recall Eqs. (12, 22)] yields the adsorption isotherm. Thus, by extrapolating the asympotic expressions (29) to where they intersect, inserting them into (24), and using ao - n-u3 to eliminate %, we obtain the results displayed in Table 3.
Table 3 Multilayer adsorption on a fractal surface, with Ag and Agc given by (5) and (30). Van der Waals wetting
Capillary wetting
Range of Ag 9 D --~ 2 9 D --, 3
Ag < Ag c range increases range decreases
Ag >__Ag c range vanishes range increases
Isotherm: 92 < D < 3
N =Nrn [ - - ~ g j1
(32a)
N=N m
N ~ [-ln(p/p0)]-(3-D)/3
(33a)
N ~ [-ln(p/p0)]-(3-~
(33b)
N = N m [ l + l l n ( -~,I x nAg)J )13
(34a)
N = N m I l + l n (-n2/36)lAg
(34b)
N ~ const- ln(-ln(p/p0))
(35a)
N ~ c o n s t - ln(-ln(p/p0))
(35b)
9D = 3
Interaction
__ (3-D)/3
Substrate potential pulls liquid-gas interface I to the surface S; f'tim grows slowly.
[ (D--2)6n2/3A] -t jq3_D
(32b)
Surface tension pushes liquid-gas interface I from the surface S; f'flm grows fast.
642 The results in Table 3 are the comerstones of multilayer adsorption on a self-similar fractal surface, as function of the chemical potential A~t or gas pressure p. They are subject to the condition that the adsorbed film is at least a monolayer (N > Nm) and that fractality starts at the length scale of the thickness of a monolayer. The regimes A~t < A~tc and All, > A~tc are identified as van der Waals wetting and capillary wetting because the isotherm depends only on the substrate potential strength a and surface tension ~, respectively The chemical potential A~tc given by Eq. (30) is therefore the transition point anticipated in Sect. 2.4 for a fractal surface. It is the fractal analog of the capillary-condensation point A~tc, Eq. (20), in a single pore. We divide the discussion into several subsections. The transition from van der Waals wetring to capillary wetting, which for simplicity we treat as sharp transition in this section, will be analyzed further in Sect. 4. Experimental examples will be presented in Sect. 5. 3.1 Earlier Derivations The power law for van der Waals wetting was first obtained in [44] (see also [ 109]). It was derived by assuming that films are sufficiently thin that surface tension can be ignored. The whole set of isotherms (32-35), including the logarthmic dependence for D = 3 and the coexistence point (30, 31) of the two types of wetting, were obtained in [101] (see also [45]). In fact, our derivation here is a streamlined version of the one in [ 101]. A more refined analytic calculation, in which the interface was not equidistant and the substrate potential was not simply a function of dist(x, S), was performed in [112] and reproduces the results (30-33) within a factor of order one. Other analytic treatments give similar agreement [ 113, 114]. The power law for capillary wetting or equivalents thereof, on the other hand, has been discovered and rediscovered in several different contexts, such as pore filling under hydrostatic pressure [ 115-117], third sound in superfluid 4He films [ 118], adsorption in micropores [ 119], and capillary condensation in mesopores [120-124]. Even the fractal generalization of the BET isotherm [109, 110, 125-128], which is unphysical beyond a few layers, coincides with (33b, 35b) in the limit p ~ P0- Most of these contexts neglect the substrate potential or model it inadequately. As a result, they give no estimate of the pressure above which (33b) would be valid, give prefactors which differ from the one in (32b), or give no prefactor at all. We therefore will focus our discussion on the present framework. 3.2 Nonclassical FHH Isotherms The classical FHH isotherm, Eq. (14), is of the form
N o, [-ln(p/p0)]-Y,
(36)
with y = 1/3. It is included in the fractal isotherm as the special case in which the surface irregularity vanishes. Indeed, in the planar-surface limit D -~ 2 the transition point Al.tc approaches zero and the associated film thickness zc diverges, so that for D = 2 the capillary wetting regime is absent in the isotherm (32) and only the van der Waals regime with y = 1/3 exists. We call (36) with y ~ 1/3 a nonclassical FHH isotherm because a departure from the value 1/3 signals a departure from the classical planar surface geometry, a departure from the classical inversedistance-cubed law for the energy of a particle at some distance from the surface, or both. Experimentally, it is weN-known that many adsorption data can be fitted to (36), but rarely with an exponent equal to 1/3. Typical experimental exponents y range between 0.4 and 0.7 [85, 87, 129-131]. Following Halsey [132], the discrepancy has been rationalized for a long time by a model in which the surface is planar, but patchwise energetically heterogeneous.
643 Each patch supports a film obeying Eq. (14), and the substrate potential strength ot varies from patch to patch. This model, under suitable assumptions for the distribution of or, leads to isotherms that can be fitted to (36) with an exponent y = 0.4 [87, 131,132]. The nonclassical FHH isotherms in Table 3, covering the whole range 0 < y < 1 by virtue of 2 < D < 3, offer a very different interpretation of experimental isotherms with y ~: 1/3. They replace Halsey's hypothesis that the surface is geometrically homogeneous (planar) and energetically heterogeneous by the hypothesis that the surface is geometrically heterogeneous (fractal) and energetically homogeneous. The merits of the fractal interpretation, as a working hypothesis, are considerable: (i) Halsey's interpretation requires the specification of a whole function, namely the distribution of ~ values, which is difficult to test by independent experiments and therefore must be regarded as phenomenological. The fractal interpretation requires the specification of a single parameter, D, which can be tested by independent experiments (Table 1) and by internal thermodynamic consistency (Sect. 4). (ii) The variable o~ values in Halsey's model may be attributed to variable-index domains in a polycrystalline surface or other defects in a planar surface. But such local energy differences are rapidly averaged out as the adsorbed film grows beyond a few layers and cannot account for y ~ 1/3 for thick films. By the same argument, the local energy differences (departures from the substrate potential (7)) that may exist on a fractal surface as a result of the geometric heterogeneity, are averaged out in films exceeding a few layers. Exact calculations of the potential U(x) for a fractal surface confirm this [133]. Even at coverages less than a layer, energetic heterogeneities are irrelevant on a fractal surface at sufficiently high temperatures [ 125] (see also [29]). Thus, under a wide range of circumstances, energetic heterogeneities of Halsey's type can be disregarded, leaving geometric heterogeneity as the most likely source of experimental exponents y ~: 1/3. (iii) Halsey's model is designed to explain exponents y > 1/3. But experimentally, one finds also y < 1/3, although less frequently. The fractal model is capable of explaining both deviations and covers to the best of our knowledge all experimental exponents that have ever been observed. The correspondence between y and the fractal dimension is illustrated in Fig. 7. It shows that, for 1/3 < y < 1, the dimension can be uniquely reconstructed from y as D = 3 - y, that the resulting D value lies between 2 and 8/3, and that the value of y necessarily implies capillary wetting. This agrees remarkably well with the earlier suggestion, predating the fractal concept, that exponents y > 1/3 are "probably explicable by some reversible capillary condensation" [ 129], and converts that suggestion into a quantitative statement about surface irregularity. For 0 < y < 1/3, the correspondence between the isotherm exponent and the fractal dimension is no longer one-to-one. That is, the fractal dimension may be either D = 3 - y (capillary wetting), giving 8/3 < D < 3, or else D = 3 - 3y (van der Waals wetting), giving 2 < D < 3.
Zyy
= 3 - D [capillary wetting]
.
.
.
.
.
.
.
.
= (3 - D)/3 [van der Waals wetting] 1/3
-ilj~176 2
8/3
3
D
Figure 7. Isotherm exponent y as function of the fractal dimension D, as given by Eq. (33).
644 Which of the two possibilities is the appropriate assignment when only y is known, is a question that has been raised in several experimental studies using the isotherms in Table 3. We will answer that question systematically in Sect. 4. A partial answer is provided by the inequality 3-3y
< D < 3-y,
(37)
which translates the two possibilities into upper and lower bounds for the sought-after dimension (Fig. 7). These bounds, which are tight when y is small, may be taken as substitute for the missing one-to-one correspondence between y and D for 0 < y < 1/3. If the y value under consideration comes from the low-coverage regime in the isotherm, i.e., from the regime starting at N/N m = 1, one additionally has the role of thumb that the closer to 1/3 the y value lies, the more likely is the choice D = 3 - 3y (van der Waals wetting) the correct one. Indeed, the only way how a value y -- 1/3 can originate from capillary wetting at low coverage is if D -- 8/3 and simultaneously the film thickness z c at which capillary wetting sets in, Eq. (31), is of the order of a monolayermwhich would be an unlikely coincidence. Together, this rule and the above bounds give fairly strong guidelines for the interpretation of exponents in the range 0 < y < 1/3 in terms of fractal dimension [ 134].
3.3 Reduced Isotherm and Law of Corresponding States The isotherms (32, 34) offer a fully developed theory of multilayer adsorption on irregular surfaces with a minimum number of parameters: The number of particles in the monolayer, N m, describes the sample size; the fractal dimension D describes the surface geometry; the potential strength ~ describes the substrate-adsorbate interaction; the surface tension ~ describes the adsorbate-adsorbate interaction; and the number density n describes the adsorbate particle size (recall a0 = n-1/3). Interpreted in this way, the five parameters are clearly independent, none of them is dispensable, and none of them is adjustable. One gains additional insight into the analytic structure of the isotherms by expressing them in reduced variables. The reduced form is N/N m = [max{~, (D-2)T~3}] 3.D
(2 < D < 3),
(38a)
N/N m = 1 + In(max{ ~, y~3 })
(D = 3),
(38b)
; :=
/ an/l/3 ~-S-ff]
y :=
t~ . t~n5/3
'
(39) (40)
The expression max {....... } selects the larger of the two arguments and automatically reproduces the van der Waals regime for ~ < ~c [N/Nrn < (N/Nm)e], and the capillary wetting regime for ~ ___~e [N/Nm > (N/Nm)c], where ~c := [(D-2)Y] -1/2,
(41)
(N/Nm) c := [(D-2)y] -(3-D)/2.
(42)
The variables ~, ~c, and T are all unitless and take the role of Ag, Ag e, and the triple (~, tj, n) in Table 3. The variable ~ is the film thickness z, measured in units of the thickness of a monolayer, one would have at chemical potential Al.t if the surface were planar. In agreement, the isotherm (38) for D = 2 reads N/N m = ~. The value ~c predicts the film thickness z c at which
645 capillary wetting sets in, Eq. (31), in units of the monolayer thickness; and the value (N/Nm) c predicts the associated coverage. By the remarks at the beginning of this subsection, the constant 3' measures the strength of the adsorbate-adsorbate interaction relative to the substrateadsorbate interaction. Since ao = n -1/3, it may be interpreted as the ratio of the liquid-gas free energy, ca~, to the potential energy, ~a~ 3, of a particle in a monolayer. We therefore call 3' the capillary-energy ratio. In wetting dynamics (spreading of liquids), a similar energy ratio, namely surface tension divided by the product of liquid velocity and viscosity, is known as the inverse of the capillary number [95, 133]. The reduced form of the isotherm displays the full D dependence of the adsorption process (note that ~ and 3' do not involve D), encapsulates the competition between the substrate potential and surface tension in the single number q,, and describes the dependence on the chemical potential through the single variable ~, the number of equivalent layers on a planar surface. As a result, it reduces the dependence on four parameters in Table 3 (D, ~, (~, n) to a dependence on only two parameters (D, 3'). The fact that 3' enters exclusively via the combination (D - 2)3' [Eqs. (38, 41, 42)] says that the liquid filling the hierarchy of small and large pores of the fractal surface has an "effective surface tension" equal to ( D - 2)~. Thus, compared to the liquid-gas equilibrium in a single pore, the fractal surface lowers the effective liquid-gas free energy by a factor of D - 2. A similar lowering of effective energy exists in chemical reactions on a fractal surface, for stationary diffusion of reactant molecules from an external source to the surface: There the effective activation energy is lowered, by a factor of 1/(D - 1), compared to the reaction on a planar surface [52, 136, 137]. The reduced isotherm leads to the following simple way of testing an experimental isotherm for fractality of the underlying surface. The idea is to employ the coverage N/N m, rather than the chemical potential A~t, as calibration scale, i.e., as variable to distinguish van der Waals wetting from capillary wetting and to determine absolute film thicknesses. Assuming that the monolayer value N m is known (say from a BET analysis of the low-pressure part of the isotherm), all one needs to do is to plot the coverage N/N m as a function of In(p/p0); test whether the data, starting at N/N m = 1, obeys one of the power laws (33a, b); andmin the event that the data obeys (33a) at low coverage and (33b) at high coverage (with the same D)---test whether the crossover from (33a) to (33b) occurs at the coverage predicted by Eq. (42). The start of the power law at N/N m = 1 reflects the start of the fractal regime at the molecular scale. The test for D = 3 proceeds similarly. Since the expression max {....... } in the reduced isotherm is the film thickness, in units of the monolayer thickness, on the fractal surface (both for van der Waals and capillary wetting), one can convert any experimental coverage into a corresponding film thickness z: / N ~l/(3-D) z = /N~-~) a~
(2 < D < 3),
(43a)
z = exp
(D = 3).
(43b)
- 1 ao
This gives the length scale of surface irregularities probed at coverage N/N m [ 134]. The smallest and largest film thickness computed from (43), as the coverage varies over the range in which the data obeys (33) or (35), gives the inner cutoff s (-- %) and outer cutoff s respectively, of the fractal regime of the surface. Representative values of the parameter 3' and related data are listed in Table 4. The film thickness ~/o~n/o is the smallest possible film thickness at which capillary wetting may set in. It
646 Table 4 Substrate potential strength or, film thickness ~/~n/c~ at which capillary wetting sets in for D = 3 [Eq. (31)], and capillary-energy ratio 3' [Eq. (40)], for nitrogen on different solids. The surface tension and number density of liquid nitrogen, in coexistence with its vapor at T = 77.347 K, are o = 8.85.10 -16 erg,~ -2 and n = 1.738-10 -2 ,~-3 [138]. The resulting monolayer thickness is a 0 = n -1/3 = 3.86 ,~. Solid
c~ (erg,~ 3)
SiO 2 (quartz) C (graphite) Si A1 Ag Au
6.19-10 -13 1.42.10 -12 1.59.10 -12 2.13-10 -12 2.35-10 -12 2.62.10 -12
[89] [89] [89] [89] [112] [89]
qo~n/o (.~)
7
3.49 5.28 5.58 6.46 6.80 7.18
1.23 0.534 0.478 0.357 0.323 0.289
measures the competition between the substrate potential and surface tension in terms of a length and is a characteristic length also for multilayer adsorption on other nonplanar surfaces (see Sect. 2.4 and [ 100-104]). When the substrate potential is so weak or surface tension is so strong that qom/c < ao, which is equivalent to ~/> 1, and if D > 2 + 1/3', then capillary wetting sets in at a film thickness that is nominally less than the thickness of a monolayer [Eqs. (31, 41)]. In this case, adsorption obeys the capillary-wetting isotherm for all N/N m > 1, i.e., the regime of van der Waals wetting is absent. This means that capillary forces may dominate already at the stage of a monolayer and conforms with the observation that the monolayer has an effective liquid-gas free energy exceeding the substrate-potential energy whenever (D - 2)7 > 1. On a planar surface, the situation 3' > 1 expresses that the lateral attraction among adparticles in a monolayer is stronger than their attraction to the solid. An instance of such a weak substrate potential is SiO 2 in Table 4. The weak substrate potential of SiO 2 can be attributed to its low polarizability (electric insulator). Accordingly, the increase of the substrate potential, as we go down in the table, reflects the increase in polarizability with increasing metallic character of the solid. If one views the capillary-energy ratios in Table 4 as significantly different, then the variety of ratios predicts a corresponding variety of different behaviors of the isotherm (38). But if one regards the capillary-energy ratio as only weakly varying, T-- 1, then the reduced isotherm has the status of a law of corresponding states for films on a fractal surface, quite analogous to the law of corresponding states for bulk fluids. Indeed, just as bulk fluids satisfy, at least semiquantitatively, a universal equation of state that makes no reference to material constants if expressed relative to the pressure Pc, temperature T e, and number density n c at the liquid-gas critical point [139], the films on a fractal surface satisfy a universal isotherm making no reference to material constants (other than the fractal dimension) if expressed in the reduced form (38). The independence of material constants manifests itself in the relation pe/(kTenc) = 0.27 + 0.04
(normal bulk fluids),
(44)
o/(otn 5/3) = 0.76 + 0.47
(adsorbed films),
(45)
respectively, where the estimate (45) is based on Table 4 and hence is preliminary.
647
3.4 Does the Roughness of the Substrate Enhance Wetting? In the study of general 3-phase equilibria between a liquid, its vapor, and a solid (Fig. 2), this question [ 140] has been investigated from many different angles (interfacial energies, contact angle, spreading kinetics, transition to complete wetting) and for several types of roughness [83, 85, 135, 140-152]. The genetic answer is yes, in the sense that the contact angle decreases or increases with increasing roughness (enhanced or diminished wetting), on a surface without overhangs, depending on whether the contact angle on the planar surface is less than or larger than rff2 [85,140]. Here, for completely wetting films on a fractal surface with Overhangs (Fig. 6), we address the question in terms of whether the coverage N/N m grows faster or slower than on a planar surface as A~t ~ 0 (enhanced or diminished wetting). We assume for simplicity that the surface is fractal at all length scales above a 0, i.e., that the outer cutoff is formally infinite. The surface then has voids of all sizes and, based on the picture that capillary condensation in voids of ever larger size systematically enhances adsorption [ 129], one might expect that the coverage always grows faster than on a planar surface. However, this is not the case. The isotherms (32, 34), or equivalently (38a, b), show that the coverage grows faster in the limit Akt ~ 0 if and only if 1/3 < 3 - D < 1. We therefore have enhanced wetting if 2 < D < 8/3, diminished wetting if 8/3 < D < 3
(46a) (46b)
[ 112]. In Fig. 7, the situation (46a) corresponds to 1/3 < y < 1 and identifies the isotherm exponents which give enhanced wetting as those from which D can be uniquely reconstructed. To explain how (46) arises, including the paradoxical consequence that the coverage grows fastest on a nearly planar surface (D ~ 2), we begin at low A~ and note that the film thickness at low A~t is the same as on a planar surface, Eq. (29a), so that the coverage depends on the fractal dimension only through the film volume, which decreases with increasing D. Hence, for van der Waals wetting the coverage always decreases with increasing D. When we enter the capillary wetting regime, the film thickness becomes D-dependent and grows faster than on a planar surface, Eq. (29b). The question then is, can this fast growth of the film thickness compensate for the slow growth of the film volume, so as to make the isotherm for D > 2 overtake the classical FHH isotherm at sufficiently high Al.t? The answer is yes if the film volume V(z) does not grow too slowly with increasing film thickness z, i.e., if the fractal dimension is not too high. Thus, a surface with 2 < D < 8/3 is sufficiently porous to induce capillary condensation and, at the same time, sufficiently "open" for the condensate to grow in excess of the growth on a planar surface. A surface with D _>8/3 is also sufficiently porous to induce capillary condensation, but too "closed" for the condensate to grow as fast as on a planar surface. On a nearly planar surface, with D very close to 2, the van der Waals regime will be very extended and the coverage will be close to that on a planar surface up to very high values of Alx; but no matter how close to zero A~tc may be, once Akt exceeds the value Al.tc, the nonplanarities of the surface at those large length scales will be sufficiently magnified to induce capillary condensation and make the coverage eventually grow as (-A[.t) -(3-o). Thus the paradox of fastest growth on a nearly planar surface is resolved by the observation that the limits Al.t -~ 0 and D --~ 2 are not interchangeable [ 112]. A convenient way to analyze the growth of the coverage in more detail and compare it with the growth on a planar surface is to plot the reduced isotherm (38) as a function of ~. Since ~ is the coverage one would have at chemical potential Akt if the surface were planar, such a plot is in fact equivalent to a t-plot or comparison plot [ 129], using the classical FHH isotherm as
648
4
3 -
~
N/N m 2
-
. ~
(b)
1
0 0
1
I ,, 2
I 3
I 4
J
5
Figure 8. Reduced isotherm (38) as a function of the unitless variable ~: (a) for D = 2; (b) for D = 2.40 and T = 0.323; (c) for D = 2.40 and y = 2.50. Depending on whether ~ is interpreted as film thickness (in units of the monolayer thickness) or amount adsorbed (in units of a monolayer) on the planar reference surface, the curves represent the t-plot or the comparison plot of the adsorption isotherm on the respective substrates.
standard isotherm. Such plots for selected D values and capillary-energy ratios y are shown in Fig. 8. The straight line (a) is the isotherm on the planar reference surface. Curves (b) and (c) are the isotherms on a 2.4-dimensional surface for two different values of y. They both lie above the planar-surface isotherm for sufficiently large ~, in agreement with (46a). In (b), the value of T, representing nitrogen on silver, is low enough for the van der Waals regime to extend up to ~ = 2.78 [Eq. (41), second dotted line] and for capillary wetting to make the isotherm cross the planar-surface isotherm only at ~ = 4.64 [third dotted line]. In (c), the value of y has been chosen high enough for the van der Waals regime to be absent, i.e., for capillary wetting to set in at ~ = 1 [Eq. (41), first dotted line]. This makes the isotherm (c) lie above the planarsurface isotherm for all ~ > 1. We note that the break points at ~ = 2.78 and ~ = 1 in (b) and (c) result from our treating the crossover from van der Waals wetting to capillary wetting as a sharp transition in this section. They are smoothed out if one solves Eq. (28) for the film thickness exactly and puts the resulting isotherm into reduced form. Likewise, the submonolayer regime in the isotherms in Fig. 8 (N/N m < 1) should not be taken at face value, because the underlying expression (32a) does not describe submonolayer adsorption properly. Figure 8 gives a fine-tuned picture of how a surface with 2 < D < 8/3 enhances wetting, by displaying the value ~e at which the fractal isotherm intersects and overtakes the planar-surface isotherm. We call this point the onset of enhanced wetting and define it generally as ~e := min{~'" (~---m-m)(D,~)>(~mm}(2,~)for all ~> ~'; ~'> 1} = max{ 1, [ ( D - 2)T]-'(3-D)/(8-3D)},
(47a) (47b)
where the condition ~'> 1 serves to exclude the submonolayer regime from consideration. The result (47b) is simply the solution of [ ( D - 2)]t~3}] 3-D = ~ if this solution is > 1, and equal to
649
20. 15. I0.
2 1.5 ~ ~
~
~
'
~
I
~
I
~ - ~
I 1
2
2 2
2 4
,
,
,
i
2.6
,
I,
~ ,
,
~ I
2.8
~
...._._. _........ 9
D
3
Figure 9. Onset of capillary wetting, ~c [Eq. (41), dashed curves], and of enhanced wetting, [Eq. (47), full curves], as function of the fractal dimension. The curves from top to bottom are for y = 0.323, y = 1.50, and 3t = 2.50. For fixed D and y, the regimes ~ < ~c, ~ > ~c, and ~ > ~e amount to van der Waals wetting, capillary wetting, and enhanced wetting. To the right of the vertical line at D = 8/3, enhanced wetting no longer exists.
one else. The significance of ~e is that it distinguishes whether in (46a) the onset of enhanced wetting occurs at low or high chemical potential (low or high ~e)" We illustrate this in Fig. 9. For D ~ 2, all curves ~ diverge in agreement with our discussion of Eq. (46a). For D ~ 8/3, the curves ~e diverge, too, if'f < 3/2. In this case capillary condensation is not strong enough to overcome, at some low chemical potential, the slow growth of the film volume as D ~ 8/3. Accordingly, the curve ~e for nitrogen on silver first drops with increasing D until it reaches a minimum of ~e = 4.41 at D = 2.32, and then rises again. If 3t > 3/2, however, capillary condensation is strong enough to make the onset of enhanced wetting drop all the way down to the chemical potential at which the planar reference surface supports a monolayer, as D increases. For example, the fact that the isotherm (c) in Fig. 8 lies above the planar-surface isotherm for all > 1 is mapped into the value ~e = 1 for D = 2.40 and y = 2.50 in Fig. 9. We note that Fig. 9 also clarifies the status of the borderline case D = 8/3 in (46): For D = 8/3, wetting is enhanced if 3t > 3/2 (~e < oo), and diminished if T < 3/2 (~e = oo). These results for the onset of enhanced wetting on a fractal surface lead to guidelines for the interpretation of t-plots and comparison plots which differ substantially from the traditional guidelines. The traditional wisdom [129] is that (i) capillary condensation, with or without hysteresis, manifests itself in an "upward swing" of the t-plot or comparison plot, i.e., in a plot that lies above the straight line representing the standard isotherm; and that (ii) the presence of micro- and/or mesopores manifests itself in an upward swing of the plot, with or without the adsorption equilibrium being controlled by capillary forces. The presence of an upward swing, also called enhanced adsorption in [ 129], amounts to a finite value of ~e in Fig. 9. Thus, Fig. 9 confirms the validity of statement (i) for 2 < D < 8/3, but shows that the statement is not valid in the range 8/3 < D < 3 because in that range capillary
650 condensation does not enhance adsorption. The validity of statement (ii) is even more restricted. Indeed, if we interpret the statement as saying that enhanced adsorption starts at ~ = dmin/a0 where dmi n is the smallest pore diameter, enhanced adsorption should start at ~ = 1 on a fractal surface with inner cutoff a 0. But since van der Waals wetting never enhances adsorption, the statement is valid only if ~e = 1. Hence statement (ii) is valid only if 2 + 1/3, < D < 8/3 [Eq. (47b)]. It is easy to see how the guidelines need to be modified to be valid without restrictions. All one has to do is to weaken them from "if-and-only-if" statements to "if" statements. That is, capillary condensation is operative and micro- or mesopores are present if there is an upward swing in the plot. The converse, however, is not true: As fractal surfaces show, pore filling may occur, by capillary condensation or otherwise, without an upward swing.
3.5 Comparison with Adsorption in a Single Pore Having obtained the transition from van der Waals wetting to capillary wetting in a single pore (Sect. 2.4) and on a fractal surface (Table 3), we now wish to highlight the parallels and differences between the two transitions. We compare the transitions by analyzing their dependence on the energy parameters and surface geometry. The chemical potential Agc, Eqs. (20) and (30), sets the energy scale for the transition. The film thickness z c, Eqs. (21) and (31), sets the associated length scale. As in Sect. 3.3, we express Agc in units of the potential energy per particle in a monolayer, czn, and z c in units of the monolayer thickness n -1/3. This reduces the dependence on o~ and ~ to a dependence on the capillary-energy ratio 3, and leads to the results in Table 5, the fractal entries being familiar from Sect. 3.3. We begin with the dependence on the energy parameters. Since ABc and z c predict the point at which capillary forces become stronger than the substrate potential, their value must decrease with increasing 3,. Lines 1 and 2 in Table 5 confirm this both for the fractal surface and the single pore. The specific dependence on 3, is quite different, however: The magnitude of the exponent of 7 is consistently larger in fractal case (by a factor of 3/2 for Agc, and 2 for Zc). Thus the onset of capillary wetting depends strongly on the competition between cz and o in the fractal case, and by comparison weakly in the single-pore case.
Table 5 Onset of capillary wetting on a fractal surface and in a single pore, with 3, given by (40). The first two entries in the last column are the leading terms of the asymptotic expansion. Fractal surface
Single pore
Agc/(om)
- [ ( D - 2)T] 3/2
-27/(Rn I/3) [R --4 oo]
Zcn 1/3
[(D - 2)3,]-1/2
(23'/3)-1/41/Rnl/3 [R --4 oo]
Dependence on energy
strong
weak
Dependence on geometry
D
R
Dependence on size
none
R
Planar-surface limit
z c ~ oo [D ~ 2]
z c --~ oo [R ~ oo]
Maximally nonplanar surface
z c = n-1/3T-1/2
Zc= n-1/3(l+ ~/l+V8T/3 ) -1
= 3-7 ~, [ D = 3 ]
= 1-2 ,~ [R = (1/2)n -1/3]
651 Next we consider the transition as function of surface geometry, restricting attention to the film thickness z c. Just as z c in the single pore depends on the surface geometry via the pore radius R, the value of z c on the fractal surface depends on the surface geometry via D. However, while in the single-pore case this dependence on geometry involves simultaneously a dependence on size, R, there is no size dependence in the fractal case because a fractal surface is scaleinvariant (D is a size-independent measure of surface irregularity [12]). Capillary effects should become weak and z c should increase, as the degree of nonplanarity of the surface decreases. This is indeed the case, as the planar-surface limits in Table 5 show; both for the fractal surface and the single pore, there is no capillary condensation at any finite film thickness in this limit. Conversely, capillary effects should be strongest and z c should be smallest when the surface is maximally nonplanar. The notion of maximally nonplanar is naturally defined as D = 3 in the fractal case, and as a pore that can hold exactly one particle in the single-pore case. The resulting values for z c in Table 5 (based on the values from Table 4 for n and 7) are of atomic size in both cases, reinforcing the conclusion that capillary forces may control the formation of already the first layer. It might be argued that the onset of capillary consensation at a film thickness of a few ,~mgstroms, on a maximally nonplanar surface, is not really surprising because, if pores with diameter comparable to the diameter of a single adsorbate particle are present, these pores are automatically full after the first or second layer. But capillary condensation at the level of one or two layers is not trivial because in the fractal case a surface with D = 3 has room for an unlimited number of layers [Eq. (34)]. In the single-pore case, it is not trivial because condensation occurs at z c < R (Table 5). In this light, we consider it as a signature of remarkable internal consistency that nonplanar surface geometries of such different types yield similarly low values for z c in the limit of maximum nonplanarity. This consistency suggests that the model of capillary condensation in a single pore, when treated including the effects of the substrate potential and applied to micropores, may in fact perform considerably better than what recent model studies of adsorption in micropores seem to suggest [ 153, 154]. A somewhat different picture of the transition from van der Waals wetting to capillary wetting emerges when we compare the fractal and single-pore case away from the transition point. On the fractal surface, the isotherm gives the full structural information about the surface (i.e., the dimension D) both below and above the transition point, as shown by the D dependence of Eqs. (32, 34) or (38). In principle, therefore, the D value can be obtained in three different ways, namely from the isotherm exponent in the van der Waals regime, from the isotherm exponent in the capillary regime, and from the transition point itself. The multiple manifestation of D is naturally a consequence of the recurrence of the same structure at all length scales. In a single pore, by contrast, the isotherm exhibits only little information about R below the transition point (the film thickness z 1 in Fig. 4 depends only weakly on R for Ag << Agc), and no information above the transition point, when analyzed in terms of incremental adsorption dN/d~g. Thus in a single pore, the structural information revealed by the isotherm is restricted to the transition point, reflecting that the surface has structure only at one length scale, R. Finally, adsorption on a fractal surface and in a single pore can also be compared from the viewpoint of decomposing the fractal surface into single pores (hierarchy of ever larger voids) and regarding adsorption on the fractal surface as the sum of adsorption events in single pores. A decomposition of a fractal surface into nominally single pores is shown in Fig. 10. The decomposition and the results for a single pore imply that, at any given chemical potential Ag, some pores are full (capillary condensation in small pores) and some pores are partly filled (van der Waals wetting in large pores). This leads to the conclusion that van der Waals and capillary
652
Figure 10. Decomposition of a fractal surface into spherical pores of variable radius R (from Ref. [44]).
wetting coexist at any Ag, and one may ask how such coexistence can be reconciled with two separate regimes as predicted by Table 3 [155]. The answer is as follows. The film thickness z c at which capillary wetting sets in on the fractal surface, Eq. (30), divides the pore hierarchy into small and large pores according to R < zc R > zc
(small pores), (large pores).
(48a) (48b)
The filling of small pores, taking place when Ag < Ag c, is governed by the substrate potential, in the sense that the isotherm jump in a single pore due to capillary condensation is small. In this sense capillary condensation is negligible in small pores. The filling of large pores, taking place when Ag > Ag c, is governed by capillary condensation, in the sense that most of the increase in adsorption comes from the jump in pores with radius R-- 2~/(-nAg) [Eq. (20)], while the contributions from van der Waals wetting in larger pores (where capillary condensation has not occurred yet) are negligible. The reason why van der Waals wetting is negligible in large pores is because the film thickness is at most R1/E(6om/tJ) TM prior to capillary condensation [Eq. (21)], which is small compared to R for large R. Thus small and large pores are indeed filled by van der Waals and capillary wetting, in agreement with Table 3. Explicit calculations of the isotherm on a fractal surface in terms of single-pore adsorption have been carried out in Refs. [44, 112].
3.6 Effective Potential and Extension to Arbitrary Surface Geometries Can the variational isotherm calculation, as underlying the results in Table 3, be extended to other irregular surface geometries? If so, can the calculation be inverted to extract information about the geometry from a general experimental isotherm? The answer is yes. To see that and gain additional insight into the nature of the adsorption equilibrium, we return to Eq. (28) which determines the equilibrium film thickness z on a fractal surface and can be written as Ueff(z) - Ag = 0 Ueff(z) := - ~ z 3 - (D - 2)~/(nz).
(49) (50)
The function Ueff(z), which we call effective potential has a simple and important physical meaning. It is the difference between the energy of adding a particle to an equilibrium film of
653 thickness z on the fractal surface and the energy of adding a particle to bulk liquid, all effects included. It separates the adsorption isotherm into two regimes, van der Waals wetting and capillary wetting, by decomposing the energy into a sum of the substrate potential -ot/z 3, dominant at short distances, and the "capillary p o t e n t i a l " - ( D - 2)cy/(nz), dominant at large distances. The slow growth of the capillary potential at large distances induces the fast film growth indicated in Table 3. More generally, the lower the effective potential is as a function of distance z, the larger is the equilibrium film thickness at given chemical potential Ag. Remarkably, the capillary potential in (50) acts like a Coulomb potential, with "electric charge" ( D - 2)~/n. The electrostatic analogy makes clear that the capillary potential is overwhelmingly stronger than the substrate potential at large distances. In the spherical pore, the equilibrium (or metastable) interface is an equidistant interface, too, and the associated film thickness z is determined by Eq. (49), too, but now with the effective potential
Ueff(z) =
- ot/z 3 Al.tc
2(y/[n(R
- z)]
if z < z c
(51)
if z c < z < R
where z c and A~tc are defined as in Eq. (21) [recall Eqs. (15, 16) and Fig. 4]. The constancy of the effective potential (51) at distances larger than z c, which implies that the film can have any thickness between z c and R at A~t = A~tc, describes the vertical jump in the isotherm (Fig. 5). This extends the effective potential to include all physically realizable states on the isotherm, regardless of whether they are equilibrium states, metastable states, or even unstable states (vertical jump in the isotherm). Under this extension, constancy of the effective potential is the most extreme form of slow growth of the effective potential and entails the most rapid growth of the film thickness. For adsorption on a general surface, in terms of equidistant trial interfaces, the condition (27) for the equilibrium film thickness z leads again to Eq. (49), with effective potential Ueff(z )
=
o~ z3
~
(3"~VP'(z) . n V'(z)
(52)
The functions V'(z) and V"(z) denote the first and second derivative of the film volume V(z), Eq. (22), and the expression is subject to the condition that the right-hand side of (52) is monotone increasing with increasing z. Under this condition, Eq. (49) has a unique solution z for every A~ and the solution represents the equilibrium state. If the fight-hand side of (52) is not monotone increasing, Eq. (49) with (52) has more than one solution z and the physically realized film configuration, depending on whether one moves along the adsorption or desorption branch of the isotherm, is found by a stability analysis similar to Eqs. (18a, b). The stability analysis yields
Ueff(z) = nde+_. - ~ +
W(z)1
n V'(z)
(53+)
for the adsorption (+) and desorption (-) isotherm, respectively. Here nde_ denotes what we call the upper and lower nondecreasing envelope, defined as
654 nde+ qo(z) := max{ qo(t)" t ___z; qo'(t) _>0 }, nde_ q)(z) := min{qo(t): t _>z; ~o'(t) > O}
(54+)
(54-)
for a function q~(z) with derivative cp'(z). The construction of the two envelopes is illustrated in Fig. 11. As in the spherical-pore potential (51), which is a special case of (53+), the potentials (53+) lead to a vertical jump in the isotherm where they are constant. Clearly, they reduce to (52) if the function on which nde_+ acts is monotone increasing. We note that V'(z) is always positive, V"(z) is zero if and only if the surface is planar, V"(z) is negative for the spherical pore and every fractal surface, and V"(z) is positive for every convex solid. Thus, to compute the adsorption/desorption isotherm on an arbitrary surface S, one has the following simple steps: 9 Compute the function V(z), i.e., the volume of points outside the solid whose distance from the surface is less than or equal to z; this is the only surface-geometric input needed. If no analytic expression for V(z) is available, the computation requires only the sorting of distances dist(x, S) of points x close to the surface [ 156]. 9 Compute the effective potential Ueff(z) according to (53). If no analytic expression for V(z) is available, use V"(z)/V'(z) = (d/dz) [In V'(z)] for robust numerical differentiation. 9 Solve Eq. (49) for z as a function of Ag and substitute the result, z = z(Ag), into N = nV(z); this is the desired isotherm, N(Ag) = nV(z(Ag)).
(55)
The procedure is remarkable because, unlike the original problem (12, 13), it does not require to solve any differential equation. It operates with a minimum of geometric input, namely a function of one variable, which automatically maps two surfaces with "similar degree of irregularity" (Fig. 1) onto the same function V(z). The presence of the second derivative V"(z) in (53) corresponds to the Euler equation (13) being a second-order differential equation. The bifurcation generated by the operator nde+_in (53) automatically selects, from the several solutions of the Euler equation, the ones that are realized during adsorption and desorption, respectively. It may lead to hysteresis loops with any number of steps in either isotherm (Fig. 11). For the case in which there is no hysteresis, the procedure has been described before [43, 157].
Ueff
(+)
f f
S Figure 11. Construction of the effective potential Ueff(z) given by (53). The solid curve is the f u n c t i o n - ~ z 3 + a V"(z)/(nV'(z)) [schematic]. The dashed lines, together with the adjoining monotone parts of the solid curve, represent Ueff(z) for adsorption (+) and desorption (-).
655 When there is no hysteresis and therefore the form (52) of the effective potential applies, it is possible to invert the procedure and deduce the function V(z) from the adsorption isotherm N(A~t): By differentiation of (55) with respect to A~t one obtains V'(z(A~I,))
= n -1N'(A~I,)/z'(A].I,)
g"(z(A].l,)) = n-l[N"(Akl,)-
N'(A].t)z"(A~l,)[z'(Akt)]][z'(A~l,)] 2,
(56)
(57)
where N', N", z', z" are the derivatives of the functions N(A~t) and z(A~). Putting (56, 57) into (52) and using the equilibrium condition (49) yields
(58)
This is a second-order nonlinear differential equation for the film thickness z(Als), in terms of the given isotherm N(AI.t), subject to Akt > A~trn where Aktm is the chemical potential at monolayer coverage. The initial data for (58) is z(A~l,m) = n-l/3
z'(Aktm) = n-l/3 N'(A~m)/N(AILtm)'
(59a) (59b)
expressin~ that the monolayer thickness is n- 1/3 and that the area of th e monolayer, V'(n-l/3), equals n-2/3N(Alarn), which after substitution into (56) gives (59b). The solution of the initialvalue problem (58, 59) gives the film thickness z(A~t) at any chemical potential A~t > Atxm. Since z(A~t) is a monotone increasing function, it can be inverted to obtain the chemical potential as function of the film thickness, Akt(z), which yields the sought-after surface geometry as V(z) = n-lN(A~t(z)),
(60)
for any z > n-1/3. If desired, the volume V(z) can be converted into the pore-size distribution Vpore(Z), the cumulative volume of pores with radius less than or equal to z, which is defined-free of any model assumptions about the surface geometry--as the volume of space outside the solid that is inaccessible to spheres of radius z [21, 36, 43, 109, 128]. The connection between the two volumes is given by Vpor~(Z) = V(z) - z V ' ( z ) ,
(61)
for a surface that is neither convex nor concave [43]. This concludes our demonstration, by explicit construction, of the existence of a general one-to-one correspondence between surface geometry and experimental adsorption isotherms as announced in Sect. 2.3. More details will be published elsewhere. The inverse part of the correspondence, which transforms the isotherm N(AI.t) into the function V(z), is optimal in the sense that from a scalar function of one variable, N(Atx), one cannot expect to get more information about the surface than another scalar function of one variable. The correspondence shows that there is nothing special about fractal surfaces with regard to the computability and invertibility, as a matter of principle, of the isotherm. What does make fractal surfaces special is that their isotherms are power laws from which all geometric information (D, groin, gmax) carl be obtained without having to solve the differential equation (58).
656 4. W E T T I N G P H A S E D I A G R A M In our discussion in Sect. 3, we treated the transition from van der Waals wetting to capillary wetting on a fractal surface as a sharp transition. The sharp transition resulted from extrapolation of the asymptotic film thicknesses (29) to the point where they are equal to each other. But in reality, when Eq. (28) for the film thickness is solved exactly, the transition is smooth. This raises the question in what sense the notion of two distinct wetting regimes is well-defined independently of any approximation, how accurate the simplification of the transition between the two regimes as a sharp transition is, and in what sense the transition is a phase transition, similar to that for capillary condensation in a single pore. The same question arises for the transition from submonolayer adsorption to multilayer adsorption, which we treated as sharp, but which in fact is smooth, too. We address these questions in an operational way. Our goal is to map the qualitative regimes of submonolayer adsorption, van der Waals wetting, and capillary wetting onto quantitative intervals of chemical potential, (-oo, A~tm)' (Aktrn' A~tc)' and (A~tc, 0), so that the intervals, when used to analyze experimental isotherms, predict the pressure ranges in which one should, or should not, expect to find one of the power laws (33a, b). The challenge is to identify A~n (transition to multilayer adsorption) and Al.tc (transition to capillary wetting) in a way that is conceptually independent of whether the surface is fractal or not and independent of whether the isotherm computation ( 12, 13) is carried out exactly or approximately. With such an identification at hand, one may then compute A].I.m and A~c as function of D, which will be the desired wetting phase diagram for fractal surfaces. Depending on what approximations are used to compute the isotherm, the resulting phase diagram is approximate, too. We begin by examining how well the adsorption isotherm on a fractal surface, computed from Eq. (24) by solving Eq. (28) for z exactly, is approximated by the power laws (32a, b) [Eq. (24) with z from (29a, b)] which describe the transition as sharp. This is done in Fig. 12, for a choice of parameters that corresponds to one of the experimental examples in Sect. 5. The isotherm is plotted over a deliberately wide range of A~t values, including very low values where the multilayer framework no longer applies (recall Sect. 3.4), so as to exhibit the full asymptotic behavior for A~ --~ _.oo and A~t --~ 0. The figure shows that the isotherm rapidly approaches the power laws (32) as Akt moves in either direction from the point of intersection of the power laws. At the point of intersection, where the difference between the isotherm and the power laws is largest, the difference is about 20 %, which is small for a power-law crossover. The crossover interval, which we take as the range of N/N m values for which the isotherm differs by more than 10 % from the respective power law, spans a coverage ratio of 2.5, which is again small for a power-law crossover. In units of film thickness, Eq. (43a), the interval spans a ratio of 3.8; and in units of A~t, the interval spans a ratio of 10. Thus we conclude that the description of the transition from van der Waals wetting to capillary wetting as a sharp transition, as given by the power laws (32), is a good approximation for most practical purposes and can easily be replaced by an exact evaluation of Eqs. (24, 28) if need arises. The evaluation of Eqs. (24, 28) for other D values and substrates yields similar results for the rate of approach to the asymptotic power laws and the behavior in the crossover region. That is, there is no significant dependence of the crossover behavior, in relative units, on the fractal dimension and the material constantsmin agreement with the universal form of Eqs. (24, 28) when expressed in terms of the reduced variables ~ and ~t introduced in Eqs. (39, 40). The only thing that depends on D and the material constants is the position of the transition point on the Al.t and N/N m axis.
657
1000
100
10
zX~t/(kT)
[mL1tl I
[illl,tt
I
hillllll
-10000
],lltili~
-10
hiil|itl
'.htli~il i
Ir
-0.01
h,,t, ll t
]u,,~,~,
N/Nm
0.1
-0.00001
Figure 12. Adsorption isotherm for nitrogen on a silver surface with D = 2.30, computed from Eqs. (24, 28) and the constants in Table 4. The dashed straight lines are the asymptotic power laws (32a, b) for van der Waals wetting and capillary wetting. They intersect at Al.t = A~tc and N/N m = (N/Nm) c, given by Eqs. (30, 42), and have slopes (3 - D)/3 and 3 - D, respectively.
Figure 12 contains an important caveat, however: If one selects a sufficiently small portion of the isotherm from the crossover region and fits a power law to it, one may find a nonclassical FHH behavior, Eq. (36), but with an exponent y neither equal to (3 - D)/3 nor to 3 - D. Thus, an experimental exponent y obtained from a small pressure range must be interpreted with care. If the underlying surface is known to be fractal, one can use Fig. 12 to conclude that the exponent must satisfy (3-D)/3 < y < D-3,
(62)
which leads back to the bounds (37) for the fractal dimension. The exponent in this case, even though it is nonasymptotic, still provides valuable information. If nothing is known about the surface and the analyzed pressure range is small (e.g., in the sense that the range of film thicknesses calculated from Eqs.(43a) and (37) spans less than a factor of two [36, 158]), a fractal interpretation of the exponent is not meaningful, as a rule. Figure 12 depicts A~tc, the transition to capillary wetting, as the point of intersection of the asymptotic power laws (32a, b). More generally, we define h~tc as follows. We write the adsorption isotherm (12) on a surface with arbitrary geometry, computed from (13) exactly or approximately, in the form
N(AB) = NmO(A~t; n, o~, <3)
(63)
where N m is the number of particles in the monolayer as before. The function | which is the coverage and is viewed as the result of the computation (12, 13), displays both the dependence
658 on the chemical potential and on the material constants. If O as a function of A~t has a singularity at one or several values of the chemical potential (--oo < Akt < 0), A~tc by definition is the lowest of these values, and the transition is a genuine phase transition. The transition is firstorder if the singularity at Alttc is a jump, and second-order else (e.g., if some derivative is discontinuous). A case where | has several singularities is illustrated by the effective potential in Fig. 11. If O has no singularity, we define A~tc as the lowest chemical potential for which O(A~c; n, o~, 0) = O(A~c; n, 0, o),
(64)
where the two sides are the coverage in the limit t~ ~ 0 (pure van der Waals wetting) and o~~ 0 (pure capillary wetting), respectively. The motivation for identifying the transition to capillary wetting through (64), when the isotherm has no singularity, is simple: In a single spherical pore, the isotherm jumps at Agc as described by Eqs. (19, 20). The jump describes a first-order phase transition. But if instead of a single pore a whole set of pores is present, described by the pore-size distribution Vpore(R) introduced in Sect. 3.6, the isotherm changes from (19) to N(A).t) = nVpore(Rc(Al.t)) + nf. ~ [1 - (1 - Zl(R, Akt)/R)3]V~ore(R)dR. Rc(A~t)
(65)
Here Rc(A g) is the radius of the pore filled at chemical potential Al.t (inverse of Al.tc in Fig. 4 as a function of R), and zl(R, Ag) is the film thickness in a pore of radius R at chemical potential Ag (solution z 1 in Fig. 4 as a function of R and Ag). The first term is the contribution from the full pores, and the integral is the contribution from the large, partly filled pores.t Equation (65) shows that, no matter how strongly peaked the derivative Vpore(R) may be, the isotherm is analytic in Al.t whenever Vpore(R) is analytic in R. In the framework of (65), the isotherm for a single pore of radius R 0 corresponds to the nonanalytic function
Vp~
=
0 ifR R 0.
(66)
Thus the slightest variation in pore size removes the singularity in (19) and erases the phase transition present in the single pore. Instead of jumping at A~tc, the isotherm now rises steeply. The rise is what is left over of the phase transition in the single pore, and the chemical potential at which the rise occurs represents the transition to capillary wetting. Taking the change of power law in Fig. 12 as paradigm for the rise, we are then led to the def'mition (64) of the transition point A~tc. The definition (64) is natural in several respects. It implements the notion that capillary wetting occurs whenever adsorption is controlled by surface tension, as opposed to being controlled by the substrate potential. It identifies the "rise point" in the isotherm as the point at which adsorption in the absence of the substrate potential exceeds adsorption in the absence of t The right-hand side of (65) can be evaluated for an arbitrarily shaped surface (recall the definition of Vpore(R)) and thus seems to offer an isotherm expression that competes with Eq. (55). But the two expressions are quite complementary: (65) approximates the surface as collection of independent spherical pores, and (55) approximates the film interface as equidistant. In the limit o~~ 0, Eq. (65) reduces to N(A~t) = nVpore(-2t~/(nA~t)), i.e., to the traditional formula for Kelvin porosimetry, which neglects the substrate potential.
,659 surface tension. If the surface has a geometry such that V~ore(R) is peaked at R, Eq. (64) recovers Ag c - -2cy/(nl~)
(67)
for R ~ o~, in agreement with Eq. (20) for the phase transition in a single large pore. Thus, Eq. (64) automatically locates what may be regarded as approximate first-order phase transition if the distribution of pores is narrow, and what may be regarded as approximate second-order phase transition if the distribution of pores is wide as on a fractal surface. If the distribution of pores is narrow, Ag c essentially coincides with the inflection point of N(AB) [which might be considered as an alternative definition of the transition point]; and if the distribution of pores is wide, Ag e is well-defined also in the absence of an inflection point (Fig. 12). We now turn to the transition point Al,tm from submonolayer adsorption to multilayer adsorption, which we define as the chemical potential for which
O(Agm; n, ct, or) = 1.
(68)
This is the lowest chemical potential for which the grand potential (6), together with the substrate potential (7), provides a meaningful description of the adsorbed film. Indeed, the expression (6) assumes that the adsorbed particles are densely packed as in bulk liquid, which leads to the condition that the number of particles in the film f(S, I) must be larger than or equal to the number of particles in a monolayer, n If(s,i ) d3x >- n If(S,in_l/3)d3x.
(69)
A slightly stronger condition would be dist(x, S) > n-1/3 for all x on I, requiring the local film thickness to equal at least the diameter of an adsorbate particle. The chemical potentials obeying Ag > AB~ are precisely those for which the minimizing interface satisfies condition (69). When AB < ABe, the minimizing interface no longer satisfies (69), and Eq. (12) no longer describes the adsorption isotherm properly. To get the adsorption isotherm in the submonolayer regime properly (from first principles), one has to replace (12) by a full-fledged statistical mechanical calculation, ~) ln(~ c e-[E(c)N = kT ~)(Ag)
AgN(c)]/(kT)),
(70)
similar to Eq. (8). Here the sum is over film configurations c that include submonolayer configurations (e.g., modeled as occupied sites on a lattice representation of the adsorption space), with respective energy E(c) and particle number N(c). Such an extension of the adsorption isotherm to submonolayer coverage is outside the scope of this chapter and is mentioned here only to illustrate why ABrn, as defined in (68), is the natural lower limit of applicability of the multilayer isotherm (12). A simple theoretical treatment of submonolayer adsorption on fractal surfaces can be found in Ref. [125]. The chemical potentials ABm and Ag e completely specify the three adsorption regimes under consideration (Table 6). If ABm < Ag e, the expression max {Ag m, Agc } takes Age as the onset of capillary wetting, leaving room for van der Waals wetting in the interval (Agrn, Agc)- If AB~ > Ag e, the expression max{ABe, Ag c } selects ABTnas the onset of capillary wetting (for
660
Table 6 Submonolayer adsorption, van der Waals wetting, and capillary wetting on a solid with arbitrary surface geometry. The chemical potentials Agrn and Aktc are defined by Eqs. (68, 64). Regime
Range of Ag
Regime exists
Submonolayer adsorption van der Waals wetting Capillary wetting
--oo < A~t < A~n A~m < A~I,< max {Ap~n, Aktc } max {All.m, AILtc } < A[.t < 0
always if and only if A[.I,m < A[.t c if and only if A~tc < 0
A~tc lies in the regime where the isotherm (12) is no longer meaningful), leaving no room for van der Waals wetting. This completes the construction of the different adsorption regimes from the isotherm (12) or from approximations thereof. To implement the construction for a fractal surface, we first consider the isotherm expression (32) which approximates the isotherm in terms of pure power laws. Equations (68) and (64) in this case give Al.tm
=
-
max
{ t~n, (D
-
2)o'n -2/3 },
A[U,c = _ ~-1/2 [(D - 2 ) ~ / n ] 3/2.
(71) (72)
The result (71) is the lower of the two chemical potentials which yield N = N m in (32a, b) The result (72) agrees with (30) because the expression (30) was defined as the point where the power laws (32a, b) intersect. The results also apply to the logarithmic isotherm (34) for D = 3. A simple calculation shows that Ala~n< Al.tc holds if and only D < 2 + otnS/3c-1,
(73)
which is precisely the condition we found in Sect. 3.3 for the van der Waals regime to be realized. The three regimes in Table 6, upon insertion of the expressions (71, 72), divide the set of all pairs (D, A~t) into three respective regions of the D-A~ plane. The regions correspond to different phases of the adsorbed film, as a function of D, and constitute the wetting phase diagram for self-similar surfaces (Fig. 13): The submonolayer region lies below the line Alarn and may be thought of as a D-dimensional gas. The region of van der Waals wetting lies between the lines Ala~nand Al.tc and corresponds to a 3-dimensional, slowly growing liquid film ("thin film") on a D-dimensional surface. The region of capillary wetting lies above the line A~tc and describes a 3-dimensional, rapidly growing liquid film ("thick film") on a D-dimensional surface. The lines are coexistence lines of the respective phases. The line which separates van der Waals wetting and capillary wetting may be viewed as analog of the prewetting line in a thermal wetting transition on a planar surface (the prewetting line, too, separates thin from thick films [83, 84, 146]). In this analogy, the fractal dimension plays the role of temperature, in agreement with the picture that the transition on the fractal surface is driven by quenched disorder (geometric disorder), while the transition on the planar surface is driven by thermal disorder. Just as the prewetting line joins the coexistence line for bulk liquid and gas (A~t = 0 for all T < T c) tangentially at the wetting temperature T w, the capillary wetting line joins the coexistence line for bulk liquid and gas (A~t = 0 for all 2 <_D _<3) tangentially at D = 2. The tangential behavior of the capillary wetting line is not visible on the logarithmic scale in Fig. 13,
661
2
2.2
2.4
2.6
2.8
3
D
_ 1 0 -4
_10 -3 [..,
_10 -2
=1.
<1
_10 -1 _10 o _101
Figure 13. Wetting phase diagram for the power-law isotherm (32, 34). The full, dashed, and dashed-dotted lines are for nitrogen on SiO 2, C, and Ag, respectively. The lower and upper three lines are Aktrn and Al.tc, respectively, calculated from (71, 72) and Table 4.
but is obvious from Eq. (72). The region of van der Waals wetting shrinks with increasing D, as discussed in Sect. 3, and disappears when condition (73) is no longer satisfied. At the point where (73) ceases to be satisfied, several things happen: (i) the lines A ~ and Alsc merge; (ii) the line A~tm switches from A~tm = - txn to AlXrn= - ( D - 2)~n-2/3; (iii) the line AlXrn switches from being coexistence line for submonolayer adsorption and van der Waals wetting to becoming coexistence line for submonolayer adsorption and capillary wetting. In Fig. 13, this occurs at D = 2.82 for nitrogen on SiO 2, in agreement with the discussion of the weak substrate potential of SiO 2 in Sect. 3.3. Other earlier instances of D > 2 + txnS/3o-1 have been encountered in Figs. 8 and 9 (curves with Y > 1). In fact, the curves for the onset of capillary wetting in Fig. 9 may be regarded as phase diagram in which the chemical potential At.t has been replaced by the reduced film thickness ~ and submonolayer adsorption corresponds to the region ~ < 1. A somewhat different phase diagram is obtained if we use the equidistant-interface isotherm, Eqs. (24, 28), instead of the approximation (32, 34). Equations (68, 64) then give At.tm = - o m - (D - 2)on -2/3, A~c = - Ix-1/2 [(D - 2)a/n] 3/2.
(74) (75)
The expression for Apt n results from noting that, for equidistant trial interfaces, the condition O = 1 is equivalent to z = n -1/3, so that the equilibrium condition (49) yields Ala~n = Ueff(n-1/3 ) for arbitrary surface geometry, which leads to (74) for the special case (50). The expression for Aktc is still the same as before because the isotherm (24, 28) coincides with (32, 34) for a = 0 and tx = 0. Here Alxm < Aktc holds if and only if D < 2 + 2.147...-omS/3t~ -1.
(76)
Thus, the two isotherms give the same Al.tc'S, but different APTn'S and different intersection points for A~tc and Aktm. The phase diagram for (74, 75) is plotted in Fig. 14. It shows that the line Apt n is shifted to lower values compared to Fig. 13 and no longer meets the line At.tc for
662
2
2.2
2.4
2.6
2.8
3
D
_10 -4 _10 -3 ~"
_10 2
<1
_10 -1
~
~-.-.... ----
~---.._.-
---..__._
~_._.
_ ..~
_10 ~ _101 Figure 14. Wetting phase diagram similar to Fig. 13, but for the equidistant-interface isotherm (24, 28). Here the lines A;lm and Abtc are given by Eqs. (74, 75). any of the three substrate-adsorbate pairs. The reason for the shift is clear from Fig. 12: The equidistant-interface isotherm lies above the power-law approximation and therefore reaches the monolayer value at a lower chemical potential. In experimental applications, the wetting phase diagram, together with Eq. (5), predicts what adsorption behavior one should find in what pressure range, for a given substrate-adsorbate pair and fractal dimension. But which of the two diagrams, Fig. 13 or Fig. 14, should be used? Will other approximations, say for the substrate potential or calculation of the isotherm, yield a significantly different phase diagram? These questions reflect the contrast between the isotherm exponents ( 3 - D)/3 and 3 - D, which are independent of the approximations used to find the isotherm and independent of the material constants, and the transition points, which do not share this independence. The answer depends on which model isotherm one seeks to fit to the experimental data. If the data is to be analyzed in terms of the power laws (33a, b), Fig. 13 is the appropriate phase diagram. For example, on a sample of SiO 2 that is known to have substantial surface irregularity at atomic length scales, suggesting a high-D surface, one may expect to find the power law (33b), but not (33a), by virtue of the narrowness or absence of the van der Waals regime at high D. Conversely, when an experimental isotherm exponent y [Eq. (36)] is translated into a D value according to the criteria in Sect. 3.2, the obtained D value can be tested for consistency by comparing the experimental pressure interval from which y was obtained with the interval predicted from Fig. 13. This test is important when the test for fractality in Sect. 3.3 cannot be carried out because the monolayer value N m is not known. When N m is not known and the test is successful, one can convert the experimental pressure interval into an interval of film thickness z by means of Eq. (29a) or (29b), depending on whether D 3 - 3y or D - 3 - y. If, on the other hand, the data is to be fitted to the full isotherm (24, 28), rather than to individual power laws, then Fig. 14 is the appropriate phase diagram. In that case, the line A~lrnpredicts the low end of the pressure interval to be analyzed, and the line A~tc may be used to decide whether a departure from power-law behavior in the data is consistent with a crossover from van der Waals wetting to capillary wetting, such as in Fig. 12, or signals the upper end of the fractal regime.
663 5. E X P E R I M E N T A L EXAMPLES
The experimental observation of nonclassical FHH exponents, y ~:1/3, and their interpretation has a long history, as mentioned in Sect. 3.2. The discovery that they have a natural interpretation in terms of van der Waals wetting and capillary wetting of fractal surfaces is much more recent [44, 101 ]. Experimental studies of exponents y ~ 1/3 can therefore be divided into "pre-fractar' investigations and "fractal" investigations. We restrict ourselves to the latter here and leave it for future research to revisit the former in terms of fractal analysis. We give particular attention to those case studies where the fractal interpretation of the isotherm exponent has been tested for consistency with the wetting phase diagram (PD) or for consistency with independent structural data. Methods which have provided independent structural data include scanning tunneling microscopy (STM), small-angle X-ray or neutron scattering (SAXS, SANS), X-ray reflectivity (XR), thermoporometry (TP), isotherm steps at low temperature (IS, test for surface planarity), adsorption-desorption hysteresis (ADH), molecular tiling (MT), and measurement of active surface areas (ASA). A survey of experimental isotherm studies is presented in Table 7. For each example we list the experimental exponent y (in the event that the isotherm exhibited more than one power law, we quote the exponents as separate entries), its interpretation in terms of van der Waals wetting or capillary wetting as given by the authors, the range of film thickness z probed by the <- Zmin power law (the low-end and high-end thickness, Zmin and Zmax, give the bounds s and s > Zmaxfor the inner and outer cutoff of the fractal regime), and performed consistency tests. Consistency with structural data from other methods than listed above is designated by OM. The examples include cases that were analyzed in terms of what sometimes is called the fractal Dubinin-Radushkevich isotherm [ 119], which assigns the fractal dimension D = 3 - y to the exponent y, just as the capillary-wetting exponent does. We list those examples as instances of capillary wetting, believing that the interpretation in terms of capillary wetting has a better understood physical basis and offers more opportunities for consistency tests. Similarly, we list cases that were analyzed by the "thermodynamic method" [ 120], which is equivalent to the analysis in terms of capillary wetting (see below), as instances of capillary wetting. We group the examples according to the chemical composition of the substrate, in the order of increasing values of the capillary-energy ratio T for nitrogen. This makes metals, with their tendency to favor van der Waals wetting, come first and silicas, with their tendency to favor capillary wetring, come near the end of the table. In each group, we order the examples by increasing fractal dimension. Case Study 1: Classical FHH exponent on planar surfaces. An important test of the theoretical framework described in the previous sections is to verify that planar surfaces experimentally exhibit the predicted exponent y = 1/3. One of the most systematic and striking verifications has been carried out on vapor-deposited gold films (first four entries in Table 7). For seven different gases, the chemical potential was varied by variation of temperature (triple-point wetting), which tests the exponent 1/3 via the temperature dependence in Eq. (5) [nonisothermal adsorption]. Direct tests, by varying the chemical potential isothermally, were done with nitrogen and argon. In all instants, not only the exponent 1/3 but also the prefactor in Eq. (14), with A taken as the geometric surface area of the sample, was confirmed with remarkable precision. This experimental confirmation is important because earlier Monte Carlo simulations of multilayer films on a planar surface, based on Lennard-Jones potentials for the adsorbateadsorbate and substrate-adsorbate interaction, exhibited significant departures from Eq. (14) [ 179]. The confirmation also corroborates the calculated values of the constant ot in the sub-
664
Table 7 E x p e r i m e n t a l determination of the fractal dimension of solid surfaces from multilayer adsorption. T h e labels 'vdW' and 'cw' specify whether D is obtained from van der W a a l s wetting or capillary wetting (Fig. 7). Substrates are specified by crystallographic indices of surface, temperature of deposition, sample name, or other labels from the references. System
y
D(vdW)
D(cw)
z (,~)
Tests
Ref.
Metals: N2/Au (111) A r / A u ( l l 1) .../Au(111)b N2/Au(300 K) N2/Ag(293 K) N2/Ag(Crystek) N2/Ag(80 K)
0.33 0.33 0.33 0.33 0.33 0.23 0.21
2.00 + 0.05 2.00 2.00 2.00 2.00 2.30 + 0.02 2.36
-
4-20 a 6-60 7-20 a 8-45 4-70
XR XR XR XR, S T M STM, PD XR
[159] [1601 [1601 [481 [161] [44, 112] [48, 1611
0.33 0.33 0.33 0.33 0.33 0.30 0.27 0.27 0.38 0.30 0.28 0.27 0.34 0.40 0.40 0.43 0.42 0.62 0.57 0.44
2.00 2.02 + 0.05 2.00+0.05 2.01 + 0.05 2.01 + 0 . 0 5 2.11 + 0 . 0 5 2.19+0.10 2.18 + 0 . 1 0 1.85 + 0 . 1 0 2.10+0.10 2.15 + 0 . 1 0 2.20+_0.10 1.97 _+ 0.09 -
< 2.60 < 2.60 < 2.57 < 2.58 2.38 _+ 0.03 2.43 2.56 _+ 0.03
7-30 a 6-24 6-16 a 7-14 a 6-14 a 4-16 a
Carbons: Ar/graphite c N2/carbon film N2/acetylene black
N2/TB#4500HT d N2/TB#4500 d N2/TB#5500 d N2/N110 d N2/N220 d
N2/N300 d NE/N550d N2/N762 d N2/N787 d N2/N990 d N2/N762 d N2/N550 d
N2/N330 d NE/V3 d
NE/VSB-32 GRo N2/VSB-32 A R e N2/WCA e N2/T-300 G R e N2/T-300 A R e N2/CEL e
0.56 0.41 0.40 N2/PIT e 0.38 N2/PAN e 0.29 0.57 N2/natural coal N2/activated carbon 0.28
-
IS (~ OM (~ C~ OM MT MT MT MT MT MT 4-18 NIT MT MT MT MT 16-190 ASA, STM, PD ASA 4-360 ASA, STM, PD, SAXS 2.44 ASA 2.59 ASA, STM 2.60 _ 0.01 f MT, S A X S 2.62 + 0.01 f MT, S A X S 2.71 + 0.02 f MT, S A X S 2.43g 25-500 ADH 2.72 + 0.01g 10-200 ADH
[162] [163] [164] [164]
[164] [164] [165] [165] [165] [165] [165] [165] [134] [134] [134] [134] [134] [134] [134] [134, 166] [134] [134] [167] [167] [167] [168] [120]
665
Table 7----continued System
y
D(vdW)
D(cw)
z (/k)
Tests
Ref.
Silicas: N2/FK320DS h N2/Tixosil h NE/Sipemat h NE/Aerosil 200 i N2/aerogel B N2/aerogel A Nz/Aerosi1200 i N2/Aerosil 150 i N2/Aerosil 5 0 i Ne/quartz Nz/cristobalite Nz/Aerosi1200 i Nz/aerogel s NE/aerogel s Nz/aerogel s N2/CPG-240 n Nz/Si 300 h Nz/Si 300 h P B D Nz/Si 60 h
0.33 0.33 0.32 0.32 0.23 0.06 0.40 0.37 0.39 0.32 0.35 0.36 k 1 m 0.88 0.79 o 0.78
2.01 + 0 . 0 5 2.01 + 0.05 2.03 + 0.05 2.03 + 0.05 2.30 + 0.05 2.83 + 0.05 2.0-2.1 2.0-2.1 2.0-2.1 2.04 + 0.10i 1.95 +_ 0 . 1 0 J 1.92 + 0.10J 2.0 2.0-2.2 _ _ -
-
4-12 a 5-12 a 4-12 a 5-12 a 6-18 a -
TP TP TP TP, S A N S TP, S A N S TP, S A N S Mr MT MT OM
-
-
-
2.5-2.6 2.12 + 0.02g 2.21 +_ 0.01g 2.21-2.28g 2.22
5-12 a 10-200 15-150 15-150 9-20 a
MT SAXS, OM SAXS, OM ADH ADH ADH SAXS
[164,169] [164, 169] [164,169] [164, 169] [164, 169] [164, 169] [134] [134] [134] [170,171] [170,171] [170, 171] [172] [173] [173] [168] [168] [174] [175]
0.20 P q 0.33 0.04
2.40 + 0.05
2.25-2.55 2 . 3 4 - 2 . 7 lg 2.68 + 0.03g 2.96 + 0.02 f
5-24 10-230 17-800 -
(~I ADH ADH (~I
[163] [176, 177] [178] [168] [170,171]
Others: N2/magnetic film
N2&H20/cement N2/CaO N2/apatite N2&CC14/zeolite Y a b c d e f g h i J k
Our estimate. Adsorbates: Ar, Kr, Xe, N 2, 02, CH4, C2H 6. Variation of Al.t by variation of temperature. Isotherm measured by ellipsometry. Carbon black. Carbon fiber. Analysis according to the fractal Dubinin-Radushekevich isotherm (see text). Analysis according to the "thermodynamic method" (see text). Precipitated silica (xerogel). Pyrogenic silica. Our reinterpretation of the experimental y value (see text). y = 0.34-0.35 for four acid-catalyzed samples. 1 y = 0.25-0.33 for nine samples, base-catalyzed samples at high water concentration. m y = 0.38-0.43 for six samples, base-catalyzed samples at low water concentration. n Controlled-pore glass. o y = 0.72-0.79 for five samples coated with bolybutadiene. P y = 0.45-0.75 for three samples of variable composition. q y = 0.29-0.66 for six samples calcinated at different temperatures.
666 strate potential. An example of the nitrogen adsorption isotherm on a planar gold surface, with y = 1/3, is shown in Fig. 15. Of all isotherms on planar gold films quoted in Table 7, it spans the largest film thickness range (6-60.4,). Measurements up to ln(p0/p) = 10-2, on annealed gold films, show an even better agreement with the predicted behavior [159]. The other two data sets in the figure illustrate the behavior of the isotherm on nonplanar substrates (the silver data describes entry 7 in Table 7). They show that different exponents y can be easily distinguished experimentally, and that not every experimental isotherm follows an FHH-type power law. The exponent y -- 1/3 on a planar surface has been confirmed also for many other solids, of course. Noteworthy examples in Table 7 are graphite, where planarity was established from the stepwise growth of solid Ar films at low temperature; various graphitized carbon blacks; several silicas; and crystalline SiO 2 (quartz and cristobalite). In none of these cases have the isotherms been measured up to high enough pressures, however, to span a film thickness range comparable to that in Fig. 15. The case of quartz, cristobalite, and Aerosil 200 studied by Lefebvre et al. [ 170, 171] deserves some explanation. Those authors measured N 2 and CC14 isotherms on six SiO 2 samples of various origins (three quartz and two crystobalite samples) and interpreted the observed exponents y in terms of the fractal Dubinin-Radushkevich isotherm. This led them to conclude that all six samples have fractal dimension between 2.55 and 2.68. These values are much larger than any previously reported D values for quartz (D = 2.02.2 [158]) and Aerosils (D = 2.0 [14]) determined by other methods. In Table 7, we therefore interpret the three quoted y values as instances of van der Waals wetting, which makes the resulting D values agree with all other known structural properties of these samples. Case Study 2: Van der Waals wetting of fractal silver surfaces. The first fractal surface analysis by multilayer adsorption was done on two silver electrodes plated on quartz crystals employed in microprocessor circuits [44]. The corresponding isotherms are shown in Fig. 16.
~
.. !.
~'x.~./,..,..i.
,
"
,
,
,
w
~ .
..
ioO
"%%
.:
I#
=
_z 10
~g(a0K)~Au(S00K)
10- I
Xx
~
X
N.
"'"e
io-2
"
m..
% \
#.
10-3
40
~
40
' ''"
'
'
'X"" ' ~ "
100 400 QUANTITY ADSORBED(hE/era')
lOO
Figure 15. N 2 isotherms on Au and Ag films, vapor-deposited under various conditions. The data for Au(300 K) follows the prediction (14) for adsorption on a planar surface (solid line) without adjustable parameters. Planarity of the Au(300 K) surface over large lateral distances was confirmed by scanning tunneling microscopy and X-ray scattering. (From Ref. [48].)
667
0r " ~ , substrate ~ 2 substrate 1 +##§ ~Ii~
\
I0 0
\\
~
~+'~
' . Zmln-8A
10-1 ,,,-,
10-2
_
,,,+, . \[ I
I
I
50
I
I
I
I
I
i
100
i
~I "
I
500
J
Quantity Adsorbed {ng/cm 2)
Figure 16. N 2 adsorption isotherms on two rough Ag electrodes. The dashed line represents the prediction (14) for adsorption on a planar Ag electrode. In all three isotherms the reference area is the macroscopic area of the electrode (geometric surface area). (From Ref. [44].)
Substrate 2 corresponds to entry 6 in Table 7. From the values y = 0.23 for both isotherms in Fig. 16 and the assumption that van der Waals wetting prevails, it was concluded that both surfaces have fractal dimension D = 2.30 + 0.02, over an approximate range of 8-20 ,~ and 8-45 .&, respectively. This is consistent with the roughness factor of 2 and 3, respectively, implied by the isotherms, and agrees with the value D = 2.30 + 0.10 over a range of 5-50 .A obtained from STM [44]. The surface topography as seen by STM is shown in Fig. 17.
20
0-I0
nm
10 10
Figure 17. STM image of a Ag electrode from the same manufacturer as substrate 2 in Fig. 16. Note the different length scale in the horizontal and vertical direction. (From Ref. [ 180].)
668 The STM topography suggests, by virtue of the absence of overhangs in the image, that the surface is self-affine rather than self-similar (introductory expositions of self affinity can be found in Refs. [2, 6-8, 11, 31, 36, 101 ]). This means that the surface height S(x,) at position x,, as illustrated in Fig. 3, satisfies ([S(x, + y , ) -
S(yll)]2)Yll
=
b(lx,l/b) H
(77)
where the average {...)., is defined as in Eq. (9). The exponent H, 0 < H < 1, is called roughall ness (or H61der) exponent of the self-affine surface, and the length b is called crossover length. In the regime Ix,! << b, called steep or local regime, the surface behaves like a self-similar surface with fractal dimension D = 3 - H. For example, the volume of a film of thickness z, V(z), on a self-affine surface is proportional to z H [Eq. (24a)] for z << b. In the regime Ix,I >> b, called shallow or global regime, the surface behaves like a surface with D = 2, however, in the sense that film volume V(z) is in leading order propertional to z for z >> b (only the correction to the leading-order term depends on H). These properties make the analysis of multilayer adsorption on a self-affine surface potentially quite different from the self-similar case treated in Sect. 3. As a result, it was suggested that the observed isotherm exponent y = 0.23 might be interpreted in terms of capillary wetting in the shallow regime of a self-affine surface [113, 114] instead of van der Waals wetting of a self-similar surface or in the steep regime of a self-affine surface [44, 45, 101, 110, 112]. This controversy was resolved when (i) a reexamination of the STM topography of the Ag surface gave H = 0.70 _+0.10 and b = 1 A, for 5 ,~ < Ix,I < 50 .~, as best estimate from various methods of analysis [ 180]; and (ii) an analytic isotherm expression for self-affine surfaces with b _
~ ~ ~
(-A~t) -1/3 (-A~I.)-1/3 (-A~) -H/(2-H)
for low A~t (van der Waals wetting), for A~t --~ 0, 0 < H < 1/2 (van der Waals wetting), for Al.t -~ 0, 1/2 < H < 1 (capillary wetting),
(78a) (78b) (78c)
earlier obtained by scaling arguments [45, 114], but found that capillary wetting on a surface with H = 0.7 sets in only at film thicknesses in excess of 103 A [ 104]. The STM analysis rules out van der Waals wetting in the steep regime because the steep regime is absent (as expected); the isotherm expression rules out capillary wetting in the shallow regime because the films in Fig. 16 are much too thin to follow the power law (78c); and the experimental isotherm rules out van der Waals wetting in the shallow regime because it does not follow the power law (78a). Isotherm calculations based on the numerical solution of the Euler equation (13b) conf'trm these conclusions [ 181 ]. Thus, we are led back to the original interpretation that Fig. 16 represents van der Waals wetting of a self-similar surface with D = 2.3. The observed van der Waals regime is somewhat larger than predicted by the phase diagram in Fig. 13, but consistent with the phase diagram calculated for N 2 on Ag without using the equidistant-interface approximation [ 112]. But a self-similar surface with D > 2 necessarly has overhangs. This implies that Fig. 17 does not image the surface faithfully. Indeed, there exists considerable experimental and theoretical evidence that solid films grown by vapor deposition, as is the case for the Ag film, often contain overhangs and voids. Columnar growth of sputtered films is an experimental example [ 182]. Molecular dynamics simulations illustrate this at the atomic level (Fig. 18). Consequently, the combined adsorption and STM study of the Ag surface highlights the generic overhang problem
669
(a)
o
E = 0.05~
~
~
~
(b)
E = 0.3~
~
(c)
E = 1.5r
) )
('~X ...............................................
:
Figure 18. Microstructure of overhangs and voids, obtained in molecular dynamics simulations of random deposition under normal incidence at different incident kinetic energies (from Ref. [183]).
of STM. The problem is that STM may not give a faithful image of the surface because it cannot detect overhangs, crevices, or deep pits: When the STM is operated at constant current and the tip moves across an overhang, there comes the point where the tip is about to lose its tunneling current and another part of the tip, is close to the surface elsewhere, begins to draw current, with no indication that the "new" current comes from another location. In this way, the STM image always looks as if overhangs were absent, and the adsorption isotherm provides a strong tool to detect the presence of surface regions invisible to STM. Case Study 3: Capillary wetting of fractat carbon surfaces. An interesting example of capillary wetting of a fractal surface was analyzed by Neimark [120-122]. The example is interesting because it has an isotherm exponent of y = 0.28, which in the absence of other information might be interpreted as van der Waals wetting of a surface with D = 2.16. However, the isotherm exhibited hysteresis in the pressure regime where y = 0.28 was observed, showing that capillary effects must be involved. Interestingly, the adsorption branch and the desorption branch gave the same exponent y within the experimental accuracy. The experimental data and analysis are reproduced in Fig. 19. Neimark's analysis, which he calls the "thermodynamic method," starts from the observation that, when the substrate potential can be neglected, the change in the surface area of the liquid-gas interface of the adsorbed film, dI, is related to the
N mmole/g
tnS, m2/g
2O
6'
12.~nm
Z _
dts,,,Z? 3 ~ ] ,
ZOnm ~
~.
~
Inm
I 0
I 1
2
-/ o
. . . .
I o, z5
,, I 0.5 a
, I o.7~
o/e 6 f
I
-6~ -6
I -5
-4
I
-3
I b
-2
I
~ -7
t.t.t~/P)
Figure 19. (a) Adsorption and desorption isotherm for N 2 on an activated charcoal. (b) Surface area of the liquid-gas interface as function of pressure. (From Ref. [ 120].)
670 change in the number of adsorbed particles, dN, by t~dI = AktdN.
(79)
Upon use of Eq. (5) and integration, this yields
I(p) = (kT/o) [ N(p) ln(p(N,)/P0)dN ,
(80)
" Nmax
for the surface area of the liquid-gas interface as function of the gas pressure p. In (80), the function N(p) is the number of adsorbed particles at pressure p, p(N') is the pressure at which N' particles are adsorbed, and Nmax is the number of adsorbed particles at complete pore filling (i.e., at the pressure Pmax for which I(Pmax) = 0). In Fig. 19, the area I(p) is denoted as S(p). Under the assumption that at every pressure p the liquid-gas interface has a constant radius of curvature r(p), given by r(p) = 2g/[-kT In(p/p0)] (Kelvin equation), it follows for a fractal surface that
I(p)
o~ [ r ( p ) ] 2 - D
or
[_ln(p/p0)]D-2.
(81)
Equations (80) and (81) together imply N(p) 0r [_In(p/p0)] -(a-D),
(82)
which is the power law for capillary wetting derived in Eq. (33b). Thus, Neimark's analysis using Eq. (81), and the direct analysis of the experimental data using Eq. (82) are completely equivalent. The virtue of the derivation in (33b) is, of course, that it also predicts the pressure at which the transition to van der Waals wetting occurs. The analysis gives D = 2.73 from the adsorption branch and D = 2.71 from the desorption branch (Fig. 19). Case Study 4: Transition from van der Waals wetting to capillary wetting on fractal carbon fibers. Our final case study reviews an investigation in which the transition predicted in Table 3 was experimentally observed [ 134]. This may also shed useful light on investigations of carbon samples in which the interpretation of observed exponents y either as van der Waals wetting or capillary wetting was ambiguous [184]. The samples studied in Ref. [134] were carbon fibers, designated as VSB-32 GR and WCA, whose adsorption isotherms are shown in Fig. 20. The isotherm for WCA exhibits a single power law with y = 0.44, starting at monolayer coverage. Since the interpretation of this exponent as van der Waals wetting would imply an absurdly low fractal dimension of 1.68, the exponent was taken as capillarywetting exponent, yielding D = 2.56 + 0.03 over a range of 4-360 ,~. For this fractal dimension, the phase diagram in Fig. 13 predicts the onset of capillary wetting at a coverage of N/N m = 1.4, which is in very good agreement with the observed low end of the power-law regime at N/Nrn = 1.1. Recent SAXS studies of the sample gave a dimension of D = 2.50 + 0.05 up to even larger length scales [ 166]. Thus, the agreement and consistency of all data is very good and makes the system a remarkable case where capillary wetting sets in already at the first or second adsorbed layer. The transition from van der Waals wetting to capillary wetting is exhibited by the sample VSB-32 GR in Fig. 20: At a coverage of N/N m - 1.5, the power law at low pressure (which is too limited to lend itself to a separate fractal analysis) changes to a power law with exponent y = 0.62, which gives D - 2.38 _+ 0.03 over a range of 16-190/~,
671
2.4 2.1
.~,,' ' ' i . . . . i "x~ i "~ "x
I ....
t ....
I ....
I .... t .... I .... I .... ~ WCA- As-received - - 4 - V S B - 3 2 G R l o w e r end ~ V S B - 3 2 G R u p p e r end
-
1.8 1.5 >
1.2
g
.9 R E G I M E II
3.8
-3.3
-2.8
-2.3
i
-1.8 -1.3 ln(ln(P~
"lk~
-.8
,.,
"
-.3
.2
.7
Figure 20. N 2 adsorption isotherms on two carbon fibers (from Ref. [ 134]).
upon interpretation as capillary wetting. Here the phase diagram predicts the transition to occur at N/N m = 1.8, which again is in good agreement with the observed change in power law. The power law at low pressure is therefore interpreted as crossover regime from van der Waals wetting to capillary capillary wetting (recall the crossover in Fig. 12).
ACKNOWLEDGMENT We thank D. Avnir, M.W. Cole, S. Dietrich, F. Ehrburger-Dolle, J. Krim, W. Rudzinski, and W.A. Steele for stimulating discussions which have influenced the presentation in this chapter in many different ways.
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W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces
Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
679
H e t e r o g e n e o u s Surface Structures of A d s o r b e n t s Katsumi Kaneko Department of Chemistry, Faculty of Science, Chiba UniversityYayoi 1-33, Inage, Chiba 263, Japan Structural causes for surface heterogeneities and a systematic approach with physical methods to understand heterogeneous strucutures of adsorbents is described. 1. THE HIERARCHY IN PORE STRUCTURES AND ORIGIN OF PORES Porous solids are morphologically classified into spongy and corpuscular porous solids 1 IUPAC recommended the classification of pores according to the pore width w : micropores (w < 2nm), mesopores ( 2nm < w < 50 nm), and macropores (w > 50 nm). 2 The micropore has two subgroups, namely, ultramicropores (w < 0.7 nm) and supermicropores (0.7 < w < 2.0 nm). The critical widths are associated with the analysis of the N2 adsorption isotherm and the statistical bilayer thickness of the adsorbed N 2 molecule. Molecules are adsorbed by these pores with different adsorption mechanisms. Molecules are adsorbed on the flat surface and macropores with the multilayer adsorption mechanism, which is described by the BET equation. The adsorbed molecules in a mesopore produce the liquid layer of the curved meniscus at the lower vapor pressure than the saturated vapore pressure of the bulk liquid, and thereby leads to a marked increase in adsorption below P/P0- 1. This adsorption is called capillary condensation. Consequently, capillary condensation occurs on the multi-layerd adsorbed film on the mesoporous surface. In the microporous system, the opposite micropore walls are so closed to each other that the potentials from the opposite surfaces are overlapped to increase the potential depth and then enhance adsorption at an extremely low pressure region; so-called micropore filling, occurs. Thus, the pore width is quite important in molecular adsorption. 3 However, the pore width is only the shortest one-dimensional parameter of a unit pore. The three-dimensional pore geometry must be described in order to understand molecular adsorption. Also three-dimensional shape is important. There are distributions of three dimensional size parameters and shape in real porous solids.
Furthermore, a higher
order connecting structure of unit pores and a spacial distribution of pores in a solid sample should be taken into account. Thus, fundamental description of pores needs the pore geometries, the pore population in solids, and the chemical nature of the pore walls.
In ordinary
680 characterization of porous solids, however, we determine only the pore width distribution, the pore volume, and the surface area with the molecular adsorption. The chemical structure of the pore walls has been approximated by the bulk chemical structures. The chemical structure of the surface layer of the solid, however, is often different from the bulk composition. For example, the transition metal oxide crystals such as ZnO and a-Fe203 have surface hydroxyls and activated carbon has surface functional groups. ~ Consequently, the surface chemical structures are necessary for description of pores. It is not easy to determine the chemical structures of pore walls compared with a single crystalline surface. The surface chemical properties are governed by the electronic structures. Hence, the electronic structures are helpful to understand even physical adsorption. Although sufficient determination of pore geometries and pore chemical structures is not easy, the origin of pores is strongly associated with both structural factors. Then, we will discuss of the origin of pores. 7
There are two types of pores of intraparticle pores and interparticle pores. The
intmparticle pores are in the primary particle itself, while the interparticle pores originate from the interpartilce void spaces. Although there is an obscurity of distinction between both types of pores in some systems, this classification is helpful to understand pore structures. There are many minerals which have intrinsic pores, s Zeolites are the most representative porous solids whose pores arise from the intrinsic crystalline pores. The pore geometry and pore connectability are evaluated by their crystal structure determined by X-ray diffraction studies.
These intraparticle pores originate from the intrinsic crystal structures, and thereby
they are named intrinsic crystalline pores. The carbon nanotube found by Iijima9 has also the intrinsic crystalline pore; the tube-wall is composed of graphitic structures. The above intrinsic crystalline pores are micropores, but they are not limited to micropores. Crysotile Mg#i4Ox0(OH)8 has the tube crystal form having mesopores of about 7nm t~
The outer
and inner surfaces of the tube crysotile crystals are basic Mg(OH)2 and acidic SiO2, respectively. Almost all crystalline solids other than the above mentioned substances have their intrinsic crystalline pores more or less, but those are not available for adsorption or diffusion due to their isolated state and extremely small size. There are other types of pores in a single solid particle. Metal hydroxide is transformed into the metal oxide by heating to create pores; the particle morphology is often preserved during the structural transformation. Thus, evolution of sublimable chemical substances introduces pores in solid materials.
Also the selective dissolution of some components
creates pores. Activated c a i r n s are the most popular adsorbent 12,~ Although the mechanism of pore formation in carbons is still actively discussed, basically pores are produced by a
specific reaction process so-called activation. Carbons are mainly composed of rnicrographitic units. The edge carbon atoms of the micrographite are more reactive than basal plane carbon atoms, developing pores along the basal plane of the micrographite. Thus, modification of intrinsic structures by specific evolution, leaching, and reaction
681 procedures can create pores in solid materials. Also control of defects is another effective way for the pore creation.
The van der Waals C60 solid has many defects, which form
channels. The defect concentration is controlled by solvent inclusion upon recrystallization and evolution of the solvent on heating. Figure 1 shows adsorption isotherms of N2 by highly defective C60 microcrystals and as-received sample at 77 K; the isotherm of the defective sample has a sharp initial uptake and a hysteresis, indicating of presence of both micropores and mesopores 14asThus, there are two subgroups in intrinsic intraparticle pores. The above mentioned modification methods such as specific evolution, leaching, and reaction can be generally applied to produce porous solids. In this case, a foreign substance is impregnated in the mother material in advance, which is removed by the above modification procedure. This type of pores should be called extrinsic intraparticle pores.
We can
introduce a pore-forming agent into the structure of solids to produce voids or fissures which work as pores.
This concept has been applied to the layered compound in which the
interlayer bonding is very weak; some inserting substance swells the interlayer space. Graphite intercalation compounds have such interlayer spaces. 16 K-intercalated graphite can adsorb great amount of H 2 gas,while original graphite cannot 1~. The interlayer space opened by intercalation is ordinary too narrow to be accessible for larger molecules than H2;Takahashi et al recently tried to prepare the graphite intercalated compound which have selective
adsorptivity
molecules TM.
for organic 12.0
Montmorillonite is a
representative layered clay compound, which swells in solution to intricate hydrated ions or even surfactant molecules 19.20. Then some pillar materials such as metal hydroxides are intricated in the swollen interlayer space in wet condition, As the pillar compound is not removed
": ~m
E =
8.0
oE = O -~,
f~. 4.0 o
m
upon drying, the swollen structure can be preserved even at the dry condition. The size of pillars can be more than several nm, being different from the above
intercalants.
kanemites were recently template and
0.0.
0
0.2
t
!
!
0.4
0.6
0.8
1
Mesoporous prepared by
pillar of the surfactant
Figure 1. Adsorption isotherms of N 2 on C60
molecular assemblies 21 .The pores in the
crystals at 77 K. O" Highly defective C~0
pillared and intercalation
crystals,
compounds
belong to extrinsic intraparticle pores.
I-l- As-received sample
682 Primary particles stick together to form a secondary particle, depending on their chemical composition, shape, and size. In colloid chemistry, there are two gathering types of primary particles. One is aggregation and another is agglomeration. The aggregated particles are loosely bound to each other and the assemblage can be readily broken down. Heating or compressing the assemblage of primary particles brings about more tightly bound agglomerate. There are various interaction forces among primary particles, such as chemical bonding, van der Waals force, magnetic force, electrostatic force, and surface tension of thick adsorbed layer on the particle surface. Sintering at the neck part of primary particles produces stable agglomerates having the pores. The aggregate bound by the surface tension of adsorbed water film has flexible pores. Thus, interparticle pores have wide varieties in stability, capacity, shape, and size, which depends on the packing of primary particles. They play an important role in nature and technology regardless of insufficient understanding. interparticle pores can be divided into rigid and flexible pores.
The
Only rigid interparticle
pores due to agglomeration are available for adsorbents or catalysts. Silica gel is representative of interparticle porous solids. Ultrafine spherical silica particles form the secondary particles, leading to porous solids. Also carbon gel which has great surface area due to interparticle pores is made of spherical carbon particles 22.
Alunite platellites gather to produce
considerably stable aggregate of slit-shaped pores 23. The oriented aggregate of boehmite platelets has ordered slit-shaped pores of interparticle gaps; their film and plug forms are available u2s. In the above description, strong correlation of pore structures with the pore origin was shown. We must introduce classification of pores according to the origin. Furthermore, communication of pores to the surroundings is a key factor in quality of adsorbents and catalysts. In ordinary cases we treat only open pores which communicate with the external surface to play an important role in surface chemistry. There are pores which do not communicate with the surroundings; such pores are called closed pores. The closed state depends on the probe size, in particular, in the case of gas adsorption. Even open pores with a pore width less than the probe molecular size must be regarded as a "closed" pore. For example, the pores which only He molecules are accessible to must be effectively closed pores. The true closed pores and the effectively closed pores may be named latent pores. 7 This is because the latent pores often change to open pores in some chemical environments. Table 1 summarizes pore classifications according to the width, origin, and communication to the surroundings of pores. However, these classifications are still insufficient to describe the pore structure.
683 Table 1 Classification of pores from origin, pore width w, and accessibility
Origin and structure Intraparticle pore Intrinsic intraparticle pore Extrinsic intraparticle pore Interparticle pore
Rigid interparticle pore
(Agglomerated)
Flexible interparticle pore
(Aggregated)
Pore width w >50rim
Macropore 2nm<w
Mesopore
<50nm
w <2rim
Micropore Supermicropore Ultramicropore
0.7 < w < 2 nm w < 0.7 nm
(Ultrapore w < 0.35 nm in this article)
2. E L E C T R O N I C STRUCTURES The electronic structures of solid surfaces have been discussed with relevance to chemisorption. Chemisorption originates from a strong electronic interaction of a molecule with the solid surface such as charge transfer interaction. On the other hand, a fundamental interaction in physical adsorption is van der Waals interaction and thereby the electronic structures of adsorbents have been almost neglected. As representative adsorbents such as silica gel, clay, zeolite, and alumina are insulators, only their acid-base properties due to local electronic structures are taken into account in the surface chemical processes. Recently, semiconductive mesoporous solids such as TiO 2, ZnO, and r
a, however, were developed. 26'z7 The
electrical conductivity of transition metal oxides is sensitive to nonstoichiometry, which is controlled by their environments and presence of impurities.
The transition metal oxides
have a wide 2p valence band and a wide 4s conduction band. There is a narrow 3d band .
.
.
. . .
between both wide bands. Generally speaki'ng, the narrow 3d band can be regarded as a localized level due to the ligand field splitting and spin orbit interaction, zs The electrical
684 charges are conducted by both band and hopping conductions. The oxide semiconductor shows the band bending near the surface. 29 The energy bands of n-type oxides bends upward with a negative charge on the surface state, giving rise to the space charge region near the surface.
For example, oxygen is chemisorbed as either O', O z" or O 2. on the n-type
oxide by electron transfer from the oxide to Oz~~
Physisorption occurs on the negatively
charged oxygen and bare surfaces. The space charge region is the order of 10 nm or less in thickness. The charged surface influences even physical adsorption in the case of adsorption of molecules having the electric multipole. The surface potential measurement is helpful to study such effect in physical adsorption. 33 Also a part of molecules can be chemisorbed on the high energy sites even under physical adsorption conditions. The knowledge on surface electronic structures of adsorbents is important in separation of minor chemisorption from major physical adsorption.
Activated carbon is mainly composed of micrographites. The
electronic structure of activated carbon is estimated from that of graphite. Carbon atoms in a graphite structure are combined by o (sp 2 hydrid orbital) and n (2pz orbital) bonds, n electronic wave functions extend over the basal plane, then n electrons can move freely in the basal plane. Minot analyzed graphite energy levels as a limit of the polyacene series with MO calculation, showing that a single sheet of graphite has the density of n states with double peaks coming from valence and conduction bands without the gap and the density of states is zero at the Fermi level which separates the double peaks. 34 This electronic structure of the two-dimensional graphite indicates semi-metal. In the case of three-dimensional graphite, the electronic interaction among graphitic sheets gives rise to slight overlap of the conduction and valence bands, although the interlayer spacing of 0.335 nm is too great to interact.
The total band overlap is only about 0.03 eV, and thereby graphite is not a typical
metallic conductor and still has many unresolved problems. 3sz~ How can we consider the electronic structure of activated carbon ? Pure activated carbon shows diamagnetism due to the conjugated n electronic structure. Graphite is made of layers which form the infinite extension of the polyacene structure, exhibiting marked diamagnetism. 3T The diamagnetic susceptibility xaof activated carbon has an empirical relationship with the micrographitic unit size, as follows, as
Zs(10 ~ emu g-l) = 0.009025 x ( number of carbon atoms) + 0.4687
(1)
They suggested the unit micrographitic unit structure of about 60 carbon atoms for activated carbon. A possible unit structure is shown in Fig. 2. In principle, activated carbon is made of micrographitic units from theelectronic structure aspect. The band gap becomes smaller as the graphitiC unit grows, because the smaller the HOMO-LUMO gap of the MO, the
685 greater the conjugated system of nelectrons.
Akamatsu and Inokuchi a9
showed that molecular solids with conjugated aromatic structures are semiconductors whose energy gap decreases with increasing molecular size. Consequently, activated carbon has the band gap due to the finite size of the graphitic sheet structure. It is wellknown that carbonous materials show the strong ESR (electron spin resonance)
Figure 2. Micrographitic unit structure of
signal, indicating the presence of stable
activated carbon
free radicals. Ingrain proposed that radicals observed by ESR are formed by the cleavage of bonds at the edges of conjugated carbon rings and that these broken bonds liberate electrons back into the ring system where they can become resonance-stabilized. 4~ Singler et al proposed that the stable free radicals have the neutral odd-alternate structure and they are formed predominantly during condensation stage of carbonization. 4~ Although ordinary activated carbons exhibit diamagnetism, superhigh surface area ca_r~ns have paramagnetism? 2 Enoki et al 'a and Ishii et a144observed that high surface area carbon has great spin concentration of
1019gq, the ESR signal disappears on exposing them to an oxygen gas, but it appear
reversibly on evacuation of O z. The exposure to a He gas induces the anomalous spin-lattice relaxation. The clear cusp of the magnetic susceptibility and temperature relationship below 4 K and a remarkable magnetic hysteresis were observed for superhigh surface area carbon, indicating the spin glass behavior. 45 Activated carbon should have the stable free radical structure in the micrographitic unit and dangling bonds in latent pores. The unpaired electrons form an
organized structure, which is associated with the micrographitic structures. The
magnetic susceptibility and ESR measurements suggest the presence of coexistence of localized and band electronic states. The electrical conductivity measurement provides information of the electronic structures. Mott described the so-called variable range-hopping process among localized levels near the Fermi level. '~47 The electrical conductivity o is expressed by eq. (2).
o = constant exp[-(T fl') t~+t]
where
n = 3,2,
constant.
or 1
(2)
following the dimensionallity of the electronic system and To .is
a
Therefore, we can know the fundamental electronic structure from the teml~erature
686 dependence of electrical conductivity. Fig. 3 shows a good linear relationship of log o vs. 1/'I"ta plot for ACE Then,
1.20
the two-dimensional electronic structure of the localized nature was deduced, agreeing
with the micrographitic
structures.
Furthermore, the electrical
1.10 0 O
conductivity due to hopping of an
\ b
electron among localized states increases
..o
1.00
0.90
with the frequency of the electric field. The measurement of ac conductivity as a function of the frequency evidences
0.80 0.15
I
I
~
!
0.16
0.17
0.18
0.19
! O 0.20
0.21
1/TIt3
the hopping conduction. ~1'48'49 The activated carbon shows the frequency dependent conductivity,s~ Therefore,
Figure 3. The temperature dependence of dc
we presume the electronic structure
electrical conductivity of ACF
having bands and localized levels, as shown in Fig.4.
Activated carbon shows p-type
semiconductivity according to the conductivity increase upon exposure to oxygen, s~ The lower localized levels work as acceptor levels.
The coexisistence of band and localized
levels should be associated with adsorptive properties. The localized electron predominantly leads to chemisorption, whereas n-band electrons influence physical adsorption. In the case
Conduction Band Localized States
F~
D(e)
Valence Band
Less-graphitized Carbon
3D Graphite
o Figure 4. Electronic structure models of less-graphitized carbons and graphite. Here E F is the Fermi energy and D(~) is the density of states.
687 of micropore filling of a molecule having no multipole by neutral micropores, only LennardJones type interaction is considered; the electronic contribution is slight. The information of the electronic structure of adsorbents is necessary for elucidation of adsorption of polar molecules by micropores. The micropore filling of SO2 by activated carbon is such an example. As the micrographitic micropore-walls of activated carbon are conductive, the interaction of the graphitic-wall with a dipole molecule of SO 2 can be approximated by the image potential approximation; the contribution of the dipolar interaction of SO 2 with ACF of the pore width of 0.7 rim is 23 % of the whole interaction energy. 5z The contribution of the Lennard-Jones interaction of SO2 adsorption with microporous oxides should become less. Thus, the electronic structure changes the interaction mode of gases with the pore walls. Although photoelectron spectroscopy such as XPS and UPS can give important informations of electronic structures of adsorbents, we have no sufficient data on the electronic structure themselves with the aid of them. XPS was helpful to determine the mixed valence state of the porous Ti-doped iron oxide filmfl 3. CHEMICAL SURFACE STRUCTURES The surface of a porous solid must be divided into the external surface and the pore surface (inner surface). The comparison analysis using t- or ct,-plot for the N2 adsorption isotherm can determine each surface area, although still we have a discussion in evaluation of the microporous surface area2,s4 The difference of the external and porous surfaces should be quite important in adsorption characteristics of adsorbents.
Accordingly, it is
preferable that the chemical structures of both surfaces are separately described in order to understand the adsorption nature.
Almost all interactions of a molecule with the surface in
physical adsorption can be approximated by the Lermard-Jones potential. Consequently, the difference in chemical surface structures is not seriously considered in physical adsorption; the chemical difference is included in the LJ parameters at best.
In particular, we tend to
neglect surface chemical structures of microporous systems because of the enhanced interaction potential predominantly determined by the pore geometry. We have, however, many experimental examples showing the importance of chemical nature of the surface even in micropore filling. The representative example is water adsorption by activated carbon. Fig. 5 shows adsorption isotherms of H20 on the silica-modified ACF samples at 303 K. Silica modification of the ACF surface was carried out using adsorption and decomposition of SiCl, ss The surface of the untreated ACF sample is hydrophobic, because the ACF surface consists of micrographites. Silica-coating increases the H20 uptake in the low P/P0 region.
In the case
of a strong interaction of a vapor molecule with the micropore, molecules adsorbed near the
688 entrance of the micropore often block the further adsorption, n-nonane has been used as a probe for the preadsorption method, s6 The one-center LJ interaction energy of a n-nonane molecule with the graphitic micropore is quite large ( 9900 K for 1 nm width), s~ It is difficult to attain the adsorption equilibrium. However, the external surface modification with long alkyl groups leads to the rapid adsorption equilibrium. Fig. 6 shows the low pressure adsorption isotherms of n-nonane by TTS-modified A CF and A CF at 303 K. s+ The chemical formula of TTS is (CHa)2CHOTi R s (here R = OCOCavHas) and TiR a replaces with the surface hydroxyl. A remarkable increase in adsorption by the TTS-modification is shown. Control of the adsorption process is not limited for vapors, but the surface modification is quite effective for adsorption for supercritical gases such as NO ss~ and CH 4. Although raicropore filling is an enhanced physical adsorption, it is not a predominant process for a supercritical gas ( a gas above the critical temperature). However, iron oxide dispersed ACF can adsorb great amount of NO up to 150-320 mgg "1 at 303 K in the subatmospheric pressure region. +t Not only the magnetic perturbation but also a weak chemisorptive mechanism by the dispersed iron oxides induce dimerization of NO molecules; as the NO dimer is a vapor, the dimerized NO molecules are filled in micropores. ~ Fig. 7 shows NO adsorption isotherms of iron oxide dispersed ACF (h-ACF) and ACF at 303 K. The amount of iron oxide dispersed on the surface is only 3 wt. %. The adsorption isotherm of h-ACF shows a marked hysteresis. Thus,
the surface modification markedly enhances even micropore filling of
lOOO
4.0 2. E
7~
"8 E
3.0
E
~ 5o
O
2.0
0~ O
=
0 I=
1.0
O.Oa 0 |
o.
-,-
,-~-
v 'm~4
o.5 P/Po
t
t
t
0.02 0.04 0 . 0 6 0.08
0.1
P/Po 1.o
Figure 5. Adsorption isotherms of H=O
Figure 6. Adsorption isotherms of n-nonane
on SiO2-modified ACF ( ZX ) and ACF ( m ) .
on TrS-modified ACF( A ) and ACF (O).
689 supercritical NO. Methane is also an important supercritical gas. The capacity of methane retained at high pressure is predominantly governed by the micropore field strength. The surface modification of A CF with MgO enhanced the gaturated amount of methane adsorption by 50 %.63
~,~ h-A C F
'**~
"
E
In the above examples, we showed that surface chemical modification can control the
~ o -o, t~
adsorption properties of even microporous solids.
o
Z
I
As the chemical structure is often
associated with the surface heterogeneities, it
ACF
should be characterized in order to understand even physical
adsorption. How can we O
~
o
characterize the change in the surface chemical
40
80
NO equilibrium pressure / kPa
structure ? We must examine the surface chemical structures using the systematic
Figure 7. Adsorption isotherms of NO
approach
on h-ACF and ACF at 303 K.
with the aid of chemical and
physical methods. At first, the amount and state of dispersed substance or surface specific
9
sites such as surface functional groups on
A
6
activated carbon should be determined. The
B
N 2 adsorption at 77 K is useful to check the 3
porosity change and the external surface change by the surface modification. Also adsorption experiments of acidic and basic
"~E'
0
A
gases are helpful to determine the acid-base properties of surfaces.
The characterization
9 -
~t-ACF
of surface heterogeneities by physical adsorption is given in other chapters, and thereby
mainly
characterizations are
other
6
physical
described
here.
Extended X-ray absorption fine structure (EXAFS) spectroscopy gives an important information on the local environment of a specific atom even in finely dispersed metal
0
0
0.2
0.4
0.6
distance/nm
oxides on the high surface area solid such as
Figure 8. Fourier transforms of kax(k)
activated
for bulk a-FeOOH and a-ACF.
carbon. ~'~s
EXAFS refers to
690 oscillations of the X-ray absorption coefficient on the high energy side of an absorption edge of the threshold energy of Ethfor a specific atom. Such oscillations extend up to 1000 eV above the edge and may have a magnitude of 10 % or more. The oscillations come from an interference effect involving scattering of the outgoing photoelectron from the neighboring atoms. The EXAFS oscillation x(k) is determined from the X-ray absorption coefficient Ix at the photon energy E vand the smoothly varying background absorption ~t0.
x(k) = [ ~t(E,)- v~(Ep)] / rt0(~
(3)
Here k is the wave vector of the outgoing photoelectron, which is given by k = [2m(E: F_~)]ta2~/h ( m = mass of an electron). Fig. 8 shows the Fourier transforms kax(k) for bulk ct-FeOOH powder and ct-FeOOH-dispersed ACF (ct-ACF). There are two main structures of [A] (0.1-0.2 rim) and [B] (0.2-0.3 nm), which are ascribed to Fe-O and Fe-Fe coordinations, respectively. The position of [A] and [B] of ct-ACF agrees with that of tx-FeOOH. The [B] peak for a-ACF is much weaker than that for ct-FeOOH, indicating that ct-FeOOH like species has a lower coordination number of nearest Fe ions on ACF.
Accordingly, the
EXAFS analysis indicated that ultrafine ct-FeOOH particles are dispersed on the ACF surface. A careful EXAFS examination of a simple system can determine the coordination number and the bond distance around the specific atom, which is helpful to understand the surface local structures. X-ray photoelectron spectroscopy (XPS) is another powerful method for surface characterization. The technique measures the kinetic energy E~, of the ejected electron by irradiation of X-ray having known energy hr. The binding energy E b of determined by eq. 4
electrons is
using the work function of the surface
F_.,= h v - Ekm-- 9
(4)
E, of even a core electron is sensitively affected by the chemical environment. Consequently, XPS provides information about the chemical state of the elements in the surface layer of about 5 nm depth, although sample must be kept under ultrahigh vacuum conditions for the XPS measurement. The surface functional groups of carbon have been determined by XPS; main studies were done for low burn-off carbons. Takahagi and Ishitani studied the surface chemical change of carbon fiber (not ACF) with oxidation by XPS. 6s They showed the increase of the Ols peak and the striking change of Cls with oxidation.
XPS is expected to
be effective for characterization of surface chemical state of activated carbon, but it is not easy to determine the surface functional groups of low concentration on activated carbon.
691 Recently, an XPS ratio-method is proposed for determination of surface functional groups of high burn-off microporous carbon such as ACF. 69 Fig. 9 shows the XPS spectral changes of C u for cellulose-based A CF. Here CEL, CEL-ox, and CEL-red express as-received sample, ACF oxidized by H20 2 solution, and ACF reduced by H2, respectively. The peak position coincides with that of graphite (284. 5 eV). The C1, spectra of CEL and CEL-ox have a shoulder at high energy side due to formation of surface functional groups such as =COH (286 eV), =CO (287 eV), and -C(O)OH (288.6 eV). The shoulder of CEL-red is very weak. As absolute concentration of surface functional groups on the A CF samples is very low, it is difficult to get a quantitative result from the comparison of these spectra and subtraction spectra.
However, the ratio-spectra
clearly shows distinction of the surface
|
|
|
~+
state of the A CF sample, as shown in
. 9 II
Fig. 10. The ratio-spectra can stress
i
|
|
i
I
1
1
= II
!
!
!
the difference of the shoulder part of
"~
the C u peak. Careful deconvolution of
--=
,9
CEL
=
the ratio-spectra will provide the detailed
Cl~-l~d
1
" - ~ z/CEL-ox
chemical structure of A CF having
~
_
2 4'a 6
functional groups of low concentration.
28a
......
290
binding energy/eV
XPS is effective for characterization of the dispersed oxides on porous solids. XPS provides the information of the
Figure 9. C u XPS spectra of CEL, CEL-ox, and
valence state of the metal ion in the
CEL-red.
dispersed oxide and the dispersed position on the surface.
X-ray can 10-,-
penetrate the whole sample, but only the ejected electrons surface
near the external
of porous adsorbents are
._~
"~
,
,
",
,
,
,
,
,
I
2 0
'
'
CEL-ox
e~
analyzed. If the oxides are dispersed in
"~
the pore, they are not detected by XPS.
"~
CEL
Therefore, a quantitative analysis of the XPS peaks gives the position of oxides over the porous media.~'~~Also Kerkhof and
02 2' 2 4'2 8'2 8
292
binding energy/eV
Moulijn proposed the size
determination method of the dispersed
Figure 10. The ratio spectra of C B peak for
fine particles on the support with the
CEL and CEL-ox. The relative intensity was
relative XPS intensities. 7~
determined with the standard data of CEL-red.
692 IR spectroscopy, in particular, Fourier transform infrared spectroscopy (FT-IR) has devoted to surface characterization. The surface structures of a variety of porous silica samples have been studied by IR spectroscopy. ~1.raPorous silica has no color and it can be cut into very thin sections. Also thin film sample can be obtained and a very finely ground sample is obtainable for the KBr pellet formation. Thus, silica has an advantage for sample preparation for IR absorption spectroscopy. Kiselev firstly proposed that the active surface of silica gel is covered by hydroxyl groups bound to the SiO z skeleton. TM Since Kiselev's proposal the presence of different hydroxyls groups was elucidated. FT-IR has contributed to the progress.
The IR bands due to hydroxyls are assigned to, as shown in Table 2. 75
There are other absorption bands due to molecular water (3400-3500 cml), inner OH groups, and interparticle hydrogen-bonded silanols. Talble 2 IR bands of hyroxyls of silica gel Wave number/era 1
Species
3746
Free OH
3742
Oeminal OH Si(OH) 2
3730-3720
Hydrogen perturbed OH SiOH---H
3520
Oxygen perturbed OH
SiOH
SiOH--O
The relationship between physical adsorption and the surface chemical structure for porous silica has been actively studied with FT-IR, as mentioned above. However, it is not easy to characterize the surface of activated carbon with FT-IR, because carbon absorbs most of the radiation. Especially activated carbon of a finely ground state cannot be easily obtained for the IR measurement. The most hopeful method is diffuse reflectance FT-IR (DRIFT).7~ The causes for diminishing beam energy upon measurement of the IR transmission spectrum are both the effective black body effect and diffuse reflection from the surface. The intensity of the diffuse reflectance spectrum is expressed by the Kubelka-Munk function fiR) which corresponds to the absorbance of the transmission spectrum, fiR) is given by eq. (5). f(R) = (1-R)2/2R (R: Diffuse reflectivity)
(5)
693 Rochester et al studied the surface oxidation of activated carbon with DRIFT. r' DRIFT is quite effective for elucidation of the changes of the surface functional groups in the presence of a reactive gas. The assignments of the bands ascribed to principal functional groups of carbons
PIT-ACF
9
~o 9
C H3
I
OH
were summarized by Fanning and Vannice. v8 Fig. 11 shows the DRIFT spectra of pitch- and cellulose-based ACFs in vacuo. We can observe clearly the absorption bands due to OH,
I
,
3,500
,
aromatic CH, CI-Ia, and C=C in the
,
,
I
'If -
i
3000'1600
,
,
I
1300
wave number / cm 4
spectrum of pitch-based(PIT) ACF, whereas the cellulose-based(CEL)
Figure 11. DRIFT spectra of CEL and PIT-ACFs
ACF has ambiguous absorption bands. ~ This distinction should be caused by the difference of the precursor and carbonization mechanism.
Temperature programmed desorption (TPD) can determine the surface chemical
species. The evolved gas analysis of porous solids at a linear-programmed heating rate (for example 1-5 Kmiff t) with a gas chromatograph or a mass spectrometer. There are many careful TPD studies on activated carbon. ~ u Representative functional groups are carboxyls (>-COOH), lactone (>-CO2), quinone (>=O), phenol groups (>-OH), and CH groups. In general, carboxylic and lactone groups begin to decompose at about 420 K, whereas phenol and quinone groups decompose between 750 and 1150 K. The evolved CO 2, CO, and H 2 are attributed to surface functional groups. The evolved CO2 is ascribed to carboxyl acid and lactones, while CO originates from phenols and quinones. H 2 comes from the dissociation of CH or OH. The surface functional groups of different ACFs were analyzed by TPD. For example, cellulose-based ACF has marked CO 2 and CO peaks at 500 K, but pre-evacuation of ACF at 773 K loses them. 8a Thus, TPD is sensitive to the surface chemical state. The TPD analysis has been combined with chemical titration, which has been helpful to understand the chemical structures of activated carbon. Recently, temperature programmed reduction (TPR) has been applied to surface chracterization.~ 4. PORE-WALL AND UNIT PORE STRUCTURES The pore-wall and unit pore structures are primarily important factors in molecular adsorption. Although only pore structures are discussed on the basis of molecular adsorption,
694 mainly, N 2 adsorption at 77 K, the pore-wall structure should be taken into account. In the case of molecular adsorption by a high surface-area solid even the pore-wall structure varies due to adsorption more or less, which clearly shows the importance of the pore-wall structure. Consequently, both structures of the pore-wall and unit pore are described here. With unit pore characterization, physical adsorption is the most representative method. 2.ss-s, Various analytical methods for physical adsorption are given in other chapters. Accordingly, physical characterizations other than physical adsorption are predominantly explained below. 4.1 Physical adsorption
Physical adsorption method, which should be denoed molecular resolution porosimetry, must show its effectiveness especially in micropore analysis. Nevertheless molecular adsorption mechanism in micropores itself has active discussions; there are many useful progresses in this field (see other chapters).
Although the Kelvin equation offers the basis for the
mesopore characterization, s9'9~ even the modified Kelvin equation becomes less appropriate for description of adsorbed layers in the micropore because of no continuous meniscus formation. 9t'92 Here,high resolution a,-analysis for N 2 adsorption and the ultramicropore analysis with He adsorption at 4.2 K are described. High resolution ocpiot for the N 2adsorption isotherm It is difficult to compare one adsorption isotherm with another, but determination of the deviation from the linearity using a standard adsorption isotherm is easy and accurate. The plot constructed with the aid of the standard data is called a comparison plot. The representative comparison plots are t- and a,-plots. The molecular adsorption isotherm on the nonporous solid of similar chemical structure which has a clear B-point has been used for the standard isotherm. Lippens and de Boer proposed the useful t-plot analysis. SSHowever, the t-plot analysis has the limitation to applicability to the microporous system due to the absence of explicit monolayer formation. Sing proposed a more general comparison plot of a,-plot 93,94 The ratio of the adsorption at P/P. to the adsorption at P/P, = 0.4 (W0.4 ) for the standard isotherm, which is designated cx, ( = W,/Wa, ), is plotted against P/P,, and thereby the P/P. axis can be expressed by a,. Here, W, is the adsorption amount at P/P0. The test isotherm can be replotted to the adsorption W a against a., which is called %-plot. The construction of o~-plot does not need the monolayer capacity, so that it can be applicable to microporous solids. The straight line passing the origin guarantees the multilayer adsorption, that is, absence of meso- and/or micropores; the deviation leads to valuable informations on pore structures.
Kaneko et al
introduced the high resolution ~t,-analysis using a high resolution standard adsorption
695 isotherm 4z-~. The high resolution as-analysis determines the plot in a small a,-region below a, = 0.7, which was not originally used for analysis by the Sing et al. The high resolution l W0
ix, plot has a characteristic feature according
....
to the pore structure. The typical types of the tx,-plots are shown in Fig. 12.
Micropore ~......
c-swing
A nonporous
-~//
Mesopore
.m4
solid has a single line passing the origin, while the line from the origin for the a,-plot of the mesoporous system bends with an upward hump in the ot,-region of 0.7 to 1.0. The slopes of the straight line through the origin 0
and the line at high a s region give the total
1.0 tt.~
2.0
and mesoporous surface areas, respectively. Also the extrapolation of the high %-region
Figure 12. Three types of a,-plot.
line to the ordinate leads to the mesopore volume.
The high resolution a, analysis is especially effective for determination of the
micropore structure. below r
The o~-plot for a microporous system has one or two upward swings
= 1.0. The swing at lower ct,-region (f-swing) and that at higher a, region
(c-swing) are designated the filling and condensation swings, respectively.
As the f- and
c-swings are ascribed to adsorption by ultramicropores and by larger micropores, respectively, the type of the oq-plot suggests presence of ultramicropores and/or supermicropores. When in the microporous system there is an evident linear region between two swings and the line can be extended to the origin, the total surface area a, in m2g1 is obtained from the relation of 2.05 x slope using the unit of adsorption in mgg "1. a,
The extrapolation of the line above
=1.5 to the ordinate and its slope lead to the micropore volume W 0 [mlg I] and the
external surface area ac~ [m2g'~]. The following modified Wicke equation gives an average slit-shaped micropore width w [nm]. w = [ 2W 0 / (a, - a,~,) ] x 103
(6)
He adsorption -Gaussian distribution approach N 2 molecules are strongly adsorbed at the entrance of necked micropores.
A H20
molecule is smaller than a N2 molecule, but the former has a strong preference in the adsorbent because of the dipole moment. A He atom is the smallest spherical monoatomic molecule and interacts nonspecifically with any solid surface. He adsorption at 4.2 K is an effective method for determination of micropore structures. Kaneko et al used the gravimetric
696 He adsorption method using a grease-free stainless vacuum line and high precision pressure t r a n s d u c e r s . 9s'97 The
He adsorption isotherm at 4.2 K and N: adsorption isotherm at 77 K for
pitch-based ACF are shown in Fig. 13.
Here the amount of adsorption is represented by
their volumes using the theoretical adsorbed He density (0.202 gml "t) on the fiat surface by Steele ~ and the liquid N 2 density (0.807 gmla), respectively. The amount of He adsorption is much greater than that of N 2 in the low P/P0 region. This is caused by better accessibility and accelerated bilayer adsorption. The density of He adsorbed in the cylindrical micropores of zeolites was examined; the value was 0.23 gml~. 99 If the He adsorption is analyzed by the Dubinin-Radushukevich plot given by eq.6, the He adsorption isotherms at 4.2 K provides the micropore size distribution using eq. 8 together with the Dubinin-Stoeckli relation zoF~ = constant for activated carbon. ~0o,~m
(7)
WJW o = exp[-(A/E)~], A=RTIn(Po/P), and E= 13Eo
we--
Wo
~l+2m62A 2
exp[- m7~2
1 + 2m62A2 ] [1 + eft(
. . . . . __)] 8~'41_ ;to + mtZA2-
(8)
Here, W, is the amount of adsorption at the equilibrium pressure P; W0 is the micropore volume; E 0 and 13are the characteristic adsorption energy and an affinity coefficient (13m= 0.03). 6 is the dispersion of the distribution, and ;to and X are the mean pore half-width (w = 2Xo ) and the half-width, respectively. Wo and ;to can be determined experimentally and 5 is chosen from the best fit conditions. This equation is derived by the assumption of the 3
0.60
He
E
/
0
E
N~
_
9 9~176. " ' ~ 1 7 6
--' OAO
i
0
He
1-
E- 0.20
O
o
o N2
r
v
0.00 -6
-5
v
v
v
-4 -3 -2 log ( P / P,)
-1
I
0
v
0
0
0.2
0.4
0.6
0.8
1
micropore half width X/nm
Figure 13. Adsorption isotherms of He at 4.2
Figure 14. Micropore size distribution
K and N 2at 77 K by PIT-ACF.
from He and N 2 adsorption isotherms.
697 Gaussian distribution of adsorption potential A.
Then the relationship dWJd x vs. X, that
is, the micropore size distribution is obtained. The calculation of this method is so easy that it has been widely applied to many carbonous adsorbents, giving a new information on the pore structure. The micropore size distribution of ACF by He adsorption at 4.2 K is shown in Fig. 14 in comparison with that by N 2 adsorption at 77 K. The micropore size distribution from He shifts to smaller value compared to that from N 2.
The potential profile-aided He adsorption method The above analysis for the He adsorption isotherm at 4. 2K is limited to activated carbon which the Dubinin-Stoekli relation can be applied to. A more general analysis for He adsorption is necessary. Steele showed that the interaction of the second layer He with the graphite surface is very important at 4.2 K and the second layer adsorption significantly proceeds during monolayer adsorption. In the micropore filling process of N 2 adsorption, the second layer adsorption is affected by the micropore field.
Then, the adsorption mechanism
for He on the flat surface can be applied to analysis of micropore filling.
Steele extended
the Dole equation having no lateral interaction to description of the He adsorption isotherm on the flat surface to obtain the following equation. 9s
{1 + c2x(2-x 1 }z{c~x ~ o =
.... - x
(9)
1 + ctx + c~c2x'l-x Where 0 is the coverage given by the ratio of the m o u n t of adsorption W,, to the monolayer capacity W= ( 0 = W a / W m ) and x = P/P0. ct and c 2 are expressed by eqs. (10) and (11) with the aid of the molecular partition function Ji of a molecule in the i-th (i =1 or 2) adsorbed layer, the difference e, in energy zero of an adsorbed molecule and a molecule in the gas, and the potential energy of an atom in the bulk liquid r~q.
c~=(jx/juq)eXp[-(e,-~q)/kT]
(10)
c2 =(,j~juq)eXp[-(e2-r~q)/kT]
(11)
Here c I agrees with the well-known BET c value. As the interaction of the second He adsorbed layer with the surface is taken into account, c 2 is necessary for the Steele's treatment.
When this approach is applied to description of micr0Pore filling of He, c x and
c 2 must be evaluated from the molecular potential profile which varies with the micropore
698 width. The potential profile is determined by calculation using the Lennard-Jones potential as a function of the pore width. This potential profile leads to both c t and c2 for different pore width. Hence, we can determine the micropore size distribution by the best fit procedure to the observed adsorption isotherm.
Two kinds of pores having different pore widths
were presumed and the fraction was determined by the computer fitting for He adsorption by ACFs.
The major distribution determined by this method almost coincides with the
previous distribution by the thermodynamic approach for ACF. Thus, this molecular potential approach for He adsorption having a general applicability is a hopeful method for micropore charactedzaton, tm The micropore field is expressed in terms of the Lennard-Jones potential. ~m Hill proposed an expression of the adsorption potential by the average molecular potentials. ~~ Horvath and Kawazoe extended this idea to evaluation of the micropore size distribution for the slit-pore system, l~
This average potential approach was applied to evaluation of the cylindrical
micropore size distribution by Saito and Foley. ~~
Also this type of approach is effective
for description of micropore filling of supercritical gases such as N 2and CH 4 by slit-shaped micropores.'~
The experimental maximum value of high pressure adsorption for a supercritical
gas is related to the micropore width through the average micropore field for the slit-shaped carbonous microporous solid, because the amount of high pressure adsorption of a supercritical gas is mainly governed by the adsorbate-adsorbent interaction potential. Therefore, high pressure adsorption gives information on the pore structure. Although the average potential method is very convenient for analysis of experimental data, only calculation of the potential is not sufficient for elucidation of adsorbed molecules in pores.
The statistical mechanical
approach-molecular simulation porosimetry can provides more reasonable an.alysis,t ~ m which will be given in another part of this book. As there is the difference between the theoretical pore width H (the internuclear distance between two surfaces) and the effective pore width w determined by physical adsorption, we must take care of the comparison.
Generally
speaking H is given by eq. 12 In the case of N2/graphitic microporous carbon w is nearly equal to H- 0.24 in nm. t~2 Here of,
and
off are the solid-fluid and fluid-fluid LJ
parameters, respectively. H = w +0.8506of, - off
(12)
4.2 X-ray diffraction X-my diffraction is an established method for determination of the crystal structure; it is a quite effective for evaluation of the pore and pore-wall structures for intrinsic crystalline pores. On the other hand, it cannot offer the conclusive information of the pore-wall structures for ill-crystalline porous solids such as activated carbon.
However, still it can provide
informations of the local structures of ill-crystalline pore-walls. In the case of activated
699 carbon, X-ray diffraction has been used to evaluate the micrographitic structures since Franklin. tt3 The half widths of the 002 and 100/101 peaks determine the stacking height and the stack width; the peak position of 002 gives the interlayer spacing. Accordingly, the fundamental unit structure of the micrographites in activated carbon can be determined by it. Furthermore, the detailed radial distribution analysis of the X-ray diffraction patterns provides the two-dimensional graphitic layer structure. Recent in situ X-ray diffraction technique showed the micrographitic structural changes
i
i
,
,
,
'
of activated carbon with gas adsorption. "+"7 Q42
Suzuki and Kaneko used the graphitizationcontrolled ACFs obtained by heating at 1773 and 2073 K in At. n6 The adsorption processes
040
E
of H20 at 303 K and N 2 at 165 K on the ACF samples were investigated by the in situ X-ray
Q38
diffraction. In the analysis of broad peaks of
\
micrographites, the strong background in the
0.36
low angle region was corrected using the Porod law. As liquid water has a peak near the 002
0.34
peak of micrographites, the reflection by
o
adsorbed water was subtracted. The adsorption
' 2'o ' 2o ' i o
' 8b
';oo
fractional filling / %
of these gases led to shrinkage of the interlayer spacing d ~ between the graphiticcarbon
layers, as shown in Fig.15. The shrinkage of
Figure 15. Changes in the interlayer
more graphitizedA C F is less remarkable
than that of as-received ACF. The d002value
distance d ~ with fractional filling in % by HzO at 303 K.
decreased hardly until0.4 of the fractional
C): ACF, A: ACF-1773, O :ACF-2073
fillingof H20 ,then itlowered markedly
with the fractionalfilling. On the other
10
hand, slightadsorption corresponding to
v~
decreased seriously the d002 value in
x
less than 0.05 of the fractional filling the case of
N 2 adsorption. This
difference comes from the difference in adsorption mechanism
, which is
associated with the distortedslit-shaped micropore structures. Also adsorption of ethanol and methanol affects the ..
micrographitic structures, but the
7.
'~"
,-5
-10
i
=
I
i
,
5
r/(A)
.
10
Figure 16. Differentialradialdistribution
15
functions of adsorbed H:O and bulk one.Solid
700 changes are less than that by adsorption
line: Adsorbed H20, Broken line: Bulk H20
of H20. Thus in situ X-ray diffraction examination is a powerful characterization method. Aslo in situ X-ray diffraction can show the intermolecular structure of adsorbed molecules in pores. Iiyama et al recently reported presence of the organized water in carbon micropores using the radial distribution. 118 Fig. 16 shows the differential radial distribution function-changes of water molecules with the amount of adsorption in the carbonous micropores at 303 K . The radial distribution of adsorbed water is clearly different from that of bulk liquid. There are double peaks at 0.3 and 0.45 nm in all cases. The peak at 0.3 nm is higher than that at 0.45 nm in the bulk liquid. In the case of the adsorbed water, the peak around 0.45 nm is higher and the peak position shifts with adsorption amount to longer side. This result suggests that the fundamental unit structure of the bulk water has excess nearest molecules due to the flexibility. The structure of the adsorbed water is rather rigid like ice. This direct evidence of the structure of the adsorbed water molecules should be helpful to understand the adsorption mechanism.
4.3 Small angle X-ray scattering Average three-dimensional structure of pores in ill-crystalline solids. In the X-ray diffraction the angular region of X-ray scatter below 9' (20x < 5 ~ 0 x is the incident angle) is ordinary called the small angle region of the X-ray diffraction diagram. In this small angle region, two types of scattering phenomenon can be observed. First, sharp maxima due to long range periodicity in solids can occur. The second type is observed to decrease in intensity with increasing angle in a continuous manner, which is not due to large internal periodic regularities but to the electronic density heterogeneities of the medium. The heterogeneous entity size is in the order of 0.5 nm to 103 nm.
This X-ray scattering,
so called small angle X-ray scattering (SAXS) gives important structural informations on pores producing the distinct heterogeneities of the electronic density.
A new SAXS
equipment with a position-sensitive proportional counter and a computer analyzer can give reliable scattering patterns in the scattering parameter s (= 4nsin0x/X ) regions of s = 0.005 to 1.0 regardless of necessity of slit correction and position adjustment of X-ray. The SAXS measurements can provides the pore structures through the electronic density discontinuities in adsorbents. The SAXS is useful for ill-crystalline porous solids to which X-ray diffraction cannot be applied. The SAXS method leads to informations on both closed and open pores whose sizes are in the range of micropores to macropores. One of the important information from SAXS is the electronic radius of gyration of a particle about its electronic center of mass, RG (an electronic deficient void may be considered for a porous system).
From the knowledge of R Gthe linear dimensions of many simple
geometrical forms can be calculated, if the shape is known. In the case of a spherical particle
701 of radius R, the radius of the sphere is related to RG with R~ = (3/5) ~ R. If the scattering particle is the slit-shaped pore of the micropore width w,
the horizontal length of the cross
section of the slit l, and the depth of the pore d, the R Gis given by eq. (13). R~=[(w2+ 12 + d 2)/12 ] in
(13)
Guinier demonstrated that the scattering curve should become exponential and the exponent should equal to - s 2 R~ 2 /3 , as s approaches zero. eq.(14). "9
Th. Guinier equation is given by
I(s) = I(0) exp(-RG2s 2/3) at s --~ 0.
(14)
The Ouinier approximation is valid for sR G< 1. The linear relationship of In I vs. s 2 at a low
i
11011r
angle region can be often observed, and then the corresponding R G can be obtained.
Figure 17
!
[1. 0
~'~.k~'%~,4.~,,~.. 6 ;~,~.~..~...~..,.:,,. " .. ..
shows the Guinier plots of SAXS spectra in the treating tCml~ratum
smallest s region for ACF samples in air at room temperature; these samples were obtained by
1473 "'~""~'~"~'$--~""-'-N~'....~.:.
,
heating ACF in Ar at different temperatures of 1473 to 1673 K for 1 h.
There seems to be a
linear region, where the requirement of s R~ < 1
. . . . ~,~,f;~,~.
is satisfied, so that the R G of the A CF sample is 1.0 rim.
Pores in real porous solids are not so "~" .~.,~%.,.
isolated that the no-correlation assumption possibly does not hold. Nishikawa et al. showed
0
t
t
t
t
I
0.05
, "~'~"l "~-,~"/"~ ''~'~':
~(k2)
that not only pores but also micrographites can
1673 0.1
cause the evident scattering in the simulation
Figure 17. Guinier plots of SAXS for
study of SAXS of ACF samples. 12~In particular,
ACF treated at different temperatures
determination of the scattering entities for a
in Ar.
highly porous solid is difficult at this stage. entities are pores, as usually presumed.
For simplicity we consider that the scattering
Also the scattering entity should be monodisperse
for the Guinier approximation regardless of slight nonlinearity.
The observed
R G value
should be an average value for scattering entities having a distribution to some extent. The analysis by Shun and Roess for a nonlinear system is available. 121
If a Maxwellian
distribution is assumed and the Guinier approximation holds for all different pores, the scattering intensity is described using the size distribution M(RG) having RG, which can be expressed by eq. (15).
702 M(Rc,) = (
2M0/r0*"r[(n+l)/2]
} RGz exp (-RG2 / r02)
(15)
Here n and r 0 are parameters, and M 0 is the total mass of scattering particles (mass difference for pores).
F denotes the gamma function.
This approximation provides the following
expression of the scattering intensity. logI(s ) = constant - [(n + 4)/2]1og (r02/2) - [(n + 4)/2]log(s 2+3/r0~)
(16)
The computer fitting determines n and r 0, and thereby the size distribution is obtained. The size distribution of nonheated ACF was considerably broad; the mean R Gvalue was obtained from the first-order moment using the distribution, so that the R G was 1.5 rim, being seriously distant from 1.0 nm by the simple Guinier analysis. ~ The R G value obtained thus provides an average geometrical size of the pore. We presume the slit-shape of the pore of ACF, and thereby the R o is related to the three dimensional parameters, w , l, and d through eq. (13). Further analysis of the scattering at angles higher than the Ouinier region leads to another information on the shape of the pore. The following thickness plot is available for analysis of the slit-shaped pore. I(s) s2= I, ( 0 )exp ( -R~2 s 2 )
(17)
Here, It( 0 ) is a constantand P-,t is the gyration radius of thickness. The R, is determined from the linear thickness plot. The linearity clarifies the slit-shape of the pore of ACF. The R, of nonheated ACF was 0.8 n m , which is close to the pore width determined by N 2 adsorption.
Porod derived the thickness and cross section plots for analysis of the SAXS
data for thin plates and rod particles, respectively, m
Approaches other than the Guinier
plot should be helpful to elucidate the pore structures regardless of necessity of careful examination. Three-dimensional parameters of pores of ill-crystalline solids can be determined by the SAXS analyses. ~ The determination of the absolute pore geometry of ill-crystalline solids promises further understanding of the molecular process on heterogeneous solid surfaces. Latent pore characterization
Combination examinations of density, N 2 adsorption, and SAXS enable us to evaluate the latent pores, m Here we must explain the porosity and density for further discussion.
The porosity is defined as the ratio of the pore volume to the total solid volume; three kinds of densities, truedensity t , apparent density p,p, and He-replacement density Pa, gives the open pore porosity ~op (=1- P~PH~) and closed pore porosity ~q,(= p,c(1/pa , - 1/pt)).
703 In order to separate the closed pores from total pores, the solid volume fraction ~, (= pq,/p,), the total porosity (the total pore volume fraction) ~p (=1 - ~,), and the ultrapore
0.012
porosity ~up ( = 1-p~, [W0 + (1/pa,)]) must be introduced. Here, P, is presumed
0.010 0.008
to be equal to the density from X-ray Therefore,
0.006
determination of p,, p~,, ~ and W0 gives
0.004
diffraction examination.
the above-mentioned porosities.
If we
determine p ,p, tbcp is obtained.
The
determination of p,p must be carefully
i
0.002 0
0
0
I
l
I
0.02
0.04
0.06
0.08
done. Not only mercury, but also non-
0.I
s : IX'~)
wetting liquid can be available for the measurement. In the case of activated carbon,
Figure 18. The Debye-Bueche plot for ACF.
water is recommended for
determination of pp. Debye et al showed that SAXS analysis can derive the total surface area of the interface originating from the electron density heterogeneity. TMThe specific surface area a, is associated with porosities ~, and ~p, the apparent density p~,, and the correlation length a , as given by eq. (18).
ax =
4 x 104~s~p a~
(m2/g)
(18)
Here, a can be determined from the following eq.(19) for the slit focus system after Williams.
I("~ =
A (1 + a2s~)a/2
A=constant
l(s) is the smeared intensity for the slit focus system.
(19)
The linear Debye-Bueche plot of
l(s~ z~ vs. s 2 leads to a from the slope and intercept( eq. (20)).
a=
slope intercept
(20)
Then, a x can be determined by SAXS and density experiments. Figure 18 shows the Debye Bueche plot for the nonheated ACF. The good linearity in a wide s
region leads to the
correlation length and the surface area. Figure 19 compares the a, values with a, ones (from
704 Nzadsorption ) for various ACF samples with different bum-offs, which were prepared from pitches.
Almost all ax values are
more than a, ones; the deviation becomes more predominant with the decrease of a,.
1800
This difference
indicates directly the presence of the closed pores and ultrapores, which
..--,
should be designated latent pores.
1400 s
If we presume that latent pores are uniform spheres (or cubes), the
1000
s
number and the size of the latent
s
pores are derived from aI, as , ~,, ~q,, and ~p.
9
s
I
1
1r
When the number
concentrations and pore sizes for
!
!
1400 as(m2g"')
!
1800
open pores and latent pores are expressed by n,p and n~,, and l,p and
Figure 19. The comparison of the surface area a I
~, respectively, eqs. (22) and (23)
from SAXS with that a, from N: adsorption.
are obtained.
+(n-a)
_.
(22)
/Ip _'q- 1
(23)
top ~-1 Here, ~ and r I are calculated from observable quantifies as follows.
*up + 'l' p = _
~P
nlpl
_
Ilop~p +nli~l 3 -- ~"
ax
=_
n pl . 2
2
= 1
(24)
nop/op+nlpllp
Consequently, we can estimate their number and size of latent pores; if l,p is approximated by the observed pore width w, /~ is derived. The ACF of the surface area of 500 m2g"t has many latent pores of 60 % and their size is about 0.4 rim, which agrees with molecular adsorption behaviors. The latent pore can work as the open pore under a chemical perturbation and desolvated ions and metal ions can be stored through the narrow entrance. Then, the latent pore characterization is quite important in new development of materials. Foster and Jensen have proposed a new SAXS
analysis using a rondom
pore structure model which is
705 effective for elucidation of structural changes of carbons) z5 Further develpment of the SAXS analysis is expected in order to understand ill-crystalline porous solids..
4.4 Nuclear Magnetic Resonance Method The strength of the local magnetic field acting on a given nuclear spin in matter is slightly different from that of the external field. The electron cloud surrounding a nucleus shields the external magnetic field, shifting the nuclear magnetic resonance(NMR) line. This effect is the so-called chemical shift. The highly sensitive chemical shift of NMR has contributed to elucidate complex molecular structures of organic molecules. The chemical shift is not necessarily caused by the molecular electron cloud, but the electronic microenvironment of the molecule. In particular, molecules in a confined solid space such as a micropore or a mesopore are affected by the electronic states of the surroundings, giving rise to the chemical shift. Fraissard and Ito observed the correlation of the NMR chemical shift with the pore size of zeolites using a probe 129Xe(nuclear spin I = 1/2). 1"6 The atomic size of Xe is 0.44 nm and its cross-sectional area is about 0.195 nm 2. Xe near room temperature is a supercritical gas, and thereby it is a good probe for the micropore which is just fit for the Xe atom. Briefly speaking, the narrow Xe NMR signal is observed for the uniform pore geometry; the chemical shift induced by the surrounding increases with decrease in the pore size and the chemical shift due to the Xe-Xe interaction increases with the Xe pressure. Although the chemical shift is governed by the electronic microenvironment mainly, other physical processes such as collisions of Xe atoms with each other or solid wall and presence of additional atoms in pores can affect the chemical shift.
Conner et al tried to apply this NMR method
to characterize the mesoporous silicas, lz7 They observed the chemical shift similar order to that of zeolite regardless of smaller amount of Xe adsorbed by an order of magnitude. The signal was also narrow. The dependence of the chemical shift on the Xe adsorbed for silicas was completely different from that for zeolite. The chemical shift slightly decreased with the amount of adsorbed Xe. They analyzed the observed chemical shift by eq. (25).
(25)
~,~--x~.<~,~> + x ~ <Sr~> They expressed the observed chemical shift of Xe,
8,~ , by the sum of
the average
contribution of adsorbed Xe species, , and that of interporous Xe species, < ~ , ~ . Here, x ~ and xp,~ are the fraction of the adsorbed and interporous Xe species. Thus, the NMR behaviors of Xe atoms in mesoporous silicas are presumed to be caused by the adsorption of the Xe atom on very small pore site( the micropore size) and rapid exchange between Xe in the micropore and Xe adsorbed on the surface.
They also examined the
706 NMR signal change for mixing of two kinds of mesoporous silicas, then they showed two distinct signals due to different pore sizes. Consequently, NMR method is effective for direct checking the heterogeneity of pore structures.
The measurement of the chemical
shift of NMR will be widely used in the adsorption sicence field hereafter. 2D-echange spectroscopy (2D-EXSY) of 129XeNMR can provide information about the rate and the pathways of the Xe exchange between the different states of Xe in pores. ~29Xe 2D-exchange NMR should become another useful technique for elucidation of the relationship between surface heterogeneities and molecular adsorption, m If there is a macroscopic orientation in solid samples, another NMR principle is helpful to elucidate the pore structure and the molecular motional state of adsorbed molecules. electric quadrupole occurs only for spins with quantum number I > 1/2.
An
Deuterium has a
spin quantum number of 1, then the interaction between the nucleus and the local electric field shifts the energy level.
One observes two resonance lines by the following equation
for hydrocarbons whose principal axis z is given by the direction of the C-D bond so that the xx component of the electric field gradient tensor V~, equals to the yy component V.. to =
too
-4-
(toQ/2) (3 cos: 0m- 1 )
(26)
Here toQ is called quadrupole frequency, which is given by eq.(27). % = (3 ~2)e 2qQ/h
(27)
where Q is the quadrupole moment, eq
=
V= is the zz component of the electric field
gradient tensor, and 0mrepresents the orientation of the magnetic field in the principal axis of the electric field gradient. Different distributed
~\\\\N%
orientations and motional states of molecules having the C-D bond provide characteristic line shape. Deuterated benzene NMR provides
.~-~-~
E
ao~, oooo
~ ~
o
0 0
an important information, because benzene has been widely used as a probe adsorptive. Recently Fukazawa et al applied this D-NMR technique to analysis of adsorbed benzene states in slit-shaped micropores of boehmite microcrystalline aggregates. 24'129
The
microporous boehmite aggregate has two kinds
~
0.4
0
.................. @'V..,.
0.0
0.0
.
0.2
x
.x\\xx~
0.4
t
0.6
Or.8
1.0
PfPo
of effective micropores of 0.8 and 1.3 nm.
Figure 20.The benzene adsorption isotherm
Fig. 20 shows
the benzene adsorption
of boehnfite aggregate at 303 K. The solid
isotherm at 303 K. They measured D-NMR
symbol corresponds to the D-NMR meas-
707 at three adsorption stages denoted by arrows
ment and the filling state is shown.
and solid circles which correspond to 0.02, 0.2, and 0.4 of PIP0- Almost smaller micropores of 0.8 nm are filled with benzene at P/P0 = 0.02, while adsorption by larger micropores of 1.3 nm begins at P/P0= 0.2. Both types of micropores are filled with benzene at P/P0= 0.4. Fig. 21 shows the D-NMR spectra of benzene adsorbed in the micropores at P/Po = 0.02, 0.2, and 0.4.
The peak becomes narrower with the increase of P/P0. At P/P0 = 0.04 benzene
molecules have a considerably definite organized structure, but they are moving with the tilt angle of 44* against the pore wall. Under the conditions of P/P0 = 0.4, benzene molecules exchange more rapidly with the configurationof the tilt angle of 51-54 ~ The surface diffusion coefficient was 3 x 10s cmZs' (liquid benzene: 2.1 x 10 s cm2sI). Three spectra are different from each other and each spectrum is not the convoluted peak due to coexistence of different molecular motional states, suggesting that benzene molecular states are characteristic to the relative pressure and even at P/P0 = 0.04 benzene molecular states are uniform regardless of adsorption on the two kinds of sites. Hence, D-NMR should give important informations on the shape and size of micropores, the pore orientation, and also the adsorbed molecular states. There are other methods for characterization of pores and pore-walls. High resolution electron microscopy is one of powerful characterization method of the pore-wall and pore structures and the higher order structures. 13~
However, quantitative analysis is difficult.
The image analysis of high resolution electron transmission micrographs is a hopeful quantitatie technique. ~2 In future, scanning tunneling microscopy
(STM)
and
atomic
j••
force
microscopy (AFM) will become one of representative methods. 133'134 The mercury
0.02 P/Po
porosimetry is an established method for evaluation of mesopore analysis, although this method has problems such as deformation or fracture occurrence in porous specimen due to application
of
high
pressure
!
and
0.2
discrepancey from N 2 adsorption. 3'135 The mercury porosimetry is not described here, because there are many articles on it. The
0.4
heat of immersion has been associated with the pore structures. The depression of the melting point of solids in pores has been
'"|
20
[
i
II
I
10
0
- 0
-20
kHz
applied to the pore size determination by Quinson et al. t~ Yoshizawa et al showed that
Figure 21.D-NMR spectra of benzene
the EELS(electron energy-loss spectrometry)
adsorbed at different relative pressures
708 can determine the sp:- and spS-carbons at 303 K separately. ~3~ It is quite important to characterize ill-crystalline porous solids, and thereby new techniques and trials are desired. 5. HIGHER ORDER STRUCTURES
Activated carbon is quite important adsorbents which have been widely used in human activities. They are produced from natural products, coals, and pitches. The adsorption characteristics is influenced by the sample history. In catalysis field, Hedvall suggested "structural heredity effect" which is strongly associated with the sample history. 138 The scientific reason is not clearly elucidated yet. Even in the modem technology activated carbons the best fit to the industrial application have been selected from intuitive experimental examinations. Probably higher order structural difference lead to observed difference in adsorption properties. As the voids are pores, the examination of the particle agglomerates or pore walls provides the information of the higher order structures of pores.
We can
understand a clear feature of the higher order structures of an adsorbent through the micro graph. The high resolution transmission microscopy and AFM will contribute to understand the higher order structures. However, analysis with microscopy is not quantitative, but qualitative. Understanding of the higher order structures requires introduction of a qualitative analysis in microscopy.
The image analysis of micrographs is one of such possibilities, as
mentioned above. The stress in the image contrast enables to determine the size and shape of primary particles and their short-range order by a statistical averaging. The development of the image analysis of micrographs has been desired. A systematic measurement of classical properties such as the electrical conductivity or dielectric constant is often helpful to understand the higher order structures. The example of the electrical conductivity measurements is shown here. The porous solid can be regarded as a binary system of primary solid particles and pores. Both have own electrical conductivity's. Here, the pore has high resistance of an interparticle region. The pore resistance is not necessarily the same as that of vacuum, but it is determined by the atmosphere and the surface conductivity of primary particles. The total electrical conductivity R t of the porous solid samples between two electrodes depends on the higher order structure of solid particle resistors R, and the pore resistors Rp. There are two types of electrical contacts among component resistors according to the two layer model. ~a9 The two types are series and parallel contacts of R, and R r
The electrical conductivity change with the pore fraction is
completely different from each other for parallel and series connections. Then, we can determine the fundamental contact structure of the aggregates of primary particles. ~4o The electrical conductivity measurement was effective for determination of the higher order structure of the oxide mixture. If the pores and solids have capacitance character, the .
equivalent circuit is expressed is in terms of the resistor and condenser. Suck-an equivalent circuit shows a frequency dependence which is characteristic to parallel or series connection.
709 Thus, the ac electrical conductivity measurement is a hopeful method to determine the higher order structures. Especially, activated carbon is composed of conductive graphite crystals. The graphitic units are highly conductive, while the pore resistance is quite high. The electrical conductive measurement should be applied to characterization of activated carbon relating to the development of a superhigh capacitor. 14L142 REFERENCES
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W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.)
Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
715
C h a r a c t e r i z a t i o n of g e o m e t r i c a l a n d e n e r g e t i c heterogeneities o f active c a r b o n s by u s i n g s o r p t i o n m e a s u r e m e n t s M. Jaroniec a and J. Chorea b aSeparation and Surface Science Center, Department of Chemistry, Kent State University, Kent, Ohio 44242, U.S.A. bInstitute of Chemistry, Military Technical Academy, 01489 Warsaw, Poland
A thermodynamic approach to the characterization of active carbons is presented including a brief review of selected methods and extensive experimental illustrations. This approach represents a comprehensive way to characterize the energetic and geometrical heterogeneities of active carbons, and is based on the differential adsorption potential distribution, which provides a quantitative measure of all changes in the Gibbs free energy of a given gas/solid sorption system. The distribution in question is associated with the differential enthalpy, differential entropy and immersion enthalpy via simple relationships. Also, it can be easily converted to the mesopore and micropore volume distributions.
1. INTRODUCTION Active carbons are most popular porous solids [1,2]. The presence of micropores in these solids increases substantially their sorption capacities over those for nonporous carbons [3,4]. According to the classification scheme of the International Union of Pure and Applied Chemistry (IUPAC), micropores have widths below 2 nm, macropores above 50 nm, and mesopores between 2 and 50 nm [5,6]. The macropores and large mesopores play an important role in the molecular transport process, whereas the remaining pores determine the sorption properties of a given carbon. By carbonization and activation of various raw materials (such as wood, coal, lignite, coconut shell, peat, polymeric resins, etc.) [1,2], it is possible to prepare carbons of differentiated surface and structural properties. In order to alter their properties, various chemical and physical modifications of active carbons can be performed [1]. One of the simplest modifications can be carry out through heat treatment in either an oxidizing gas or solution [1,2]. The types and concentration of surface functional groups are determined by the degree and extent of a given surface oxidation. Other modifications of the carbon surface include impregnation of carbons with various inorganic salts or organic compounds; coating with oils, waxes, and other high molecular weight liquid phases; chemical bonding of different ligands; and deposition of finely dispersed metals and their oxides [1,2]. A further progress in the preparation and applications of novel carbons of the controlled porosity and surface properties can be accelerated by creating novel
716 characterization methods. Among various methods used to study the surface and structural properties of these materials, various sorption-based techniques such as adsorption [7-10], chromatography [11] and thermal analysis [12] are still popular because they provide direct information about adsorbate-adsorbent interactions. For instance, the low-temperature nitrogen adsorption isotherm is recommended by the IUPAC to evaluate the BET specific surface area and the mesopore volume distribution function, which are essential for characterizing the sorption properties of mesoporous solids [5,6]. However, these classical quantifies are not sufficient to characterize active carbons, which usually are highly microporous solids of a complex surface functionality [1,2]. A more sophisticated characterization of these solids, which includes the evaluation of their energetic and geometrical (structural) heterogeneities, is desirable. In the current chapter a consistent thermodynamic approach to the characterization of energetic and geometrical heterogeneities of active carbons is presented with a brief discussion of selected methods. In this approach the adsorption potential distribution, which provides information about changes in the Gibbs free energy for a given system, is a key thermodynamic function. This distribution can be used to calculate the differential enthalpy, differential entropy and heat of immersion as well as it can be converted to the pore volume distribution. In addition, this approach demonstrates clearly the relationship between energetic and geometrical heterogeneities of active carbons.
2. ACTIVE CARBONS AS ENERGETICALLY AND GEOMETRICALLY HETEROGENEOUS SOLIDS The porous structure of active carbons is complex. These solids possess mostly slit-like pores of different sizes, which are contained between the twisted aromatic sheets forming the matrix [1,2]. Although the main feature of good active carbons is their high microporosity, they also contain large pores (i.e., mesopores and maeropores [1]). Thus, these materials are geometrically heterogeneous due to the existence of pores of different sizes and shapes. Their geometrical (structural) heterogeneity can be characterized partially by the mesopore volume distribution function. The IUPAC [5,6] has made some recommendations for evaluating this distribution, which can be evaluated from the high-pressure part of the equilibrium adsorption-desorption isotherm. Also, other methods (e.g., mercury porosimetry) can be applied for determining the porosity of solid materials [1]. While the mesopore distribution evaluated from the high-pressure part of the adsorption-desorption isotherm is useful for characterizing the structural heterogeneity of mesoporous and macroporous solids, it does not provide information about the micropore effects. This information can be obtained from the low-pressure part of the adsorption isotherm. An experimental illustration of the adsorptiondesorption isotherm from the gas phase is shown in Figure 1 for benzene vapor on Ambersorb 572 synthetic carbon (Rohm and Haas Co., Philadelphia, PA) at 293 K [13]. Of course, the shape of isotherm depends on the adsorbate-adsorbent interactions as well as on the structural and surface heterogeneities of the carbon. The classification of the gas-solid adsorption isotherms and the adsorption-desorption hysteresis loops are discussed elsewhere [7]. Here, it should be mentioned that adsorption occurs more or less gradually. At very low pressures, the most energetic adsorption sites are occupied
717 and adsorption takes place in the micropores, which are filled gradually. Adsorption data measured in the range of the micropore filling provide information about the structural heterogeneity of micropores and about the surface heterogeneity. At higher relative pressures, the layer-by-layer adsorption occurs on the surface of mesopores. Finally, capillary condensation takes place in mesopores and small macropores. The multilayer and condensation segments of the adsorption isotherm provide information about the mesopore and macropore volume distributions. 12.0 13) 10.0 0
E E
~
Amb572
Capillary Condensation
8.0
(D .Q
6.0
L_
0
<
4.0 qJ~v,
E
2.0
0.0
0.0
Layer-by-layer Adsorption
Micropore
"
0.2
0.4
0.6
0.8
1.0
Relative Pressure, P/Po
Figure 1. A complete adsorption-desorption isotherm of benzene vapor on Ambersorb 572 synthetic carbon at 293 K. Experimental data taken from Ref. [13]. Microporosity plays a substantial role in adsorption on active carbons and its evaluation is one of the key problems in the characterization of these adsorbents. In addition to microporosity, the surface heterogeneity is essential especially for active carbons with a large specific surface area of mesopores. The mesopore surface can possess various irregularities, strongly bound impurities, and functional groups, which are the source of this heterogeneity [8]. The amount and kind of surface groups depend on the type of raw materials used for preparation of an active carbon and on the conditions of carbonization and activation processes. This amount can be reduced significantly by additional thermal treatment of the carbon in a neutral gas atmosphere [1]. The total adsorbent heterogeneity of an active carbon includes its structural and surface heterogeneifies. While the structural heterogeneity can be characterized by the micropore volume distribution, evaluation of the surface heterogeneity is not as easy. The surface heterogeneity plays an important role in adsorption on porous carbons with a large fraction of mesopores (i.e., carbons with a relatively large external surface area), e.g., carbons impregnated or coated with different compounds, carbons with deposited fine particles or thin films, and chemically.modified carbons [1]. For carbons with a
718 large micropore volume the variability of their surface heterogeneities is limited because the free space available for surface groups in fine pores is small. In this case the dispersive carbon-adsorbate interactions dominate and the intrinsic features of adsorbate molecules are not strongly manifested [3]. Micropores of different sizes and the surface heterogeneity are sources of the energetic heterogeneity of active carbons [8,14,15]. The energetic heterogeneity is a relative quantity, defined most often in terms of the adsorption energy. The distribution function of the adsorption energy is commonly accepted as a quantitative measure of this heterogeneity [8,9]. This function characterizes only the global energetic heterogeneity and does not provide information about distribution of adsorption sites on the carbon surface, which can follow an intermediate pattern between patchwise and random topographies.
3. FUNDAMENTAL ADSORFFION QUANTITIES 3.1. Adsorption isotherm and characteristic adsorption curve
The equilibrium adsorption isotherm, which expresses the amount adsorbed a as a function of the equilibrium pressure p at a constant temperature T, is the fundamental experimental dependence in adsorption (see Figure 1). The quantity a provides the amount of adsorbed adsorbate (expressed usually in moles or cc ST/') per unit mass (sometimes per unit surface area) of the adsorbent. The amount adsorbed can be easily converted to the volume of liquid adsorbate, which provides information about the pore volume access~le to adsorption. While the graphical presentation of adsorption data in the isotherm form is suitable for demonstration of the multilayer adsorption and capillary condensation range, this form is not good to show characteristic features of the low-pressure adsorption data, which are essential to characterize the surface and geometrical heterogeneities of porous solids. Although the logarithmic scale of the pressure axis can be used to display the low-pressure adsorption data, the use of the adsorption potential instead of pressure has some additional advantages. The adsorption potential A is defined as the change in the Gibbs free energy taken with the minus sign [3]: A = - A G = RTln(p o/p)
(1)
Here Po denotes the saturation vapor pressure, T is the absolute temperature, and R is the universal gas constant. An experimental illustration of the amount adsorbed plotted against the adsorption potential is shown in Figure 2 for benzene on Ambersorb 572 synthetic carbon on at 293 K (open circles). As can be seen in this figure, the graphical presentation of the low-pressure adsorption data (which correspond to high values of the adsorption potential), is excellent because A is a simple logarithmic function of the equilibrium pressure. Another advantage of this presentation is its temperature invariance for many microporous solids [3]. The dependence of the amount adsorbed on the adsorption potential is often called the characteristic adsorption curve [3]. It will be shown t h a t the characteristic adsorption curve is a key thermodynamic function in characterization of porous solids such as active carbons.
719
.0
r O
E E
~
5.0-
Amb572
4.0-
-o"
-
.ca 3.0L_ O
t/} "O
<
r =5 O
<E
2.01 . 0 -0.o
10
0
20
30
40
Adsorption Potential, A (k J/tool)
Figure 2. Characteristic adsorption curves for benzene vapor on Ambersorb 572 synthetic carbon at 293 K. Open circles present the total amount adsorbed as a function of the adsorption potential, whereas filled circles denote the amount adsorbed in mieropores only. The amount adsorbed in micropores was extracted from the total amount adsorbed by using the a~-method [7]. Adsorption data taken from Ref. [13]. 3.2. Comparative analysis of adsorption isotherms In many active carbons, the fine pores contn~oute mostly to the total amount adsorbed. The overlapping of the adsorption forces generated by the opposite walls of the fine pores causes a significant increase in the adsorption, which occurs according to the volume-filling mechanism [3]. At low equilibrium pressures, molecules are adsorbed first on the micropore walls, and then the internal volume of micropores is filled gradually [3,16,17]. For microporous active carbons, the amount a~a adsorbed in the micropores gives the main contribution to the total adsorbed amount a [18], which is defined as follows: a
-- anti
+ ante.
(z)
Above a ~ is the amount adsorbed on the surface area of mesopores. Although the value of a ~ is often small, for active carbons with high surface area of mesopores (i.e., high external surface area), it can not be ignored. For strongly mieroporous earbons, a ~ is neglig~ly small and a = a n . In this case the uncorrected adsorption data can be used to obtain information about mieropores. Equation (2) can be used to evaluate adsorption in mieropores. According to this equation, the amount adsorbed in mieropores is:
720 o a ~ : a-a,,,,,O,,,,
(3)
where Om= a.ja o
(4)
Above 0,,, denotes the relative adsorption on the surface of mesopores and a ~ is the monolayer capacity of this surface. The relative adsorption 0., on the mesopore surface can be identified with the relative adsorption 0, on a reference nonporous carbon, which is measured in a separate experiment; i.e.,
o.,
:
o,
(s)
for a given equilibrium pressure. The reference carbon should possess the same surface properties as the mesopore surface of the carbon studied. The standard isotherms for nitrogen, argon, n-butane, benzene, and neopentane are available in the literature [1925]. Using the standard adsorption isotherm, 0,, it is possible to extract a,i from the total adsorbed amount a: o
ami = a -am0,
(6)
An analog of equation (6) was used frequently by Dubinin and co-workers [26-28] to calculate the amount adsorbed in micropores. The value of a ~ necessary for evaluating a.i according to equation (6), can be estimated by comparing the shape of a given isotherm with that of a standard isotherm on the reference nonporous carbon [7,29,30]. Two methods, viz., the t-method [7,31] and the a,-method [7,32], are used frequently for evaluating the micropore capacity a ~ and the monolayer capacity of the mesopore surface, am. o In the t-method, the adsorbed amount a is plotted against the thickness of the multilayer formed on the reference adsorbent. The a s-method is based on the plot of the adsorbed amount a on the reduced standard adsorption a , , which is defined for a reference nonporous adsorbent as the ratio of the adsorbed amount a, to the amount a0.4 adsorbed at P/Po = 0.4 [7]. Theoretical foundations of the as-method, which apply also to other comparative methods, were discussed elsewhere [32]. The comparative methods are based on the assumption that in the multilayer range, the layerby-layer adsorptions on the reference nonporous carbon and on the mesopore surface of the active carbon studied are proportional, i.e., equation (5) is valid. In the multilayer range, the micropores are completely filled and
ami =
o
ansi
(7)
The symbol a~ denotes the adsorption capacity of micropores. Equations (4), (5) and (7) allow to transform equation (2) to the following linear form with respect 0,:
721
a
~
= dmi
+
~
(8)
as~0 r
Equation (8) shows that in the multilayer range the dependence between the adsorbed amount a and the standard adsorption Or is linear with the slope equal to the monolayer capacity a ~ of the mesopore surface. In the region of low relative pressures, the proportionality analogous to that in equation (8) is not observed because the mechanism of the volume filling of micropores differs from that for adsorption on a nonporous solid. Because the micropore filling occurs in this region, i.e., equation (7) is not fulfilled, the dependence between a and 0, is not linear (see Figure 3, which presents the schematic dependence between a and 0,). It follows from Figure 3 that the intercept of the linear segment gives the micropore capacity, a=~, whereas its slope provides the monolayer capacity of the mesopore surface, a ~ . An experimental illustration of the comparative r s-method is shown in Figure 4 for benzene vapor at 293 K on the BPL commercial active carbon from Calgon Carbon (Philadelphia, PA) [33].
O.
Standard Relative Adsorption, (9r
Figure 3. Schematic representation of the comparative plot. 3.3. Adsorption potential distribution The characteristic adsorption curve a(0), which for many active carbons shows a temperature invariance, is a primary thermodynamic characteristics of the adsorption system studied. The amount adsorbed a (after conversion to the adsorbate volume) measures the total pore volume accessible to adsorption. If a t is the maximum amount adsorbed, the difference at-a represents the unoccupied pore volume associated with the adsorption potentials smaller than A [34,35], and denotes the non-normalized integral distribution function of the adsorption potential, X~(A) Its first derivative with respect to A is the non-normalized differential distribution function X,,(A), i.e.,
dX~,(A)
d(a~-a)
da(A)
(9)
w
In terms of the condensation approximation [8] the adsorption potential distribution
722 7.0 6.0 m
0
E E
5.0
-~- 4.0 om 3.0 "~ 2.0
I
<E
BPL
1.0
0.0 0.0
0.5
1.0
1.5
2.0
2.5
Standard Adsorption, o~s
Figure 4. The r =-plot for benzene adsorbed on the BPL carbon at 293 K. Adsorption data taken from Ref. [33]. gives essentially the same information as the distribution function of the adsorption energy because in this case A is equal to the adsorption energy e expressed with respect to the energy e o that characterizes the standard state [36,37], i.e., A = e-eo
(10)
Because for microporous carbons the amount adsorbed plotted against the adsorption potential is a temperature'independent function, use of equation (9) for calculating the adsorption potential distribution is fully justified [34]. Since for simple molecules adsorbed in the carbon micropores the entropy changes during adsorption process are small, the adsorption potential distribution can be used to characterize the energetic heterogeneity of active carbons. Division of the non-normalized distribution X~,(A) by the adsorption capacity gives the distribution X(A), which satisfies the following normalization condition:
f X(A)~ = 1
(11)
b,4
where A a is the integration region of the adsorption potential A. The average adsorption potential A and the dispersion o,4 associated with the distribution function X(A)
723 can be evaluated by means of the following equations:
(12)
0,4
and
A,t n
Analytical equations for A and o a associated with the different adsorption potential distributions were discussed in Refs. [34,35,38,39]. The adsorption potential distribution can be calculated by a simple numerical differentiation of the characteristic adsorption function 0(A) according to equation (9). In the micropore range the adsorption potential distribution can be evaluated by the Jaroniec method [40], which utilizes an exponential polynomial with respect to A to describe the characteristic adsorption curve 0(,4):
a(A) = e x p
[n ]
(14)
The approximation coefficient B0 can be related to the adsorption capacity a ~ aO
=
o o a,,,/ + a,,,,,
=
exp (-B,,)
(15)
For special sets of the parameters Bj, equation (14) reduces to the Freundlieh and Dubinin-Raduslrkevich (DR) equations, which are popular in gas adsorption on mieroporous carbons [3,7,8]. An attempt at the derivation of equation (14) in terms of statistical thermodynamics was presented in Ref. [40]. Later, Chert and Yang [41] provided some statistical thermodynamics arguments for the DR adsorption isotherm. The exponential equation (14) gives a good representation of many adsorption isotherms on heterogeneous solids [7], especially for adsorption on heterogeneous microporous carbons [42-45]. Also, equation (14) was used successfully to represent adsorption data at high pressures [43]. At extremely low pressures, the exponential polynomial should be modified in order to satisfy Henry's law [46]. This modification is not necessary when equation (14) is used to calculate the adsorption potential distribution, especially for active carbons. For these solids the Henry range appears often at extremely low pressures, at which the measurement of the equilibrium adsorption is very difficult.
724 Differentiation of equation (14) according to equation (9) gives:
Bj A
(16)
Because equation (16)contains a large number of adjustable parameters Bj for j= 1,2,...,N, it allows the complex-shaped distributions X,,(A) to be determined. For many active carbons, the adsorption potential distributions calculated from the adsorption isotherms by means of equation (16) consist of one asymmetrical peak broadened in the direction of higher values of A or two more or less overlapped peaks [8,14,42,47,48]. A comparison of the distribution functions X(A) calculated by using equation (16) for adsorption isotherms of argon, nitrogen and benzene on the PA active carbon (Bamebey-Cheney Co., Columbus, OH) [49] is shown in Figure 5. The functionX(A) calculated from benzene adsorption data is a symmetrical single-peak distribution. However, the adsorption potential distributions calculated from argon and nitrogen adsorption isotherms are similar and consist of two overlapped peaks. The physical interpretation of the adsorption potential distributions for active carbons is difficult because they provide information about the global energetic heterogeneity. As mentioned earlier, the source of the energetic heterogeneity can be both nonuniform mieropores and various functional groups. 10.0 --~ 0 E
8.0
PA
m
(,,q 0 .g
X c" o
argon / "~ nitrogen /.' i', benzene /.' ~",
...... ....
v
6.0
/...
4.0
. m
3 ~
--t~ a t,=.
2.0
.=._
0.0
0
'
,
I
t
I
10
,
,
,
,
I
20
L
,
,
,
L
,
30
J
40
Adsorption Potential, A/J3 (kJ/mol)
Figure 5. A comparison of the adsorption potential distributions calculated according to equation (16) from argon (77.5 K), nitrogen (77.5) and benzene (293 K) adsorption data on the PA active carbon. The symbol fl denotes the adsorbate similarity coefficient [3]. Figure taken from Ref. [49].
725 0.4
AG-5
i
0.3 o v
X
0.2
i5 0.0
0
'
I
3
6
I
9
I
I
,I, I
I
12
I
15
Adsorption Potential, A (kJ/mol)
Figure 6. A comparison of the adsorption potential distributions for nitrogen on the AG-5 active carbon at 77.5 K calculated by using equation (16) (solid line) and a regularization method described in Ref. [50]. As shown in Figure 6 the adsorption potential distribution calculated with equation (16) is similar to that obtained by inverting numerically the integral equation of adsorption with respect to the adsorption energy distribution ~(e):
/"
0(p) = J 0,~,e)~(e)de
f o r T = const
(17)
A
where 0 is the overall relative adsorption in the submonolayer pressure range, Or is the relative local adsorption on the sites with the adsorption energy r and A is the range of the adsorption energy. The value of ~(e)de denotes the fraction of adsorption sites with the adsorption energies between e and e + de. Many attempts have been made to solve the integral equation (17) with respect to the energy dism'bufion function,(e) (see books [8,9] and references therein). An accurate evaluation of the energy distribution function ~(r requires an inversion of the fundamental integral equation (17) by using numerical procedures. Extensive literature devoted to this problem is discussed critically in a few monographs [8,9,51]; new approaches are still being published [52-60]. It appears that the regularization method is most often used to invert the integral equation (17) [50]. This method was used to calculate the energy distribution for the AG-5 carbon, which after reseating according to equation (10) is compared with the adsorption potential distribution in Figure 6. A great similarity of
726 the adsorption potential distributions shown in Figure 6 suggests that the adsorption potential distribution, which is a function of the Gibbs free energy, can be also used to characterize energetic heterogeneity of active carbons. It should be mentioned that the total distribution function X(A) can be separated on two parts [36]:
X(A)
=
I..X.~(A) + I.,X.,(A)
(18)
where fmi and/me denote respectively the fractions of adsorption sites in the micropores and on the mesopore surface, whereas X~(A) and X ~ ( A ) are the adsorption potential distributions that characterize the energetic heterogeneities associated with the mieropores and with the surface heterogeneities of the mesopores. Equation (18) is useful for evaluating the effects of energetic heterogeneity in adsorption on active carbons, which are structurally heterogeneous and often show a strong surface heterogeneity. 3.4. Thermodynamic relations for the adsorption potential distribution
Since through equation (1) the adsorption potential A is defined in terms of the G~bs free energy, the differential enthalpy and differential entropy of a given adsorbate can be expressed as follows:
AS
= -
c3T ),, =
'~
,,
(19)
and AH = A G-
T A S = - A + T(OAIOT ),,
(20)
In addition to equations (19) and (20) the following relationship is valid:
(~AA) ( ~ T ) ( f f ~ ) T
a
=-1
(21)
A
Bering et al. [61] combined equations (19), (20) and (21) in order to express the differential entropy as follows:
AS
--
-~ o
+
a
( ) 0A
r
(22)
is the thermal where 0 = a[a ~ is the relative adsorption and a = - ( d lna~ coefficient of the limiting adsorption a ~ taken with the minus sign. Jaroniec [62] showed that equation (22) can be expressed in terms of the characteristic adsorption
727 curve and the adsorption potential distribution: 04) AS :
-~
o
-
a
O(,4) X(A)
(23)
An analogous relationship can be obtained for the differential enthalpy A H = - A + T A $ after substitution AS by equation (23). If the characteristic adsorption curve shows the temperature invariance, as is often observed for microporous carbons, the first term in equation (23) is equal to zero. An experimental application of equation (23) for active carbons was reported in Ref. [63]. Another important thermodynamic relationship expresses the immersion heat for a porous solid in terms of the average adsorption potential [64]: AH~,,, = -A(1 + aT)
(24)
!
where A is calculated from the adsorption potential distribution via equation (13)anda is the thermal coefficient of the limiting adsorption. Equations (23) and (24) demonstrate that the adsorption potential distribution, which is a model-independent function, is an important thermodynamic characteristics of a given adsorption system. This function provides a quantitative information about the distribution of the Gibbs free energy for a heterogeneous porous solid and through equations (23) and (24) it permits the estimation of the enthalpy and entropy effects in adsorption on active carbons. As it was shown in Figure 6 this distribution can be also used to estimate the energetic heterogeneity of active carbons. It will be shown later that the adsorption potential distribution is also useful to characterize the geometrical heterogeneity of active carbons.
4. INFORMATION ABOUT SELECTED ACTIVE CARBONS Argon, nitrogen and benzene adsorption isotherms measured on various active carbons are used in this article to illustrate some theoretical considerations. Basic information about all active carbons used is summarized in Table 1, which provides the carbon code, the source of each carbon sample, some information about its preparation and application, and the BET specific surface area. Adsorption isotherms for the active carbons listed in Table 1 as well as their characterization were reported elsewhere [6570]. The selected carbons show different surface and structural parameters. As can be seen from Table I their BET specific surface area vary from 450 to 1500 mZ/g except the ACZ carbon, which has much higher surface area. Their porous structures are also different. For instance, the microporous structure of the ACS is highly uniform and its external surface area is small. For other carbons the pore volume distributions are much broader. The 563 and 572 Ambersorbs from the Rohm and Haas Corporation are examples of commercial synthetic carbons, which were produced by controlled carbonization and activation of highly sulfonated styrene/divinylobenzene ion exchange resins. The remaining commercial carbons were prepared by using different raw
728 materials such as bituminous coals, coconut shells and pit coal. Table 1 Basic information about selected active carbons. Active carbon
Additional information
Source of carbon ~i
code
BET surface area
,(m2/g)a ACS
Dubinin's laboratory
ACT-K
Dubinin's laboratory |
ACZ
i
"Dubinin's laboratory
Hajnowka, Poland
AG-5
Rohm and Haas Co., Philadelphia, PA, USA
AM563
t
,
9
microporous carbon; obtained through electrochemical reduction of polytetrafluoroethylene in contact with lithium amalgam
1050
1340
|
prepared from saccharose by combined chemical (K2CO3) and gas (COz) activation in a rotary furnace ! at 8500C
2760
prepared from pit coal, granulated, particle size about lmm, used in gas 9 adsorption .
1150
synthetic carbon obtained from a highly sulfonated styrene/ divinylobenzene ion exchange resin; used for removal of chlorinated hydrocarbons from water and volatile organic compounds from humid air streams
450
9
Rohm and Haas Co., Philadelphia, PA, USA
AM572
!
BPL
highly microporous active carbon; obtained from eopolymer of vinylidene chloride and vinyl chloride (saran) by thermal degradation under a temperature slowly increasing to 800~
synthetic carbon obtained from a highly sulfonated styrene/ divinylobenzene ion exchange resin; used for removal of chlorinated hydrocarbons from water and volatile organic compounds from humid air streams iii
Calgon Carbon Co., Pittsburgh, PA, USA
1030
ii
prepared from selected bituminous coals, used in gas adsorption
945
,
729
CAL
i Calgon Carbon Co., ~ Pittsburgh, PA, USA
,
,.
prepared from selected bituminous coals, used for decolorizing liquid mixtures
1000
prepared from plant products, basic surface, used in gas and liquid adsorption
950
Calgon Carbon Co., Pittsburgh, PA, USA
prepared from selected bituminous coals, used for decolorizing liquid mixtures
900
Calgon Carbon Co., Pittsburgh, PA, USA
prepared from bituminous coals, used for purifying water
900
crushed, vapor phase applications
1430
_.
,
,
Comp. of Carbon Electrodes,Rac~orz, 'Poland
CWZ-3
j
i
F-200
,
|
GW
,a
|
Barnebey-Cheney Co., Columbus, OH, USA
MI
l
i
Bamebey-Cheney Co., Columbus, OH, USA
PA |
PC
i
,,
,
,
9
,
!
'
'
.
"
,
,
680
,
980 9
!
crushed, vapor phase applications
930
granulated, obtained from selected coconut shells, used in gas adsorption , ,
1200
i
Calgon Carbon Co., Pittsburgh, PA, USA
PCB RIAA
,
,,
.
crushed, vapor phase applications |
i
Bamebey-Cheney Co., Columbus, OH, USA
PE
9
i
crushed, vapor phase applications |
Bamebey-Cheney Co., Columbus, OH, USA i
,
|
' Norit Co., Amersfoort, granulated, particle size of 0.5 mm, i The Netherlands _ liquid phase applications
1500
. . . . . . . . . . . . 7
RIB
! Norit Co., Amersfoort, i The Netherlands
granulated, particle size of 0.5 mm, liquid phase applications
1150
Norit Co., Amersfoort, granulated, particle size of 0.5 mm, ... The Netherlands . liquid phase applications
980
j
RIC
.
..
, ,,
..
,
i
i
a The values of the BET specific surface area were calculated in the range of the relative pressures from 0.01 to 0.2.
4. GEOMETRICAL HETEROGENEITY OF ACTIVE CARBONS 4.1. Integral equation for heterogeneous porous solids A quantitative measure of the structural heterogeneity of a porous solid is the pore volume distribution J(x), where x is the pore width [7,8]. The expression J(x)dx denotes the fraction of the pores of widths between x and x+dx. The pore volume distribution
730
J(x) is related to the amount adsorbed a through the following integral equation:
o
where a, is the maximum amount adsorbed, 0x(A,x) descn~oes adsorption in the pores of the width x, and 12 denotes the integration region with respect to x. The integral equation (25) is analogous to equation (17). Both integral equations represent the overall adsorption isotherm, which is measured experimentally. Equation (17) represents this isotherm in terms of the adsorption energy distribution and the kernel 0~(p,e) is a function of the adsorption energy. According to equation (25) the overall isotherm is expressed in terms of the pore-volume distribution and the kernel function depends on the pore width. The local adsorption in the integral (17) can be represented by any isotherm equation derived for monolayer adsorption on an energetically homogeneous solid (e.g., Langmuir and Fowler-Guggenheim equations), and therefore numerous analytical and numerical solutions of this integral are known [8,9]. This is not the case for equation (25) since there is no analytical equation to descn~oe the complete adsorption process in uniform pores. Also, the pore geometry (e.g., cylindrical, spherical or slit-like) should be assumed in order to evaluate the pore-volume distribution on the basis of equation (25). Therefore, approximate methods are often used to invert this integral equation with respect to the pore-volume distribution. The most popular solutions of equation (25) are limited to the range of the multilayer adsorption and capillary condensation [7]. By replacing the kernel 0x(A,x) by the condensation isotherm, one can express the function J(x) as the derivative of the amount adsorbed with respect to the pore width (the condensation approximation method). In order to carry out this differentiation one needs to express a(A) as a function of the pore size. This can be done by using a simplest form of the Kelvin equation, which is valid for the mesopore range [7]: x -
-2ov./A
(26)
where the adsorption potential A is defined by equation (1), x is the radius of the equivalent hemispherical meniscus, a is the surface tension and Vm is the molar volume of the liquid condensate. Depending on the pore geometry a correction is made for the thickness t of a layer already adsorbed on the pore walls [7]. Since x and t are functions of the adsorption potential A, the characteristic adsorption curve a(A) can be converted to a function of the pore width, which after differentiation with respect to x gives the mesopore volume distribution. A detailed description of this method is given elsewhere [7,71-73]. The methods based on the Kelvin equation can be applied to the mesopore range only. In 1983 Horvath and Kawazoe [74] proposed a method to derive analytical equations for the average potential in a micropore of a given geometry, which in fact relate the adsorption potential A with the pore size x. These equations are used to express the amount adsorbed in micropores as a function of the pore width and subsequently to calculate the micropore volume distribution. Thus, the HorvathKawazoe (HK) procedure is a logical extension of the method based on the Kelvin
731 equation to the micropore range, and can be considered as an adaptation of the condensation approximation method to the region of fine pores [75]. Further improvements and modifications of this method were published recently [76-81].
4.2. Relationship between the adsorption potential and pore volume distributions Since the relationships between A and x are known for different pore ranges and different pore geometries [7,74,76-81], they can be utilized to convert the adsorption potential distribution to the pore volume distribution via the following equation:
J(x) == da/dx = (da/dA)(d,4/&)= -X,,(A) (da/&)
(27)
According to equation (27) the pore volume distribution J(x) can be obtained by multiplication of the adsorption potential distribution by the negative derivative dA/dx. The derivative dA/dx depends on the pore range and pore geometry. For instance, for slit-like micropores (which are commonly used to model microporous carbons) the relationship between A and x can be expressed as follows:
,4 = x'd
(x+b)3
Cx+b)9 + c,
(28)
where C~ C~ C3 and (74 are constants for a given adsorbate-adsorbent system, which can be calculated on the basis of parameters given in Ref. [74,76,77]. The symbol do denotes the adsorbate diameter, and 8 = (dA-d,,)/2, where d,4 is the diameter of adsorbent atom (in this case the carbon atom diameter). The derivative of A expressed by equation (27) with respect to x is:
(29)
The HK micropore volume distribution for a slit-like microporous structure can be obtained by multiplying the adsorption potential distribution by the expression (29). For cylindrical and spherical micropore geometries another expressions for the derivative dA/dx should be used. An illustration of the HK pore volume distribution is shown in Figure 7 for the BPL carbon [76]. Similarly, the mesopore volume distribution can be calculated from the multilayer and capillary condensation range of the adsorption isotherm. In this case the corrected Kelvin equation should be used to calculate the derivative d/l/dr. While the adsorption potential distribution is a model-independent thermodynamic function, the pore volume distributions are obtained by assuming a definite pore geometry, i.e., by assuming the relationship between the adsorption potential and the pore width. Thus, the adsorption potential distribution can be considered as a unique and primary characteristics of a given adsorption system, whereas the pore volume
732 distribution is its secondary characteristics, which can be obtained from X,,(A). Equation (27) defines the relationship between these distributions. E r
1.0 9
o
v
0.8 -
.6.
o
4.,.w
BPL
,,
x
.~.
0.6 -
~o~
-
.m L_
9 - nitrogen
o-argon
6
_
r
i:5
0.4
E "~ > (9 0 cL 0 L_ L_.
....
0.2 (3"0-0 0
--
_
~
0.0 0.5
1.0
1.5
2.0
2.5
:) 3.0
Micropore Size, x (nm)
Figure 7. The HK micropore volume distributions for the BPL carbon obtained from argon and nitrogen adsorption isotherms [76]. Pore a n a l y s i s m e t h o d s b a s e d on the integral equation of adsorption An attempt to solve analytically the integral equation (25) was made by Stoeckli [82] who expressed the kernel function by Dubinin'Radushkevich equation and performed the integration for the Gaussian micropore-size distribution. Further modifications of this approach are presented elsewhere [8,14,42]. The most advanced method based on the potential theory of adsorption was proposed by Jaroniec and Choma [14]. In the latest formulation of this method the kernel of equation (25) is represented by the Dubinin-Astakhov (DA) equation [39,83]:
4.3.
e
_
A A_ "
(3o)
where A is the adsorption potential defined by equation (1), fl is the similarity coefficient for the adsorbate, n is the exponent (n = 2 or 3 is usually assumed for active carbons), and Eo is the so-called characteristic energy directly proportional to the average adsorption potential [39,84]. According to this approach the linearity of log 0 on A 3 denotes that the microporous structure is uniform. Since the plot of log 0 vs. A 3 is almost linear for the ACS active carbon (see Figure 8), the microporous structure of this
733 carbon is considered to be uniform. Deviation from the linear behavior of the DA plot, as shown in Figure 8 for the ACT-K carbon, indicates that its microporous structure is heterogeneous.
0.0
r
-0.5
9~
_ A C S carbon
,..=-
d o
-1.0
0
-1.5
. ~ , .
to
"o
<
(D
.->
-2.0
(D
s
A C T - K carbon
-2.5 -3.0
0
10000
20000
30000
Adsorption Potential Cubed, A 3 (kJ/mol) 3
Figure 8. The DA plots for benzene adsorption on the ACS and ACT-K active carbons at 293 K. Adsorption data taken from Ref. [65]. In order to represent the kernel in the integral (25) by the DA equation (30) one needs to relate E o to the pore width x. It was pointed out previously [83] that an accurate relation between Eo and x is very important for calculating the micropore volume distribution. Its experimental determination is difficult [85]. Some theoretical foundations for this relationship were published recently [41]. It appears that suitable computer simulations and density functional theory calculations for adsorption in uniform pores are required to establish the relationship in question. According to Jaroniec and Choma (JC) method the following integral is solved instead of equation (25):
0(A) = f exp [-(Az/I3)n] F(z) dz
01)
o
where z = l I E o. It was shown elsewhere [42] that gamma-type distribution fulfills all physical requirements and gives a good representation of the function F(z) for microporous active carbons:
734 F(z)
z "-1
:
[-(pz)']
(52)
where v and p are the parameters. Then the overall adsorption isotherm becomes:
0(,4)
=
[1 + (A/[3p)n] -'/n
(33)
Equation (33) was found to provide a good description of experimental adsorption isotherms on strongly heterogeneous mieroporous active carbons [8,14,86]. For the purpose of illustration Table 2 contains the parameters of the JC equation (33) for benzene at 293 K on all active carbons listed in Table 1. In addition, the results of the a t-plot analysis are summarized in Table 2. As can be seen the values of the micropore capacity obtained by the a t-method and the JC equation (33) are very similar. Note that the JC parameters summarized in Table 2 were obtained for the amounts adsorbed in micropores only, i.e., the adsorption in micropores was extracted from the total adsorption according to equation (6). When the distribution function F(z) is narrow, the lower integration limit in equation (31) should be non-zero, e.g., Zm~ = 1/E~m~; then the isotherm equation has the following form [87]:
0 : exp [- (A/ l~Eo~) n] [1 + (A/13p)n]~/~
(34)
For a broad distribution F(z), it is easy to show that the exponential factor in equation (34) approaches unity and the overall isotherm can be represented by equation (33); however, for a very narrow distribution F(z), the second factor in equation (34) approaches unity and the overall isotherm reduces to the DA isotherm (30). If the relationship X(Eo) is known, the function F(z) can be converted to the micropore-size distribution by using the following equation [83]" J(x) " (dMdz)-lF(z) for z --" 1lEo = z(x)
(35)
Calculation of the micropore volume distribution according to equation (35) requires only division of F(z) by dr/dz. The values of v and p required to calculate the function F(z) can be obtained by fitting equation (33) to the overall adsorption isotherm (see Table 2). An exemplary distribution function F(z) calculated by means of equation (32) is shown in Figure 9 for benzene on the RIAA active carbon. Note that this distribution characterizes well the RIAA active carbon because equation (33) gives a good representation of the experimental adsorption of benzene on this carbon. However, one need to keep in mind that the calculation of F(z) is based on two assumptions: (i) description of adsorption in uniform micropores by the DA equation (30), and (ii) the representation of the structural heterogeneity of micropores by the gamma distribution. Although these assumptions limit the range of applicability of the JC method, it has been found to be useful for characterizing not only microporous carbons [8,14,86] but also other microporous solids [88,89].
735 Table 2
Parameters obtained by the as-method and Jaroniec-Choma equation (33) for adsorption of benzene on the active carbon studied at 293 K. Active carbon code
a=-method o
ami
S
mmol/g ACS
Jaroniec-Choma equation (33) o
4.81
i
P
ami
m~g
mmol/g
10
4.72
160
14.38
kJ/mol 25.5
2.81
ii
ACT-K
15.61
6.4 i
ACZ
5.91
250
5.59
i
AG-5
4.41
98
2.06 ,i
8.9
1.32
8.3
1.45
,
i
4.17 i
AM563
1.52
125
1.57
29.5
,
AM572
3.85
185 i
BPL
u
3.94
65
4.14
i
CAL
3.96
CWZ-3
2.96
4.54
22.3
6.37
i
i
i
i
112
3.86
14.4
2.46
69
2.81
18.4
2.34
i
3.69
i
60
3.55
,,,
GW
25.9
i=
4.47 i
F-200
i
6.12 i
18.0 i
3.82
66
3.79
i
2.75 i i
12.9
1.81
i
MI
5.98
PA
2.92
,
. ,..
PC PCB
R~I'~
RIB
.
2.70
40
2.89
27.3
6.66
4.16
19.8
2.84
ii
52
i
i
5.04 i
i
46
5.04
17.3
2.71
70
3.87
24.3
3.73
4.64
17.1
2.83
4.27
18.3
2.81
r|
4.24 ~ ~ l
~l
4.66 4.32
15.6
.=
149 i
RIC
6.05
,
4.20 i
PE
,..
87
.....
i
49
The distribution F(z) shown in Figure 9 was converted to the micropore volume distribution J(x) (see Figure 10) by using equation (35) and the Stoeekli's relationship between z and x [83,85]. The Stoeckli's relationship generates the decreasing mieropore volume distributions because in the range of small z the curve x(z) is almost parallel to the z-axis (el., Ref. [83]). In this region, the derivative dx/dz is about zero, and according to equation (35) the function J(x) increases to infinity. If one assumes that the Stoeekli's
736
relationship is accurate for microporous carbons, then the decreasing functions J(x) are characteristic for these materials. 2o o
E v
15
:~
10
N
5
RIAA
It.
5 i5
o o.oo
0.05
O.lO
o.15
Quantity z = l I E o, (mol/kJ)
Figure 9. The distribution function F(z) for benzene on the RIAA active carbon calculated according to equation (32) for the parameters listed in Table 2. E
3.0
•
2.5
RIAA
:
c .o 3 ..Q
2.0 _
_
.m=
.~_
1.5
E ~
1.0
121
D
D
_
o
>
~-
o
0.5
o
L_
.o_
.... o.o " , , , , I o.o 0.5
IlliJltllllJll~ll,~,
1.0
1.5
2.0
2.5
3.0
Micropore Width, x (nm)
Figure 10. Micropore volume distribution for the RIAA carbon obtained from the function F(z) shown in Figure 9 by using equation (35).
737 Recently, a significant progress has been made in modelling adsorption in the micropores. For instance, Seaton et al. [90] used computer simulations of adsorption in uniform micropores to evaluate the structural heterogeneity of active carbons. Aukett et al. [91] and others [92-94] employed the functional density theory to describe the local adsorption in equation (25). Olivier et al. [95] combined the functional density approach with the regularization procedure [50] and proposed an elegant method to calculate the pore volume distribution from experimental adsorption data. This method is a remarkable breakthrough in evaluating the pore volume distribution because it does not require to assume a definite shape of the distribution and is applicable to the whole range of pores. An illustration of this method is presented in Figure 11. The lowpressure parts of nitrogen adsorption isotherms were used to evaluate the micropore volume distributions for the PA and CAL active carbons. As can be seen from Figure 11 the PA carbon possesses much more small micropores than CAL. 0.10
-~
p.. o
0.08
~: ,,~
0.06
nO
0.04
o.o o.oo
0.5
~
- PA
i
I -CAL
t ,,
,m
1.0
,
1.5
~,,
2.0
,FI,,I;I, B , , , ,
2.5
3.0
Pore Width (nm) Figure 11. A comparison of the micropore volume distributions for the PA and CAL carbons calculated from nitrogen adsorption isotherms by using the DFT method [95]. 4.4. Fractai characterization of active carbons
The ffactal dimension D of the surface accessl%le for adsorption is an operative measure of the surface irregularity. In general, the adsorbent heterogeneity includes the surface and structural heterogeneities of a porous solid [8]. Various irregularities of the surface such as cracks, steps and flaws as well as impurities deposited or bonded to the surface contribute to the ffactal dimension. In the case of active carbons a major contribution to the fractal dimension comes from the micropore distribution. Therefore, for microporous carbons the ffactal dimension has a value often close to three, which is its theoretical upper limit [96]. The lower value of the ffactal dimension is obtained for mesoporous carbons [97].
738 Simple relationships were proposed to evaluate the fractal dimension from various types of experiments including adsorption and related data (see book [96] and references therein). One of the most popular methods used to evaluate the fractal dimension is that based on the dependence of the monolayer capacity on the adsorbate size [98]: ao
=
~-otz
(36)
where a ~ is the monolayer capacity, and ~ is the area occupied by one adsorbate molecule. Although evaluation of D on the basis of equation (36) is simple, this procedure has some disadvantages related to evaluation of the monolayer capacity and selection of suitable adsorbates in order to avoid the effects associated with orientation of adsorbate molecules on the surface and with adsorbate-adsorbate interactions. Also, the range of co for available adsorbates is relatively narrow. These problems become particularly important for adsorption on microporous solids [99]. Another popular method is that utilizing the log-log plot of the pore volume distribution. The slope of this plot is related to the fractal dimension [98]: log
J(x)
= const
-
(D-2)log x
(37)
where J ( x ) is the pore volume distribution. In addition to the relationships given by equations (36) and (37), several isotherm equations have been derived for various models of physical adsorption on fractal surfaces [96,100]. These equations contain the fractal dimension D as a parameter and describe the surface coverage as a function of the equilibrium pressure. One of most popular relationships is that based on the Frenkel-Halsey-HiU (FHH) equation [101]: In a
= const
-
(3-D) In A
(38)
It was shown recently [99] that in the Kelvin range equation (38) is equivalent with the so-called thermodynamic method proposed by Neimark [102,103]. The theoretical principle of the Neimark's method is a very simple relationship between the surface area of the adsorbed liquid film S= and the average pore radius x: In s= = c o n s t - (D-2) h x
(39)
where at
soO,Ip.)
= o -~ f a aa
(40)
a
where
a,
denotes the maximum amount adsorbed.
Equation (40) is employed to
739
calculate the surface area of the adsorbed film and the Kelvin equation (26) is used to convert the equilibrium pressure to the average pore radius. Two later methods seem to be useful to evaluate the fxaetal dimension from single adsorption isotherms. 2.0 7-
cz~
1.5
-,,,=
c
1.0
c o
0.5
m
0.0
9
-0.5
.,...
Q.. 0
"o
"~ Iv'
9
~o
SAO e,~__
D=2.59
-1 .o -1.5 -2.0
i
-2.0
i
,
,
I
~,
-1.0 Adsorption
,
,
I
~
I
I
0.0
i
l+I
]
,
,
1.0 .
Potential,
I
~
~
t
2.0
3.0
In A (A, k J / m o l )
Figure 12. Adsorption data for benzene on the SAO mesoporous carbon black at 293 K plotted according to the FHH equation (38). Figure taken from ref. [97]. 2.5 c~ 04
E
2.0
-
SAO
o0 v
D=2.61
"'N..
1.5
-
I:::
9
-
9
o9
0.5
0.0
i I L i Ill 0.5
i l I I I z z I l 1.0 1.5
Pore Width,
I
I
l
2.0
log x (x, n m )
Figure 13. Adsorption data for benzene on the SAO carbon black at 293 K plotted according to equation (39). Data taken from Ref. [97].
740 Shown in Figures 12 and 13 are benzene adsorption isotherms on the SAO carbon black at 293 K plotted according to equations (38) and (39). The linear segments of these plots were used to evaluate the fxactal dimension of the SAO carbon. Both plots give very similar values of D = 2.60, which suggests a high geometrical irregularity of the mesoporous carbon studied. This relatively high value of D for the SAO carbon seems to be caused by the geometrical irregularity of its surface, not by microporosity, because its specific surface area is low (about 110 m2/g).
5. CONCLUDING REMARKS Although characterization of the energetic and geometrical heterogeneities of active carbons on the basis of gas adsorption isotherms contains a number of questions which need to be addressed in future studies, a significant progress has been done in this field in the last decade. Recent developments in the sorption instrumentation allow accurate measurements at the low pressure range for various probe molecules, which are essential for evaluating the adsorption potential and micropore volume distributions. As demonstrated in the current review, the adsorption potential distribution is a modelindependent function, which allows a unique thermodynamic characterization of a gas/solid adsorption system. This distribution provides information about possible changes in the Gibbs free energy, which are caused by the energetic and geometrical heterogeneities of an active carbon as well as by the adsorbate-related entropic effects. It appears that in the case of adsorption of simple gases on active carbons their energetic heterogeneity does not change significantly the entropy effects since the adsorption potential and adsorption energy distributions are similar. A general character of the adsorption potential distribution is clearly visible by its direct relation to the differential enthalpy and differential entropy. Also, the average adsorption potential is directly proportional to the heat of immersion, which through this proportionality can be estimated on the basis of vapor adsorption isotherms. Another important conclusion concerns the geometrical heterogeneity of active carbons, which is usually characterized by the micropore and mesopore volume distributions. The current work demonstrates that in terms of the condensation approximation both these distributions are directly related to the adsorption potential distribution. As shown the pore volume distribution can be obtained by multiplication of the adsorption potential distribution by the derivative of the adsorption potential A with respect to the pore width x. However, the pore volume distribution is a secondary characteristics of a given adsorption system because the derivative dA/dx depends on the pore geometry and adsorbate. In order to evaluate the pore volume distribution one needs to assume a model of the porous structure, e.g., slit-like, cylindrical or spherical pores. A brief review of methods based on the integral equation (25) of adsorption showed that they are attractive to evaluate the pore volume distribution. The analytical solution of this integral for subintegral functions represented by the Dubinin-Astakhov equation and a gamma-type distribution is extremely simple and provides a good description of experimental adsorption data on active carbons. In particular, application of the gamma distribution leads to simple analytical equations for the adsorption potential distribution and other thermodynamic functions that characterize the process of the micropore tilting
741 and provide valuable information about structural and surface heterogeneities of active carbons [34,35,39,62,64]. This description can be extended easily to adsorption of organic compounds from dilute solutions on active carbons [104-110] as well as to adsorption of liquid mixtures in the whole concentration region [111-113]. An interesting perspective for future studies is the description of differences and similarities between adsorptions in micropores from gaseous and liquid phases [107-109]. A significant progress has been also made in modelling adsorption in the micropores [90-95,114-117]. Computer simulations and density functional theory calculations have been recently utilized to evaluate the structural heterogeneity of active carbons [91-95]. Methods which combine the functional density theory approach and computer simulation data with the regularization algorithm [50] seem to be very attractive for evaluating the pore volume distribution from experimental adsorption isotherms because they do not require to assume a definite shape of the distribution and are applicable to the entire range of pores. The future studies should focus on the improvement of the existing numerical methods of the pore and surface analysis, which are based on the adsorption measurements, and extension these methods to thermodesorption, calorimetric and spectroscopic data. Another important issue in the characterization of active carbons is the elaboration of simple methods based on the liquid/solid sorption data. Although interpretation of these data is more complex, they are useful to study heterogeneous active carbons [108,109,117].
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743 51. M. Jaroniec and P. Brauer, Surf. Sci. Reports, 6 (1986) 65. 52. J. Roles and G. Guiochon, J. Phys. Chem., 94 (1990) 6785. 53. J. Cortes, J. Chem. Phys., 91 (1989) 1932; 88 (1988) 8011. 54. V.A. Bakaev, Surface Sci., 198 (1988) 571. 55. J.L. Riccardo, V. Pereyra, J.L. Rezzano, S.A.A. Rodriguez and G. Zgrablich, Surface Sci., 204 (1988) 289. 56. J. JagieUo, Langmuir, 10 (1994) 2778. 57. J.A. Lure Wan and L.R. White, J. Chem. Soc. Faraday Trans., 87 (1991) 3051. 58. L.I~ Koopal, M.M. Nederhof and W.H. Van Riemsdijk, Progr. Coll. Polym. Sci., 82 (1990) 19. 59. B. McEnaney, T.J. Mays and P.D. Causton, Langmuir, 3 (1987) 695. 60. W.A. House, Colloid Sci., 4 (1982) 2. 61. B.P. Bering, M.M. Dubinin and V.V. Serpinsky, J. Colloid Interface Sci., 38 (1972) 185. 62. M. Jaroniee, Langmuir, 3 (1987) 795. 63. M. Jaroniec, R. Madey and J. Chorea, J. Colloid Interface Sci., 126 (1988) 69. 64. M. Jaroniec and R. Madey, J. Phys. Chem., 92 (1988) 3986. 65. M.M. Dubinin, Carbon, 25 (1987) 593. 66. J. Choma and M. Jaroniec, Karbo-Energochemia-Ekologia, 40 (1995) 101. 67. J. Chorea and M. Jaroniec and J. Klinik, Karbo-Energochemia-Ekologia, 40 (1995) 160. 68. J. Chorea and M. Jaroniec, Przemysl Chemiczny, 72 (1993) 324. 69. M. Jaroniec, R.K. Gilpin and J. Chorea, Carbon, 31 (1993) 325. 70. J. Chorea and M. Jaroniec and J. Klinik, Koks-Smola-Gaz, 36 (1991) 74. 71. E.P. Barrett, L.G. Joyner and P.P. Halenda, J. Amer. Chem. Sot., 73 (1951) 373. 72. K. Kaneko, J. Membrane Sci., 96 (1994) 59. 73. D. Dollimore and G.R. Heal, J. Appl. Chem., 14 (1964) 109. 74. G. Horvath and K. Kawazoe, J. Chem. Eng. Jpn., 16 (1983) 470. 75. M. Jaroniec, in "Acces in Nanoporous Materials", T.J. Pinnavaia and M. Thorpe (eds), Plenum Press, New York, 1996. 76. J. Chorea, M. Jaroniec and J. Klinik, Karbo-Energochemia-Ekologia, 10 (1994)258. 77. L.S. Cheng and R.T. Yang, Chem. Eng. Sci., 16 (1994) 2599. 78. A. Saito and H.C. Foley, AIChE, 37 (1991) 429. 79. R.K. Mariwala and H.C. Foley, Ind. Eng. Chem. Res., 33 (1994) 2314. 80. R.D. Kaminsky, E. Maglara and W.C. Conner, Langmuir, 10 (1994) 1556. 81. L.S. Cheng and R.T. Yang, Adsorption, 1 (1995) 187. 82. H.F. Stoeckli, J. Colloid Interface Sci., 59 (1977) 184. 83. M. Jaroniec, X. Lu, R. Madey and J. Choma, Mater. Chem., 26 (1990) 87. 84. M. Jaroniec and R. Madey, Carbon, 26 (1988) 107. 85. H.F. Stoeckli, F. Kraehenbuehl, L. BaUerini and S. DeBernardini, Carbon, 27 (1989) 125. 86. R. Madey and M. Jaroniec, Trends in Phys. Chem., 3 (1992) 281. 87. M. Jaroniec, J. Chorea, F. Rodriguez-Reinoso, J.M. Martin-Martinez and M. Molina-Sabio, J. Chem. Soc. Faraday Trans. I, 85 (1989) 3125. 88. R.T. Yang and M.S.A. Baksh, AIChE, 37 (1991) 679. 89. M.S.A. Baksh and R.T. Yang, AIChE, 38 (1992) 1357. 90. N.A. Seaton, J.P.R.B. Walton and N. Quirke, Carbon, 27 (1989) 853.
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P.N. Aukett, N. Quirke, S. Riddiford and S.R. Tennison, Carbon, 30 (1992) 913. C. Lastoskie, K.E. Gubbins and N. Quirke, J. Phys. Chem., 97 (1993) 4786. C. Lastoskie, K.E. Gubbins and N. Quirke, Langmuir, 9 (1993) 2693. C. Lastoskie, K.E. Gubbins and N. Quirke, in "Characterization of Porous Solids III," J. Rouquerol, F. Rodriguez-Reinoso, K.S.W. Sing and K.K. Unger (eds.), Elsevier, Amsterdam, 1994, p. 51. J.P. Olivier, W.B. Conklin and M. v.Szombathely, in "Characterization of Porous SolidsIII," J.Rouquerol, F. Rodriguez-Reinoso, K.S.W. Sing and K.K.Unger (eds.), Elsevier, Amsterdam, 1994, p. 81. D. Avnir (ed.), The Fractal Approach to Heterogeneous Chemistry, Wiley, New York, 1989. J. Chorea and M. Jaroniec, Polish J. Chem., 69 (1995) 281. P. Pfeifer and D. Avnir, J. Chem. Phys., 79 (1983) 3558. M. Jaroniec, Langmuir, 11 (1995) 2316. D. Avnir, D. Farin and P. Pfeifer, New. J. Chem., 16 (1992) 439. D. Avnir and M. Jaroniec, Langmuir, 5 (1989) 1431. A. Neimark, Physica A, 191 (1992) 258. A. Neimark and K.K. Unger, J. Colloid Interface Sci., 158 (1993) 412. M. Jaroniec, Langmuir, 3 (1987) 673. M. Jaroniec, M. in "Fundamentals of Adsorption", L.A. Liapis (ed.), Amer. Inst. Chem. Eng., New York, 1987, pp. 277-285. M. Jaroniec, R. Madey, J. Chorea and J. Piotrowska, J. Colloid Interface Sci., 125 (1988) 561. M.Jaroniec, J. Chorea and W. Burakiewicz-Mortka, Carbon, 29 (1991) 1294. J. Chorea and W. Burakiewicz-Mortka, M. Jaroniec and R.K. Gilpin, Langmuir, 9,(1993) 2555. M. Heuchel and M. Jaroniec, Langmuir, 11 (1995) 1297. A. Derylo-Marczewska and M. Jaroniec, Surf. Coll. Sci., 14 (1987) 301. M. Jaroniec, R. Madey and A. Dabrowski, Langmuir, 5 (1989) 987. A. Dabrowski, M. Jaroniec and J. Oscik, Surf. Coll. Sci., 14 (1987) 83. A. Dabrowski and M. Jaroniec, Advan. Colloid Interface Sci., 31 (1990) 155. V.A. Bakaev and W.A. Steele, Langmuir, 8 (1992) 1372. M.J.Bojan and W.A. Steele, Langmuir, 9 (1993) 2569. V.A. Bakaev and W.A. Steele, Langmuir,8 (1992) 1379. M. Jaroniec, R.K. Gilpin, P. Staszczuk and J. Choma, in: "Characterization of Porous Solids III," J.Rouquerol, F. Rodriguez-Reinoso, K.S.W. Sing and K.K.Unger (eds.), Elsevier, Amsterdam, 1994, p. 613.
W. Rudzifiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
S t r u c t u r e of P o r o u s A d s o r b e n t s :
Analysis Using Density Functional
Theory and Molecular Simulation C.M. Lastoskie 1, N. Quirke 2 and K.E. Gubbins 3 1Department of Chemical Engineering, University of Michigan, Ann Arbor M148109 2Department of Chemistry, University of Wales at Bangor, Gwynedd LL57 2UW 3School of Chemical Engineering, Cornell University, Ithaca NY 14853 Abstract
The pore size distribution (PSD) analysis method based on nonlocal density functional theory (DFT) and on molecular simulation is reviewed and compared with classical PSD methods. Applications to carbons and oxides are given. The DFT method offers several advantages over classical methods: (a) a valid and accurate description for small pores; (b) a description of the full adsorption isotherm (not just the capillary condensation pressure), as well as other properties such as heats of adsorption; (c) it can be used for supercritical conditions; (d) it accounts for effects of pore shape; (e) it can be improved in a systematic way, since it rests on fundamental statistical mechanics. A critique of the method as currently applied is also offered. In common with most other PSD methods, the model neglects connectivity and pore blocking, and changes in pore size and geometry with pressure and temperature, and assumes that heterogeneity due to differences in pore shape and surface chemical groups can be approximated by an effective porous material, in which all heterogeneity is due to a distribution in pore sizes. Additional tests, using molecular simulation and experiment, are needed to determine whether these neglected effects exhibit signatures in experimental results that are distinct from the PSD effects. Molecular simulation studies of pore connectivity effects have been made for a simple network model; the model seems able to provide a detailed molecular explanation for the several hysteresis types found in Type IV and V isotherms. 1.
Introduction
Classical methods [ 1] for determining the pore size distribution (PSD) from nitrogen adsorption isotherms have relied on thermodynamic models based on the Kelvin equation for the capillary condensation pressure, or on semi-empirical treatments such as that of Horvath and Kawazoe (HK) [2] for slit pores, or of Saito and Foley (SF) [3] for cylindrical pores. The Kelvin-based methods are only applicable for large pores; while the HK and SF methods have a better range of application, they also underpredict pore sizes and break down for small pores. Methods based in statistical mechanics are much more accurate, and provide the full isotherm for pores of all sizes. Our recent work [4-8] in developing an accurate method for analysis of pore size distributions is aimed at replacing the phenomenological methods with a more rigorous approach based on density functional theory (DFT). In this paper we review the DFT method and show some recent applications to pore size distribution analysis. In both the classical and DFT approaches, the adsorption F(P) at pressure P is usually approximated as
745
746
I"(P) = ~F(P,H)f(H)dH
(1)
H.,,,
here H is pore size (e.g. width for slit pores, diameter for cylinders), f(H) is the pore size distribution, and F(P, H) is the adsorption for a material whose pores are all of width H. The latter is calculated from the macroscopic correlation or theory. Equation (1) does not explicitly account for many effects present in real porous materials, such as pore networking and blocking, heterogeneity due to chemical groups on the surface, or variation in pore shape. The inclusion of all these effects is not practical at the present time. The use of eqn. (1) replaces the real material by an effective porous material, in which all the heterogeneity of the real material is approximated by a distribution of pore sizes [7]. 2.
Classical Methods
The experimental nitrogen isotherm is a composite of the individual adsorption isotherms of the various sizes and types (shapes, surface chemical nature) of pores present in the sorbent. Historically, the Kelvin equation has been the most commonly employed model to describe capillary condensation in pores of a given size and geometry. The Kelvin equation is derived from classical thermodynamics and is accurate in the limit of large pores for temperatures that are sufficiently below critical that the gas phase can be treated as ideal. It is based on the assumptions that there is a definable gas-liquid inteffacial region, and that the liquid phase is incompressible. Since it only accounts for the effect of surface curvature on gas-liquid surface tension, it does not take into account the influence of fluid-wall forces, wetting, etc. In an attempt to roughly account for these wall effects, it is common to use a modified form of the Kelvin equation, in which the pore width H is replaced by H-2t, where t is the adsorbed film thickness at the pore condensation pressure Pc:
gn(Pc / Po ) =-27'g / RTR g(H - 2t)
(2)
where Po is the vapor pressure of the bulk fluid, 7g and ps are the gas-liquid surface tension and liquid phase density, R is gas constant and T is absolute temperature. In practice, the film thickness t is usually estimated from a standard isotherm, or "tcurve", measured on a nonporous surface of the same chemical type. While this equation works satisfactorily for large pores, it fails for small mesopores and for micropores, due to the failure to account properly for fluid-wall interaction effects, and the highly inhomogeneous nature of the pore fluid. These effects lead to enhanced adsorption at a given pressure, so that the Kelvin and modified forms overestimate the capillary condensation pressure; this leads to pore size predictions that are too low. Simulation studies [9] indicate that 75/~ is the approximate lower bound of pore size that can be determined from such methods (see also Fig. 1). An alternative method, originating from the microporosity model put forth by Everett and Powl [10], is to calculate an average potential function inside the micropore. Using thermodynamic arguments, this average potential can be related to the free energy change of adsorption, yielding a relation between filling pressure and pore width. This approach was developed for slitlike pores by Horvath and Kawazoe [2], and later extended to cylindrical pores by Saito and Foley [3]. For nitrogen adsorption in carbon slit pores, the Horvath-Kawazoe (HK) relation between filling pressure and slit width is
747
N A (ANCNN + AcCcN )
oz( Pc l Po) =
RT~c4 (H - 2GCN ) (3)
x[
x'lO
x'4
9G9 N
x'lO
)9 +
3G3CN 9( H - CrCN
)3 ]
r4
3(H - OCN
where N^ is Avogadro's number, A s and A c are the monolayer areas of nitrogen and carbon, C ~ and CcN are the Kirkwood-Muller dispersion constants for nitrogennitrogen and nitrogen-carbon interactions, CcN is the mean nitrogen-carbon diameter and ~ is the zero-energy adsorbate-surface separation distance. The HK method is an improvement over the Kelvin approach, in that it accounts for the strong solid-fluid attractive forces in micropores. However, it gives poor results for mesopore PSDs, as it does not account for pore wetting, and it must be combined with a Kelvin-type method to describe the full PSD (Fig. 1). Furthermore, the HK method has an oversimplified view of pore filling, in that it assumes a pore is completely empty if it is below its tilling pressure and completely full is above its filling pressure. Since pore falling is in fact a continuous process, with monolayer and multilayer formation occurring prior to pore condensation, this assumption leads to inaccuracies in the HK model. Several more empirical methods have been proposed to determine the PSD of rnieroporous sorbents. An example is the Dubinin-Stoeckli equation [ 11], in which the Dubinin-Astakhov equation for estimating micropore volume is combined with an assumed Gaussian micropore size distribution. This method has two principal drawbacks: it constrains the PSD to an arbitrarily chosen functional form, and it requires the use of empirical energy parameters in the solution of the PSD. 1E+00
-
1E-01 1E-02 o
a. 1 E - 0 3 "o
1 E-04 r 1 E-05 -
a_ 1 E-06 O1 C ~. 1E-07 1E-08
i
1E-09 1E - 10 , 0
.
i
i
.
I I
5
.
i
. . ,. . . . . . . . . ., 10
15
,' . . . . 20
,' . . . . . . . . ., . . 25
30
', . . . . . . . .,. . . . . . 35
40
, 45
50
Pore W i d t h (A)
Figure I. Filling pressures Pc predicted by the modified Kelvin equation (MK), the Horvath.Kawazoe equation (HK), density functional theory (DFT), and molecular simulation (points) f o r nitrogen adsorption in a carbon slit pore at 77 K.
748 Attempts to analyze hysteresis loops in type IV isotherms have been largely conf'med to percolation theory methods [12-18]. In percolation theory, individual pores are regarded as bonds on a lattice, and percolation of the system occurs when a connected cluster of pores containing vapor spans the lattice. Application of this probabilistic method to experimental desorption data yields estimates of the mean connectivity in porous materials. The utility of the percolation approach is limited, however, as this model cannot be used to examine the dynamic aspects of desorption at the molecular level. 3.
The Model
Many porous materials contain an interconnected network of pores, each with its own shape, size and chemical heterogeneities. While we cannot hope to realistically model such a complicated structure in its entirety, we can reduce the problem to a more tractable form by assuming that (1) the pores have a simple geometry (e.g. slits or cylinders), (2) the aspect ratio of pore length to width is large, so that pore junction effects can be neglected, and (3) the concentration of surface functional groups is either uniformly distributed or low enough to be disregarded. With these assumptions, the task of determining the pore size distribution reduces to the solution of eqn. (1). To model nitrogen adsorption on activated carbon, a slit pore geometry is chosen for the individual pores. Each pore is bounded by two semi-infinite parallel graphitic slabs separated by a physical width H, the distance between the centers of the surface carbon atoms (Figure 2). The graphite layers in each slab are separated by a uniform spacing A. The fluid-fluid interaction potential is modeled using the Lennard-Jones 12-6 pairwise potential, with parameters fitted to the bulk fluid properties of nitrogen. The solid-fluid interaction potential ~,f for nitrogen interacting with a single graphitic slab is described by the Steele 10-4-3 potential [19]
3A(z + 0.61A) 3
(4)
where z is the distance from the graphite surface, p, is the solid number density, andEa and c a are fitted parameters for the nitrogen-carbon well depth and intermolecular diameter. We take p,=0.114 /~-3 and A=3.35 /~ [19]. The 10-4-3 potential is obtained by integrating the Lennard-Jones potential between one fluid molecule and a carbon atom over the individual carbon atoms in one graphite plane, and then summing over all planes. The "10" and "4" terms represent the repulsive
H
Figure 2.
Schematic of model carbon slit pore.
749 and attractive interactions of the fluid molecule with the surface graphitic plane, while the "3" term results from the summation of the attractive part of the potential over the remaining layers of the solid. (The repulsive interactions of the f u i d molecule with the subsurface graphite planes are small and therefore neglected.) For a slit pore, the fluid molecule interacts with two graphite slabs; hence, the full external potential Vex' is
(5)
V,,, (z) = r ss (z) + Css (H - z)
To model nitrogen adsorption on silica and other aluminosilicates, it is assumed that the pore network is composed of an array of noninterconnected cylindrical pores of varying radius. Each individual pore is bounded by an infinite structureless solid slab. It is further assumed that any cation interactions with the adsorbate molecules are small and may be neglected. This premise, which is no doubt inaccurate for sorbents with strong localized charge centers (e.g. cationexchanged zeolites), nonetheless provides a practical starting point for the analysis. Usually, the cation species are situated beneath the surface oxide layer of the solid, and hence for nonpolar adsorbates the oxide interactions with the sorbate can be expected to dictate the f'flling behavior. The fluid-sorbent potential for nitrogen interacting with a semi-infinite slab of unstructured oxide has been derived by Peterson et al. [20]; the external field for a pore of physical radius R (the distance from the pore centerline to the center of the oxide surface atoms) is of the 9-3 form
r =;7~Ps~'sfl~'sftTJ L"~(~sFf)619(~sF f ) 13
(6)
where r is the distance from the centerline of the pore, Ps is the number density of the oxide in the solid (0.0706 ~-3 for alumina), and
I,, (x) = So dO[-x cosO+ (1 - x 2 sin 2 0)i~2 ]-.
(7)
The "9" and "3" terms represent the repulsive and attractive interactions of the adsorbate with the oxide solid. Fluid-fluid interaction parameters were obtained by fitting the DFT predictions to the bulk thermodynamic data (liquid density and pressure) for saturated liquid nitrogen at its normal boiling point; this gives efr/k = 93.98 K and are = 3.572,4,. The solid-fluid potential parameters are fitted so as to reproduce the nitrogen isotherm on a nonporous material of the same composition as the porous materials of interest, using the "t-curve" method [21]. For carbons the nonporous material was Vulcan, and for the oxides it was a nonporous NBS alumina. The solid-fluid parameters found in this way are: Carbons:
Glk = 53.22 K
tyse= 3.494 .~
Oxides:
Esf/k = 193.6 K
crsf= 3.166/~
750
4.
Density Functional Theory
Each individual pore has a fixed geometry, and is open and in contact with bulk nitrogen. The temperature is fixed at the nitrogen boiling point. For this system, the grand canonical ensemble provides the appropriate description of the thermodynamics. In this ensemble, the chemical potential It, temperature T, and pore volume V are specified. In the presence of a spatially varying external potential Vc~, the grand potential functional f~ of the fluid is s
= F[p(r)] - f drp(r)[p - V,~ (r)]
(8)
where F is the intrinsic Helmholtz free energy functional, p(r) is the local fluid density at position r, and the integration is over the pore volume. An analytic equation of state for F is not available for the Lennard-Jones fluid; hence, F is expanded to first order about a reference system of hard spheres of diameter d,
eta(r)1 =
F~rp(~).,al+l~a~aepr
)@,,,(Ir- r' l)
(9)
where F h is the hard sphere Helmholtz free energy functional, p(2) is the pair distribution function, and ~,, is the attractive part of the fluid-fluid potential. In principle, if the pair distribution function of the reference fluid is known, the second term on the fight hand side can be evaluated numerically. It is usual, however, to invoke the mean field approximation, wherein correlations due to attractive forces are neglected (i.e. the two-body correlation function g(2)(r,r') is set equal to unity). The mean field density functional is thus
Ftp(r)] = p~[.o(,-); a] + ~jj drdr' p(r)p(r' )r
r'l)
(10)
The attractive part of the fluid-fluid potential is represented by the Weeks-ChandlerAndersen division of the Lennard-Jones potential > r
(11)
where r==2~/ran is the location of the minimum of the Lennard-Jones potential. The hard sphere term F hcan be written as the sum of two terms,
Fh[p(r);d] = kT~ drp(r)[ln(A3p(r)) - 1] + kT~ drp(r)f,,[-~(r);d]
(12)
where A = h~ (2zankT) u2 is the thermal de Broglie wavelength, m is the molecular mass, h and k are the Planck and Boltzmann constants, respectively, and f,~ is the excess (total minus ideal gas) Helrnholtz free energy per hard sphere molecule. The latter is calculated from the Camahan-Starling equation of state for hard spheres [22]. The fh'st term on the fight side of (12) is the ideal gas contribution, which is exactly local (i.e. its value at r depends only on p(r) at r), while the second term on the fight is the excess contribution, which is nonlocal.
751 The density ~(r) that appears in the last term of (12) is the smoothed or nonlocal density, and represents a suitable weighted average of the local density p(r),
(13)
~(r) = j'dr' p(r' )w[Ir- r'l;~(r)]
The choice of the weighting function w depends on the version of density functional theory used. For highly inhomogeneous confined fluids, a smoothed or nonlocal density approximation is introduced, in which the weighting function is chosen to give a good description of the hard sphere direct pair correlation function for the uniform fluid over a wide range of densities. In this work, Tarazona's model [23] is used for the weighting function. This model has been shown to give very good agreement with simulation results for the density profile and surface tension of LJ fluids near attractive walls. The Tarazona prescription for the weighting functions uses a power series expansion in the smoothed density. Truncating the expansion at second order yields 2
~(r)] = ~ w,(~'-
(14)
i=0
Expressions for the weighting coefficients w, are given by Tarazona et al. [23]. The equilibrium density profile is determined by minimizing the grand potential functional with respect to the local density, gff~[p(r)] =0 6p(r)
at p = p.~
(15)
A numerical iteration scheme is used to solve this minimization condition for p.q(r) for each set of values of (T,I.t,H); the hard sphere diameter is determined from the Barker-Henderson prescription [24] for each temperature. The chemical potential is related to the bulk pressure through the bulk fluid equation of state, (16)
P(P) = Ph (P) - ~Tc~ak_,..~ 2
where a is a van der Waals interaction parameter given by (17)
a = 4z~o~,~,(r)rZdr
For large pores, two minimum density profiles often arise; these are the liquid and vapor branches associated with thermodynamic hysteresis in individual slit pores. When more than one minimum exists, the density profile which has the lower grand potential energy is the stable branch. The chemical potential at which condensation occurs is the value for which the two minima have the same grand potential energy. In subsequent sections, the following dimensionless quantities are used: H * = H~ or#
T*= k T / e f l
p*= p ~ f
z* = z / a n
752 5.
Molecular Simulation
To check the accuracy of the density functional theory results, adsorption isotherms have been calculated using Gibbs ensemble simulation. The Gibbs ensemble method provides a direct route to the determination of the phase coexistence properties of a fluid, by Monte Carlo simulation of the fluid in two distinct physical regions that are in thermal, mechanical and material contact. Gibbs simulation was originally developed to determine phase equilibria in bulk fluids [25] and in fluids adsorbed in cylindrical pores [26]. In order to test the theory, Gibbs ensemble calculations are performed for fluid adsorption in the slit pore geometry of Figure 2. Two types of Gibbs simulation are performed: pore-pore and pore-fluid. The porepore calculations yield the coexisting liquid and vapor densities at the falling pressures of pores in which capillary condensation occurs. A schematic of the pore-pore simulation method is shown in Figure 3. The fluid molecules are confined in the slit pore geometry of each of the two simulation cells, designated regions I and II. At each simulation step, one of three perturbations is attempted: (a) particle displacement in each region; (b) particle interchange between the two regions; and (c) exchange of pore surface area between the two regions, such that the total surface area remains constant. The acceptance probability for each of these moves is chosen in a way that ensures that the system obeys the laws of equilibrium statistical mechanics [8,25,26]. Sampling configurations using these three perturbations ultimately brings the two regions into thermal, material and mechanical equilibrium. Such a state is phaseequilibrated, and thus the Gibbs method yields the equilibrium densities of the coexisting liquid and vapor phases. The potentials described in Section 3 are used to model the fluid-fluid and fluid-solid interactions. Starting from an initial facecentered cubic lattice configuration in each region, the system is equilibrated by successive perturbations over a sufficient number of configurations. Additional configurations are then sampled to measure the properties of the equilibrated phases. a
Io
! I
oO-
Ioko 101 0
L.-o I
II
l..--I 9 Ii"I ~ oo#o
0 0
L:-~
0
o
0
0
0
Ul.-"
0
o
0
0
0
Ul.-"
"o
"
b
.--'I
0-:ol .--" !o
"'i
o
L,
I oi ~ -
L.-~
o_.1 ~
Ul--"
I1'-'
I
l.-'~o
o
I
12
Figure 3. Schematic of Gibbs ensemble Monte Carlo simulation method for pore-pore equilibria calculations in slit-shaped pores. Solid lines denote pore walls; dashed lines denote periodic boundaries.
753 A typical calculation requires approximately 1.5 million configurations for equilibration and an additional 1.5 million configurations for collecting property data. For the inhomogeneous confined fluid, long range corrections are not computed because of the associated computational difficulties. However, the system size is chosen so that the minimum edge length of the pore surface is 1 0 ~ (i.e. all molecular interactions up to a distance of 5Grr are explicitly included in the calculations). This cutoff is presumed to be large enough so that long range corrections may be neglected. While the pore-pore calculation yields the equilibrium vapor and liquid states that coexist in a pore of specified width, it does not specify the pressure at which the equilibrium state occurs, nor does it indicate the amount adsorbed at pressures below and above the equilibrium pressure. Therefore, the f'flling pressure and the state points along the vapor and liquid branches of the adsorption isotherm are calculated using the Gibbs pore-fluid ensemble. In this variation of the Gibbs ensemble method, the fluid in region I is again confined in a slit pore, but region II is a homogeneous bulk fluid (all boundaries are periodic). In the pore-fluid calculation, no exchanges of pore volume are attempted, since the condition for mechanical equilibrium is automatically satisfied if the chemical potentials in regions I and II are equal (i.e. material equilibrium is achieved) [27]. Hence, only displacement and particle interchange steps are performed, with the same acceptance probabilities as stated for the aforementioned pore-pore simulation. In calculating the bulk fluid properties, a long range correction for the cubic simulation cell geometry is included [25,28]. At equilibrium, the adsorbed fluid density corresponding to the bulk fluid pressure is obtained. To facilitate comparison of theory and experiment, the bulk pressure is scaled with respect to the saturation pressure P0 of the bulk Lennard-Jones fluid, given by the modified Benedict-Webb-Rubin equation of state [29]. To determine'the pressure at which condensation occurs, the vapor coexistence density from the pore90 80
~'0 a.
I-
m6o E u
~
'u
E3o
_= o
>20 10 0
0
2
4
6 8 10 Film Thickness (A)
12
14
16
Figure 4. Surface area measurement of Vulcan nonporous carbon. The slope of the linear region of the plot is the specific surface area o f the sorbent.
754 pore calculation is interpolated so that it coincides with the vapor branch of the isotherm constructed from the pore-fluid results. 6.
P r e d i c t e d Nitrogen Adsorption Isotherms: Slit C a r b o n Pores
Calculations using several different values of the solid-fluid potential strength e J k showed that the pressure at which the monolayer forms, and the pore filling pressure for micropores, are both sensitive to this value, but the general form of the isotherm is not much affected. The solid-fluid potential strength was fitted to adsorption data for a nonporous material of the same chemical structure as the porous material. For carbons this was a Vulcan provided by BP Research. The surface area A of this material was estimated using the method of deBoer et al. [21], from the slope of the plot of volumetric uptake against the universal film thickness, or "tcurve" (see Figure 4); A is related to the slope St~ of the linear region as A = S ti,, / (P ,,~Vsrp)
(18)
where P.ds is the density of the adsorbed film, and Vsrp is the molar volume of nitrogen at standard conditions. Here P~ is usually taken to be the bulk saturated liquid nitrogen density. This procedure leads to A=78.6 m 2, as compared to A-71.76 m '~ from BET measurement. The ratio A/AB~T=I.10 is consistent with deBoer's results for Graphon and Spheron carbon blacks. The solid-fluid potential parameter Ej'k was then estimated by fitting the mean nitrogen density in a large mesopore, pDrr, as calculated from the DFT theory, to experimental data, using the relation
0.8 0.7
! ]
0.6
i
i
0.5
p'0.4 0.3 0.2 0.1 0
0.1
0.2
0.3
0.4
0.5 P/Po
0.6
0.7
0.8
0.9
1
Figure 5. Nonlocal theory isotherms for nitrogen adsorbed in carbon mesopores at 77 K. The pore widths, reading from left to right, are" H*=6, 7, 8, 9, 10, 12, 14, 20, 40, 60, and 100.
755
V,a, = lporrHA Vsre
(19)
where V ~ is the uptake of nitrogen (cm 3 at STP/g of carbon). This procedure gave a value of eJk=53.22 K as the one best fitting the pressure at which the monolayer forms. For oxide materials a similar procedure was followed, using adsorption data for a nonporous NBS alumina [8]. With these fitted potential parameters, individual pore isotherms were generated using nonlocal theory for arange of pore sizes. For carbons these ranged from H*=1.68 to 100 (6.0 A to 357 A) at T=77 K. Nitrogen adsorption isotherms in mesoporous carbon slits are shown in Figure 5. Pores in this size range exhibit type IV capillary condensation behavior. At low pressures, a monolayer is formed; as the pressure is increased, additional multilayers are adsorbed, until the condensation pressure is reached, whereupon a phase transition to the liquid state occurs. Above the condensation pressure, there is a gradual increase in the mean density due to compression of the nitrogen in the liquid-filled pore. As the pore width is reduced, the condensation pressure decreases, as shown in Figure 6. This follows from consideration of the solid-fluid slit potentials, shown for several pore widths in Figure 7. As the slit walls move closer together, the bulk-like region in the center of the pore, where V~,, is approximately zero, disappears, and adsorption is enhanced throughout the pore space. In addition, the proximity of the adsorbed fluid layers on opposing walls increases fluid-fluid interactions, and further promotes the adsorption of multilayers. A transition from capillary condensation to continuous filling occurs at a critical width of Hc~*=3.8, or 13.6 A (e.g. isotherm H*=3.75 in Figure 6). Interestingly, there is a second region of discontinuous pore filling in the nonlocal theory isotherms, separate from the condensation region, at pore widths below the critical width Hc~*. From Fig. 8 we see that for pore widths between Hc3"=2.55 (9.1 A) and H,2"=3.6 (12.8 A), a 0---~1 monolayer transition occurs wherein the 0.8 0.7 0.6 0.5
p'0.4 0.3 0.2 0.1 0 0.00001
0.0001
0.001
0.01
0.1
1
PlPo Figure 6. Nonlocal theory isotherms for nitrogen adsorbed in carbon supermicropores at 77 K. The pore widths, reading from left to right, are: H*=3.75, 4, 4.25, 4.5, 5, and 6.
756 5-~ .
-5 ,It
,,,,, X
~10 -
3
-15 -
-20
2
-25
-4
-3
-2
-1
0 Z*
1
2
3
4
Figure 7. Wall-fluid potentials for nitrogen on graphite for selected slit pore widths H* (indicated by the numerals). The wall-fluid potential V,=t* = V,=,/e:! is plotted as a function of position in the pore.
0.8
0.7,-[ 0.6 0.5
p'0.4 0.3
0.2 0.1
0 1E-07
1E-06
1E-05
IE-04
1E-03
1E-02
1E-01
1E+O0
P/Po
Figure 8. Nonlocal theory isotherms for nitrogen adsorbed in carbon supermicropores at 77 K. The pore widths, reading from left to right, are: H*=2.5, 2.6, 2.75, 3, 3.25, 3.5, and 3.75.
757 incomplete monolayer on each pore wall abruptly fills to completion. At "I"=77 K there is a narrow band of continuously fLlling pores between He2* and H:I*. Although the pores in the size range from He3* to H:2* fall within the IUPAC supermicropore classification (9.1 to 13.6 A), the phase transition in these slits is atypical of the continuous filling normally expected for such pores. However, Gibbs ensemble Monte Carlo simulations (Section 5) conf'u'm the presence of a 0--->1 monolayer transition in pores that can accommodate approximately two complete layers of adsorbate. For still smaller pore widths, a return to continuous pore f'dling is observed (Figure 9). Pores of this size, corresponding to the IUPAC ultramicropore range, arc too narrow to accommodate more than a single layer of adsorbate. The shape of the ulu'amicropore isotherms arc similar to the IUPAC Type I isotherm, characteristic of micropore adsorption, although the IUPAC representation uses a linear rather than a logarthmic pressure axis. As Figure 7 illustrates, the two minima of the solid-fluid potential coalesce into a single minimum at a pore width of H*=2.25 (8.0 A). This enhances the potential well strength, which is maximized to roughly double its original depth at a pore width of H*= 1.94, or 6.9 A. A corresponding reduction in the f'dling pressures is seen, with the minimum (at H*=1.94) occurring at approximately P/Po=10 "~~ a pressure on the order of 0.1 microtorr. As the pore width is reduced beyond this minimum, the repulsive portions of the opposing wall potentials begin to overlap. Hence, there is a rapid rise in f'dling pressure as pore width decreases below H*=1.94 (Fig. 9). For pores with physical width narrower than H*=1.69 (6.0 A), the entire solid-fluid slit potential is repulsive, and thus the pore space is inaccessible to nitrogen and no adsorption occurs. It is observed that 0.8
0.7 0.6
0.54: p'0.4 /t
0.3
/
I I/
,
/
I : i
0.2 0.1 p
0 1E-12
i/
1E-10
1E-08
I II
1E-06
//
iI
I
/
/
I
i
! s
1E-04
sI
~*
I J
1E-02
1E+O0
P/Po
Figure 9. Nonlocal theory isotherms for nitrogen adsorbed in carbon ultramicroPores at 77 K. The pore widths, reading the solid lines from left to right, are: H*=1.94, 2.25, and 2.5. For pore widths smaller than H*=1.94, the filling pressure increases with decreasing width, as shown by the dashed lines, reading from left to right: H*=l.8, 1.75, 1.72, 1.7, and 1.69.
758 the mean density of nitrogen in the ulwamicropores (H less than 9.1 ,1,) is considerably reduced, due to exclusion of the adsorbate from the region near the slit walls. Only in the largest mesopores (e.g. Fig. 5) does the mean fluid density in the pore approach the bulk saturated liquid density of p*=0.792. Although the IUPAC designation of pore sizes is a useful guide to anticipating pore filling behavior, it is evident from the results presented in this section that the nature of the adsorption depends as much upon the adsorbate characteristics as it does upon the structure of the adsorbent. For example, if the size of the adsorbate molecule was increased, and all other potential parameters were held constant, some of the slits that exhibited capillary condensation for the original value of o'~ would fill continuously were they to adsorb the larger fluid molecules. Therefore, it is more relevant to devise a classification scheme that uses a pore size scaled with respect to the adsorbatc molecular diameter, and that also accounts for the influence of temperature. A comparison of the filling behavior predicted by nonlocal theory and Gibbs ensemble Monte Carlo shows good agreement for both the density profiles in the pore and the adsorption isotherms, for the whole range of pore widths from the ultramicropore region to large mesopores. The Gibbs result also verifies the presence of the 0-->1 monolayer transition found in the theoretical isotherms for supermicropore-sized slits. Adsorption isotherms calculated from theory and simulation are compared in Figure 10, for a range of pore widths. In each case, the filling pressures predicted by nonlocal theory and Gibbs simulation are in excellent U.8 ,
4
0.7 0.6 0.5
p'0.4 u
0.3
9
0.2 0.1 O
:
1E-10
|
| |
I
i
1E-08
1E-06
~
l
'
1E-04
'
' '''":
1E-02
I
'
' ''"~
1E+O0
P/Po
Figure 10. Comparison of nitrogen adsorption isotherms calculated from nonlocal theory and Gibbs simulation for adsorption in carbon slit pores at 77 K. Lines denote the nonlocal theory isotherms f o r pores of width, reading from left to right: H*=2, 2.5, 3, 3.75, 5, 8 and 12. Symbols show the corresponding Gibbs simulation isotherms. Open circles indz'cate equilibrium densities from pore-pore calculations; solid circles indicate pore-fluid equilibrium results.
759 agreement. Furthermore, the vapor and liquid branches of the theoretical and simulation isotherms agree quantitatively over the range of pressures sampled. There are some differences between theory and simulation for the adsorbed densities in smaller pores; this may arise because mean-field density functional theory predicts a higher bulk fluid critical temperature T~ than is obtained from simulation results for the Lennard-Jones fluid [30]. Hence, the nonlocal theory isotherms at T=77 K are at a lower reduced temperature T/To than the corresponding Gibbs simulation isotherms, and the phase splitting in the theoretical calculation is therefore more pronounced than in the Gibbs simulation. Overall, however, the nonlocal theory provides a quantitatively accurate description of pore filling. The variation of the adsorption isotherm with temperature has also been studied [6] for the range 70-85 K. The critical slit widths, Hc~*, Ha* and H~3* depend on temperature. At 70 K there are no continuously filling pores, but rather a range of pore widths which exhibit two discontinuous jumps in the isotherm. These "stepped" or IUPAC Type VI isotherms are typical of low temperature adsorption of simple gases on carbon surfaces [4,19]. At the higher temperature of 85 K (which is still subcritical) the range of continuous filling is broader than at 77 K. The results suggest that an upper critical temperature for the monolayer transition in nitrogen adsorption should occur slightly below T~*=I.0 (94 K). This critical temperature lies intermediate between the bulk Lennard-Jones fluid two-dimensional [31] and three-dimensional [32] critical temperatures of Tin*=0.515 and T3D*=l.316, respectively. The pores which exhibit the 0--->1 monolayer transition condense two layers of adsorbate, one on each surface. Thus, it follows that T~,,Tm, since the adsorbed molecules interact with molecules in the opposing film layer as well as those in their own layer (i.e. the fluid is not strictly two-dimensional). 7.
Pore Size Distribution: Slit Carbon Pores
The overall sorbent structure is envisioned as an array of noninteracting individual slit pores with a distribution of pore widths described by a function f(H). Clearly this distribution function must be non-negative for all pore widths H. For amorphous sorbents such as carbons, it is reasonable to assume also that f(H) is continuous. Two functions which satisfy these requirements are the gamma distribution and the lognormal distribution. The gamma distribution is
,~, lxi (Ti H) [J' f(H)=~=zT~ F(fl,)H exp(-7,H)
(20)
while the lognormal distribution is
f(H) = ~ a, -[ln H - 13i12 = 7iH(2z01/2 exp 27/2
(21)
where m is the number of modes of the distribution, and ai,/3i and 7i are adjustable parameters that define the amplitude, mean and variance of mode i. These equations are used to represent the PSD in the fitting of adsorption integral. The choice of the PSD function is discussed at greater length below. To determine the PSDs of porous carbons from experimental nitrogen adsorption data, the set of model isotherms presented in Section 6 are correlated as a function of pressure and pore width. The adsorption integral, equation (1), is then
760 solved numerically, inserting one of the model pore size distribution functions (equations (20) and (21)) and employing a simple minimization algorithm to optimize the parameters /xi, ]~iandTi of the PSD function. A least squares error minimization criterion is used to determine the optimum fit. The choice of the number of modes in the PSD function is arbitrary, provided that enough are used to give f(H) sufficient flexibility. In practice, the number of inflection points in the experimental isotherm can be used as an estimate of the number of modes required to yield an acceptable fit. This assertion is made on the basis of the one-to-one correspondence of filling pressure to pore width in the nonlocal theory results in Figure 1 (excluding the special case of the ultramicropores, with H<6.9 A). All PSD results that follow were obtained using a trimodal gamma distribution, eqn. (20) with m=3, except where otherwise noted. The PSDs of three carbons have been fitted using this method. The standard pretreatment for all of the samples was a bakeout at 300~ for 16 hours at 10 .6 ton'. The nitrogen uptake data was collected by static flow measurement on a Coulter Omnisorp. In Fig. 11 and Fig. 12 we present the isotherm and calculated PSD for carbon CXV, a micro/mesoporous carbon. The Kelvin-type models yield predictions of unphysical pore widths, and are clearly not applicable in this pore range. The PSD from the HK method is offset to lower pore sizes relative to the nonlocal DFT results, due to differences in the predicted micropore filling pressures. The DFT isotherm is in good agreement with the experimental data. There is some deviation around P/P0 = 10", which is thought to arise from the mean-field approximation of equation (10). In the present version of nonlocal theory used, it is not possible to reproduce the large uptake near the saturation pressure without slightly overestimating the uptake at the monolayer filling pressure. It is known that density functional theory predicts a higher bulk fluid critical temperature than is found from comparable Lennard-Jones molecular simulations. Therefore, at subcritical temperatures, the nonlocal theory exhibits too sharp a rise in the isotherm at the monolayer pressure, as is seen in Figure 11. Nonetheless, since nonlocal theory gives a reasonably good description of micropore filling, the nonlocal results are considered the most reliable interpretation of the PSD. In Figs. 13 through 16 we show the isotherms and PSDs for two highly microporous carbons, AC610 and AX21. Nonlocal theory again gives a good fit over the full pressure range of the experimental isotherm. The PSDs which correspond to these isotherm fits are also shown in these figures. Only the DFF and HK results are shown, since the Kelvin-based methods give non-physical results for such small pores. Although the Horvath-Kawazoe method was specifically developed for interpreting the PSDs of microporous carbons, and gives a plausible fit to the experimental sorbent isotherms, the HK theory consistently underpredicts the position of the maximum in the PSD relative to the DFT results, due to differences in the predicted micropore filling pressures from the two theories. Since DFT is the most realistic model of pore filling, the DFT results are the most reliable. Comparisons of these predictions for the isotherms and PSDs with those calculated from the local form of DFT [33] have also been made and described elsewhere [7]. The local theory is based on the approximation that the free energy density depends only on the local density at the point r of interest, i.e. ~'(r) = p(r) in eqn. (12). It yields PSDs that have too sharp a peak in the micropore region for all three carbons; for CXV local theory also gives this peak at too small a pore size.
761
900 ~800
-
~700 s
~E600 . -o500 ~400 "0
<300 (I) E
4,"
~".
:::=200 0
>100
.-~
9 oO-
.."
...
0
_r ~-~--"
I E-06
i
i
i
i
1
"
1E-05
1 E-04
1E-03
1E-02
1E-01
1 E+00
PIP0
Figure 11. Nitrogen adsorption on microporous carbon CXV at 77 K. Symbols denote the experimental uptake measurement; the solid, shortdashed and long.dashed lines indicate the fitted isotherms from nonlocal theory, the HK equation, and the Kelvin equation. 0.2 0.18
0.,6
il
I ]i
.<0.14
I
I
I
I
i
I
I
I
! I
I i
,
'
a i
A
0.12 0.1 u
I
"0.08 ~'0.06
:
I
I
I
v
:
:
/"
",
'
1:
" ....... i ..... 0.02 0
0
5
10
15
20
25 30 3 5 40 Pore Width (A)
45
50
55
60
Figure 12. Pore size distribution of carbon CXV. The solid, longdashed, and short-dashed lines indicate the fitted distributions from nonlocal theory, the HK equation, and the Kelvin equation.
762
600 A
~500
IX.
-
l-r~
~E400
-
U "O
~
-
.12 L. 0 "0
<200
-
a)
E
>o 1 0 0 I
i
i
i
i
i
1E-06
1E-05
1E-04
1E-03
1E-02
1E-01
1 E+O0
P/Po Figure 13. Nitrogen adsorption on microporous carbon AC610 at 77 K. Symbols denote the experimental uptake measurement; the solid and dashed lines indicate the fitted isotherms from nonlocal theory and the IlK equation.
0.4 0.35 0.3
-
.<
~.25 n_ I-
E u
0.20.15
0.1
I
0.05
]
"",.
o 0
5
10
15
20
25
Pore Width (A)
30
35
40
Figure 14. Pore size distribution of carbon AC610. The solid and dashed lines indicate the fitted distributions from nonlocal theory and the HK equation.
763 900
.....
A800 O~
i..700
rt
or)
~E600 t.)
-0500 &,,,
o 4 0 0 "0
<300 (1)
E =200
...,,, 0 >
100 9 |
el
1E-06
/ " !
1
i
|
i
1 E-05
1 E-04
1E-03
1 E-02
1 E-01
i | |l||
1 E+O0
P/Po
Figure 15. Nitrogen adsorption on microporous carbon AX21 at 77 K. Symbols denote the experimental uptake measurement; the solid and dashed lines indicate the fitted isotherms from nonlocal theory and the HK equation.
0.6
0.5
.
.
.
.
.
.
.,
II II II II I t I ! I l I I I I I l I I I I I I I I I I I I I I I I I I I l I t I l I I I t
~
~AO.4 n_ I-.
U)O.3 E
r .,...
=_-0.2
0.1
] ......, 0
,
,,
-','"
J.
5
,
10
15 20 25 Pore Width (A)
,
30
35
40
Figure 16. Pore size distribution of carbon AX21. The solid and dashed lines indicate the fitted distributions from nonlocal theory and the HK equation.
764 8: Pore Pores
Size
Distributions:
Cylindrical
Aluminosilicates
and Oxide
The density functional theory model and pore size analysis method described in previous sections have been extended to the interpretation of the isotherms of aluminosilicate-based sorbents (e.g. silicas, aluminas, zeolites). As discussed in Section 3, aluminosilicate pore channels are to a first approximation cylindrical in shape, in contrast to the slit pore geometry of carbon sorbents. The pore size distributions vary considerably within the aluminosilicate family, from the highly discrete pore geometries typical of zeolites and aluminophosphates to the disordered network of pore channels characteristic of amorphous sorbents such as a-alumina. Characterization of amorphous microporous aluminosilicates presents similar challenges to the analysis of micropomus carbons. As in the carbon pore size analysis, nonlocal density functional theory is used to generate model isotherms for the pore fitting analysis of silicas. Aside from the change in geometry from a slit pore to a cylindrical pore, and from a carbon surface to an oxide surface, the density functional procedure is identical in all respects to the method outlined in the previous section. Nonlocal density functional theory isotherms were calculated for pore radii ranging from R*=2 to 50 (7.0 A to 179 A) at T=77 K. Both the theory and simulation produced the same filling features: monolayer adsorption at P/P0=10-4; multilayer formation at P/P0 --0.05; and capillary, condensation slightly below PJPo ---0.2. For the micropores, some discrepancies are evident, as nonlocal theory predicts a smaller 1E+O0 K
o n_
1E-01
o
'o IZ.
(9 1E-02
t._ ::3 u) U) (9 t_
~" 1E-03 C u. IL
1E-04
1E-05 4 0
5
10
15 20 Pore Width (A)
25
30
35
Figure 17: Filling pressures predicted by the Kelvin equation (K), t h e Saito-Foley equation (SF), density functional theory (DFT) and molecular simulation (points) for nitrogen adsorption in cylindrical oxide pores at 77 K. The Saito-Foley results are calculated from the area-averaged case[8] using the sorbent oxide ion parameters reported in the original paper[3] and the sorbate nitrogen parameters reported by Horvath and Kawazoe[2]. The result for the Saito-Foley lineaveraged case overlaps closely with the Kelvin equation curve.
765 critical pore radius than simulation for the transition from capillary condensation to continuous filling. For example, nonlocal theory suggests that a vapor-liquid phase transition should occur in the oxide pore of radius R*=2.5, whereas Gibbs simulation indicates that no such transition occurs. The micropore filling differences between theory and simulation may arise in part because mean-field density functional theory prexficts a higher bulk critical temperature T c than is obtained from simulation; thus, the nonlocal theory isotherms are at lower reduced temperature T/To than thecorresponding Gibbs simulation isotherms. Also, in the limit of small pore radius, nonlocal theory does not produce the correct one-dimensional limit of the free energy functional in contrast to the two-dimensional case where the theory is accurate for narrow slit pores. Hence, as the pore radius is reduced, nonlocal theory gives a progressively poorer representation of the pore filling behavior. This imposes a lower bound on the pore size range for which the nonlocal theory results are valid.
In Figure 17 the capillary filling pressures predicted by Gibbs simulation and nonlocal theory, as well as by the Kelvin equation and the Saito-Foley method, are compared. The filling pressures of pores that do not undergo a phase transition are estimated from the inflection point in the adsorption isotherm. The density functional theory and Gibbs simulation filling pressures are in close agreement, whereas the Kelvin and Saito-Foley relations both overestimate the capillary filling pressures relative to the statistical mechanics calculations. For example, according to nonlocal theory, a 9 ]k radius oxide pore condenses nitrogen at P/P0=0.003; for the same pore size the Saito-Foley correlation reports a much higher filling pressure of P/P0=0.1. As the pore radius decreases, the disparity between the theories increases. Because the classical methods overestimate the filling pressures of the pores, these adsorption models will underestimate the mean pore size distribution. Another example illustrates this point: according to the Saito-Foley model, a pore which fills with nitrogen at pressure P/P0=0.01 has a radius of approximately 6 A. Nonlocal theory, however, interprets the pore size in this case to be 10.5/~. The DFT model isotherms for N 2 in cylindrical pores have been used to fit experimental adsorption data for sorbents which have oxide surfaces. As in the carbon pore size analysis a least-squares minimization algorithm is employed to optimize the coefficients of a multimodal gamma distribution, equation 20, which is chosen to represent the sorbent PSD function. The adsorption integral, equation 1, provides the appropriate minimization criterion. The fitted isotherm of a meso/macroporous silica is shown in Figure 18. The experimental isotherm is reasonably well fitted by the theoretical model. The nonlocal theory pore size distribution is shown in Figure 19. The silica has a mesopore peak at 70/~ and a broad macropore band which extends beyond the resolution of the nitrogen adsorption measurement. These results indicate that the nonlocal theory pore filling model can interpret adsorption data for a wide spectrum of pore sizes. It should also be applicable to the pore size analysis of other aluminosilicates such as activated alumina and pillared clays.
766 250 A
o.200 I..E O l 50 "O JQ I,,,,
O
mlO 0
I0 ,,r
-
r
E -= 50o
>
i
l
l
i
l
0.001
9
i
l i l |I
!
!
I
i
i
I l i~
0.1
0.01 PIP0
Figure 18. Nitrogen adsorption on silica KA160 at 77 K. Symbols denote the experimental uptake measurement; the line denotes the f i t t e d isotherm from nonlocal theory.
1.5E-05
J A
"<1.0E-05 I'-
E o
~5.0E-06
0.0E+00
| | ~ i i l l l | | l l l l l l l l | l l l | | l | l l l l | l l l l | l l l | i I ! ! I !
50
Figure 19. theory.
100
150 200 250 Pore Radius (A)
300
!
350
400
Pore s~e distribution of sifica KA160 fi~ed from nonlocal
767
9. Simulation Studies of Pore Blocking Hysteresis Carbon
in Model Porous
In previous theoretical[7], simulation[34,35] and experimental[36] studies, thermodynamic hysteresis has been observed to arise from fluid metastability in pores of uniform size, where pore blocking cannot occur. Connectivity effects, however, are believed to account for the desorption hysteresis observed in experiments on sorbents which have a size distribution of multiply connected pores[5]. When the pore size distribution is accounted for, the independent pore model of section 3 can describe much of the qualitative behavior of experimentally measured sorption isotherms. Nevertheless, pore blocking and networking effects are believed to be important for some adsorption classes, particularly types IV and V. Type IV isotherms have been divided into four subclasses in the IUPAC scheme[l], based upon the shape of the hysteresis loop; the variations in the shape of the loop are attributed to connectivity effects between pores of different size. In our studies of the effect of connectivity, the simplified slit pore junction model of Figure 20 is used to model nitrogen adsorption in porous carbon at 77 K. The pore volume is situated between two semi-infinite graphitic slabs, with the innermost slab truncated to produce a cavity connected to the pore channel as shown in Figure 21. The lateral boundaries of the simulation cell are periodic. The graphite layers are assumed to be smooth and structureless, and are held rigid, the nitrogen-wall potential is obtained by summing over all carbon atoms in the cavity/pore regions[8] . The nitrogen-nitrogen pair interaction is modeled using the Lennard-Jones potential described in section 3. and the larger cavity region (slit In both the narrow channel region (slit width 4r width approximately 6r n ) there is a potential well, adjacent to each pore wall, where the initial monolayer forms. Additional low energy regions are located at the comers of the cavity adjacent to each graphitic half-plane; these sites are particularly attractive to adsorbing molecules. The external potential is weakest at the center of the cavity, and thus the filling pressure of the cavity can be expected to be higher than the filling pressure of the narrow channel. In order to reproduce pore blocking (a non-equilibrium process) a simulation technique is required which maintains phase equilibrium between the bulk vapor phase and the surface-connected pores, while allowing the development of a metastable liquid phase in the blocked interior pores. In the current study we choose to do this using a grand canonical molecular dynamics (GCMD) method[8], adapted for the simplified model pore junction geometry of Figure 20. The feasibility of grand canonical molecular dynamics GCMD was f'wst demonstrated by Cielinski and Quirke in 1985137]. They tested an ad hoe GCMD method for the properties of bulk LJ fluids. The chemical potential of a molecular dynamics simulation was controlled by superimposing the particle insertion and deletion step from grand canonical Monte Carlo simulation. As long as the number of creations and deletions was small, thermodynamic and transport properties (diffusion coefficients) were accurately predicted. In their work a trial insertion and deletion was attempted every time a molecule crossed the periodic boundaries. The velocities of the inserted molecules were chosen from a Maxwell-Boltzmann distribution and the overall temperature maintained by temperature scaling. Later workers have developed other GCMD methods[38,39]. The Cielinski and Quirke method has recently been used successfully in a study of nonequilibrium mass transport in micropores[38].
768 J
A
~L~~ Figure 20.
~Ls~
Schematic of model carbon slit pore junction.
~L~~ A H
[~ Lb ~ ~Lp~
~ Lb ~ [~Lp~
Figure 21. Cross-sectional schematic of the model pore junction showing the regions comprising the pore network.
769 For the present model pore junctio.n, contact between the pore network and a bulk phase is maintained by partitioning the narrow pore into two regions, as shown in Figure 21" A "bulk" region, of length L b, spanning the periodic boundary in the xdirection and a "pore" region, of length Lp, adjacent to the junction with the cavity region, where L + L =L, In this work, I.~,=L =3off. In the cavity region and the pore regmn normal canonical (constant NVT) mo{ecular dynamics employed. In the bulk region the chemical potential is maintained at a fixed value using GCMD. In order that a metastable liquid state can develop within the interior cavity during desorption, the geometric parameter Lp is assigned a sufficiently large value (here 3o~r) to produce a buffer zone between the cavity region and the bulk/equilibrated region. Thus the interior cavity can only adsorb and desorb molecules by diffusion across the pore region of Figure 21. If GCMD were imposed in both the bulk and the pore region, the liquid-filled cavity could equilibrate at the interface between the pore and cavity regions, and pore blocking would not occur. .
--D
p
"
During the GCMD the rate of insertion/deletion attempts must be carefully selected to be large enough to equilibrate the bulk region, but not so large that the trajectories of the molecules are unduly influenced by large fluctuations in the number of molecules. For bulk systems it has been shown[40] that the rate of insertion/deletion attempts can be set equal to the flux of molecules across the periodic boundaries of the simulation cell. However it is found that this procedure is ineffective for GCMD simulation of inhomogeneous systems (see also [41]). Because of the low flux rates associated with pore diffusion a substantial drift from the bulk-equilibrated state occurs; i.e. for low insertion/deletion rates, the system becomes uncoupled from the mass reservoir
0.7
.
i I I
9
0.65
-
I
0.6
i
i
I
p" 0 . 5 5
ii
0.5 0.45
0.4
l l
"~ .
- 8
I
I
I
I
| I
-7.5
I
'
,
i
I I
- 7
I I I
h
i
,
,
I I
-6.5
,ll
i
i
I I
- 6
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I
I
,
I I
-5.5
,
.
i
,
I I
- 5
i
i
,
.
-4.5
Figure 22" Hysteresis loop for nitrogen adsorption isotherm in porous carbon network of Figure 20 at 77 K, calculated from GCMD simulation. The open and solid circles denote the adsorption and desorption results, respectively. The spinodal limits of stability predicted by nonlocal density functional theory for nitrogen adsorption in a carbon slit pore of width H ' - 6 are superimposed on the j u n c t i o n isotherm. The dashed lines show the spinodal limits for desorption (left) and adsorption (right).
770 and a constant chemical potential is not maintained in the bulk region. In this work the insertion/deletion trial rate varies between 25 to 50 trials per timestep, with more trials made at higher densities due to the decreased acceptance probability. At this rate, the measured chemical potential in the bulk region (by test-particle methods) was found to be consistent with the imposed chemical potential. Figure 22 reports results for the adsorption and desorption of nitrogen in the carbon pore junction model of Figure 20 using CQ-GCMD simulation[8]. The reduced chemical potential ~t'= la/eff is increased from a low initial value in increments of 0.5, each increase followed by an equilibration period. The desorption isotherm is similarly obtained by step decreases of 0.5 starting from the saturated liquid state. The complete sorption isotherm required 3.5 miUion timesteps due to the slow diffusion in the cavity region. The spinodal adsorption limit g'=-6.38 and desorption limit ~t'=-6.94 from nonlocal theory are superimposed on the network isotherm. Spontaneous adsorption and desorption occurs when the respective limits of thermodynamic stability are crossed. This hysteresis loop resembles IUPAC type H4, representative of porous sorbents which have a significant fraction of large pores, coupled with some much smaller pores. Because the filling pressure of the small pores is very low (Figure 1), the fluid in the large pore reaches its spinodal pressure before emptying occurs in the small pores. Immediate vaporization occurs in the large pore, driving liquid out of the smaller connecting pores and yielding a hysteresis loop of type H4. By suitable variation of the pore junction geometry, the other IUPAC hysteresis subclasses should be reproducible. Larger pore network dimensions are expected to yield the H1, H2 and H3 subclasses, while increasing the pore width ratio Hc/Hp is likely to produce a more pronounced type H4 loop. Future work will address the relation between the network geometry and the observed hysteresis behavior. 10.
Discussion and Critique of Method
Error in the PSD calculation can arise from three sources: (1) the experimental uptake data FfP); (2) the model used for the adsorption isotherms; and, (3) mathematical uncertainties associated with the selection of the PSD function f(H) and the solution of the adsorption integral. We briefly consider each of these factors in turn. 10.1 Experimental limitations The characterization of strongly adsorbing materials such as carbons and oxides requires careful measurement of the low pressure isotherm. If such pressures cannot be attained, the PSD calculation must necessarily be truncated at the experimentally imposed lower bound. For the fitting procedure, the full range of pore widths is employed in the calculations, since changes in the density of the liquidfilled micropores contribute to the overall isotherm. It is only after the optimum PSD is determined that the distribution is truncated. For porous graphitic carbon, the theoretical calculations suggest that the minimum filling pressure is approximately 10"l~ or 10-7 tort. The experimental isotherms presented in this work sample pressures as low as 10"6P0, so a portion of the ultramicropore range is not accessible using these measurements. Recently, we have been able to modify the Coulter Sorptometer to achieve relative pressures P/P0 down to less than 10 "7, and to
771 determine adsorption isotherms for nitrogen and methane on nonporous carbons that include this low pressure range [33]. When calculating PSDs for microporous sorbents such as AX21 or AC610, it is important to allow for kinetic limitations in the adsorption measurement due to the slow rates of diffusion through the narrow pores. For AC610, for example, static uptake measurements showed a markedly increased adsorption at low pressure over automated flow measurements using the Coulter Sorptometer, since the diffusionlimited micropore volume f'Rls at these pressures [7]. The implication is that improperly equilibrated flow adsorption measurements will yield PSDs that underestimate the population of micropores in the sorbent. For all of the PSD fitting analysis, only the adsorption branch of the sorbent isotherm is used in the solution of the adsorption integral. We avoid fitting the desorption branch, as there may be hysteresis effects in this branch associated with pore blocking, due to the connectivity of the pore structure. This type of blocking, termed network hysteresis, is commonly observed in mesoporous sorbents. Pore blocking moves the desorption branch to an emptying pressure below that of the adsorption pressure. Consequently, fitting the PSD to the desorption isotherm would incorrectly skew the PSD to smaller pore widths. ,10.2. Accuracy of the adsorption model A crucial step in assembling accurate model isotherms from nonlocal theory is to obtain the best possible estimate of the solid-fluid potential parameters. To do so, it is desirable to fit the parameters to the isotherm of a nonporous sorbent that has a surface which closely resembles that of the porous specimens whose PSDs are to be determined. The work presented here uses a nonporous isotherm measured on Vulcan carbon or a NBS alumina. Clearly it is unlikely that either the nonporous sample or the porous materials considered in Sections 7 and 8 actually have ideal surfaces. While the pretreatment conditions should be sufficient to remove any physisorbed gases, the removal of chemisorbed species such as carbon monoxide would require elevated temperatures that could alter the pore structure of the sorbent [11]. Hence, it is more accurate to view the solid-fluid parameters as representing the effective potential of a graphite-like surface. The main assumption in the model, then, is that the surface concentration of functional groups is the same for pores of all widths. These factors may account for the differences in the nonlocal theory fit to the CXV isotherm at the monolayer filling pressure. Several key assumptions concerning the structure of the porous material are implicit in the model. One is the neglect of connectivity in the modeling of adsorption in the individual pores. If the aspect ratio of pore length to pore width is not large, then it is possible, in fact likely, that the adsorption in a pore would be influenced by the condition of adjacent pores. This type of pore-pore correlation would make the solution of the adsorption integral difficult. A second assumption is that, because the PSD results are obtained by assuming that all of the pores have either a slit- or cylinder-shaped geometry, it is not possible to infer from the PSD additional information about the true morphology of the sorbent. In principle, it is possible to add shape heterogeneity to the modeling of the pore structure, although this necessarily complicates the solution of the adsorption integral. For carbon pores there is experimental evidence [10] that suggests a slit-like geometry. Alternatively, the use of triangular-shaped pores of uniform size has been proposed [42] as a model for microp0rous carbon. This geometry introduces an energetic heterogeneity due to the pore shape, which gives better agreement to experimental measurements of the isosteric heat of adsorption than is obtained using uniformly sized slit pores as the model structure. However, it has been demonstrated [43] that it is possible to model
772 the molar heat of adsorption of methane on carbon by using a size distribution of slit shaped pores, a similar approach as is used in this work for modeling nitrogen adsorption. By using a distribution of pore sizes, a different sort of heterogeneity is introduced, which nonetheless seems equally capable of modeling calorimetric data for porous carbons. A third assumption is that the pore size and geometry is not changed on changing the pressure or temperature (no pore swelling, etc.). There is no theoretical limitation to modeling the PSD using supercritical adsorption data, in place of the subcritical isotherms presented in this analysis. Indeed, it would be advantageous to compare, for consistency, the PSD results calculated from measurements at multiple temperatures. However, the interpretation of supercritical isotherms may prove more difficult, as the filling transitions in these isotherms tend to be less distinct than the condensation jumps observed in subcritical isotherms. A further limitation of the model is the use of simplified intermolecular potentials to describe both the fluid-fluid and fluid-solid intermolecular potentials. More accurate nitrogen-nitrogen potentials are available, based on site-site LJ terms with or without the addition of quadrupole forces [e.g. 44]. As pointed out by Nicholson [45], these models are effective potentials, so that in fitting parameters to data multibody effects are incorporated in some way; it is likely that such potentials optimized using bulk fluid data may require some modification for confined fluids. Nevertheless they provide a suitable starting point, and potential parameters can be refined as needed through comparisons with low pressure adsorption data. More sophisticated models are also available for fluid-solid interactions; these will vary with the material considered, but should include both direct and induced electrostatic interactions between nitrogen and the wall atoms. For the direct electrostatic interactions between the fluid molecules and the carbon surface electric field, the use of surface quadrupoles located in the carbon basal plane has been suggested by Vernov and Steele [46], and further studied by Bruch [47]. The influence of induction effects, due to interaction between charges or multipoles on the fluid molecules with the induced dipole in the graphite can be included using image methods [e.g. 48,49]. For oxides, aluminosilicates and similar materials, a sum of atom-atom potentials (LJ and Coulombic terms) can be used to approximate the dispersion, repulsion and direct electrostatic terms, with image terms for induction forces. 10.3 Mathematical consideration~ The solution of the adsorption integral presents a difficulty in that one is attempting to construct part of an integrand, f(H), from information about the integral F(P). In principle, this mathematical problem is ill-posed and will not give a unique solution for f(H) unless an infinite number of data points F(P) are available [50]. In practice, however, it is found that a sufficiently large sample of data (typically thirty to fifty points) constrains the shape of f(H) such that the numerical values of the pore size distribution are unique, provided a sufficiently flexible functional form is chosen for the PSD. The functional form of f(H) itself is arbitrary: any description that is suitably flexible to model the carbon PSD is acceptable, provided that the function is nonnegative over all pore widths. To check the uniqueness of the numerical values of the PSDs, the fitting process was attempted using the trimodal lognormal distribution, equation (21), in place of the trimodal gamma distribution. It is found that the lognormal distribution results are essentially indistinguishable from the gamma distribution results [7].
773 There are some small fluctuations of the fitted isotherms about the experimental isotherms that might suggest a constraint imposed by the fitting procedure, given that the nonlocal theory model of adsorption otherwise seems accurate. Other convolution techniques can be adapted to the solution of the adsorption integral; a review of regularization methods is presented by Szombathely et al. [50]. 11.
Conclusions
The DFT pore size distribution analysis method presented in this work offers a more quantitatively accurate method for predicting the adsorption behavior for pores of well-defined geometry, and since it is based in fundamental statistical mechanics, offers the possibility of systematic improvement, through the use of more sophisticated potential models, more flexible models for pore structure, etc. Future work is needed to explore the influence of using more accurate potentials for the fluid-fluid and fluid-solid interactions, and to account for networking effects and changes in pore structure under pressure. Further systematic experimental work is needed using different adsorbate gases and different temperatures to see if consistent PSD results can be obtained for selected test adsorbents. Careful attention will be needed to the low pressure part of the isotherm, and to determination of fluid-solid potential parameters from such data. Such experimental studies, when combined with appropriate molecular simulations, are also needed to determine whether such neglected effects as pore shape and chemical heterogeneity exhibit signatures in experimental results that are distinct from the PSD effects.
Acknowledgments. We are grateful to the National Science Foundation for support of this work under grants CTS-9122460 and INT-8913150. CML was a National Science Foundation Fellow during the period when this work was carried out.
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W. Rudzitiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces Studies in Surface Science and Catalysis, Vol. 104 9 1997Elsevier Science B.V. All rights reserved.
777
D y n a m i c s of A d s o r p t i o n in H e t e r o g e n e o u s Solids D. D. Do Department of Chemical Engineering, Umversity of Queensland, Qld 4072 AUSTRALIA
1. I N T R O D U C T I O N The importance of diffusion process as one of the limiting steps in adsorption has been recognized almost a century ago [1]. Since then engineers and scientists have studied m details the role of diffusion in adsorption dynamics. The simplest way of modelling a diffusion process reside a particle is to treat the particle as a homogeneous medium and use the Fickian type mass balance equation. This approach yields simple analytical solution, a welcome sight in t h e early days when computers were not widely available. Unfortunately its use is very limited especially when one wishes to have a better understanding of the diffusion process in a complex medium. This can only be achieved with a state-of-the-art model, allowing for all known phenomena that could happen in the particle. What we will do in this chapter is to give a brief account on the dynamic model development in the past, and then discuss in details the recent development of models to explore the predictability of model for heterogeneous solids such as activated carbon.
2. PAST A D S O R P T I O N M O D E L S 2.1 A d s o r p t i o n
Models for Homogeneous
Particles
2.1.1 Linear models Diffusion and adsorption in particle is recognised in 1935 by Damkohler {2] as an important problem to understand the sorption rate reside the particle. He formulated a problem with pore and surface diffusion, and the adsorption isotherm is assumed linear between the fluid and adsorbed phases. The equation obtained by Damkohler is: ~ -~~ ) -+~(1~C
~q = q~DpV2C+ (1- ~)DsV2q
(1)
778 where ~ is the voidage of the particle (cc void/cc of particle), C is the fluid concentration (mole/cc of fluid), q is the concentration in the adsorbed phase (mole/cc of adsorbed phase), Dp is the pore diffusivity and D s is the surface diffusivity. The volume of the adsorbed phase in the definition of the adsorbed concentration is the volume of the particle excluding the void volume. Linear problems as a result of the use of the following linear isotherm q = KC (2) were fully exploited in the early days. With this linear isotherm, the equation describing the adsorption process is simply:
~C_
D~ppV2 C
~t
where
D~pp - ~Dp + ( i - ~ ) K D ~ + (1-~)K
(3)
where Dap p is called the apparent diffusivity because it embeds both diffusion coefficients and the slope of the isotherm, K. If the rate of surface diffusion is negligible compared to that of pore diffusion, i.e. (1-~)D s << ~Dp, the apparent diffusivity will become: D ape =
~D,
~ + (I-~)K
(4)
and the diffusion model is reduced to the pore volume diffusion model widely used in studying adsorption of gases in low surface area solid, where surface diffusion is negligible. For solids having large affimty (higher K) the apparent diffusivity will be lower, implying longer time for the adsorbate to equilibriate the particle. This is so because higher K means higher capacity; hence longer time must be required to reach equilibrium. This is mathematically supported by the half time, obtained from the solution of the diffusion equation for a spherical solid, given below [3]: to ~ = 0.03055 [@+ (1 - @)K]R2 (5) @Dp where R is the radius of the solid. However, if the surface diffusion is larger than the pore diffusion, i.e. (1@)Ds >> @Dp, then the apparent diffusivity is:
m~pp
(:- r
~ Ds (6) r +(1-r This case is applicable to solids having very high affinity, typically in the order of 1000 to 10000. The model is called the solid diffusion model, still commonly used for describing adsorption in solids such as resins. In contrast to the pore volume diffusion model where longer time is needed to equilibrate the particle of higher affinity, the solid diffusion model states that the same time is required to equilibrate the particle no matter how high the affinity K is. This is so because the higher is the affinity the larger is the driving force. The half time for the solid diffusion model is [3]: R2 to5 = 0.03055 (7) Ds =
779 The limitation of the linear models is that it can not be applied to most systems where solids are not ideal or the isotherms are not linear or the system is not isothermal. 2.1.2 Nonlinear models: Recognizing the nonlinear nature of many isotherms, researchers solved the diffusion equations for the case of nonlinear isotherm, for example the following Langmuir isotherm, one of the most widely used isotherm bC (8) q = f(C) = qs 1+ bC Weisz and co-workers [4-7] published a series of papers solving diffusion and adsorption in a particle with nonlinear isotherm. Nonlinear isotherm problems were generally solved by numerical techniques, but problems with rectangular isotherms were obtained analytically as in this case, the adsorption is so strong (irreversible) the adsorption occurs like a penetration process into the particle. By splitting the particle domain into two, by which one domain is saturated with the adsorbed species while the inner domain is free from any sorbates, the problems can be solved to yield analytical solution [8]. Using a rigorous perturbation method, Do [9] has proved that the penetration of the adsorption front in the case of rectangular isotherm can be derived from the full pore diffusion and adsorption equations under the condition of strong adsorption kinetics relative to diffusion, high sorption affinity and low capacity in the fluid phase compared to that in the adsorbed phase. Using the same technique, the fixed bed problem with rectangular isotherm can also be solved analytically [10]. The result is that the breakthrough curve is a almost linear function of time, an explicit form which is useful for design purposes. 2.1.3 Nonisothermal models: Adsorption m gas phase systems is an exothermic process, i.e. heat evolves during the adsorption. The adsorption heat is typically in the order of 10 to 100 kJoule/mol, with the high end usually associated with sorption of hydrocarbons m zeolite particle. The consequence of the heat release due to adsorption or heat uptake due to desorption will affect the rate of diffusion of molecules into the particle as well as the instant equilibrium capacity of the particle. For example, m the case of adsorption, heat released will increase the particle temperature ff the rate of energy dissipation to the environment is not fast enough (especially in systems under vacuum or stagnant environment). As a consequence the diffusion coefficients of bulk, Knudsen and surface diffusion increase and the adsorption affinity decreases in the early stage of adsorption. After this, the rate of adsorption will decrease and hence the rate of heat release will be more compensated by the heat of dissipation; the temperature will drop and finally the particle temperature will be equal to the environment temperature at the time when there is no further adsorption.
780 Early work of nonisothermal effect in adsorption system has been carried out [11, 12]. Detailed analysis of nonisothermal sorption in a single particle is due to Brunovska et al. [13]. They considered pore diffusion is the only mass transport mechanism into the particle, and allowed for heat conduction within the particle. No film heat resistance was considered by the authors. The case is applicable only to large particles in rigorously stirred environment, and surface diffusion is not important relative to pore diffusion. From the simulations they concluded that the temperature rise within the particle may be of order of 10 - 50 oc. This order of temeprature rise was confirmed in their experiment of adsorption of n-heptane in 5A zeolite particle [14]. Brunovska et al. [15] later proposed a lumped thermal model to describe adsorption in nonisothermal systems. They allowed for intraparticle mass transfer but heat transfer was limited to that through a stagnant film surrounding the particle. Pore diffusion and Langmuir isotherm were considered in their work. Later, Brunovska et al. [16] used this lumped thermal model to investigate the case when the adsorption affinity is very strong, i.e. rectangular isotherm. The maximum adsorptive capacity is assumed to be a linear function with respect to temperature: q ~" qs = qso[1 - c~(W-To) ] (9) where qso is the maximum capacity at temperature To. Haul and Stemming [17] allowed for heat conduction as well as heat transfer to the environment in their model. A better model was presented by Kanoldt and Mersmann [18] and they tested their model with water adsorption in molecular sieve 4A, carbon dioxide m molecular sieve 10A and water on silica gel. Surface diffusion in addition to the pore diffusion was considered in nonisothermal model [19]. Generally, numerical procedures are used to obtain the solution to the coupled heat and mass balance equations. Hills [20] obtained an analytical solution to a system where there are internal and external mass transfer and external heat transfer. However, linear isotherm was dealt with, limiting the applicability of his model. Further limitations of his models are (1) diffusion coefficient and slope of isotherm are independent of temperature and (2) solution is valid for short time, hence curvature of solid is lost. He compared his analytical solutions with the experimental results of water vapor sorption onto activated alumina obtained by Bowen and Rimmer [21]. The agreement was reasonably good. In the eighty and ninety, a lot more papers have appeared in the literature dealing with a variety of situations [22-26], such as linear driving force for the mass transfer [27], surface barrier for mass transfer [28], bidispersed particle [29], multicomponent systems [30], systems with macropore, surface and micropore diffusion [31]. 2.2 L D F M o d e l s
Solving diffusion and adsorption models by computer many decades ago requires a large amount of computing time. This is due to the fact that the model
781 equations are generally nonlinear partial differential equations and coupled, and that early computers were comparably slow. To overcome this, Glueckauf [32] developed a simple lumped model, in which the Laplacian operator was replaced by a constant; hence the driving force term is replaced by the difference between the surface concentration and the mean concentration in the particle. Glueckaufs model has the proportionality constant is ~2/R2. Jury [33] later used the frequency domain analysis to obtain a value of 15, instead of ~2. This model, he claimed, was derived from a sound mathematical foundation. This factor 15 was later proved by Liaw et al. [34] that it can be derived from the original Fick diffusion equation by assuming that the concentration profile at any time t is a parabolic function with respect to distance. They then applied this model to a fixed bed configuration where the external concentration varies with time. Film mass transfer was considered in their model. The parabolic profile assumption was later further utilized by Rice [35] to study the batch, packed tube and radial flow adsorber. Same as in the work of Liaw et al., Rice considered only linear adsorption isotherm, and the diffusion model is the homogeneous model, that is there is no distinction between the two phases existing reside the particle, i.e. the fluid and adsorbed phases. Hills and Pirzada [36] reanalysed the work of Liaw et al. and Rice and examined the approximate solution with the finite difference solutions. Hills [37] further studied the applicability of the linear driving force to a few cases and concluded that the LDF should be used with care when dealing with the batch adsorber. The parabolic profile approximation is not restricted to the homogeneous diffusion model, it is also applicable to the pore diffusion model. If the adsorption isotherm is linear the pore diffusion and homogeneous diffusion models reduce to the same nondimensional form [38]. The validity of the LDF model is justified when ~ (nondimensional time) is greater than 0.05, the LDF model can be used to approximate the system behaviour for both single particle as well as the batch adsorber. Do and Rice [38] also extended the parabolic assumption to a quartic profile assumption, and have found that the quaxtic approximate solution only improves slightly over the LDF model but the complexity involved with the quartic model does not justify its use in assessing system behaviour. Recognising the limitation of the LDF model especially at short times, Do and Mayfield [39] developed a so called power law model, and the new approximate solution, available in analytical form, improves signfficantly over the LDF model. This power law model was later improved [40]. Their solution is not only valid over the whole time domain but also exhibits the correct short time behaviour. For a solid diffusion model, they obtain m
qR which is very close to the exact solution at the short time: q _ 6 ~Dt
11 Dt
The following table lists the various LDF models used in the literature.
(11)
782 Table 1 Various LDF models used in the literature Authors Model equation Glueckauf [32] d_~ = ~2D(q R _q) dt
R "~
!
dq dt Glueskauf[32]; Jury [33]
m
R "~
2q
dq 15Dlq R[ -~- = R----V -
Do and Mayfield [39]
d:9 dt Do and Nguyen [40]
I o(qR
- 3 (3
a--0.59;
t21
~=1.9
The success of the power law model [39, 40] was applied to deal with more complicated problems, such as nomsothermal adsorption in a particle [41], and adsorption m a bimodal particle [42]. This model was later applied [43] to solve multicomponent diffusion problem, and they found that the power law model is the preferred choice over other approximations for systems having large differences in adsorption selectivity among components. Buzanowski and Yang [44] proposed a model by approximating the concentration profile as a cubic profle, and they have successfully applied it to single step changes as well as m cyclic adsorption - desorption processes. Applying the LDF concept was extended to nonlinear isotherm [45-52]. The popularity of the LDF model was later extended to reaction systems [53-55].
2.3 Bimodal Diffusion Models Since the wide spread use of molecular sieve adsorbents, researchers were concerned with ways how to describe adsorption kinetics in these particles. The particles normally have bimodal pore size distribution. The pellets consist of small microparticles (crystals in the case of zeolites), and the pores within the crystal provide space to store molecules while the space between the crystals provide space for the molecules to diffuse from the exterior into the particle interior. The pores in the crystals are called the micropores while the pores between the crystals are called the mesopore and macropores. The IUPAC classification is that pores having size smaller than 2nm are called micropores.
783 Pores larger than 50 nm are called macropores, and pores between these two are called the mesopores. With the bimodal structure, the mechanism of adsorption into particle is generally taken as follows. Molecules diffuse within the macropore and mesopore space by molecular and Knudsen diffusion mechanism. During their path of diffusion, they adsorb onto the pore mouth of the micropores. The adsorbed molecules at the pore mouth diffuse into the micropore via a process called micropore diffusion. This diffusion process is activated, i.e. molecules have to overcome certain energy barrier to diffuse into the particle. The mobility is far slower than that in the macropore space, but the length scale (typically 1~) over which the molecules have to diffuse is far smaller than the particle scale (typically 1 ram). Therefore, the time scale of diffusion in the micropore could be comparable to the time scale of diffusion in the macropore. For a pore diffusion mechanism in the macropore space, a parameter which characterizes the importance between the two time scales of diffusion is defined as [56] [~ + (1 - ~)K]DR 2 Time scale for macropore diffusion ~DpR~2 - Time scale for micropore diffusion
(12)
where ~ is the particle porosity (macropore and mesopore), K is the slope of the isotherm if the isotherm is linear or the ratio of the adsorbed concentration to the bulk concentration at equilibrium, D is the ~ s i v i t y in the micropore, Dp is the pore diffusivity, R is the particle radius and R~ is the radius of the microparticle. This parameter is basically the ratio of the time scale of macropore diffusion corrected by the adsorption capacity to the time scale of diffusion in the micropore. If this parameter is less than unity, the adsorption process is dominated by the micropore diffusion. The macropore diffusion will dominate the transport ff it is greater than unity. When it is of order umty both diffusion processes will control the uptake into the particle, and the following set of equations describe concentration distribution within a bimodal particle [57-58]. ~-~~ ) - +~ -(1/ ~ C 0q = ~DpV2C ," V2 = Laplacian operator in particle coordinate aq = D~V~q V, = Laplacian operator in microparticle coordinate 5t subject to the following boundary condition at the surface of the microparticle: r~ = R, ; q = f(C) (isotherm equation) (13) They have used the above set of equations for linear isotherm, i.e. f(C)=KC to review a number of systems in the literature and has found that many of them are controlled by both diffusion processes. The first paper published to deal with bimodal particle is due to Ruckestein et al. [59], which was later justified as the point sink approximation from a more exact analysis [60]. Since then a number of papers have appeared to investigate the various aspects of adsorption, for example in fixed bed chromatography [61-63], in fixed bed breakthrough curve [64-65], in adsorbent particle [66-70], and in nomsothermal adsorbent particle [29].
784 The bimodal diffusion model enjoys its application m zeolite particles or other particles that exhibit a bimodal structure. The carbon molecular sieve studied by Chihara et al. [71] shows that the bimodal diffusion model is applicable, and the microparticle (grain) was observed using the electron microscopy. 2.4 P o r e a n d S u r f a c e D i f f u s i o n Models: It is noted that the bimodal ~ s i o n model does not consider the transport of mass into the particle by surface diffusion. Surface diffusion was observed to occur in many systems, particularly those that exhibit large surface and with strongly adsorbing species. Surface diffusion was considered by Damkohler [2]. Experimentally, surface diffusion was observed [72-78]. The correlation between the activation energy of diffusion and the heat of adsorption was observed [79-80] to fall between one third and one. If the surface diffusion flux is expressed in terms of the adsorbed concentration gradient, the proportionality is called the surface diffusion coefficient, which is observed as a function of adsorbed concentration. This dependence does not follow any particular pattern, but generally it increases with the loading. To explain for this behaviour, a number of theories have been put forward [81-83]. Surface diffusion is no doubt the most difficult phenomenon to investigate. It always occurs in parallel with pore diffusion and it is affected by the equilibria between the fluid and adsorbed phase. Moreover, it is affected by the way solid surface is structured. If the surface were energetically homogeneous, the situation would be much simpler. Unfortunately, the surface of a real solid is complex and hence the study of surface diffusion relies on how we model the surface. Most work assume the surface as a homogeneous domain, and treat the diffusion flux in terms of the total adsorbed concentration gradient. This is no doubt the simplistic view at surface diffusion, but it serves a means for us to describe kinetics in systems revolving surface diffusion. The first experimental work to recognise the surface heterogeneity is probably due to Horiguchi et al. [84]. Review on surface diffusion was carried out by Kapoor et al. [85]. They pointed out the importance of surface diffusion, especially the role of heterogeneity of the surface on the phenomenon. This aspect of heterogeneity is studied in a systematic way by Zgrablich and co-workers [86-87]. Their approach is very general, but to develop a more analytical model for describing surface dii~sion on heterogeneous substrates Seidel and Carl [88] and Kapoor and Yang [89-90] have obtained expressions for the surface diffusivities for heterogeneous surface. Although the heterogeneity of the surface was recognised by researchers working in the area of sorption e q u ~ b r i a for quite some time, its utilization m dynamics studies was rather limited. This is due to the complexity os heterogeneity when sorption dynamics was considered. The first reason is our inability to describe the surface structure. The second reason is the description of surface diffusion rate in terms of surface structure, and the third reason is the complexity in mathematical modelling. Accepting the difficulties at hand, early researchers working with high surface area solids have to resort to the simple
785 Fickian type model for surface diffusion [2, 91-100]. Because of the importance of this model for high surface area, it has been used quite extensively in the literature. The surface diffusivity is normally found by subtracting the pore diffusion rate from the total observed rate, or it can be found by increasing the temperature to the extent that the contribution of the surface diffusion is negligible. This is due to the fact that the surface flux is a product between the surface diffusivity and the surface loading. As temperature increases, the surface diffusivity increases but not as fast as the decrease of the surface loading; therefore, the surface flux decreases [101-102]. This methodology has two limitations. One is that the temperature beyond which the surface diffusion is negligle may be too high for the solid, and the other limitation is that the surface diffusion rate is negligible compared to the pore diffusion rate as the temperature increases only when the adsorption isotherm is linear. Another possible way to overcome this problem is to use a method first proposed by Do [3,103] to extract the surface diffusivity from a simple linear plot. Starting from the set of equations: OC Oq ~DpVBC bC dp-~- + (1 - ~)-~+ (1- qb)DsV2q with q = qs 1 + bC (14) =
the following equation for the half time was obtained: to.5 =
oj
1 + Y b-Co)
(15)
where Co is the external bulk concentration and qo is the adsorbed concentration which is in equilibrium with Co and oc, 13, Y are defined in the following table. Table 2 Values of (z, 13, y for three .d~erent shapes of particle Particle shape a. ...... 13 . Slab 0.19674 0.25 Cylinder 0.06310 0.26 Sphere 0.03055 0.30
? 0.686 0.663 0.750
The experimental fractional uptakes were obtained at various bulk concentrations over which the isotherm curve is nonlinear [103]. Half times were collected for each bulk concentration. Rearranging the analytical haft time equation (eq. 15), we obtain the following equation which is useful for parameter determination.
oLR-~ , + (1- *)C7
1-
786
I bCo 1( 0/ we This equation suggests that ff one plots the LHS versus \l---------~--Jt,.------J~bC,0(-;ol+ would obtain a straight line, of which the slope is (1-r s and the intercept is ~Dp. This technique is only applicable when experiments are carried out over the nonJmear range of the isotherm. If experiments were to be carried out over the Henry's law range (i.e. bC 0 << 1 and qo/Co = K, which is the slope of the isotherm at zero loading), the half time equation (eq 16) would become: aR*[r162
= CDp +(1-r
(17)
t0.5 Inspecting this equation shows that the separate contribution of the pore and surface ~ s i o n s can not be delineated. Instead one can only obtain the apparent diffusivity D~pp - ~Dp + (1 - r -
r + (1 - ~)K
(18)
Do and Rice [103] applied this procedure for a system of n-butane on activated carbon, and Gray and Do [104] later applied the same procedure for systems having adsorption isotherm obeying a Freundlich equation, and tested the theory with a system of sulfur dioxide on activated carbon with good success. 2.5 P o r e & S u r f a c e
& Micropore
Diffusion
Models:
We have addressed two major classes of adsorption models: the bimodal diffusion model and the pore and surface diffusion model. The first class is applicable to zeolite type adsorbents and was used quite successfully to characterize dynamics of many commerial synthetic zeolites. The second class is mainly used to describe adsorption rate in activated carbon type adsorbents. Surface diffusion is important in activated carbon because of its high surface area and strong affinity of many sorbates on activated carbon. The pore and surface diffusion model works rather well for large activated carbon particles, where the half time is proportional to the square of the particle radius. When the particle size is small, it was found that the dependence of the half time on the particle radius follows a R a dependence, where a is between 0 and 2. A value of zero means that the sorption dynamics is not affected by the reduction in the particle size. The dynamics is rather controlled by the local adsorption kinetics, characterizing the mass exchange between the fluid phase and the adsorbed phase. Recognizing this dependence on particle radius, R a, Do and coworkers [102, 105] proposed a model allowing for the pore dis163 surface dJs163 in the particle coordinate, and the micropore diffusion into the gram. The surface diffusion is neccessary to explain the high rate of adsorption (which could not be explained by just pore diffusion) observed in the experiments, and the micropore diffusion is necessary to explain the experimental observation of the half time dependence on particle radius as R a (0
787 w
r 18C 62
8q = (~D~V2C +(1-r
-
where
-q = ~1q d V
(19)
bq = D~V2,q with r~ = R," q = f(C) (isotherm) (20) 8t The model was tested successfuly with sulfur dioxide adsorption on activated carbon over a wide range of conditions of bulk concentration, particle size, particle shape and temperature [105]. The surface and micropore diffusion coefficients extracted for each temperatures show correct temperature dependence, that is they increase with temperature. The activation energy was found to be about 5 kJ/mole for both surface and micropore diffusions. The same model was used successfully by Mayfield and Do [102] to investigate the adsorption of ethane, n-butane and n-pentane into activated. The model again fits nicely the experimental data of single component uptake of all paraffins tested. Further test of the model was carried out to study the adsorption of carbon dioxide onto activated carbon [31]. The theory again agrees well with the experimental data over a wide range of conditions tested. Because of the constant diffusivity used for both surface and micropore diffusions, we call the model as Constant Macropore Surface Micropore D_iffusion model (CMSMD). When this model is used to predict the multicomponent data of ethane, n-butane and n-pentane onto activated carbon, it fails to predict the overshoot of the light component (i.e. weakly adsorbing species). This is due to the assumption of constant diffusivities used in this model. To overcome this, Do et al. [106] proposed a model called M_acropore and Surface D_iffusion model (MSD) and allowed for the chemical potential gradient as the driving force for the surface diffusion. This model ignores the micropore diffusion, hence it is applicable to large particle only. The model equations are:
r OC + (1- r
Oq = CDpV2C + (1 - r
sV ~ln q V
with
q = f(C)
(21)
The term 01nC/01nq is called the thermodynamic correction factor, resulted from the use of the chemical potential gradient as the driving force. Applying this model to large activated carbon particle, they were successful m predicting the binary adsorption data of n - p a r a ~ n s onto activated carbon. This suggests the importance of the correct description of the surface diffusion flux as a function of the chemical potential gradient. When the chemical potential gradient is converted into the adsorbed concentration gradient, the proportionality between the flux and the adsorbed concentration gradient is a function of loading, which is what has been observed in many experiments dealing with surface diffusion. Using this MSD model, Do and coworkers [107] tested the effects of the multicomponent adsorption equilibrium equation on the binary system dynamics behaviour. They observed that the choice of the isotherm (in terms of the goodness of fit between the isotherm equation and single component data) is very important in the dynamics studies.
788 The MSD model was extended [108] to allow for the contribution of the micropore diffusion in small particles. Like surface diffusion in MSD model, the micropore diffusion is assumed to governed by the chemical potential gradient. The model is called Macropore, Surface and Micropore D_iffusion model (MSMD). The model equations are: p
r
- CDpV2C +(1-r f
--~nqV
with
q- V
%
~_~q= D,V ( a l n Cim V~ql with r, = R,; q = f(C) (isotherm) (23) ~t ~ ~ln q where Cim is the imaginary gas phase concentration which is in equilibrium with the adsorbed phase concentration q. This model was tested with binary sorption data of n-paraffins over a range of bulk concentration, temperature and particle size, and the results are very good [108-111]. In particular, the model correctly predicts the time at which the overshoot of the light species reaches its maximum during the course of adsorption. However, when this model was used to predict desorption or displacement data, it underpredicts the data. This seems to indicate that the surface structure is heterogeneous, with weak sites releasing molecules first and stronger sites doing so at a much slower rate, resulting in slow desorption rate data.
2.6 Heterogeneous Diffusion & Sorption Models: Recognizing the heterogeneity of practical adsorbents and its implication in dynamics studies, Do and Hu [112] proposed a model to allow for surface energetic heterogeneity to study single component adsorption in a large particle. Patchwise topography was assumed, and the local adsorption isotherm for each patch was assumed to take the Langmuir form, that is the amount adsorbed, q, at the site of energy E is expressed as follows: b(E)C (24) q(E) = qs 1 + b(E)C where qs is the maximum adsorbed concentration, C is the fluid phase concentration and b(E) is the affinity between the sorbent and the adsorbate, which is given by: b(E) = boeE/RT (25) Other forms of the local isotherm can be used such as the FowlerGuggenheim equation given below q(E) = qs 1 +b[E;b[E;q(E)]q(E)]Cc where
E z w q (qs E) 1 b[E; q(E)] = b o exp [ --+RT RT
(26)
which would allow for the local adsorbate-adsorbate interaction. Here, w is the interaction energy and z is the number of neighboring sites. The surface flux for the group of sites having an energy of E is given by the following equation:
789 0~(E) J~(E) = -L(E)q(E) ~ Or
(27)
where L(E) is the mobility constant and ~ is the chemical potential. When the adsorbed phase is in equilibrium with the gas phase, we can write the above equation in terms of the adsorbed concentration gradient as follows: D~(E) ~q(E) (28) J ' ( E ) - - I 1 _q(E)l 5rq~ where D,(E) is the surface ~ s i v i t y
(L(E)RT) and is given by:
Here D~0 is the surface diffusivity of the adsorbed species having zero interaction surface energy or at infinite temperature. The parameter a in the above equation has been observed experimentally to take a value between 0.3 and 0.8 [80]. Having derived the surface flux at a given energy level E, the total flux at the position r is the integral of that local flux over the full energy distribution, assuming a patchwise surface topography: j , = _~
D,(E) 0q(E) F(E)dE [ 1 - q (q~ E ) lj ~r
(30)
Do and Hu [112] used this form in the differential mass balance equation to formulate the model, and they have validated the model using experimental data of n-butane and sulfur dioxide on activated carbon. Uniform and Gamma energy distributions were used in their work. The parameters characterizing the distribution were found by optimally fitting the adsorption isotherm with the theory. Their model is called the Heterogeneous Macropore and Surface D_iffusion model (HMSD). This model was later extended to allow for the micropore diffusion [113]. The model is designated as Heterogeneous Macropore, S_urface and Micropore model (HMSMD). Using this HMSMD model, the desorption dynamics is better predicted than the MSMD model, illustrating the importance of surface heterogeneity in desorption. The importance of the heterogeneity was even further illustrated by Do and his workers to study the multicomponent adsorption dynamics, desorption and displacement of ethane and propane [114, 115]. Again the desorption and displacement data were well predicted by the HMSMD model, indicating the importance of the allowance for the heterogeneity in the model equations. The following table shows the various models proposed by Do and coworkers.
790 Table 3 Evolution of kinetic models Model CMSMD MSD MSMD . . . . . HMSD HMSMD , , ,
References [31, 102, 105] [ 106, 107] [10S, 110, 111] [ 112, 114] [113, 115]
3. EXPERIMENTAL SECTION 3.1 A d s o r b e n t a n d G a s e s The model adsorbent we used in our program is activated carbon (Ajax chemical company, Australia) in the form of extrudate having a diameter of around 1.8ram. The structural properties of the Ajax activated carbon are determined from the mecury porosimeter (Micromeritics model 9200) and the BET nitrogen adsorption method (Micromeritics ASAP 2000). They are summarized in the following table.
Table 4 Characteristics of Ajax activated carbon Particle density 733 kg/m 3= 0.733 g/cc Total porosity 0.71 Macropore porosity 0.31 0.40 Micr0Pore porosity 8x10 -7 m = 0.8 ~m Average macropore diameter 8.2x104 m 2 / k g - 82 m2/g Mesopore surface area 0.44x10 "3 m3/kg = 0.44 cc/g Micropore volume 1.2x106 m2/kg = 1200 m2/g Nitrogen surface area The cylindrical particle supplied with different length. They are carefully cut to the desired length using a sharp medical surgical blade. The cylindrical geometry is made by coating the two flat surfaces of the extrudate with a high temperature expoxy resin. The slab particle is made by coating the cylindrical surface with the resin leaving only the flat end faces for diffusion. Particles coated with epoxy resin were degassed at 200 oc and under vacuum to clean the solids of any unwanted residue. Degassmg at temperatures higher than 200 oc is not recommended as the expoxy may lose its characteristics. The integrity of the resin was tested by completely coating one particle with resin, and adsorption test was carried out with this fully coated particle. It was found that this particle is impermeable to any adsorbates used, confirming the impermeability of the resin used.
791 Nitrogen gas was used in our test as the carrier gas, supplied by CIG (Australia) as high purity oxygen free nitrogen (OFN). Ethane and n-butane were supplied in chemical pure grade (CP) by Matheson gases. Premixed of 30 % ethane, propane and n-butane in nitrogen were supplied by CIG.
3.2 Differential Adsorption Bed Apparatus (DAB): Adsorption dynamics (adsorption, desorption and displacement) are carried out with a device called differential adsorption bed. This simple device is designed with two purposes. One is to provide a constant environment reside the adsorption cell, and the other is to maintain a constant temperature within the particle. These two purposes can be achieved by passing a very high flow rate through the cell such that the molar rate supply of the adsorbate is much greater than the adsorption rate by the particles reside the cell. Also with this high flow rate heat released by the adsorption process will be dissipated very quickly by the fast flowing stream. Typically we use about 1000 cc/mm of flowing gas and a mass of adsorbent in the cell is about 0.1 gram. The apparatus is consisted of four parts (Figure 1): a gas mixing system, the adsorption cell (C), the desorption bomb (B) and a gas chromatograph for concentration analysis. The gas mixing system is a bank of MKS mass flow controllers. The adsorption cell is a swaglok plug modified to house exactly about 0.1 to 0.2 g of adsorbent. This plug has a stainless steel mesh gasket a t the bottom to prevent the entrainment of particles out of the cell. The feature of this cell design is the small dead volume occupied by the gas space. The cell has a thermocouple embedded inside the cell to detect any temperature rise or drop. Tests were carried out at various flow rates, and it was found that at low flow, for example 200 cc/min, temperature rise (drop) of a few degrees was detected for adsorption (desorption). When the flow rate is increased to about 1000 cc/mm, there is no detectable temperature increase within the cell [116]. F .
f
....
From gas mixing system
to vent
B
I
1o Figure 1. Schematic diagram of differential adsorption bed
to GC
792 In this setup of DAB, F is the four way valve, allowing the cell to be isolated from the flowing gas stream or allowing the gas stream flowing into the adsorption cell (C). T is the three way, either allowing the gas stream to go to the four way valve and thence to vent or allowing the gas to flow into the desorption bomb, B. The bomb is fully instrumented with a pressure transducer as well as a thermoucouple for pressure and temperature measurement. This means that when pressure and the temperature of the bomb are known the total number of moles in the bomb can be calculated from the gas equation of state. The volume of the desorption bomb is about 120 cc, and inserts can be used to reduce the volume size in certain applications. The gas chromatograph used is of the FID type for hydrocarbon gases, and TCD for non-hydrocarbon gases.
3.3 Differential Adsorption Bed Procedure: The procedure of the Differential Adsorption Bed is as follows. With the bed isolated by the four way valve and the three way valve set to the position such that the adsorption cell is connected to the desorption bomb, the bed as well as the desorption bomb are initially heated overnight at around 200 oc and vacuum is applied to desorb any unwanted impurities from the carbon surface. In some occasions, high purity nitrogen is used to desorb impurities, instead of applying vacuum. We found that there is no difference between the two methods of cleaning the particle. After the carbon is cleaned, the three way valve is set to the position to isolate the cell from the desorption bomb, the adsorption cell is brought to the adsorption temperature with an aid of a flowing inert dried nitrogen gas. Once this has been achieved by observing the temperature of the cell, the cell is isolated from the flowing gas by using the four way valve F. 9 Step 1: With the adsorption cell isolated, mix the adsorbate to the desired concentration to achieve a total flow of about 1000 cc/min. This stream is passed to the GC to determine the chromatogram area at this concentration. This is used to calibrate the GC. At the same time the desorption bomb is evacuated by using a vacuum pump and the pressure is checked with the pressure transducer connected to the bomb. Once the bomb is fully evacuated, the bomb is isolated. 9 Step 2: At time t=0, the adsorbate stream is allowed to pass through the adsorption cell by switching the four way valve. After some predertermined time t ~, the adsorption cell is again isolated. In that time t*, the total amount inside the cell will be the amount adsorbed by the solid up to time t* as well as the amount in the dead volume of the cell. 9 Step 3: Next, turn the three way valve T to connect the adsorption cell and the pre-evacuated desorption bomb. Adsorbates in the cell will slowly desorb into the bomb. To enhance with this desorption, heat is applied to the cell as wen as a small flow of pure inert gas to flush the adsorbate into the bomb. The desorption temperature is around 150 to 200 oc. This step normally
793 takes half an hour to one hour, depending on the adsorbate used. After this desorption is completed, the three way valve is switched to isolate the adsorption cell from the bomb. Pressure and temperature of the bomb are recorded; hence the total number of moles, including the inert gas as well as the adsorbate, is calculated. At the end of the desorption step, the pressure of the bomb is normally around 1.5 atm. Step 4: Pass the gas in the bomb to the sampling valve of the GC for analysis of the adsorbate concentration, from which we can calculate the number of moles of adsorbates in the bomb. This amount must be subtracted from the amount in the dead volume of the adsorption cell to obtain the amount adsorbed by the solid up to time t*. The amount m the dead volume is determined experimentally in the next step. Step 5: Replace the solid with the same amount except this time the solids are fully coated with expoxy resin; hence, there will be no mass uptake by the particle. Pass the adsorbate through the cell as we do in step 2 and then follow the step 3 and step 4. The amount determined by the GC will then be the amount of adsorbate in the dead volume. By repeating the above steps for different exposure times, we will obtain the full uptake curve as a function of time.
3.4 Volumetric Adsorption Apparatus: Adsorption equilibria can be obtained from the DAB technique by allowing the exposure time large enough to ensure equilibrium to establish between the gas and adsorbed phases. Alternately, the adsorption equilibria can be obtained using a volumetric adsorption apparatus. This has two sections separated by a dosing valve. The first section is the dosing section and the other is the sample section. The pressure in each section is monitored by MKS Baratron transducers. The materials used m the construction of this apparatus are stainless steel with VCR fittings and valves (supplied by Cajon company). The adsorption cell (made from glass) is 1/2" in diameter with a round bottom to house the sample. The sample cell has a thermocouple (K-type) immersed into the sample to measure the solid temperature during the course of adsorption or desorption. The operation of the volumetric adsorption rig is as follows. 9 Step 1: Sample is first loaded into the adsorption cell. Next, the two sections are cleaned from any residues by heating up sample cell and high vacuum is applied. After this is done the two sections are separated by closing the valve connecting the two sections. The system is then brought to the adsorption temperature. 9 Step 2: Adsorbate gas or vapor is then supplied to the dosing section. The number of moles of the adsorbate is readily calculated from the knowledge of the dosing temperature, pressure and the known volume of that section.
794 9 Step 3: The dosing valve is slowly opened to allow N moles of adsorbate to enter the sample section, after which the valve is closed. The N moles are calculated from the knowledge of the initial and final pressures of the dosing section. 9 Step 4: Sufficient time is allowed for equilibrium to establish. This length of time depends on many factors, such as the type of solid, the gas, the particle size, the pressure and the adsorption temperature. Once the equilibrium is achieved, the pressure of the sample section is recorded as P~; hence the number of moles of adsorbate, M, left m the gas phase is readily calculated from the knowledge of the known volume of the sample section as well as the temperature of that section. The number of moles adsorbed inside the solid medium is then simply N-M. Knowing the mass of the sample loaded (mp) the equilibrium amount adsorbed per unit mass is (which is in equilibrium with the final pressure of the sample section): N-M I moles adsorbed7 q = mp L mass j 9 Step 5: A plot of q versus Poo will give one point on the adsorption isotherm. By repeating steps 3 to 4 with different amount N supplied will yield the complete adsorption equilibrium isotherm. The operating conditions and parameters used for our activated carbon are tabulated in the following table. Table 5 Operating conditions and parar.a, eter s used in our work Dosing section volume 159 cc Sample section volume 38.4 cc Particle size less than 0.1 mm to minimize the adsorption time Mass loaded about 0.5 g 250 o C Cleaning temperature about lx 10 -4 Tort Cleaning pressure 12 hours Degassing time between half an hour to 5 hours Adsorption time
4. FORMULATION OF THE SURFACE DIFFUSION MODEL SYSTEMS
HETEROGENEOUS MACROPORE (HMSD) F O R SINGLE C O M P E N T
In the formulation of the model to describe the surface heterogeneity, the following assumptions are made: 9 The system is uni-directional 9 The system is isothermal 9 The particle geometry is either slab, cylinder or sphere
795 9 The pore diffusivity and mass transfer coefficient are constant 9 The surface diffusivity at zero loading and zero adsorption energy is constant 9 The adsorbed phase is assumed to have patches of the same energy along the direction of mass transport. The last assumption means that we have assumed that the adsorbed phase has a patchwise topography, i.e. sites of the same adsorption energy are grouped into one patch. There are no interaction between those patches during the transport of molecules in each patch. Let q(E) is the adsorbed concentration m the adsorbed phase of the patch having an adsorption energy E. Thus, if F(E) dE is the fraction of site having energy between E and E+dE, the accumulation of the adsorbed concentration at the position r within the particle is: ~ [iq(E) F(E)dE]
(31)
The total flux of the adsorbed species contributed by all patches is: J~=-
i
D,(E) ~q(E) F(E)dE [1-q(E)/qs] ~r
(32)
Carrying out the mass balance around a thin shell at the position r within the particle, we derive the following mass balance equation:
r
~)~!q(E) F(E)dE = ~Dp Ir S ~0 rS
+
(33) (1-r
0{ i D,(E) ~q(E)ar F(E) dE} r-7~ r~ o [1-q(E)/q~]
where r is the porosity of the macropore, Dp is the pore diffusivity based on the empty cross section, and s is the shape factor of the particle. A slab geometry will have a value of 0, while a cylinder and sphere will have a value of 1 and 2, respectively. The first term in the LHS of Eq. (33) is the accumulation term in the void space while the second term is the accumulation of the adsorbed species. Except in solids of low capacity or system with extremely high pressure, the accumulation term contributed by the gas phase is much smaller than that contributed by the adsorbed phase, which is usually about 100 to 1000 times larger than the gas phase contribution. The terms m the RHS of Eq. (33) are the flux terms contributed by the gas and adsorbed phases, respectively. For highly immobile species, the first term would dominate. However, for many adsorption systems involving activated carbons these two terms are comparable in magnitude despite the pore diffusivity is about 100 to 1000 times larger than the surface diffxlsivity. This is because the flux is the product between the diffusivity and the concentration, and since the adsorbed concentration is about 100 or 1000 times larger than the gas phase concentration, these fluxes are comparable.
796 The mass balance equation revolves the two concentrations: gas phase and adsorbed phase concentrations. We assume equilibrium is established between the gas and surface phase; hence at any point within the particle, the adsorbed concentration at the patch of site having an adsorption energy E is related to the gas phase concentration, C, according to the following relation: b(E) C(r, t) (34) q(r,t;E) = qs 1 + b(E) C(r,t) If we rearrange the above equation as: C(r, t) =
0(r, t; E) where 0(r, t; E) = q(r, t; E) (35) b(E) [1 - 0(r, t; E)] q~ then for a given concentration of the gas phase the adsorbed phase concentrations are related to the gas phase concentration according to the above equation, and they are related to each other according to the same equation. That is if the patches of sites having adsorption energy of El, E2, and etc, the adsorbed concentration of those sites are related according to: C(r, t) = 8(r, t; EI) = 8(r, t; E~) = 8(r' t; E3) .... (36) b(E1) [ 1 - 8 ( r , t ; ~ ) ] b(E~)[1-8(r,t;E2) ] b(E3)[1-8(r,t;E3)] The sites having higher adsorption energy will have higher occupancy than those sites having lower adsorption energy. Before adsorption is proceeded the particle is equilibriated with a stream of adsorbate having a concentration of Ci, and the surface concentration is in equilibrium with this gas phase concentration, that is b(E)Ci (37) t - - 0 ; C=C~ and q ( E ) = q S l + b ( E ) C i The boundary condition at the center of the particle is: ~C 0q(E) = 0 and =0 (38) r=0; Or ~r The other boundary condition is that at the exterior surface of the particle. Assuming there is no mass accumulation at the particle-fluid interface, the total flux contributing by the pore and surface diffusions at the particle must be equal to the diffusion flux through the stagnant film surrounding the particle, that is: r = R; ~Dp ~+(1-~)~C(R't) ! D~(E) ~q(R,t;E) F(E)dE = km[C b -C(R,t)] Or [ 1- q(R't;E)l qs 0r
(39)
This set of equations describes the concentration distribution of gas phase as well as adsorbed phase within a particle. The model is valid for any form of the energy distribution. So far we have not addressed the source of energy distribution. The energy distribution is a result of structural heterogeneity or distribution of surface defects or other surface factors that cause the variation in the adsorption energy. Two extremes we could expect for the energy distribution. One is the ideal surface where the energy distribution is the Dirac delta function, i.e.
797 F ( E ) - 5 ( E - E)
(40)
With this form, the model equations will reduce to the pore and surface diffusion model used by Do and coworkers [106, 107], written below: ~b~ ~ C ( rt)' + (1 - ~) 0q(r,0tt; E) = ~bDp--71 r
r--2~-
rS ~C(r,0rt)
tl
+
[1 - q(r,t; E)/q~]
b(E) C(r,t)
~
(42)
q(r,t; E) - qs 1 + b(E) C(r,t)
This equation has been used with good success in the description of adsorption of various hydrocarbons (ethane, n-butane and n-pentane) onto activated carbon [106]. If the surface diffusivity is a constant, the model will be further reduced to the pore and surface [118]. The other extreme of energy distribution is the bimodal distribution: F(E) = czS(E- E 1) + ( 1 - a ) 5 ( E - E 2) (43) where cz is the fraction of the sites having an energy of E 1- This represents a dual adsorbed sites model, where the diffusion into the particle is proceeded through three distinct paths, one is the pore diffusion and the other two are diffusions in adsorbed phase of energies E 1 and E 2, respectively. Other distributions between these two extremes often used in the literature are the uniform and Gamma distributions: 1 F(E) = (44) Em~ -E.~n K
n+l
F(E) = ~ ( E F(n
Eo)" exp[-K(E- Eo) ]
(45)
+ 1)
The uniform and Gamma energy distributions have been used by many investigators in equilibria study [119-122]. The mean and the variance of these two distributions are: = E=~ + E~x and cy~ _ ( E m ~ ~ - E=~)2 (46) 2 12 --
E-
n+l
Eo+ ~
and
K
Another form of G a m m a
F(c)
= ~
K
c
n+]
n
e
cY2 _
-
(n+l) K
2
(47)
distribution [123] is:
-
(48)
r ( n + 1)
where c is related to the adsorption energy as follows: (49)
798 4.1 N o n d i m e n s i o n a l E q u a t i o n s To investigate the consequence of the surface heterogeneity on the adsorption as well as desorption dynamics, it is convenient to cast the equations into nondimensional form as given below.
(50)
5xS ~x ~
~
X $
[l-(qo/%)Y~(E')] f>x
y.(x,.;E.)= (qo) k(E*)Y(x,x) 1 + %(E*) Y(x,z) subject to the followingnondimensional boundary conditions: x : 0;
~Y(0,~) : 0 ~x ~~Y(1,z) + 5
and i
6Y,(0,z;E*) : 0 ~x
(52)
~Y,(1,z;E*) F(E*)dE* = Bi[Yb - Y(1,z)]
H(E*)
1-
(5I)
(53)
Y~(E')
The initial condition written in nondimensional form is: 9 =0;
Y(x,0)=Y i
and
Y~(x,0;E*)=(q~-~-) k(E*)Yi 1 + k(E*)Y~
(54)
The nondimensional variables and parameters equations are defined in the following table.
of the
above
set of
Table 6 Definitions of nondimensional variables and p .arameters Independent r ~Dpt variables x = ~; 9= R2[~ + (1- ~)(qo/Co)] Dependent variables Energy parameters
C. Y=C-~'
Cb. Yb = ~ o '
Dynamic parameters
q(E) ~ b(E)Co F(E) dE ; qo=qsf qo ~ 1 + b(E)Co oo
E* = E RT
- f(E*)dE* = F(E)dE '
k(E*) = (boeE')co Capacity parameters
Y,(E*)-
=[~+(l_~)qo/Co] (1
-
~)D~oe-aE"qo ~DpCo
; E* = ~E*F(E*)dE* o
; H(E*) = exp[a(E* - E*)] ; o~=l-c ; Bi= kmR ~D,
799 4.2 S i g n i f i c a n c e of P a r a m e t e r s : Before discussing the usefulness of the model in terms of its predictability, we first study the nondimensional variables and parameters. The nondimensional time has been scaled with respect to the pore diffusion time [124]. This pore diffusion time is: tpore
--
R2[r + (1 - r162
(55)
The significance of this pore diffusion time is that in the absence of the surface diffusion and film diffusional resistance the half time of adsorption dynamics is equal to 0.03 tpore when the particle is spherical [3]. When the particles are cylinder and slab in shape, the half times are 0.063 tpo,~ and 0.197 tpo,~, respectively [103]. The nondimensional energy is scaled with the molar thermal energy, RT. It could be scaled with the following mean adsorption energy
-E =I
oo
E F(E) dE
(56)
0
but the choice of the molar thermal energy is more convenient and hence is used in our analysis. The parameter k(E*) represents the adsorption affinity of the patch having energy E (or E*). If it is much greater than umty, the affinity is very strong. While ff it is much less than unity the aff~ity is weak and hence the local adsorption isotherm for that patch of sites will follow the Henry law isotherm. The function H(E*) measures the deviation of the surface diffusion coefficient from its mean value D---~= D~o e x p ( - a E / R T )
(57)
The parameters o and o~ are capacity parameters. Let W be the total amount of adsorbates taken up by the particle at equilibrium. The parameter G is the fraction of W that resides in the void space inside the particle. The second capcacity parameter, op, is the fraction of W residing in the adsorbed phase. For most practical adsorbents, the first parameter is very small, usually in the order of 10* and 10-~. The second parameter is, therefore, of order of unity for most practical sorbents, especially microporous adsorbents. The parameter 5 is the dynamic parameter. It describes the ratio of the adsorbed phase diffusion flux to the pore diffusion flux. When this parameter is close to zero, i.e. the adsorbed phase diffusion flux is negligible compared to the pore diffusion flux, the mass balance equations reduced to the usual pore diffusion model.. When this parameter is of order of unity, the two ~ s i o n processes are comparable, equally contributing to the total flux. 4.3 S o l u t i o n Methods: The mass balance equations are coupled parabolic partial differential equations. They are readily solved by applying the method of orthogonal collocation, in which the spatial domain x is discretized into N discrete points.
800 The r e s u l t of such process will be a set of coupled o r d i n a r y differential e q u a t i o n s with r e s p e c t to time, which can be solved by any s t a n d a r d i n t e g r a t i o n solver. I n t e g r a l s involving t h e a d s o r p t i o n e n e r g y are e v a l u a t e d u s i n g t h e G a u s s i a n q u a d r a t u r e . The n u m e r i c a l c o m p u t a t i o n s were done on a p e r s o n a l c o m p u t e r 80386/33 Mhz, a n d t h e c o m p u t a t i o n time is of order of 30 secs to 5 m i n u t e s , d e p e n d i n g on the n u m b e r of discretization points used, t h e n u m b e r of p o i n t s u s e d in t h e q u a d r a t u r e e v a l u a t i o n a n d the s t r e n g t h of a d s o r p t i o n affinity. Generally, the c o m p u t a t i o n t i m e i n c r e a s e s with the n u m b e r of p o i n t s u s e d a n d with t h e s t r e n g t h of a d s o r p t i o n affinity.
4.4 S i m u l a t i o n s : It is i m p o r t a n t for the applicability of a n y models t h a t t h e y m u s t be t e s t e d for t h e i r p r e d i c t i n g b e h a v i o u r as t h e key p a r a m e t e r s are varied. A n u m b e r of p a r a m e t e r s t h a t we will be i n v e s t i g a t i n g are the form of e n e r g y d i s t r i b u t i o n , t h e m a x i m u m a d s o r p t i o n capacity, C~s, the e x t e r n a l bulk c o n c e n t r a t i o n , C 0, a n d t h e particle t e m p e r a t u r e . T h e p a r a m e t e r s listed in t h e following table are u s e d as t h e b a s e case in the s i m u l a t i o n . T h e s e p a r a m e t e r s come from the fitting of t h e t h e o r y w i t h t h e e x p e r i m e n t a l d a t a of n - b u t a n e onto Ajax a c t i v a t e d carbon at 101 K P a a n d 303 K. We use t h e s e p a r a m e t e r s to g e n e r a t e a base case from w h i c h o t h e r b e h a v i o u r s o b t a i n e d by t h e v a r i a t i o n of p a r a m e t e r can be c o m p a r e d with. Table 7 Base parameters used in the simulations Uniform Energy distribution Single Site distribution Particle ' ~ = 0.31 " R = 10-3m (slab half length) characteristics Parameter a a=0.5 Energy Era.X= 22.7, Emi" = 8.3 kJoule / mole E = 15.5 kJoule / mole distribution = _l (EmaX+ Emin) = 15.5 kJoule / mole 2 B
(5"
Co
= 2
Ema x -
E
rain
) = 4.16 kJoule ! mole /
C O = 8.286 • 10 -2 kmole ! m 3 n
Equilibria parameters
b = 1231 m 3 /kmole
b o = 2.62 m 3 /kmole : b o exp(E/RT) : 1231 m3 / kmole
q, = 5.016 kmole ! m 3
q, = 5.016 kmole / m 3 Dynamic parameters
Dp = 1.24 x 10 -6 m 2 /sec
Dp = 1.24 x 10 -6 m 2 !sec
Duo = ]_895 x 10 -s m ~/sec
D, = 8.758 x 10 -'~ m 2 /sec
D---~= D~o exp(-a ~ T I = 8.74 x 10-]~ m2 sea
801 We first study the effect of the variance of the adsorption energy, a, (a measure of the surface heterogeneity) on the adsorption and desorption dynamics. A value of zero implies a homogeneous surface. We use other three values of r to study the influence of heterogeneity (~ = 2.16, 4.16 and 6.16 kJoule/mole). Note that when this parameter is increased, the range of the variation of the surface diffusivity is wider. The patch having strongest sites will have the lowest surface diffusivity, while the patch having the weakest sites will have the highest surface mobility. Figure 2 shows a plot of the adsorption and desorption dynamics versus time with the variance as the varying parameter. The dynamics curves for different variances are practically superimposed, implying that the surface heterogeneity has no effect on the adsorption dynamics. This can be explained as follows. The patch of strongest sites has the highest mass density but this high density is compensated by its low mobility. On the other hand, the patch of weakest sites has the lowest density but the highest mobility. Hence, the overall kinetics of a solid having a wide energy distribution will behave almost identically to a solid having a narrower energy distribution with both having the same mean energy (E = 15.5 kJoule/mole) and the same mean adsorption affinity (b=1231 m3/kmole). 1 0.9
UJ
0.s
I-13. ~) _.1
0.7
< <
Z 0 !-,,.,..
~
-~- Adsorption --~=....Desorption(variance=O)
=~ ?
0.6
0.s
0.4
0
0.3
~;
0.2
U., 0.1 0
!
I
I
I
I
100
200
300
400
500
-'
I
I
!
'!
I
600
700
800
900
1000
Time (seconds) Figure 2. Effect of variance on the fractional uptake curve versus time Now we consider the case when we pre-equilibriate the solid with a stream of adsorbate of constant concentration and then desorb the adsorbate into a stream of inert gases. Figure 2 shows the desorption dynamics curves for heterogeneous solids as well as a homogeneous solid. We see a clear influence of
802 the surface heterogeneity on the desorption kinetics. This is quite understandable that the patch having weakest site will desorb quickly, while the patch having the strongest sites will desorb at a slower rate. Hence, the solids having much wider energy distribution will take longer time to desorb, provided that they have the same adsorptive capacity, which is the basis we have used m this comparison. Next, we investigate the influence of the saturation capacity, i.e. high capacity sorbent versus low capacity sorbent. We used a variance of 4.16 kJoule/mole in this investigtaion. We found that the adsorption curve is more sensitive to the change of the saturation capacity than the desorption curve is. This is due to the fact that in the adsorption mode there are more sites to be filled with molecules at all patches; hence longer time is required to equilibrate solids having wider adsorption energy distribution. In the case of desorption, even though the amount to be desorbed is higher the driving force for desorption is higher for all patches. This is because for patches with high energy even though the affinity is strong its high density provides high driving force for desorption; hence, the desorption mode is insensitive to the saturation capacity. 1 W
0.9
o.8
0.7 0.6
m
~
z 0.4
~
o.3
~
0.2
~
0.1
...m....A d s o r p t i o n C 0 = 2 0 . 6 % Adsorption C0=30.6%
O.
0
I
I
I
I
I
''1
50
100
150
200
250
300
Time (seconds)
Figure 3. Effect of the external bulk concentration on the kinetics The effect of the external bulk concentration can be seen in the following two figures. Three external concentrations used in the simulation are 10.6, 20.6 and 30.6% of n-butane at 300C and 101kPa (Figure 3). Increase in the bulk concentration results m a reduction m the adsorption time. This is because when the external concentration increases the adsorptive amount does not increase as fast as that in the bulk because of the concave (favorable) isotherm, hence the driving force per unit adsorptive capacity decreases, leading to the reduction in the adsorption time. In the desorption mode, the kinetics curve is not sensitive to the adsorbate concentration with which the solid is initially equilibrated (Figure 4). Similar behaviour was observed by Ruthven [125].
803 UJ F" (1. ..J < Z 0 .,,.. !-0 LL
1 0.9
Desorption C0=10.6%
0.8
0.7
-w- Desorption C0=20.6%
0.6
Desorption C0=30.6%
0.5 0.4 0.3 0.2 0.1 0 I
!
0
'"
I
200
.... I
400
! ....
600
I
800
1000
Time (seconds)
Figure 4. Effect of the external bulk concentration on the desorption rate
4.5 Experimental Results & Discussion: We used a model system of ethane, propane and n-butane as adsorbates and an Ajax activated carbon as the adsorbent. We also used sulfur dioxide as another adsorbate to show the effect of the interaction between the sulfur atom and the surface atoms. We will discuss the heterogeneity effect on the equilibrium data first, and then discuss its effect on the dynamic data. 4.5.1 Ethane Adsorption Equilibria: First, we study the equilibrium of ethane onto activated carbon. weakest adsorbing species among the three paraffins investigated.
This is the
. ~...-..~._~-~ .U~- - ~
mmolelg
J
..... -"
~[ 0
."
I 0
20
. . . . .
I' 40
'
'
......
Un''"" _
I'
I
I
60
80
100
Pressure
I
Langmuir
]
I
I
120
140
(kPa)
Figure 5. Adsorption isotherm of ethane on Ajax activated carbon at 283 K
804 For ethane/activated carbon, the adsorption equilibria at three temperatures (283, 303 and 333 K) were collected, and the data at 283 K are shown in Figure 5. The theoretical curve, assuming a local Langmuir isotherm with a uniform distribution, has the form: q = ~ s l n I l + b_C e S I l + b C e -s
where
s = x/~o RT
and
-b = b oexp (~_E_T)
(58)
which is the integration of the local Langmuir isotherm over the uniform distribution of adsorption energy. This form is called the Unilan equation [126]. Other forms of energy distribution could be used but the choice of the form of energy distribution does not significantly affect the goodness of fit of the experimental data, provided that they have the same mean adsorption energy and the same variance. The fitting between the theory and the experimental data of all temperatures was carried out simultaneously. The parameters bo and E are assumed to be independent of temperature, while qs and s are assumed temperature dependent. Theoretical isotherms using the uniform distribution and a local Langmuir isotherm (Unilan) fit the data very well. This is not surprising because of the additional parameter of energy variance, describing the surface heterogeneity. Langmuix equation is also used to fit the data. The fit is not as good as that observed for the case of Unilan equation. The good fit of the Unilan equation does not prove that the surface has an uniform distribution but it does indicate to the fact that the surface is not homogeneous. The optimal parameters for the Umlan isotherm are tabulated in Table 8 and those for the Langmuir equation are shown in Table 9. w
Table 8 Parameters for the Uni]an equation T qs b0 Emin ~ kmole/m 3 (kPa-1) kJ/mole 10 30 60
13.0 13.0 13.4
8.47x10 -7 8.47x10 7 8.47x10 "7
6.26 6.26 6.26
Emax kJ/mole
E (kJ ! mole)
b (kPa -1)
30.75 30.75 30.75
18.5 18.5 18.5
2.20x10 3 1.31x10 -3 6.77x10 "4
Table 9 Parameters for the Langrnuir equation Ethane W oc b (kPa -1) qs (kmole/m 3) 10 0.0516 5.49 30 0.0320 5.00 60 0.0167 4.58
s-
~o RT 5.20 4.86 4.42
805 The parameters of the Unilan equation show that the mean adsorption energy of ethane is about 18.5 kJoule/mole, and an energy variance of 7 kJoule/mole, significant enough to show the heterogeneity of the activated carbon surface toward ethane molecule. The maximum capacity obtained from the fitting of the Langmuir equation decreases with temperature. This seems to suport the concept of pore filling with liquid condensate. With this concept, the saturation capacity is: qs =
V, (59) VM(T) where V~ is the micropore volume per unit solid volume, and vM(T) is the liquid molar volume. Since the liquid molar volume increases with temperature [127], the saturation capacity decreases with temperature. 4.5.2 Propane Adsorption equilibria We also used the Unilan equation as well as the Langmuir equation in the fitting with experimental data. As we would expect the fit using the Langmuir equation is not as good as the fit we obtained for the Unilan isotherm. This is an indication of the surface heterogeneity toward propane. Figure 6 shows a typical fit between the theory and the experimental data of propane at 283 K. We note that at the same temperature, the difference between Unilan and Langmuir is greater for the case of propane than for the case of ethane. This indicates that the activated carbon surface is more heterogeneous toward propane than ethane. 6 ~___
5
9
4 mmole/g
Data
3
of propane
283K ---t--- Unilan
Langmuir 1 o o
A
!
at I
i
!
i
i
i
i
i
20
40
60
80
100
120
140
Pressure
(kPa)
Figure 6. Adsorption equilibria of propane at 283 K The equilibria parameters obtained from the fitting are listed in Tables 10 and 11. In the fitting between the data and the Unilan, we fixed b 0, Emi n, E max for all temperatures. The saturation capacity, qs, is set the same as that for ethane but temperature dependent. The reason for the last choice is that we are constrained by the same saturation capacities for all species to be thermodynamically consistent. Even with this constraint, we note in Figure 6 that the fit of the Unilan isotherm to the experimental data is excellent.
806 Table 10 Parameters for the Unilan equation (propane) T qs b0 Emi n Ema x oc kmole/m 3 (kPa-1) kJ/mole kJ/mole 10 30 60
13.0 13.0 13.4
1.93x10 -7 1.93x10 -7 1.93x10 -7
0 0 0
.....
(kJ / mole)
42.75 42.75 42.75
21.38 21.38 21.38
(kP a -1) 1.70x103 . 9.34x10 -4 4.35x10 -4
S-"
RT 9.08 8.48 7.72
Table 11 Parameters for the Langrauir equation (propane) T oc b (kPa 1) qs (kmole/m3) 10 0.470 5.00 30 0.217 4.69 60 0.0863 4.36 The same conclusion is observed for the other two temperatures investigated, 303 and 333 K, but as the temperature increases the difference between the Unilan and Langmuir equations is not as great as t h a t in the case of lower temperature. The following figure (Figure 7) shows the data as well as the fit at 333 K.
3.5 3
mmole/g
2.s
P
a_'33TM
~.5 ~
~
.... -lit- .... U n i l a n
.
_
---AmLangmulr
0.5 0 0
20
I
I
I
I
40
60
80
100
Pressure
I 120
I
140
(kPa)
Figure 7. Adsorption equilibria of propane at 333 K From the parameters obtained from the fitting of Unilan equation, we note that the mean adsorption energy of propane is greater than t h a t for ethane (21.4 compared to 17.5 kJoule/mole) and the energy variance is also greater (12.3 compared to 7 kJoule/mole). 4.5.3 n-butan, e Adsorption E q ~ b r i a : The adsorption isotherm of n-butane on the activated carbon is measured at 10, 30, 60 & 150 ~ Results of 60 ~ are shown in Figure 8. The theoretical isotherm using a local Langmuir isotherm with a uniform energy distribution is used to fit the data. The parameters derived from the
807 fitting are b o, E, and s (a measure of the surface heterogeneity), and they are listed m Table 12. 4
3.5 3
mmole/g
2.5 2
1.5 1 0.5 0 0
10
20
30
40
50
60
70
Pressure (kPa) Figure 8. Adsorption equilibria of n-butane at 333 K Table 12 Parameters for the Umlan equation T b0 oc (]cmolelm 3) (m3/kmole) q $
Q
E (kJ / mole)
w
b
s-
(mS / kmole)
RT
,,,
10 30 60 150
5.545 5.016 4.559 3.754
2.62 2.62 2.62 2.62
15.5 15.5
i5.5 15.5
1780 1440 610 91.4
,,
2.89 2.86 2.62 2.58
What we note from this fitting is that the saturation capacity decreases with an increase m temperature, typical for the micropore filling mechanism. Comparing between the two paraffins, ethane and n-butane, we also note that n-butane has a higher adsorption affinity at infinite temperature, about twice as large as that for ethane. The mean adsorption energy, E, is about the same for two adsorbates, 14.3 for ethane and 15.5 kJoule/mole for n-butane. However, the n-butane / activated carbon system has a larger energy variance. Take 30 ~ for example, the parameter s for ethane is 1.2 while for n-butane it is 2.86, indicating that the heterogeneity in the case of n - b u t a n e is twice as significant as that in the case of ethane. One may attribute this fact to the heterogeneity of the adsorbate. 4...5.4 Ethane Dvna_mics Fitting: Having the isotherm parameters from the equilibria fitting, now we turn to the fitting between our theoretical dynamic model with the fractional uptake curves obtained in our laboratory. The only p a r a m e t e r required from the fitting
808
is the surface diffusivity at infinite temperature, D~o. For the activated carbon used m this study, we have measured independently in our earlier work that the tortuosity factor for pore diffusion is about 8 [102]. Knowing this the pore ~sivity for ethane can be calculated as 1.852 x 10 -6 m2/sec at 30 oc and 101kPa. The surface activation energy is assumed to be half of the adsorption energy, i.e. a=0.5. With the one parameter to be determined from the kinetic curve fitting, Dl~o, we fit the theoretical model with adsorption curves for three different particle sizes [112]. The shape of the particle is slab and the half lengths are 1, 2 and 4 mm. The external bulk concentration and the temperature are 29.4% and 30 ~ respectively. The following figure (Figure 9) shows the fit between the experimental data for 2 mm half length slab and 4 mm half length slab with the theory. The fit between the theory and data of l m m half length slab is also equally very good. The extracted surface diffusivity at infinite temperature is D~o = 7.59 • 10 -s m ~ / sec
(60)
The half times observed for these three particle sizes are 18, 70 and 290 seconds, which are proportional to the square of the particle half length, that is t0., oc R-~, justif3ring the model we proposed with the time scaled against the square of the particle radius. t o.g 0.8 .~9
0.7
= "~ fo
o.5
~ L
o.3
/
0.4
/ r
.,omha, . Th.o sab ength I
a~
/
o.2 o.1
o 0
i
i
100
200
i
time
i
i
i
i
400
500
600
700
(second)
Figure 9a. Fitting between the theory and experimental data of 2ram half length slab 1 {19 .~r
0.8 0.7
::3 m (~
~ 0 ~ U.
Q6 Q5
,,•
-,-
4rrrn ha~ k~ngt
- - m - -- s31~)~ r
Q4 113 112
Ill 0
J
i
!
|
|
I 3OO0
time(second)
Figure 9b. Fitting between the theory and experimental data of 4mm half length slab
809 4.5.5. Ethane Dynamics: Comparison between homogeneous and heterogeneous models To compare the homogeneous and heterogeneous models, we used ethane sorption data. The conditions were: slab particle of haft length 2.2 mm, 101 kPa, three temperatures (10, 30 and 60 oc) and three external bulk concentrations (5, 10 and 20 %). Table 13 Diffusivity T oc 10 30 " 60 .
.
.
.
.
parameters Dp m2/sec 1.5 lxl0 -6 1.68x10 -6 1.96x10 -6
Homogeneous model Heterogeneous model D~(m2/s) '" D~o(m2/s ) D---, 2.58x10 -9 702x10 -9 13.76x10 "9 3.41x10 -9 702X10 -9 17.84x10 9 6.26x10 -9 702x10 -9 24.83x10 "9
Using the heterogeneous model proposed here, we obtain a surface diffusivity at infinite temperature as 7.02x10 -7 m2/sec. We note the fit between the data and theory is excellent even though we use data of all temperatures and three bulk concentrations simultaneously m the fitting with only one fitting dynamic parameter, D~0. This justifies the validity as well as the potential of the mathematical model. If we use the homogeneous model (MSD) with Unilan as the overall isotherm to fit the data, the fit is also equally good (almost overlap with the fit by the HMSD model). As we have indicated before in the simulation that the adsorption is not too sensitive to the surface heterogeneity, this good fit by the homogeneous model (MSD) is not a surprise. However, even though the homogeneous model (MSD) fits the adsorption dynamics data of ethane well, it does not have the advantage of the heterogeneous that for each temperature the homogeneous model requires a different value of surface dJffusivity at zero loading. Thus, for three temperatures tested there are three values of the surface dLffusivity. On the other hand, in the heterogeneous model we need only one value, which is the surface diffusivity at infinite temperature and zero loading, D,o. Another point worthwhile to note from the fitting with the two models is that the mean surface diffusivity calculated from the heterogeneous model is about 4 to 5 times greater than the value of the surface diffusivity calculated from the homogeneous model. Take the 303 K case for example, the surface diffusivity of the homogeneous model is 3.41x10 -9 m2/sec while the mean surface diffusivity of the heterogeneous model is: D---~ = D~o exp(-aE/RT) -17.84• 10-9 m2/sec (61) which is about 5 times larger than the value of the homogeneous model. This is the case because in the heterogeneous model the surface dif~sivity of energy higher than the mean value is much lower and the fractional occupancy at any given time is much higher at those high energy sites; hence the overall surface
810 flux calculated from the heterogeneous will be the same as that calculated by the homogeneous model despite the mean D~ of the heterogeneous model is greater than the corresponding value of the homogeneous model. Knowing the value of the surface diffusivity at infinite temperature and zero loading, we then use the model to predict the desorption data of ethane from 2.2 mm half length slab activated carbon initially equilibrated with 10% of ethane at 30 oc and 101 kPa. The desorption environment is an inert gas stream. As can be seen in Figure 10 the desorption prediction is very good. 1
o.s
- * - Desorption data
0.7
.... a ....
0.8
0.6 0.5 0.4 0.3 0.2 0.1 0
O
,m ,I,,,
Q. L.
O U}
Q
0
HMSD model
I 1000
500
! 1500
....
I
2000
Time (seconds)
Figure 10. Prediction of HMSD model for desorption of ethane at 30 C
4.5.6. Propane Dynamics: The experimental data of slab particle having a half length of 2.2 mm, three temperatures (10, 30 and 60 ~ and three external concentrations (5, 10 and 20 % @ 101 kPa total pressure) were collected by the DAB method. The following figure (Figure 11) shows a typical fit of the heterogeneous model with the adsorption data at 30 oc and a bulk concentration of 10%. 1
.....
a
0.8 -~ as
.~J"
O.7
~-
0.6
--~ 9
0.4
0.5
as 0.3 ,-_ u. 0.2
/,
J.--~.,,sor~,,on,a,aI
if
I---m---H~D model ]
I
I
I
I
I
I
I
I
200
400
800
800
1000
1200
1400
1600
Time (seconds)
Figure 11. Fitting between the theory and the adsorption data of propane at 30 ~ for 2.2 mm half length slab
811 The fit between the heterogeneous model and the experimental data is very good and the parameters are listed in Table 14 for the three temperatures considered. Table 14 Diffusivity parameters Dp
Heterogeneous model
Homogeneous model D~(m 2/s)
oc
m2/sec
10 30 60
1.20x10-6 1.30x10-6 1.56x10 -6
D~o(m2/s) 1340x10-9 1340x10-9 1340x10-9
0.789x10 -9 0.928x10 -9
1.750x10 -9
"
~ (m2/s) 14.27x10 -9 19.26x10 -9 28.23x10 -9
The homogeneous model is also used to fit the data of propane. The fit is also good as the adsorption kinetics of a single component is not sensitive to the extent of the surface heterogeneity. The parameters extracted from the fitting of this homogeneous model for propane are listed in Table 14. To test the predictability of the two models, homogeneous and heterogeneous, we test them with the desorption data of activated carbon preequilibrated with 10 % propane and 30 ~ The particle size is 2.2 mm half length. The following figure (Figure 12) shows that the heterogeneous model predicts the data better than the homogeneous model, indicating that the activated carbon is heterogeneous toward propane.
---4k-....
I
I
I
2~
4~
6~
Desorption data .... H M S D m o d e l
I
I
I
I
I
I
I
800
1000
1200
1400
1600
1800
2000
Time (seconds)
Figure 12. Prediction of HMSD model for the desorption of propane 4.5.7 n-Butane Dynamics Fitting: We now turn to the dynamics of n-butane on a Ajax activated carbon particle having slab geometry with a half length of lmm. The conditions of the experiments are 30 ~ and 101 kPa. Three different bulk concentrations of nbutane were used, 12.9, 20.6 and 31.6 %. Figure 13 shows the adsorption fractional uptake curves both from experimental data of 20.6% external bulk concentration and from the fitting. We note that the fit is good and the fit for the
812 other two external concentrations is also equally good, with an observation that the dynamics is faster with an increase in the external bulk concentration. Like in the case of ethane, we calculate the pore diffusivity using the correlation [127] with a value of tortuosity factor of 8 obtained independently by Mayfield and Do [102]. The only parameter in the dynamic fitting is the surface diffusivity at infinite temperature and zero loading, Dgo. Fitting between the theoretical model and the experimental data of all three external bulk concentrations yields a value of D~o = 1.895 x 10 -8 m 2 / sec (62) For the case of ethane dealt with earlier, this value was found to be 7.59 x 10 -8 m2/sec, which is about four times higher than that of n-butane. This result seems to be consistent that a larger molecule such as n-butane having a higher adsorption ~ffinity toward the activated carbon surface should have a lower mobility than a smaller molecule, such as ethane. 1 0.9 o.e
~
0.7 .0.6 o.s
~9 U,.
~.~r
.--4F---Adsorptiondata[ 1C0=20.6%) ~ HMSDmodel
o.4 O.3
O.2 0.1 0
I
I
60
1 O0
I
I
I
I
15O
200
250
300
Time (seconds)
Figure 13" Fitting between the theory and the adsorption data of n-butane 4.5.8 The Predictability of The Model: So far we have applied our theoretical model to fit three hydrocarbons, ethane, propane and n-butane. In the ethane case we used adsorption dynamics curves of three different particle sizes (1, 2 and 4 mm half length slab) at 30 ~ 101 kPa and 29.4 % external bulk concentration to extract the only dynamic parameter, Dgo. In the other cases of propane and n-butane, we used the adsorption dynamic curves of three different bulk concentrations (12.9, 20.6 and 31.6 %) for one particle size and conditions of 30 oc and 101 kPa. Now we would like to use the p a r a m e t e r extracted from one set of data to predict data obtained for other conditions. Let us take the case of n-butane, the strongest adsorbing species _zmong the three paraffins tested. We now use the model to predict the adsorption dynamics curves for a slab particle with a half length of 2 ram, twice the size we used in the fitting. The other conditions are 30 oc, 101kPa and an external concentration of 20.6 %. The following figure shows the predictions of the model as well as the experimental data. As clearly shown m Figure 14, the prediction is excellent, showing the potential of this model as a predicting tool for adsorption kinetics in heterogeneous solids such as activated carbon.
813
We further test the model with another set of data for n-butane at 60 oc. The particle size is lmm half length slab, and the other conditions are 20.6% and 101 kPa. Figure 15 shows the predictions of the model, and the prediction is also excellent. 1 0.9 0.8 -~ 0.7 ~-
~ " 0.6
Adsorption data
( C 0 = 2 0 . 6 % , 30 C,
"~ 0.5 0 ;~ 0.4
[]
~ 0.3 U_ 0.2
2mm half length slab) HMSD model
0.t 0
t
I
I
I
I
I
I
200
400
600
800
1000
1200
1400
Time (seconds)
Figure 14: Prediction of HMSD for adsorption data of n-butane in 2mm half length slab 1 0.9
0.8
/.
o.7 . 0.6
"~ o.s .L
/
Oo~f
I.~ o.3
//
/'~
]
(C0=20.6%,
I
~,a~
I
g/
60 C, I
lmm half length I
I
0.2 0.1 0
0
50
100
150
200
250
Time (seconds)
Figure 15: Prediction of HMSD model for adsorption data of n-butane at 60 C with lmm half length slab 4.5.9. Sulfur Dioxide Adsorption: The predictions we have done so far show for single component systems that this model is of great potential as a predicting tool for design purposes. We now test the potential of the heterogeneous model by extending to another class of adsorbate, sulfur dioxide. Experimental data of equilibria and dynamics were collected [105]. The equilibrium data are presented in Figure 16 for three temperatures 25, 50 and 100 oc. The theoretical model using a local Langmuir isotherm and a Gamma distribution (Eq. 48) is used to fit the data [128]. The fit is excellent and the parameters resulting from this fit are listed in Table 15.
814
4I
_ 4 , _ Oata at 25 C I
s9 I
----~---Data at 50 C I Data at 100 C I
3
Z5
mmole/g
2
.~...---m
....~....11~..~ - ~ ' ' ~ ' ~ ' ~
1.5
1
"~
..- . - ~ ' ~
0.5
~""~'""
o 0
1
2
3
4
5
Pressure
-
-
I
I
" I
I
I
6
7
8
9
10
(kPa)
Fizure 16: Adsorption equilibria of SO 2 on activated carbon Table 15 Equilibrium parameters T qs oc (kmole/m 3) 25 9.207 .... 50 5.059 100 2.647
b0 (mS/kmole) 2.75 2.75 2.75
a ~ m o l e / m 3) 1.190x10 -5 8.242x10 "6' 6.037x10 -6
z
Dp, (10-6m2/sec) 3.8 4.2 5.0
0.03876 0.02963 0.01341
Having obtained the optimum parameters for adsorption equilibria, we now turn to adsorption dynamics and fit our model with one set of adsorption data to extract the surface diT~sivity at infinite temperature and zero loading. We choose the particle size of 2ram half length slab. The other conditions are 101 kPa, external bulk concentration of 2 % and three temperatures, 25, 50 and 100 ~ Like the other adsorbates tested so far, ethane, propane and n-butane, the pore diffusivity for sulfur dioxide is calculated from the correlation and tabulated m the above table for three temperatures. The pore diffusivity shows a modest increase with temperature.
0.8 ,,~
O.7
/" ..1~ ~"
0.6
"~
0.5
';
0.4
ml
0.3
p,
~
/
Adsorption data (C0=2%, 25 C, 2mm half length slab) HMSD model
0.2
I
I
I
I
I
5OO
1000
1500
2OO0
2500
Time (seconds)
Figure 17: Fitting between theory and experimental adsorption data
815 The fit was carried out between the heterogeneous model and the experimental data for three temperatures simultaneously. Figure 17 shows the typical fit between the theory and the data at 25 oc. An excellent fit is observed (theoretical fit is practically superimposed on the experimental data), and the optimum parameter for the surface diffusivity at infinite temperature and zero loading is 8.9 x 10 -9 m2/sec. This value is lower than the corresponding values obtained for ethane and n-butane, indicating a slower mobility of sulfur dioxide compared to ethane and n-butane. This is possibly due to the stronger interaction between the sulfur atom and the surface atoms. Having the dynamic parameter extracted, we tested the predictability of the model for other experimental conditions. Although not shown here, the predictions by the model agree very well with the experimental data. The success of the mathematical model shows the importance of the surface heterogeneity. To illustrate this heterogeneity, we will further test its role by applying the heterogeneous model to multicomponent systems. 5. MATHEMATICAL FORMULATION OF S O R P T I O N DYNAMICS IN M U L T I C O M P O N E N T SYSTEMS Surface heterogeneity in equilibrium studies has been recognised for a long time. Numerous efforts have been spent to study this influence in multicomponent equilibria, for example the approach proposed by Myer [126]. Another approach is to assume a local multicomponent adsorption isotherm at a single site, and then assuming a patchwise topography of the surface the overall muticomponent isotherm can be obtained by integrating it over the full adsorption energy distribution [129, 130] (see Figure 18). The approach assuming a local extended Langmuir isotherm and a uniform distribution [131] will be adopted here for our dynamics calculation.
S
.oMoGENous
TEROGENEOOS H
SITE LEVEL
MACROSCOPIC LEVEL
IAS or RAS .
.
,,
.
.
.
J ....... l.
1
INTEGRATION OVER ENERGY DISTRIBUTION
Figure 18" Two approaches of handling the multicomponent systems
816 The reason why we adopt this approach because it does allow the computation time to stay within a reasonable limit. We now consider a solid particle exposing to an environment containing N different adsorbate species of constant concentrations. The surface topography is assumed to have a patchwise configuration so that it will allow us to obtain the overall behaviour by a simple integration. For the development of the model we make the following assumptions: 1. The system is isothermal 2. The particle is large enough so that the resistance to the mass transfer is due to diffusion of free and adsorbed species along the particle coordinate 3. The pore diffusivity, film mass transfer coefficient and the surface diffusivity at infinite temperature and zero loading are constant 4. Both free and adsorbed species diffuse their local concentrations are related by the adsorption equilibrium 5. Extended Langmuir isotherm is assumed to be valid at the single site level 6. The cummulative energy distribution is the same for all species [ 129] 7. The adsorbed flux is driven by the chemical potential gradient 8. At any given site, the ratio of the activation for surface diffusion and the adsorption energy is a constant 9. The adsorption energy distribution is uniform Having made the assumptions, we now consider first the multicomponent equilibria, and then consider the multicomponent dynamics.
5.1 Adsorption Isotherm: Let k to denote the adsorbable species k. The local adsorption isotherm of this species takes the following extended Langmuir form: q[k;E(k)] = q=(k) b[k;E(k)] C(k) (62) N
1 + ~ b[j;E(j)] C(j) j=l
where b[k;E(k)] is the adsorption affinity of the component k at the energy level E(k), and C = [C(1), C(2)..... C(N)] are the concentrations in the gas. Here q=(k) is the saturation capacity of the component k. The adsorption affinity, b[k;E(k)], is assumed to relate to the adsorption energy and temperature according to the following form: b[k; E(k)] = bo(k) exp[E(k) / aT] (63) where the adsorption offinity at infinite temperature or zero energy level is assumed species dependent. If the distribution of adsorption energy for the species k is denoted as F[k;E(k)] and with the assumption of uniform distribution, it has the form: 0 1 F[k; E(k)] = Ema~(k) - E,~=(k) 0
E(k) < E=, (k) E,~ n(k) < E(k) < Emax(k) E(k) > Em~x(k)
(64)
817 The energy distribution has been normalised, i.e. oo
I F[k; E(k)] dE(k) = 1
(65)
0
We must note that the range of the adsorption energy is different from species to species. With the assumption of patchwise topography, the overall adsorption isotherm of the species k in a multicomponent mixture is: oo tlk; E(k)] C(k) qobs(k) = qs (k)I N F[k; E(k)] dE(k) (66) o 1 + ~ b[j;E(j)] C(j) j=l
To evaluate this integral with respect to E(k), we must relate the energy E(j) m terms of E(k). This is achieved by assuming the equality between the cummulative energy distribution, i.e. the correlation of adsorption energies between different species, all having uniform distribution, is [ 131]" E(j) - E~n (j) E(k) - E~n (k) = (67) E m a x ( j ) - Era1 n ( j ) E m a x (k) - Emin(k) Since we use the extended Langmuir isotherm to describe the local adsorption isotherm, it is required that all species must have the same saturation adsorption capacities in order to be thermodynamically consistent. 5.2 L o c a l Flux of S p e c i e s k: Assuming the driving force for the surface flow is the chemical potential gradient, we can write the local surface flux of the adsorbed species k at the energy level E(k) as follows
J , [ r , t ; k ; E ( k ) ] - - D ~ [ k ; E ( k ) ] q[r,t;k;E(k)] 0C(r,t;k) (68) C(r,t;k) Or This means that at any given time and any position within the particle the flux of the species k at the energy level E(k) is equal to the product of the surface diffusivity D[k;E(k)] and the ratio of two concentrations at t h a t energy level q[r,t; k; E(k)] (69) C(r,t;k) and the concentration gradient of the gas phase concentration. The ratio of concentrations can be calculated from Eq.(62), that is q[r,t; k;E(k)] b[k;E(k)] = qs (k) (70) C(r,t;k) 1 + ~ b[j;E(j)] C(r,t; j) j=l
Thus, the local flux written in terms of the gas phase concentrations is:
J~[r, t; k; E ( k ) ] - - D ~ [ k ; E(k)] q~(k)
b[k;E(k)] N 1 + ~ b[j;E(j)] C(r,t; j) j=l
0C(r,t;k) Or
(71)
818 The term in the curly bracket is the slope of the line connecting the origin and the concentrations {C(k), q[k;E(k)] } on the local multicomponent isotherm. This ratio of two concentrations is higher for energy sites than that for the lower energy sites. But even though the high energy site has a higher ratio, one must note that its surface diffusion coefficient is lower than that of the lower energy sites. If we express the local surface flux in terms of the adsorbed concentration gradient instead of the gas phase concentration gradient, we can use the total differentiation: 0C(r,t;k) dq[r dC(r,t;k)= _- 0q[r,t;j;E(j)] ,t;j;E(j)] (72) With this, the local flux can be written as: J~[r,t;k;E(k)] =-D~[k;E(k)] q[r,t;k;E(k)]~' 0C(r,t;k) ~q[ r, t; j; E( j)] (73) C(r,t) j_-i ~q[r,t; j;E(j)] Using the local extended Langmuir isotherm, we obtain the following equation for the local flux written in terms of only the adsorbed concentration
[106]: q[r,t; k; E(k)] N
qs(k) ~q[r,t; j; E(j)] (74) J,[r,t;k;E(k)] = -D,[k;E(k)] ~ qs(k) 5(k,j)+ ~r j_-~ q~(j) 1_ ~N q[r,t;i;E(i)] i--~ qs(i) This form is more complicated than that when written in terms of the gas phase concentration gradient (Eq. 71). Furthermore, the numerical computation using the gas phase concentration gradient is more stable than using the adsorbed phase concentration gradient. The surface diffusion coefficient is taking the form: D,[k;E(k)] = D~o(k) exp E- a(k)E(k) RT 1
(75)
where a(k) is the ratio of the activation energy for surface diffusion to the adsorption energy. In general one would expect that this is a function not just on the species but also on the adsorption energy as well, i.e. a[k; E(k)] (76) In the absence of any information regarding this parameter we will treat it as a constant for all species.
5.3 Mass Balance Equations: The mass balance equation of the species k in the particle is simply: ~~C(r, + t) at
(1- ~) ~ i q[r,t;k;E(k)] F[k;E(k)]dE(k) = (77)
819 for k=l,2 .... N. Here Jg is the surface flux determined from Eq. (71). This equation is for surfaces with patchwise topography. For random surface topography, the effective medium approach [90] is more appropriate. The adsorbed concentration at any point is related to the gas phase concentration at any point as follows: q[r,t;k;E(k)] - qs(k)
b[k;E(k)] C(r,t;k)
(78)
N
1 + ~ b[j;E(j)] C(r,t; j) j=l
The boundary condition at the exterior surface of the particle is the balance between the total flux and the flux through the stagnant film surrounding the particle: oo
r = R;
CD ~C(R,t____~)_ (1 - ~b)f J,[R,t;k;E(k)] F[k;E(k)] dE(k) - km[C b - C(R,t)] (79) P ~ o
The particle is assumed to be initially equilibriated with a stream of adsorbate at concentration of C i = [Ci(1), Ci(2) .... , Ci(N) ], i.e. b[k;E(k)] Ci (k) (80) t = 0; C(r,0;k) - C i (k) " q[r,0;k,E(k)]- qs (k) N 1 + ~ b[j;E(j)] Ci (j) j=l
We have defined a set of equations which describe the sorption kinetics of a multicomponent mixture in a single particle. When N=I, this set of equation reduces to the set for single component systems dealt with in the last section.
5.4 Nondimensional Equations: It is again convenient from the numerical computation standpoint to cast the dimensional equtaions into nondimensional form where all variables will be scaled with respect to their characteristic variables. For the gas phase concentration, the characteristic variable is either the concentration in the bulk for the adsorption mode or the concentration used to equilibrate the particle prior to desorption. Let this characteristic gas phase concentration be C0(k). For the adsorbed phase concentration, the characteristic variable is the value which is in equilibrium with the gas phase characteristic concentration, C O =[Co(D, C0(2), .... C0(N) ] , i.e. to
qo(k ) = qs(k)i
b[k;E(k)]NC~
F[k;E(k)] dE(k)
(81)
o 1 + ~ b[j;E(j)l Co(j) j=l
where E(j) is related to E(k) according to the matching between the cummulative of energy. For the particle coordinate, the characteristic length is the half length for the case of slab geometry or the radius in the case of cylinder and sphere. For the time scale, the charactersitic time should be chosen such that it does reflect the time scale for pore and surface diffusions. This is done as follows. Let E(k) be the mean adsorption energy defined as follows
820 oo
E(k) - r E ( k ) F[k;E(k)] dE(k)
(82)
o
then N a(j)E(j) +(1-~b)}-'D~o(j) exp qo(J) \L
J:~
-
0
j= 1
R
(83)
would represent the maximum mass transfer rate into particle over a distance R with C O being the concentration at one end and zero at the other end. Here A is the area for diffusion. The capacity to fill the particle with gas of concentration C O is:
E"
V , ~ C O(j) + (1 - ,)~-~ qo (J)
"
j=l
j=l
(84)
1
then the time it takes to fill this capacity is simply: V , C O(j) + (1 - ,)~-~ qo (J) L J--~ J--~
t*= A ,
(85)
Dp(j) Co (j) + (1- ,) =~D~o (j ) exp -
R For different shapes of particle, the ratio VR/A is R 2, R2/2 a n d R2/3 for slab, cylinder and sphere, respectively. Thus, apart from a constant factor, one can choose the time scale for diffusion is:
R2 ,
Co(j) + (1- @)Y',qo(j)
t" =
j--1 ~
Dp(j) C o ( j ) + ( 1 - , )
(86)
D~o(j) exp -
RT
-=
We will use this characteristic time to scale the time variable. If we now define the nondimensional variables and p a r a m e t e r s as shown in the following table Table 16 Nondimensional variables and parameters Independent { N variables ~b~-'Dp(j)~__lC~176
N
I a ( j ) E ( j ) ] (,)] exp - ~-~ J-~oJ
T-
o ~
R2 ,
Co(j) + (1 - ,)~--' qo(J) j=l
Dependent variables
C(k). Y(k) = Co(k)'
yb(k ) = Cb(k). Co(k)
Y,[k;E*(k)l = q[k;E(k)] qo (k)
r X
--
m
R
821 Energy parameters
E'(k)
=
E(k) RT
; f[k;E'(k)] dES(k)
F[k;E(k)l dE(k)
=
E" (k) = i E" (k) F[k;E" (k)l dE" (k) o
X.[k;E'(k)] = bo(k) e E'(k~Co(k) " H[k;E~(k)] : exp{a(k)[E'(k)-E'(k)]} Capacity parameters
G(k)
~Co(k)
=
*
% (k) =
Co(j) + (1 -*)~}-'~qo(J)
(1 - ~) qo(k) d~
Co(j) + (1 - *)~-'~qo(J)
j=1
Dynamic parameters
n(k) =
)=I
CDp(k) Co(k) { j~ N I a(])~(j)lq(j) l Dp(j) Co(j)+(1-~))-~D~o(j)~:. exp - ~-~ j o ]
I
(1 - ~b)D~o(k) exp 5(k) =
1
a(k)E(k) qo(k) RT
~b~ Dp(j) Co(j) + (1- ~)~D~o(j) exp j=~
Bi(k)
qo(J)
RT
j=~
k~(k)RCo(k)
=
Dp(l) Co(]') + (1 - ~)
.=
D~o(j)exp -
RT
qo(J)
the nondimensional mass balance equations are: G(k) aY(x,-c;k) +~.(k)
! Y. [x, "c;k; E*(k)] Nk;E'(k)ldE*(k)
=
n(k) 1 ~
X s
OY(x,vk)
.
x
5Ok)lx--T-:-V~x~x ~? H[k; E" (k)] Y, [x, Y(x, z; k;z;E" k)(k)] " aY(x"; ( kk) )"F[k; } E" c (k)]" g xdE* (87) Y. Ix, ~;k; E" (k)] = qs (k) h[k;NE" (k)] Y(x, z; k) q~ 1 + ~ h[j;E'(j)] Y(x,z;j)
(88)
j=l
where qo is defined m Table 16. The nondimensional boundary conditions are: i Y~(1, ~; k; E* (k)] 0Y(1,~;k) ~ -~)x FIk;E*(k)] dE'(k) = x = 1; Tl(k)~Y(1,~;k) +5(k) H[k;E*(k)] Y(1,~;k) 0x o Bi[Yb(k)- Y(1,x;k)] (89)
+
822 This set of nondimensional equations is readily solved by the combination of the orthogonal collocation method, which discretizes the spatial variable x, and the differential-algebraic equations solver, which integrates the resulting set of coupled discretized equations. The integral terms revolving energy distribution are evaluated by the numerical Gaussian quadrature. 5.5 S i g n i f i c a n c e
of Parameters:
The nondimensional energy is scaled with the molar thermal energy, RT. It could be scaled with the following mean adsorption energy oo
E(k) = ~E(k) F[k;E(k)]dE(k)
(90)
0
but the choice of the molar thermal energy is more convenient and hence is used m our analysis as we did m the single component analysis. The parameter )~[k;E*(k)] represents the adsorption affinity of the patch having energy E(k) (or E*(k)). If it is much greater than unity, the affinity is very strong. While it is much less than unity the affinity is weak and hence the local adsorption isotherm for that patch of sites will follow the Henry law isotherm. The function H[k;E*(k)] represents the deviation of the surface diffusion coefficient from the mean value -( a ( k ) E(k)l Dr (k) = D,o (k) exp ~ -) (91) The parameters o(k) and o~(k) are capacity parameters of the species k. Let W is the total amount of adsorbates taken up by the particle at equilibrium per unit particle volume, i.e. N N I moles of all adsorbatesl W = ~ Co(j) + (1 - ~ ) ~ qo(J) (92) j=l j=l particle volume The parameter o(k) is the fraction of W contributed by the species k that resides m the void space inside the particle. The parameter, ~ ( k ) , is the fraction of W contributed by the species k residing m the adsorbed phase. This means that N
j=l
For most practical adsorbents, the fisrt parameter, ~(k), is very small, usually in the order of 10 .2 and 10 -3 . The second parameter is, therefore, of order of unity for most practical sorbents, especially microporous adsorbents. The parameter 5(k) is the dynamic parameter. It describes the ratio of the adsorbed phase diffusion flux of the species k to the diffusion flux of all species. The parameter ~(k) is also a dynamic parameter, describing the ratio of the pore diffusion flux of species k to the total diffusion flux. With these two definitions N
)-~ [5(j) + ~(j)] = 1 j=l
(94)
823 The parameter Bi(k) is the measure of the film diffusion flux of species k relative to the total diffusion flux into the particle. 5.6 R e s u l t s & D i s c u s s i o n : We used a model system of ethane and propane as adsorbate and Ajax activated carbon as the adsorbent to illustrate the predictability of the multicomponent heterogeneous model. 5.6.1 Binary Adsorption Kinetics Predictions The parameters for the multicomponent heterogeneous model are basically those obtained in single component equilibria and dynamics fitting. Thus, there are no fitting parameters in the multicomponent heterogeneous model. This section will deal with binary adsorption data, that is the particle is initially free from any adsorbates. It is then exposed to a constant environment containing two adsorbates, and the adsorption process begins. One would expect that in this simultaneous adsorption mode the species that has lower affinity and higher mobility will penetrate the particle faster. Hence it will adsorb onto the surface to the extent as if it is the only species present m the system. The other species, which is the species that has higher affinity and lower mobility, will penetrate at a lower rate, but when it does come in it will displace the adsorbed molecules of the light species, resulting in the overshoot of the light species in the plot of the amount adsorbed in the particle versus time. The conditions of slab having half length 2.2 mm, 10 oc, 101 kPa total pressure, and the constant environment has 5% propane and 5% ethane. Figure 19 shows the predictions of the heterogeneous model as continuous lines and the experimental data are shown as symbols. As shown in the figure, the prediction is excellent. The prediction of the homogeneous model, although is not shown m the figure, underpredicts the experimental data. 2
~ . K/ _
1.8 1.6 ~ 1.4
.2
0.8
I~
0.6
~
~
--4F-- Ethane data ~ Propane data ~ HMSD (Ethane) ~ ~ H ' MS "D ~ (Propane] ' " "
~
0.4 0.2 0 0
500
1000
1500
2000
2500
3000
3500
4000
Time (seconds)
Figure 19: Prediction of HMSD model for the binary adsorption of ethane and propane
824
We note from the figure that the prediction for the heavy species (propane) is better than that for the lighter species (ethane). Within the first 600 seconds (i.e. before the maximum in the uptake is reached) ethane is adsorbing onto the surface. Beyond 600 seconds, some of the ethane molecules are displaced by propane causing an increase in the ethane intraparticle concentration inside the particle, thence a net desorption of ethane. This phenomenon of displacement of a light species by a heavier one will cause an overshoot m the plot of uptake versus time (Figure 19). Due to this complex evolution in the concentration profile of the light species inside the particle, it is therefore not surprising that the prediction of the light species is not as good as that for the heavy one. Nevertheless, the prediction of the binary adsorption is excellent. We then further test the heterogenous model with the following conditions, and the results are shown in Figures 20. 9 10 oc, 101 kPa, 2.2 mm half length slab, and 10% ethane and 5% propane ~ 10 ~ 101 kPa, 2.2 mm half length slab, and 20% ethane and 5% propane 1.0
1.6 1.4
e ~9 1.2
~
/r j~/ /~
"~'~
o.2 o
/
Ethane
data
Propane
data
{10%) (5%)
HMSD (Ethane) HMSD (Pr~
f~$: ....
o.0
~ o.6 ft. o.4
~ ~ "'--."~..... ~ "~~/~___
/.~f
t
I
600
I
1000
1500
I
I
2000
2500
I
I
3000
3500
I
4000
Time {seconds)
Figure 20a- Prediction of HMSD model for binary adsorption data of ethane (10%) and propane (5%)
1.4 at
~
1.2
"0 o.e ._
~
o.s o.4
.~sS
~. :.~.~-~" . . . .
-.-~
..~__. Ethane data (20%1 --~-- Propane data 15~ ~ HMSD (Bhane) I --~,:---HMSD (Propane) |
9 I
5OO
i
1000
I
1500
i
2ooo
I
25oo
i
30oo
Time ( s e c o n d s )
Figure 20b: Prediction of HMSD model for binary adsorption data of ethane (20%) and propane (5%) Detailed testing of the heterogeneous model with other combinations of concentrations and temperatures was given in Hu and Do [114, 115].
825 5.6.2 Binary D.esorption Kinetics Predictions We now turn to the predictability of the heterogeneous model as well as the homogeneous model in the simultaneous desorption mode. The slab particle of half length 2.2 mm is initially equilibrated with 10% ethane and 10% propane at 30 ~ and 101 kPa. It is then exposed to the environment containing with an inert gas stream. The predictions of the heterogeneous model and the homogeneous model are shown in Figure 21. It is clear from the figure that the heterogeneous model is a better choice in terms of the predictability. 1 0.9 0.8
-~ ~
~
&
..~ 0.7
~ .... MSD (Propane)
"~ O.S ~
.4 0.3
HMSD (Ethane) HMSD (Propane)
. 0.6
~.
Ethane data (10%)
---~--- Propane data (10%)
.
0.2 0.1 0
, 500
-,1000
-.... ,--~ 1500
...... 1............ 2000
2500
3000
T i m e (seconds)
Figure 21. Prediction of HMSD model for binary desorption data of ethane and propane 5.6,3 Binary Displacement Kinetics Predictions: To finally test the models, we study their predictability under the displacement condition, i.e. the particle is pre-equilibrated with one adsorbate and is then exposed to a constant environment of another adsorbate. The first species will diffuse out while the second species will diffuse countercurrently into the particle. We first study the pre-equilibration of the activated carbon particle with 10 % ethane at 30 oc and then its displacement from the particle under a constant environement of 10 % of propane. The particle size is 2.6 mm and the total pressure is 101 kPa. The following figure (Figure 22) shows the trajectories of ethane and propane under such displacement condition. We note from the figure of displacement data that the predictions are not as good as those which we have observed for the adsorption and desorption cases. The poor prediction is perhaps due to the strong sites of the carbon surface which may not be properly accounted for. This is reflected in the lower prediction of the ethane desorption data in Figure 22.
826 1
I
......
_~.~
o.g
0.8
Wx ~k ~
0.7 I ~ 0.6 tO 0 L IJ=
0.5
,,~"~ "~_,,.-~"-'~I'"~J'-:-r ---Ethane desorption /.~..m''" I data (10~
"~...>//;x /~
I---m.... Propane I adsorption data
OA
g, HMSD (Ethane) ~ o p a n e )
0.3
~
(1.2 0.1
1
.........
i
"t
. . . . . .
0
100
200
300
400
500
600
Time (seconds)
Figure 22: Prediction of HMSD model for binary displacement data of ethane and propane We now further test the displacement by swapping the role of ethane and propane. This time the activated carbon was pre-equilibrated with 10% propane and then propane was displaced with a constant environment containing 10% ethane. The total pressure is 101kPa and the particle is 2.6 mm slab. The following figure (Figure 23) shows the good prediction of the multicomponent heterogeneous model. 1 0.9 0.8
\
..~ 0.7 - 0,6 "~ 0 "~ U.
0.5 0.4 o.3 0.2
~':~':2::~...:L.y...:__.............
o.1 o~ o
I
I
I
I
I
500
1000
1500
2000
2500
Time (seconds)
~'~
I
3000
Figure, 23: Prediction of HMSD model for binary displacement data 5.6.4 Ternary Adsorption Kinetics Predictions: We finally test the heterogeneous model to a ternary system of ethane, propane and n-butane. The particle has a slab geometry with a half length of 1.2 ram, and is initially free from any adsorbate. At time t=0 +, the particle is exposed to a constant environment of 10 % ethane, 10% propane and 10 % n-
827 butane, and the adsorption process begins. The temperature of the system is 30 oc. Figure 24 shows the trajectories of all three adsorbates as function of time. We see that the predictions of ethane (the lightest species) and n-butane (the heaviest species) are excellent, while the prediction of propane dynamics is only reasonable. Other conditions for simultaneous adsorption, desorption and displacement were tested, and the agreement between the data and the predictions are generally very good [132, 133].
4.5
0 ..~
3.5
Q-
3
C O (J ,14.
I
i:~
]
/
~ Ethane data ~ Propane data --&-- n-Butane data ~ HMSD (Ethane) .....~,-... HMSD (Propane) ....-$......RUeD (n-Butane)
\
/ ~
\~ \~_ ~~X.~.lllk
2.5 2 1.5
m
0.5
0
100
200
300
400
500
600
700
800
900
1000
Time (seconds) Figure 24: Prediction of HMSD model for ternary adsorption data of ethane, propane and n-butane The study of multicomponent predictions of binary adsorption dynamics, binary desorption dynamics, binary displacement dynamics and ternary adsorption dynamics has shown the promise of the heterogeneous model as one of the most sophisticated model available to date to deal with multicomponent mixtures.
6. MICROPORE SIZE DISTRIBUTION INDUCED HETEROGENEITY The effect of heterogeneity was accounted for by the use of the energy distribution as shown m Sections 4 and 5. The heterogeneity is due to the adsorbent and adsorbate pair. One aspect of the adsorbent that could give rise to
828 the heterogeneity is the micropore size distribution. This heterogeneity is called the micropore size-induced heterogeneity. The energy of interaction between the micropore and the adsorbate molecules is a strong function of the size of the adsorbate as well as the size of the micropore. The effect of micropore size distribution in the study of equilibria is fairly well studied [134-136]. It was studied in the steady state analysis of surface diffusivity [137]. However, its role in dynamics study is only recently investigated by Hu and Do [138, 139]. 6.1 Effect o f M i c r o p o r e S i z e D i s t r i b u t i o n on A d s o r p t i o n i s o t h e r m The local adsorption isotherm in a pore having a width of 2rp is assumed to take the form of fundamental equation, such as the Langmuir equation, or the more sophisticated Hill-de Boer equation [135]. The choice of the local isotherm is of secondary importance than the micropore size-induced heterogeneity. So we will take the Langmuir form as the local isotherm
b(E)P boeE/RTP q(E) = qs 1 + b(E)P = qs 1 + boeE~RTP
(95)
where the interaction energy E between the micropore and the adsorbate molecule is a function of the micropore size, that is E = E(rp)
(96)
Thus, if the micropore size distribution is known, we can write the following equation for the overall adsorbed concentration. r.,~,
boeEiR,rp
(97)
q = f qs 1 + boeE~RTp • f(r~)d~ rmm
where f(rp) is the functional form for the micropore size distribution. The above equation can be quite readily evaluated if the functional form between the interaction energy between the adsorbate and the micropore having size rp is known. This relationship is possible with the assumption of the geometry of the micropore and the use of the following equation for the Lennard-Jones potential between two bodies
12o12
A
(98) k r)
If we assume that the micropores of activated carbon is slit shape and is infinite in extent, we can integrate the above equation over the whole layer. The result is then the well-known 10-4 potential [134] given below. 5
2
lo
4 2( (99) -
2
-z
829 where z is the distance between the adsorbate molecule a n d one of the lattice plane, rp is the half width of the slit-shape micropore. The potential energy e~ is the depth of the Lennard-Jones potential m i n i m u m for the case of a single lattice plane and ~2 is the position at which this m i n i m u m occurs. Investigation of the potential energy equation (99) shows t h a t when the two lattice planes are far apart, the m i n i m u m potential energy is s~. When the two lattice planes are getting closer, the attractive forces of the two layers enhance this minimum and when the half width rp is equal to the collision diameter c~2 the potential minimum is twice as much as s~. When the two lattice planes move even closer, the repulsive forces become dominant; hence the minimum potential energy becomes smaller t h a n 2s~. The following figure shows the schematic relationship between the m i n i m u m potential energy and the pore haft width, rp.
E
rp Figure 25. The relationship between the interaction energy a n d the pore size
Thus, knowing the relationship between the interaction energy and the pore size, the adsorbed concentration can be readily evaluated from eq.(97). The lower and upper limits of eq.(97) need some clarification. The lower limit r ~ , is taken to be either the value at which the interaction energy is equal to t h a t of a single lattice or the value at which the interaction energy is zero. If the former is chosen, the minimum pore size is 0.8885~2, and if the latter is used the minimum pore size is 0.858~2. The results using these two limits do not differ much because the pore volume between these two m i n i m u m pore sizes is negligible, compared to the total micropore volume. Using the Gamma distribution of the following form
830
f(~)=
q'+l r~" exp(- qr~)
r(v+l)
(100)
Using this form in eq. (97) the adsorbed concentration can be readily calculated as E is related to the pore size according the minimization of eq.(99) or given in Figure 25. Applying this to the isotherm data of ethane and propane on activated carbon at three temperatures 10, 30 and 60 ~ shows that the fitting is excellent [138]. The fit is slightly better than that using the uniform energy distribution. The reason for this slight improvement over the traditional use of the uniform energy distribution is that when the gamma micropore size distribution is converted into an energy distribution, this pore size-induced energy distribution exhibits a high density at the high energy site as shown in the following figure (Figure 26).
F(E)
E Figure 26. Micropore size-induced energy distribution
The micropore size-reduced energy distribution is then fitted by a polynomial for the subsequent use in the dynamics calculation. The model equations for the dynamics studies are the same as those presented in Section 4. The only difference is that the energy distribution is obtained from the gamma micropore size distribution, instead of the uniform energy distribution. Hu and Do [138, 139] have studied this in great details, and we have shown that the approach using the micropore size distribution as the source of system heterogeneity seems to provide a better description of the desorption data than the approach using the uniform energy distribution. Further work [140] is being carried out to extend this concept to multicomponent systems.
831 7 CONCLUSIONS AND F U T U R E WORK We have shown a new mathematical model, utilizing the surface energy distribution to understand the sorption dynamics into the particle. The model has shown great promise in its role as a predictive tool to understand dynamics under a wide range of conditions. Regarding the future work, this author suggests that the following aspects could be explored to further our advances in this area 9 The separate role of surface energy and structural heterogeneity 9 The role of pore size distribution in structural heterogeneity 9 More experimental data of other binary and ternary systems 9 The heat effect in the heterogeneous model 9 More detailed structured models 9 Effect of pore evolution on the equilibrium and dynamics parameters of the heterogeneous model
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W. Rudzitiski, W.A. Steele and G. Zgrablich (Eds.) Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces
Studies in Surface Science and Catalysis, Vol. 104 9 1997 Elsevier Science B.V. All rights reserved.
837
Sorption rate processes in carbon molecular sieves
J.M.D. MacElroy a , N.A. Seaton b and S.P. Friedman b a Department of Chemical Engineering, University College Dublin, Belfield, Dublin 4, Ireland. b Department of Chemical Engineering, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, United Kingdom.
1. INTRODUCTION In the design of laboratory or industrial fluid/solid sorptive separation processes the relative adsorbate selectivity of a given microporous adsorbent is, with few exceptions, one of the most important issues to be taken into consideration. This aspect of sorption processes is acknowledged by recent advances in separation science and technology which are, in large part, associated with the development of structurally and/or chemically modified porous substrates. Examples include (i) zeolites, which have had a significant impact on the petrochemical industry; (ii) chemically bonded silica supports in gas and liquid chromatography; (iii) monoclonal antibodies immobilised on inert supports such as agarose or porous silica for affinity separation of biochemical mixtures; and (iv) activated carbons and carbon molecular sieves. In many of these cases the tailoring of the adsorbent for a specific application is influenced by the presence of highly nonuniform pore structures and/or energetically heterogeneous internal surfaces and both of these characteristics must be given careful consideration during substrate design and/or sorbent selection. An additional feature of the solid substrates involved in a number of the examples cited above, and in the case of many other adsorbents currently employed in laboratory analysis or industrial practice, is that the principal constituent of the solid phase is either silica or graphite. This suggests that future development of new, highly selective adsorbent materials would be greatly assisted by accurate theoretical models of the (heterogeneous) surfaces of these two materials and the underlying phenomena involved during pore formation in the bulk solid phase. In this chapter a specific class of heterogeneous carbon systems, carbon molecular sieves (CMS), are investigated. Carbon molecular sieves are of particular interest in this work in view of the intimate interaction believed to exist between the microscopic and macroscopic levels of description. A unique case of this interaction is provided by one of the principal uses to which this material has been put, namely the separation of air into its major constituents [Juntgen et al.,
838 1981], and it is the underlying mechanism associated with this process which is examined here. It is now generally accepted that the physical step which controls the separation of oxygen/nitrogen mixtures in CMS is kinetic in origin and is a direct result of the existence of pores in a very narrow molecular size range within the carbon medium. The raw materials most frequently used in the production of CMS are naturally occurring bituminous coals. These coals possess a wide range of pore sizes, including those which are largely responsible for permselectivity of oxygen and nitrogen; however in order to achieve both the capacity and selectivity required for commercial separation of air, extensive physical and chemical modification of the basic raw material is required [Juntgen et al., 1981]. The key steps in the treatment of coal involve oxidation in air to form oxicoal followed by heat treatment under an inert atmosphere with subsequent reduction in pore width via carbon deposition. The precise conditions employed in the latter step are central to the production of a CMS material which is suitable for air separation. In one study [Moore and Trimm, 1977] it was found that if carbon deposition was carefully controlled at 16 mg/g of sample, nitrogen adsorption within the carbon was dramatically reduced without significantly influencing the oxygen sorption capacity and therefore even a small amount of deposition can result in high selectivities. 02/ N 2 selectivities of between 3.0 and 30 have been reported [Chihara et al., 1978; Chihara and Suzuki, 1979; Ruthven et al., 1986; Ruthven, 1992; Kikkinides et al., 1993; Chen et al., 1994] which are very significant in view of the fact that the kinetic diameters of these two molecules differ by less than 0.03 nm. While extensive experimental work on adsorption and diffusion of oxygen and nitrogen within CMS has been conducted it would appear that very little theoretical work has been undertaken to explain the above selectivity effect at a molecular level. One of the few attempts to develop such a molecular model was reported by Chihara et al. (1978a). They approached the problem from the point of view of absolute rate theory and with the aid of simplifying'assumptions they arrived at the following expression for the adsorbate diffusivity at low loadings in graphite pores.
DM
=la./Ea exp(- Ea ) 6 V2m kBT
(1.1)
where a is the distance between nearest-neighbour sorption sites on the graphite surface, E a is the activation energy for diffusion, m is the molecular mass of the diffusing particle, k B is Boltzmann's constant, and T is the absolute temperature. The primary objective of the studies reported by Chihara et al. was to assess the validity of the preexponential term in Equation (1.1) by using experimentally determined values for the activation energies for a variety of adsorbates. While agreement was fair in a few cases for simple atomic or molecular species, it was concluded that the above expression could only provide qualitative estimates for the diffusivity in CMS. Chihara et al. also attempted to relate the activation energy E a to slit width and although reasonable results were obtained it was later shown [Sakoda et al., 1993] that their analysis was founded on an incorrect premise, namely that the activation barrier to diffusion does not arise within a slit pore formed by two parallel homogeneous graphite planes, as assumed by Chihara et al., but is located at the edges of these crystal planes. More recently, Chen et al. (1994) investigated the loading dependence of the diffusivities in
839 oxygen/nitrogen mixtures by generalising a model originally proposed by Yang et al. (1973) for a pure adsorbate
E 1 ]
DM--DMI0_0 1-(1-~)0
(1.2)
where 0 is the fractional amount adsorbed based on the saturation limit of the Langmuir isotherm and ~, is the ratio of the sticking probabilities for an adsorbate molecule interacting with a preadsorbed molecule and with a vacant sorpfion site. The coefficient DMI0=0 in this expression is equivalent to Equation (1.1) and therefore, while Equation (1.2) (and its generalised multicomponent form [Chen et al., 1994]) permits correlation of the loading dependence of DM, no additional insight is provided into the underlying mechanism for diffusion. Furthermore, both Equations (1.1) and (1.2) are based on general assumptions of surface homogeneity and the effects of energetic heterogeneity and the influence of a pore size distribution and pore connectivity are not taken into consideration. We believe that an atomistic description of the micropore structure coupled with a realistic pore network model can provide the basis for a rational approach to this problem. Within the last fifteen years a theoretical technique which has grown in popularity in studies of surface and pore phenomena is direct computer simulation of sorbate/sorbent systems. The simulation method of choice depends on the level of description needed to adequately represent the sorbate/sorbent phenomenon of interest and to date two independent procedures have emerged: (i) lattice simulation and (ii) molecular (or particle) simulation. Of primary concern in lattice simulation is the influence of pore size distribution and connectivity and it is usually assumed that the properties of the sorbate within the individual pores of the pore network can be represented by a thermodynamic analysis or transport model which is itself unaffected by the topology of the network. In molecular simulation, the influence of the atomic structure of the pore surface and pore apertures is of most interest. Early work in molecular simulation focussed on the development of methods for simple structureless atomic fluids in idealized pores (slits, cylinders) and in the last five years the incorporation of realistic interfacial atomic features and pore spaces for both silica [MacElroy and Raghavan, 1990, 1991; Brodka and Zerda, 1991; MacElroy, 1993; Kohler and Garofalini; 1994] and carbon [Bojan and Steele, 1993; Sakoda et al., 1993; Segarra and Glandt, 1994] based materials have been undertaken. These studies have demonstrated that molecular simulation of adsorbents of direct industrial interest is now feasible on current computers. Rather than give a detailed review of simulation procedures themselves since the techniques involved have been described and discussed rigorously in a variety of sources [Nicholson and Parsonage, 1982; Allen and Tildesley, 1987; Sahimi et al., 1990], in this paper we will demonstrate the general approach to simulation of microporous adsorbents by considering CMS as a specific example. In the next section we describe a new model for the micropore structure and the pore network in carbon molecular sieves which incorporates the principal structural features currently considered to exist in these materials. In Sections 3 and 4 the properties of oxygen and nitrogen adsorbed within the pores of this model are evaluated via Monte Carlo, molecular dynamics, and lattice simulation techniques.
840 2. A M O D E L PORE STRUCTURE FOR CARBON MOLECULAR SIEVES The requirements of a pore structure model are that (i) it should incorporate the main features of the pore structure that affect the process of interest - diffusion in our case; (ii) it should be as simple as possible, consistent with an adequate representation of experimental data. It is convenient to look at the pore structure at two levels, each of which corresponds to a different length scale: (i) the single pore model and (ii) the pore network model (a collection of pores, individually represented by the single pore model and having a distribution of sizes, combined with a topology). The effective diffusion coefficients of nitrogen and oxygen are calculated by first solving the diffusion problem at the single pore level, using the adsorbateadsorbent interatomic potentials as an input. This gives the diffusion coefficients in an individual pore as a function of pore size. These data then serve as inputs to the pore network calculation which yields the effective diffusion coefficients of the CMS. As we will see, the calculation of the effective diffusion coefficients is effectively a process of successive "integration" from the atomic level to the single pore level and then again to the pore network level. Of course, for a real solid, the distinction between pores and junctions where the pores meet is arbitrary; the pore space cannot be uniquely attributed to one or the other. Nevertheless, in the absence of detailed structural information of a kind not currently obtainable, this distinction must be made.
2.1. The Randomly Etched Graphite Single Pore Model Pores in activated carbons (including carbon molecular sieves) are largely bounded by graphitic planes. This suggests a single pore model formed by graphite planes of some thickness (perhaps assumed to be essentially infinite) at some orientation to each other. In a real carbon the planes will certainly not be parallel but, in the absence of detailed orientational information, we assume for simplicity that they are (see Figure 2.1a). This model has been widely used in theoretical studies of adsorption [Steele, 1974; Nicholson and Parsonage, 1982; Talbot et al., 1985; Rhykerd et al., 1991; Lastoskie et al., 1993a, b; Bojan and Steele, 1993] and in a number of cases it has been employed to investigate diffusion in liquids conf'med within porous carbon materials [see, for example, Rhykerd et al., 1991]. The individual graphite basal planes are also most frequently assumed to be structureless i.e. the fluid particle/solid interactions are considered to take place with an infinite series of smeared carbon planes whose averaged atomic number density corresponds to the actual carbon atom surface density in an individual basal plane (n c = 38.6 atoms/rim2). Further details of the fluid/solid interaction potential involved in this idealized model will be discussed below in Section 3.1; however at this point we recall a comment made earlier in Section 1 with regard to efforts by Chihara et al. (1978) to theoretically quantify the activation energy for diffusion within CMS. It has been shown [Sakoda et al., 1993] and confirmed in an independent study [MacElroy, unpublished] that the zero loading diffusion coefficient is very large in a graphitic slit-shaped pore irrespective of whether the walls of the pore are atomically smeared or possess the explicit atomistic structure of the basal planes of graphite. Therefore, within the scope of the single pore/lattice models under consideration in this work such an idealized
841 model slit pore was deemed unsuitable. For this reason a new approach has been developed which implicitly incorporates the crystal edge effects suggested by Sakoda et al. in a more general way and which, as will be shown later, provides realistic estimates for both the equilibrium and transport properties of the oxygen-nitrogen system in CMS.
............
~::~i~i~:r....
i
~l~~,,dl~/'~ Spacing = 0.335 nm . ~ . ~ ~ _ _ _ _ ~ ~ , . . . . . ~ ~ - ' N- - ' ~
~
~
~
'
~
B
.
~
Pore
', ~ " ~ "
I
'
i
,
,
y
C-C bond = O.142 nm X
Figure 2.1. (a) Schematic diagram of an idealized slit pore in crystalline graphite; (b) Schematic diagram of a slit pore with etched graphitic walls.
The new model is illustrated schematically in Figure 2.lb. In this model the innermost basal planes on both sides of the slit pore are etched to the same degree with the result that significant energy barriers to the motion of the admolecules exist in the direction parallel to thesolid surface. When the pores are wide enough to admit at least one admolecule to the space between these innermost planes then the diffusion mechanism is primarily via surface "hopping" trajectories (Figure 2.2a). However when the pore width is smaller (Figure 2.2b) the admolecules are severely hindered due to their physical size and we suggest here that it is structures of this type which form the rate controlling diffusional barriers within CMS. (It is of interest to note that the structure shown in Figures 2. l b and 2.2 could also be considered the result of carbon deposition onto the graphite surface and the fact that deposition is one of the most important steps involved in the production of 0 2 / N 2 selective CMS lends tentative support to the physical realism of the proposed model). The simulation technique employed here to generate randomly etched graphite (REG) pores of the type shown in Figure 2. lb is summarized as follows: (i) Two graphite basal planes, periodically imaged [Nicholson and Parsonage, 1982; Allen and Tildesley, 1987] in the x and y directions and fully occupied with carbon atoms, are
842
Carbon atom (a) ~ Background basal plane
T h
z=+h/2
' ....
OG-O~
.ji !ii!ii
d!i!~, ~
-#~
z = - h/2
Figure 2.2. Schematic diagram of the side view of the randomly etched graphitic pore. In (a) the pore is wide enough to accomodate gas molecules between the two etched faces; in (b) the molecules are confined to holes generated by the etched surfaces.
constructed a distance (h - 2A) nm apart where A is the distance, 0.335 nm, between the basal planes in bulk graphite. These two planes are also offset relative to one another in the same manner as two neighboring planes in solid graphite (Figure 2.1a). The basal planes at z = _(h/2) (see Figure 2.2) and at distances further from the origin are all assumed to be structureless. (ii) Carbon atoms are randomly selected and removed from the atomistically modelled surface planes subject to the restriction that no dangling (singly bonded) carbon atoms are generated during this "etching" process. Etching continues until the desired partial occupancy of each individual plane has been achieved and the resulting surfaces contain only doubly or triply bonded carbon atoms. (An implicit assumption in the model is that the third coplanar bond of a carbon atom on the outer edges of a given cluster of hexagonal rings involves a univalent species such as hydrogen. The latter components are not included in the overall fluid/solid interaction to be described below.) An example of one such surface is shown in Figure 2.3 for a final carbon atom occupancy of 50% (the etched plane occupancy employed in all of the simulations reported in this chapter). This figure also provides some of the details of the size of the fundamental cell used in the computations (L x = 10~/3 and Ly = 18, both in units of the C-C bond length g cc = 0.142 nm). Figure 2.3 also demonstrates that a significant range in carbon ring cluster sizes can result from the etching algorithm described above and the cluster size distribution obtained for an ensemble containing 1000 etched surfaces is provided in Figure 2.4. The average number of hexagonal rings per cluster in this case is 4.56 and as may seen from Figure 2.3 a significant portion of the clusters appear as isolated "aromatic" rings. In the interests of simplicity no provision was made for the possible relaxation of the etched planes during the etching process and the (stationary) low molecular weight groups of aromatic rings were retained in the model. The significance of these simplifications will be investigated and, if necessary, eliminated in future work.
843 y = +9
y=0
y---9 -5 ,~
X - - -
Diatom
Carbon atom
Figure 2.3. Plan view of an etched graphitic surface. The relative sizes of the collision diameters of the carbon atoms and the atoms of the gas molecules are illustrated schematically. All length scales are in units of the C-C bond length, 0.142 nm, in graphite.
0.5 0.4 0.3 f(NR) 0.2 0.1 0.0
i
0.1
|
|
i
1|1
i
1
NR
10
|
|
i
|11
100
Figure 2.4. Carbon ring cluster size distribution. N R is the number of rings in a cluster.
844 2.2.
The Pore N e t w o r k Model
We now turn to the formulation of a model pore network consisting of interconnected REG pores, and in particular to the selection of a topology. A real pore network is likely to be highly disordered, with pores assuming a wide range of orientations, lengths and degrees of connectedness to their neighbours. As we do not have this information, we are free to adopt any one of a very large number of pore network models. It is natural to ask at this point whether the results are likely to be sensitive to the model we adopt. If they were, we could have little confidence in the generality of our results. Fortunately, it turns out that, as long as the network has reconnections, the transport properties of a network depend strongly on the dimensionality and the mean coordination number of the network, but only weakly on other aspects of the topology [Jerauld et al., 1984; Arbabi and Sahimi, 1991]. Thus, provided we have the right dimensionality (three in our case) and can incorporate an appropriate range of coordination numbers, we can choose the form of the network for convenience. In this work, we use the simple cubic lattice; the bonds of the lattice represent the pores and the nodes represent the pore junctions. (We shall use the term bond and pore interchangeably in the remainder of this chapter.) It is worth noting in passing that the Bethe lattice, which has an endlessly branching structure, and has the advantage of greater tractability, is unsuitable because of its lack of reconnections. In incorporating the REG single pore model in the pore network model, it is necessary to assign a f'mite length and width to the pore (see Figure 2.5). Constant pore length is imposed by the geometry of the simple cubic lattice. This is certainly unrealistic but, in the absence of experimental information, there is little point in modifying the underlying lattice to incorporate an arbitrary distribution of pore lengths. Similarly, we assume that the breadth of the pores is fixed. If the pore length is much greater than the pore widths of interest, the motion within the pores forming the lattice will be diffusive and the molecular dynamics simulation results (Section 3) for diffusion between the essentially inf'mite graphitic surfaces of the REG model are applicable to these f'mite pores. Likewise, if the breadth of the pore is much greater than its width, the effect of that boundary (which is not present in the REG model) on diffusion can be ignored. It will be shown below that the pore length and breadth are indeed much larger than the pore widths in the range of interest.
Direction of flux
Figure 2.5. Characteristic length scales for the slit pores in the network model.
845 The specification of the pore network model is completed by assigning a pore size distribution (PSD) and a mean coordination number, Z. The PSD is expressed as a probability density function for the pore width, f(h) = dN/dh (where N is the pore number), normalised so that oo
I f(h)dh = 1 o
(2.1)
The coordination number is reduced from its original value (Z = 6) by "diluting" the simple cubic lattice, i.e. by deleting its bonds at random until the desired value of Z is attained. The probability that a bond is not deleted is the bond occupation probability, p. For an infmite network, p is also the fraction of bonds remaining; p = Z/6. If a sufficiently large fraction of the bonds is removed, the network loses its long-range connectivity and diffusion ceases. The value of the bond occupation probability at which this occurs is Pc = 0.2488 [Sahimi, 1994] for the simple cubic lattice. In terms of the coordination number of the diluted network, Z c = 1.493 at the percolation threshold. For all threedimensional networks, percolation occurs when the average number of connections between a node and its neighbours is about 1.5, i.e. Z c ~ 1.5 is a dimensional invariant. Clearly, we are interested here only in networks above the percolation threshold. Nevertheless, we shall see in Section 4.3 that percolation ideas are useful in interpreting our results. The PSD, the mean coordination number and the porosity are related by
bZ((h))
(2.2)
where W is the porosity, Z is the pore length and b is the pore breadth. The mean pore width is defined by oo
((h)) = I hf(h)dh o
(2.3)
It is instructive to consider plausible values for the parameters of the pore network model. Let us assume that the mean pore width is 0.5 nm, and that the length Z and the breadth b are both four times this value (this corresponds to assuming that the graphite layers bounding the pore are roughly square and are four times larger in linear dimension than the mean separation between the layers). If we further assume that Z = 4, Equation (2.2) gives ~g = 0.5. None of the parameters of our pore structure model can be obtained experimentally using current characterisation methods, but this does not seem to be a fundamental limitation. There has been some success in measuring the PSDs of microporous solids using adsorption measurements, but the data analysis is invariably based on a simple pore geometry - smooth parallel planes, or smooth cylinders. In recent years, statistical-mechanical methods have
846 been used to obtain the PSDs of microporous solids, by using realistic results fbr adsorption in individual pores to deconvolute experimental adsorption isotherms [Seaton et al., 1989; Lastoskie et al., 1993b; Lastoskie et al., 1994]. It is likely that by carrying out Monte Carlo simulations of adsorption in REG pores, this approach could be adapted to obtain the PSD in terms of our model. We are unaware of any method currently in use for measuring the connectivity of microporous solids. However, the use of probe molecules of different sizes, combined with an accurate PSD, should in principle allow the calculation of a measure of the network connectivity. Thus, in the long term, there is a prospect of being able to measure the parameters of the model we use here. Finally, we point out that in our model the nodes of the network offer no resistance to diffusion. This assumption keeps the complexity of the model within reasonable bounds and, in any case, we have no separate knowledge of the contributions of pores and junctions (the distinction between which is in any case arbitrary). So, we present this model as a first attempt to simulate the sieving effect of the CMS pore network, but make no claim that the pore structure model incorporates a full description of the micropore diffusion process.
3. M O L E C U L A R SIMULATION STUDIES OF OXYGEN AND NITROGEN IN REG PORES 3.1. Potential Energy Functions for the Carbon/O 2 and Carbon/N 2 Systems
The interactions of the individual atoms of the oxygen and nitrogen molecules with the carbon atoms of the etched graphitic surface are modelled using the Lennard-Jones (12-6) potential energy function
r
12
= 4e--I I -~t- I Ok,(rij )
~,ro/J
(3.1)
where eij is the potential energy minimum for the interaction of atoms i and j, crij is the codiameter of the two atoms, and rij is the relative separation of atoms i and j. For the simulations reported in this work the oxygen and nitrogen parameters provided by the AMBER force field [Wiener et al., 1986] were employed in the computations and are reported in Table 3.1. Included in this table are the carbon/carbon Lennard-Jones (12-6) parameters cited in [Steele, 1973, 1974] and the cross-interaction parameters evaluated using the Lorentz-Berthelot rules eij = "~(giit~jj)and aij = (~ii+ajj)/2. The oxygen and nitrogen molecules were also assumed to be rigid diatomic structures with atom-atom bond lengths, g, equal to 0.1169 nm (oxygen) and 0.1097 nm (nitrogen). The atomic interactions described by Equation (3.1) are reserved for the admolecule interactions with the etched planes only. The interaction of the atoms of the oxygen and nitrogen particles with the graphite basal planes further from the centre of the pore are modelled via the smeared 10-4-3 potential function proposed in [Steele, 1973]:
847 T a b l e 3.1.
Lennard-Jones (12-6) atom/atom interaction parameters for the carbon/oxygen/nitrogen system. Atom i/Atom j
eij/ kB(K) 75.49 60.39 28.0 45.98 41.12
O/O N/N C/C O/C N/C
crij(nm) 0.2940 0.3296 0.340 0.3170 0.3348
[ ( ) (~ic) 4 c~ic4 2 0.2 (11/2) t~ic- z 10_ 0.5 (h/~)- z 6A((h/2) - z + 0.61A) 3 ~ig(Z) = 4n sic nc~ic 4 (~ic 110- 0.5( eric 14cYie 1 + 0.2 (h/2) + z (h/2) + z 6A((tg2) + z + 0.61A) 3
(3.2)
where A is the interplane spacing, 0.335 nm, in bulk graphite and n e is the carbon atom surface density in an individual basal plane. In the studies to be reported below, the Henry's law region of adsorption is of primary interest and for this reason higher multipole interactions are omitted. For nonzero loadings, however, the admolecule-admolecule potential functions would need to incorporate these terms as demonstrated for example in [Talbot et al., 1985]. In the simulations proper, the Lennard-Jones pair interaction potential in Equation (3.1) is modified to limit the range of atom/atom interactions. This cutoff is a common feature of molecular simulations and may be constructed in a number of ways, either with the potential itself or the corresponding interatomic forces [Allen and Tildesley, 1987]. In this work the shifted force modification to the potential function has been employed and this is formally given by ~~F(rij)= ~ij(rij)-r
dr
drij
) ] (rij - rc)
rij -< rc
rc
=0
rij > rc
(3.3)
where ~ij is the potential function in Equation (3.1). As indicated, the shift in the potential is achieved by subtracting ~ij and its derivative (the magnitude of the force) times rij - r c, both evaluated at rij = re, from ~ij(rij) and this eliminates discontinuities in the potential function at rij = r c which could give rise to instabilities during the simulation. The cut-off radius r e was taken to be 3.5Oic while no cutoff was employed with the smeared 10-4-3 interaction in Equation (3.2).
848
3.2. Background Theory and Simulation Method for Sorption and Diffusion In the limit of zero loading [see Suh and MacElroy (1986), MacElroy and Suh (1987), and MacElroy and Raghavan (1990) for the more general case of high loadings for pure fluids and mixtures in micropores] the isothermal diffusion flux of a pure adsorbate in the x-direction parallel to the graphitic basal planes within a single pore of volume V in the CMS medium is given by j (x) = _ L(X)d___~_g dx
(3.4)
where kt is the chemical potential of the diffusate and
L(X) _
1
VkBT
~ ~a(x)(t) dt
(3.5a)
0 or
L(X) =
1 lim d <(x(t)- x(0))2 > 2VkBT t -+ oo dt
(3.5b)
Equation (3.5a) is the Green-Kubo [Green, 1952, 1954; Kubo et al., 1985] form which relates the macroscopically observed kinetic coefficient L(x) to microscopic fluctuations in the fluid particle momenta. The term q)(x)(t) is the velocity autocorrelation function (VCF) for the xcomponent of the centre-of-mass velocities, v(X), of the individual noninteracting particles of the (low de~ity) pore fluid which is simply expressed as
q,(x)(t) = < v(X)(O v(X)(o)>
(3.6)
Equation (3.5b) is obtained by carrying out the integration indicated in Equation (3.5a) and is known as the Einstein relation for L(x) [see Berne, 1971]. The angular brackets in this expression and in Equation (3.6) refer to averaging over an equilibrium ensemble of fluid particles dispersed within a statistically large sample of equivalent pores which have been randomly generated using the technique described earlier in Section 2.1. The flux equation may be expressed in the more familiar Fickian form by noting that the coefficient L(x) is related to the diffusion coefficient DM(X) and the pore fluid number density, n, by [Suh and MacElroy, 1986; MacElroy and Raghavan, 1990, 1991]
L (x) = nDl~)
(3.7)
Furthermore, for a bulk external phase at a chemical potential ~tb in local equilibrium with the pore fluid at x, ~t = ktb = l.t~ + kBT In ~,b where ~,b is the activity of the bulk fluid which
849 satisfies the limiting condition ~,b ~ nb as nb ~ O. Substituting this expression into Equation (3.4) gives
j (x) = _nD! x) "d In ~.gb M dx
(3.8a)
= _ D(X) d ha ~.br dn M d l n n dx
(3.8b)
D(X) K d In ~,b dn b
(3.8c)
, . . , -
M
d l n n b dx
where K is the distribution coefficient for the fluid defined by
K
=
n nb
(3.9)
n is the number density of the fluid within the accessible volume of the pores and nb is the corresponding density within the same volume but in the absence of the carbon planes. For zero loading conditions the bulk external phase corresponds to the ideal gas limit nb ~ 0 and Equations (3.8b) and (3.8c) simplify to
J
(x) = _ D (x) dn M dx
(3.10a)
= _ D~,I) H dnb dx
(3.10b)
where H is the Henry's law constant. For diatomic species this coefficient may be expressed as
lim K = g = V nb_..~0
( exp -
kBT
) 000 dr
(3.11)
where ~(r,00,o) is the sum of pairwise interactions between the diatom and the carbon atoms of the etched planes (N c in total) and the background structureless basal planes for a given diatom centre-of-mass position r and orientation ~,0 relative to the space-fixed cartesian axes x, y, and z, i.e.
~(r,d~,0) =
SF ~ ~ ffij (rij)+ ~ i=1,2 j=I,N c i:1,2
~ig(zi)
(3.12)
850 The angular brackets inside the integral in Equation (3.11) refer to numerical averaging for randomly selected solid angles uniformly distributed over the surface of a sphere and the estimation of H is readily achieved by applying Monte Carlo methods [Allen and Tildesley, 1987] to the integral in this equation. The diffusion coefficients DM(X) for oxygen and nitrogen confined within a given REG pore at low loadings are determined by numerically solving the equations of motion for the individual 0 2 and N 2 molecules subject to the potential field described by Equations (3.1), (3.2) and (3.12). These computations provide the centre-of-mass coordinates, orientation angles, and momenta of the molecules as functions of time and statistical averaging of the xcomponent of the center-of-mass velocity correlation functions and/or the mean square displacement of the particles permits estimation of DM(x) using Equations (3.5)-(3.7). Similar expressions apply for diffusion in the y-direction and the results reported later in this chapter refer to the average diffusivity within the REG slit
1 (D(x)
D(y)~
DM = 2 " - M + M
:
(3.13)
Note that since, by definition, for a low density pore fluid the component of the VCF normal to the pore walls fails to decay to zero before the oxygen and nitrogen molecules experience the influence of the system boundedness in this direction [see for example Schoen et al., 1988], then the Fickian diffusion coefficient does not exist for the z-direction. The numerical algorithms employed to solve the equations of motion of the rigid diatomic molecules of oxygen and nitrogen are described in detail in [Allen and Tildesley, 1987] and are briefly summarized in the following. The translational motion of the centre-of-mass of the individual molecules was obtained by solving Newton's equations of motion using the f'mite difference Verlet "leapfrog" scheme [Hockney, 1970] v(t +12 8 t) = v(t- ~-8 1 t) + 5 t a(t)
(3.14a)
r(t +8 t) = r(t) + 8 t v(t +1 8 t) 2
(3.14b)
where v, r and a are the velocity, position, and acceleration of the centre-of-mass of an individual molecule with
1
a(t) = ~m (fl + f2)
(3.14c)
In this last equation m is the mass of one of the homonuclear atoms in the diatomic particle and fi is the total force acting on atom i
851
fi = - ~ ~ j=l,Nc rij
(rij) d~ig(Zi) drij - k ~dzi
(3.15)
The orientation and rotational motion of the particles, which are characterised by the unit vector e(t) along the axis of the individual particles and the rate of change of e(t) defined by u(t) = de(t)/dt [Allen and Tildesley, 1987], were computed using the finite difference algorithm [Fincham, 1984] 1 1 1 g-i- (t) u(t + ~5 t) = (u(t- 7 8 t)- 2 [u(t- ~5 t). e(t)] e(t)) + 8 t I
(3.16a)
1 e(t + 8 t) = e(t) + ~5t u(t + 7 5 t)
(3.16b)
where e
(3.16c)
and the quantity I in Equation (3.16a) is the moment of inertia of the diatom which is equal to (ms 2/2). The force fi-t- is the component of fi normal to the axis of the molecule. The general procedure which we have employed in this work to compute the low density diffusion coefficient for either of the fluid species within a given REG pore was as follows: (i) A diatom was placed at a randomly chosen position and with a random orientation within the REG pore and the insertion was accepted only if the kinetic energy satisfied the relation X = E - 9 > 0 where E is the total energy of the system (a conserved quantity in the microcanonical ensemble MD computations conducted in this work). The total energy was fixed by the requirement that the time averaged kinetic energy, , (which appears in the expression E = + <37>) for the particles in an ensemble of diatom/pore realizations, corresponded to the desired simulation temperature which is statistically related to <37> via
T -
2 . (z) 5k B
(3.17)
(With the exception of a number of simulation runs to be discussed later in Section 3.4, all of the computations reported here were conducted for a mean thermal energy <37> corresponding to 300 K. A few trial simulations runs were initially conducted for each of the pore widths investigated here to determine the appropriate values of the total energy E needed to satisfy this requirement; these values of E are reported in Table 3.2). (ii) The initial components of the centre of mass velocity, v(X), vfY), and v(z) and the two independent components of the angular velocity o(1) and 0(2) were next assigned according to the equipartition principle which states that the kinetic energy of each of the five degrees
852 of freedom is equal to (1/5)~. The components of the rotational velocity u(0) are then determined using both co(l) and co(2) and the known axial orientation of the diatom, e(0). (iii) The trajectory of the diatom in a single realization of an REG pore was then monitored as it evolved through a discrete sequence of time steps, 5 t, and the position and velocity of the particle at equispaced time intervals were stored for later use in the computation of the diffusion coefficients. The time step employed in all of the simulations reported here was 5 t = 0.001 in units of xi = ~?cc~/(mi / Sic) (1;O2 -- 1.3 ps and "I;N2 "- 1.285 ps) and a typical trajectory in a single pore was tracked for 105 time steps. The number of pore realizations of a fixed width, h, was generally between 200 and 2000 depending on the statistical accuracy required for the velocity correlation functions and the mean square displacements of the fluid particles. Specific details of the MD simulation runs conducted in this work are provided in Table 3.2.
Table 3.2. System characteristics for the molecular dynamics simulations.
ah 3.00 3.25 3.375 3.5 3.625 375 4.0 45 50
b Neoaf 2000 2000 2000 2000 800 400 200 200 200
c E (0 2 ) -33.43 -26.77 -22.63 -19.60 -16.65 -15.00 -14.40 -5.464 -1.364
c E (N 2 ) -39.10 -34.48 -30.88 -26.64 -22.75 -20.00 -18.06 -8.993 -2.247
a h is the pore width in units of the distance between the basal planes in graphite, A = 0.335 rim.
b Nconf is the number of pore realizations. c E is the total (conserved) energy of the diatom, in units of eio in a given simulation run which ensures an average thermal energy, <X>, corresponding to a temperature of 300 K.
In addition to the diffusion computations described above and the Monte Carlo estimation of the Henry's law constants, H, for oxygen and nitrogen, a number of complimentary properties which permit a deeper insight into the behaviour of the carbon/oxygen and carbon/nitrogen systems have been determined in these studies. These include the number density profiles, the isosteric heats of adsorption, and the rotational diffusion coefficients, D R, of the diatomic particles within the REG pores. The latter are of particular interest in that they offer a unique insight into the influence of REG pore width on the freedom of alignment of the molecules and were determined using the expression
853 1 oo
(3.18)
D R = ~ f dt 0
where c0(t) = e(t) x u(t). The angular velocity autocorrelation function, , was readily estimated during the execution of the MD program. The number density profiles across the pores were also determined during the MD simulations primarily for comparison with the results provided by the Monte Carlo integration analysis employed to compute H. The isosteric heat of adsorption, qst, was also estimated during the execution of the Monte Carlo code. This quantity is defined by
qst = RT2
c3In P ) 0T Nex
(3.19)
where N ex is the excess number of moles sorbed within the pore. In the zero pressure limit N ex is given by (PV/RT)(H-1) and Equation (3.19) simplifies to
I'm qst _-RT(1 d nb~0
I (~* (r, ~,0) exp(-~* (r, ~, 0)))~o dr = RT 1-
9
(3.20)
where ~*(r,r = O(r,d~,0) / kBT. Both H and qst were evaluated for each pore width at a temperature of 300 K. 3.3. The Henry's Law Constant and the Isosteric Heat of Adsorption at Zero Loading The Henry's law constants at 300 K for both oxygen and nitrogen sorbed within single graphitic pores are shown in Figure 3.1 a as functions of the pore width defined in Figure 2.2. The trend displayed by H as the pore width is increased is explained by the accessibility of the various regions of the pore space to the diatomic particles. For the smallest pore (h = 3 A) the distance between the etched planes shown in Figure 2.2 (h - 2A) corresponds to the interplane distance in the graphite lattice (i.e. A) which is inaccessible to the diatoms. As the width, h, is increased the average sorption potential drops and hence H decreases. At a distance h = 2A +Occ + ~ii, however, it is just possible to accomodate a diatom within the pore volume confined between two groups of etched carbon rings on opposite sides of the slit pore and H increases at this point. A slight increase in the pore size leads to a shallow
854 25
25.0
(a)
(b)
20 o O.xyg~~n
22.5 00.XYog:en
15
20.0
10
qst 17.5
H
15.0
2.5
l
3.0
I
3.5
I
4.0 h/A
I
4.5
l
5.0
5.5
12.5
2.5
I
3.0
I
3.5
I
4.0
I
4.5
I
5.0
5.5
h/A
Figure 3.1. (a) The Henry's law constant as a function of pore width; (b) isosteric heat of adsorption, qst (kJ/mol), as a function of pore width. (The standard errors on the results are smaller than the symbols shown).
maximum in H which ultimately decreases again for pore widths greater than 4A. In the wide pore limit the Henry's law constant may be simply expressed as
lim H = 1+ 1 ~ f (( exp ((1)(r'r h ---~oo B
=1+~
. )~0 -1) dr
(3.21)
where B is a constant which is independent of the pore width and A is the projected area of the graphite surface. Similar trends in the results obtained for the isosteric heat of adsorption are illustrated in Figure 3.1 b. The isosteric heat is largest for the smallest pore and decreases with increasing pore size, the slight ridge at 4A corresponding to the opening of pore space between the etched carbon planes. In the wide pore limit both the numerator and denominator in Equation (3.20) approach values which are independent of the pore width and qst will equal the isosteric heat of adsorption on a free surface. 3.4. M o l e c u l a r Pore Model
Dynamics
S i m u l a t i o n R e s u l t s for 0 2 a n d N 2 D i f f u s i o n w i t h i n t h e R E G
Examples of the single particle center-of-mass velocity autocorrelation functions for oxygen and nitrogen for two representative pore widths are shown in Figure 3.2. For the
855
narrower of the two pores the particle velocity is, on average, reversed in direction at t 0.7z and a distinct backscattering minimum is observed at t ~ 1.5z. This backscattering is due to the severe hindrance experienced by the diatoms as they attempt to diffuse along the pore either by remaining close to the background planes at z = +h/2 or by undergoing a sequence of jumps which brings the particles from one side of the pore to the other (the small pore width, h = 3.375A, corresponds to a situation in which the gas molecules must circumnavigate the obstacles created by neighboring sections of the etched planes on opposite walls of the pore). For the larger pore however the diatoms freely diffuse between the etched planes as well as along the pore walls which, in this case, results in the absence of a negative tail in the VCFs.
1.2
1.2
(a)
(b)
1.0
1.0 o Oxygen
0.8
o Oxygen
0.8
e Nitrogen 0.6 VCF 0.4
e Nitrogen 0.6 .~, VCF ~. 0.4
o,
0.2
o
0.2
a'~
~
~~~,__
o
"~
0.0
:i~ . . . . . .
,,mabmeoOeeooo,,,,oH'n"
0.0
--~.
,~..~,~.___-
,_ ~
-,iooliiooo,ooOm, . . . . . .
0.0
1.0
2.0
3.0
tl~
4.0
5.0
0.0
.0
8.0
12.0
16.0
20.0
tlX
Figure 3.2. The normalized centre-of-mass velocity autocorrelation functions for (a) h = 3.375A ; (b) h = 5A. In principle, the diffusion coefficients for the different pore widths are determined by combining the VCFs (and/or the mean squared displacements of the center-of-mass) with Equations (3.5) and (3.7); however, in practice, this is not always as straightforward as it first appears. The upper limit of oo in Equation (3.5) is a strict requirement for correct estimation of the stationary Fickian diffusion coefficient which is the quantity generally measured in permeation or dynamic sorption experiments reported in the literature. A simplification of Equation (3.5) results if no long-time tails exist in the correlation functions, i.e. if the VCF within the integral in Equation (3.5a) approaches zero within the time scale of the MD 'experiment' or if the mean square displacement increases linearly with time (both conditions necessarily taking place at the same time). If this is the case then the upper limit in Equation (3.5a) may be replaced by that time at which q)(t) goes to zero, e.g. t', and/or the slope of the mean square displacement may be obtained over a time range t' < t < tmax where tmax is of the order of the length of a given simulated trajectory. In this work t' was generally taken to be 20x i and tmax was taken to be 50z i and while these time scales satisfied the above criterion for
856 t' for pores wider than 3.75 A, this was not the case for narrower pores. The VCFs shown in Figures 3.2a and 3.2b illustrate this point most clearly. The VCFs in Figure 3.2b for a pore width of 5 A decay rapidly to zero and the stationary oxygen and nitrogen Fickian diffusion coefficients are reliably estimated by integrating these functions over the time range 0 < t < 12.0x i. However, for the pore width h = 3.375 A, the tail of the VCF is seen to persist to long times, an observation which was found to be characteristic of all of the simulation runs for pore widths h < 3.75 A. It is very important to note that although the tail in Figure 3.2a may appear to be very close to zero care must be exercised in extrapolating these results to very long times. A simple and direct way of extrapolating the data which also assists in reducing the influence of the statistical noise in the simulation results is to compute the time dependent diffusion coefficient obtained by changing the upper limit in Equation (3.5) to a finite value
D~)(t) = f q~(x)(t)dt
(3.22a)
0 or
D
1 d )(t) = ~ ~ <(x(t)- X(0)) 2 >
(3.22b)
with similar expressions for the y-direction parallel to the pore walls. Plots of DM(t) (see Equation (3.13)) for the pore width corresponding to Figure 3.2a are provided in Figure 3.3. The abscissa in this figure is the inverse power 1/tl3-1 which results from an assumption that the time correlation function q~(t) decays as 1/tB at long times i.e. lim r
-- - ~ot-
(3.23)
t ---~ oo
Substituting this expression into Equation (3.22a) gives lira D ~)(t) - D ~)(oo) +
t--> oo
. o~ (13 1)t13-1
(3.24)
where DM(X)(oo) is the desired stationary value of the diffusion coefficient. Negative power law tails of the type predicted by Equation (3.23) are characteristic of systems close to a percolation threshold and have been widely investigated via kinetic theory [Keyes and Mercer, 1979; Masters and Keyes, 1982], mode-coupling theory [Gotze et al., 1981a, b; Emst et al., 1984; Machta et al., 1984], and scaling theory [Havlin and BenAvraham, 1987]. The model oxygen/nitrogen/carbon systems under investigation in this work are also seen to conform with the long time 'anomalous' diffusion predicted by Equation (3.24). Figure 3.3 demonstrates this behaviour for one pore width and similar results were obtained for all other pore sizes in the range h < 3.75A.The power law expression in Equation (3.24) wasgenerally found to correlate the data for times greater than 10~i and the
857 0.40 O Oxygen (DM = 0.05,13 = 2.00) 0.32
9 Nitrogen (DM = 0.0, 13 = 1.81)
O
0.24
0
G~t) 0.16
J
0.08
0.0
I
0.0
0.04
i
I
i
I
0.08 0.12 l/(t/~) (1~-1)
i
I
0.16
I
0.20
Figure 3.3. DM(t ) as a function of 1/(t/x )(~-1). The stationary values for DMare provided in the legend in units of g ccX/(~ic/mi)
results obtained via nonlinear regression for DM(OO) as well as ct and 13are reported in Tables 3.3(a) and 3.3(b). The values listed for 13are in good agreement with prior simulation results for anomalous diffusion within random media [Park and MacElroy, 1989], particularly in the case of N 2 at the single pore percolation transition (3.5A < h < 3.625 A) which is in excellent agreement with the power law exponent reported by Raghavan and MacElroy (1991) for nonpercolating rod-like particles confined within a random overlapping spheres model of microporous media. This provides indirect support for the extrapolation procedure employed here and, in our view, confirms the semipermeability of the REG pore model (subject to 50% etching) for pore widths less than 3.625 A. The kinetic selectivity of the model KEG pores is most clearly demonstrated in Figure 3.4 where the stationary Fickian diffusion coefficients reported in Table 3.3 are plotted. The break in the behaviour of the micropore fluid properties as observed in Figures 3.1 and 3.4 as the pore width drops below h = 4A is also observed in the results obtained for the rotational diffusion coefficients which are plotted in Figure 3.5. For pore widths greater than 4 A the reorientational dynamics of the nitrogen and oxygen diatoms are primarily determined by comparatively free rotation within a surface plane parallel and adjacent to one of the walls of the slit pore (i.e. the z-component is the dominant term in the angular velocity vector and
858
Table 3.3(a) Power law decay parameters and stationary diffusion coefficients for oxygen. ah 3.00 3.25 3.375 3.5 3.625 3.75 4.0 4.5 5.0
cz 1.2(2) 1.7(3) 1.2(2) 1.0(3) -
13 2.1(1) 2.2(1) 2.0(1) 1.9(2) -
b DM(OO) 0.017(4) 0.033(3) 0.050(6) 0.113(12) 0.290(4) 0.99(4) 3.97(3) 6.13(4) 6.23(4)
Table 3.3(b) Power law decay parameters and stationary diffusion coefficients for nitrogen. ah 3.00 3.25 3.375 3.5 3.625 3.75 4.0 4.5 5.0
cc 0.6(1) 0.7(2) 0.6(2) 0.15(4) 0.28(5) -
13 1.87(7) 1.90(8) 1.81(9) 1.42(6) 1.44(7) -
b DM(OO) 0.0 0.0 0.0 0.0 0.014(0.04)* 0.350(5) 2.99(3) 6.03(9) 7.38(4)
a As in Table 3.2. bDM(oO) is in units of ~ee'~(eic/mi) where ~ee = 0.142 nm, the C-C bond length in graphite. The standard errors are reported in parenthesis. * The standard error in this case reads as 0.014+0.04 which would suggest that the single pore percolation transition for nitrogen may occur at this pore width.
o decorrelates slowly with time). For smaller pores however the diatoms are primarily confined to 'holes' within the etched graphite layers (Figure 2.2b) and the particles experience simultaneous interactions of similar magnitude with both pore walls. Under these conditions the component of o in the z-direction decorrelates much more rapidly with time. In the absence of a heat reservoir the simulation procedure described earlier in Section 3.2 provides results which are strictly microcanonical (fixed total energy E) and this could raise questions concerning the applicability of the values for D M obtained in this manner to the
859
10 2
_
"
lO
O
Oxygen
1 O
.
10~ D
M -1
10
-2
10
-3
10
-
-
I
2.5
I
3.0
3.5
I
J
4.0
,.
I
4.5
5.0
5.5
h/A Figure 3.4. The diffusion coefficient as a function of pore width. D M is in units of cc4(eic / mi). (The standard errors on the results are provided in Table 3.3).
103 O Oxygen 9 Nitrogen 102
DR
_
~
m
**O1[8~
101
l0 0
2.5
I
!
I
1
!
3.0
3.5
4.0
4.5
5.0
5.5
h/A Figure 3.5. Rotational diffusion coefficient as a function of h. D R is in units of ~/(sie / mi)/gee
860 more usual experimental setting of fixed temperature (for example, conditions corresponding to the canonical ensemble in which the distribution of the thermal energy of the particles is Maxwellian). A specific example of the disparity in the results which can arise for one of the configurational properties for the systems under investigation here is demonstrated in Figure 3.6a for the density profiles obtained via MC integration at a fixed temperature of 300K (the integrand in Equation (3.11)) and microcanonical MD at an average thermal energy corresponding to the same temperature. It is clear that, although the qualitative features of the profiles in both cases are similar, major differences exist, most notably near the pore walls at z = +0.77A where the density peaks are much sharper for the canonical MC profile. Whether a comparable disparity can occur for a dynamical property such as the diffusion coefficient is, at this point, an open question and therefore to check the efficacy of the results reported in Table 3.3 and Figure 3.4, the microcanonical properties over a wide range of total energies, E, were determined for a single pore size, h = 3.75 A. The diffusion coefficients for oxygen and nitrogen as a function of total energy are provided in Figure 3.7 and to compute the corresponding canonical averages the following transformation is used [Allen and Tildesley, 1987].
('~)B -- f exp(-IBE)ZE-~,-7Z,,(~)EdE
(3.25)
f exp(-13E)ZEdE
1.5[r n microcanonical MD 1.2
1.5
(a)
a canonical MD
9 canonical MC
1.2
n* 0.9
n* 0.9
(103)0.6
(16~ 0.6
(b)
9 canonical MC
o
0.3
0.3 0.0 -2.0
-1.0
0.0 z/A
1.0
2.0
0.0 -2.0
-1.0
0.0 z/A
1.0
2.0
Figure 3.6. (a) Normalised nitrogen density profiles for h = 3.75A. Comparison of microcanonical MD and Monte Carlo predictions. (b) Normalised nitrogen density profiles for h = 3.75 A. Comparison of canonical MD and MC profffles.
861 1
lO
llI
m
rrr
1~
9
~
9
o
lO
1K
D~E)161 7-
-2
t
10
o
Oxygen
9
Nitrogen
-3
10
I
-50
I
-40
I
-30
I
-20
[
I
-10
I
I
()
10
20
E Figure 3.7.
The microcanonical diffusion coefficient as a function of energy for h =
3.75A. DM(E ) is in units of s
/ m i) and E is in units of sic.
2
10 10
1 : 0
10 10 10
-1 -2 -3
Z*(E) lO 10 10 10 10
-4 -5 -6
/ / oxyg
-7 -8
10_70
I
-50
l
I
-30
l
I
-10
i
I
10
i
,,
30
E Figure 3.8. The reduced microcanonical partition function as a function of energy for h = 3.75 A. E is in units of eie.
where ~1 is an arbitrary property of the system, 13 = 1/kBT and ZE* is the reduced microcanonical ensemble partition function which, for a single rigid diatomic particle, is given by
862
ZE = S (E - U) 3/2 Z c (U)dU
(3.26)
with
Zc(U) = S ~i(U - O(r, ~, 0))sin0d0d~dr
(3.27)
The integration in Equation (3.27) is carried out via Monte Carlo sampling [Allen and Tildesley, 1987] and a straightforward numerical integration of Equation (3.26) then provides the desired values for ZE* for a range of total energies E. The results obtained in this manner for ZE* are provided in Figure 3.8 and the canonical diffusivities for oxygen and nitrogen are plotted in Figure 3.9 as functions of the inverse reduced temperature, l/T* = eic/kBT. The two full lines shown in this figure represent the true canonical diffusion coefficients obtained from the above transformation while the discrete data points correspond to the microcanonical diffusion coefficients obtained from the individual isoenergetic simulation runs plotted as functions of the reduced temperature computed using Equation (3.17). The agreement, between both methods (except at temperatures well below those normally encountered in practice) confirms the applicability of the results reported in Table 3.3 and Figure 3.4.
10
10
1
0
DM(T) 10-1
o 9
O
-2
10
9 Nitrogen -3
10
,
0.0
I
..,
0.25
I
0.5
,
0.75
1/T*
Figure 3.9. The microcanonical (symbols) and canonical (lines) diffusion coefficients as functions of the inverse reduced temperature for h = 3.75A. DM(T ) is in units of g cc~/(eic/mi) and T* is in units of eic/k B (i = 0 2 or N2). The standard errors on the microcanonical diffusivities are provided in Figure 3.7.
The nitrogen density profiles determined for each of the energies specified in Figure 3.8 were also transformed using Equation (3.25) and the corresponding canonical results are
863 provided in Figure 3.6b. Much better agreement is now obtained between the two methods and it is believed that the residual differences observed in the results are due primarily to statistical inaccuracies, particularly in the MD profile.
4. DIFFUSION IN THE PORE N E T W O R K 4.1. Problem Formulation
We begin by writing a variant of Fick's law describing diffusion in the CMS at the macroscopic level. Consider an element of the adsorbent which is large compared with the size of the pores so that the concentration of the adsorbed species can be viewed as a continuous function of position. Diffusion in the adsorbent is described by
J = -D~ffVn p
(4.1)
where J is the flux of the adsorbate within the bulk CMS, DM eft is the effective diffusion coefficient, and np is the local concentration of the adsorbate in the pore space (this definition differs slightly from n as defined in Equation (3.9) in that n p is the concentration of the adsorbate averaged over a large number of pores occupying a volume that is nevertheless infinitesimal compared with the size of the adsorbent particle). Our task is to evaluate DM eft by performing a simulation of diffusion in the pore network, taking into account the PSD and coordination number of the network. As the equilibrium loading (expressed by the Henry's constant) differs from pore to pore, it is convenient to rewrite Equation (4.1) in terms of the bulk species concentrations, rather than adsorbed phase concentrations. In the low density limit, the concentration of pure adsorbate in pore j, nj, is related to the Henry's constant for pore j, Hj, and the concentration of the adsorbate in the bulk gas phase, n b, by (see Equation
(3.9)) nj = Hjn b
(4.2)
The mean Henry's constant over a large number of pores in a small element of the adsorbent is
=
((h))
(4.3)
where as before (see Equation (2.3)) ((...)) indicates an average over the pore size distribution f(h). It follows that the mean adsorbed phase concentration in this element is given by
864 np = h r nb
(4.4)
Substitution of Equation (4.4) in Equation (4.1) provides Fick's law in terms of the gradient in the bulk concentration that would be at equilibrium with the adsorbed phase at that location in the particle: J : -D~ff/~rVn b
(4.5)
In order to calculate the effective diffusion coefficients, we must consider the microscopic variant of Fick's law, describing diffusion through an individual pore (i.e. Equation (3.10)). The molar flow of diffusate through pore j is given by
Fj = - - ~ D M ( h ) A n j
(4.6)
where Anj is the difference in the concentration across pore j. Here we have assumed that the pore length is greater than its width and breadth so that the concentration gradient within the pore is essentially one dimensional. The plausibility of this assumption was demonstrated in Section 2. As in the macroscopic variant, it is convenient to write Fick's law in terms of an equivalent bulk concentration: hb Fj = - - z D M ( h ) H ( h ) A n b
(4.7)
where Anjb is the bulk concentration difference equivalent to Anj. It is convenient to make use of the analogy with electrical conduction and write Fj = -gjAn b
(4.8)
where gj is the "diffusional conductance" of pore j: hb gj = --~-DM(h)H(h )
(4.9)
The effective diffusion coefficient is obtained by creating a model pore network with a specified PSD and mean coordination number Z, and solving for each species a set of mass balances, either directly or using an approximate theory. Conservation of mass requires that for every node in the network
865 ~Fj =0
(4.10)
where the summation is over all pores that meet at the node. Substitution of Equation (4.8) in Equation (4.10) gives
g jAn b = 0
(4.11)
An equation of this form is written for each node in the network (except for nodes on boundaries where the concentration is fixed), amounting to a set of linear equations in the nodal concentrations.
4.2. Computational Algorithm The most direct way to solve the set of mass balance equations is to impose a concentration difference across the network and to solve these equations directly using an iterative method. In order to obtain good values for the effective diffusivities, it is necessary to simulate a large number of realisations of the network, each of which notionally represents a small element of a macroscopic network, and to average diffusion rates over these realisations. A single realisation involves the following steps: (i) if the network has a coordination number Z < 6 (the coordination number of a fully occupied simple cubic network), each bond is deleted with probability 1-p (where p = Z/6); (ii) diffusional conductances are allocated to the bonds by sampling randomly from the PSD; (iii) the mass balance equations are solved to obtain the concentration at each node in the network; (iv) the molar flow through the network is calculated by summing the contributions from all the pores crossing a surface perpendicular to the imposed concentration difference, using Equation (4.7); (v) the effective diffusion coefficient is calculated using Equation (4.5). This is a stochastic process, which for convenience we shall call a "network Monte Carlo" (NMC) simulation. As the real solid that is being modelled has a very large (i.e. essentially infinite) number of pores, it is necessary to use several large network sizes in order to evaluate and then attempt to eliminate the finite-size effect. This solution method is not a practical proposition if a large number of lattice calculations is contemplated, as in the present study, and an approximate method must be used. We employ here the Monte Carlo Renormalized Effective Medium Approximation (MCREMA) method of Zhang and Seaton (1992). This method is based on earlier work of Sahimi and coworkers [Sahimi et al., 1983; Sahimi, 1988] and combines elements of the Effective Medium Approximation [Kirkpatrick, 1973; Burganos and Sotirchos, 1987] and renormalisation group theory. We do not derive the MC-REMA method here but rather restrict ourselves to outlining its physical basis. We divide the simple cubic lattice into an array of topologically identical unit cells (or "renormalisation cells") with linear dimension 22, where as before 2 is the pore length (Figure 4.1a). Each bond in the network has a different conductance, reflecting the distribution of pore widths; the array of renormalisation cells forming the network therefore offers a distribution of diffusional conductances along the three principal axes of the cell. As in an NMC calculation, bonds are absent with probability 1-p to obtain the desired
866 coordination number. In the renormalisation process, each of the original cells is replaced by a new cell consisting of only three "renormalised bonds" (Figure 4.1b), each of which is twice as long as the original bond. The renormalised bonds have a conductance distribution different from that of the original bonds, and chosen so that the renormalised bonds offer the same distribution of diffusional conductances as the original cells. The conductance distribution of the renormalised bonds is calculated by solving the mass balance equations (of the form of Equation (4.11)) for a large number of realisations of the unit cell. Figure 4.1 c shows the conductor network that must be solved to calculate the effective conductance between two faces of the renormalisation cell (indicated by A and B in the figure). This calculation amounts to an NMC simulation for the renormalisation cell (rather than for the network as a whole). One characteristic of the renormalisation process is that the renormalised network is further away from the percolation threshold than the original network. Thus, provided the original network is percolating, the renormalised network has fewer absent bonds than the original.
2~
B (a)
Co)
(c)
Figure 4.1. Renormalised cell configurations.
In the pure form of renormalisation group theory, the renormalisation process is repeated many times until the final, many-times renormalised, network is composed of effectively identical conductances [Bemasconi, 1978]. The effective di~sion coefficient is then readily calculated. This process is computationally very demanding. Sahimi et al. (1983) made use of the observation that each renormalisation moves the network further away from the percolation threshold to develop a hybrid method. In their approach, a single renormalisation step is carried out and then the renormalised conductance distribution is used as the input to the Effective Medium Approximation (EMA) of Kirkpatrick (1973). In the EMA, each bond is notionally replaced by a constant, "effective" conductance, chosen so that a network made up of the effective conductances would offer approximately the same resistance to diffusion as the original network. The EMA is computationally very fast and is known to be accurate far from the percolation threshold. However, it is significantly in error near the percolation threshold; in particular, the percolation threshold predicted by EMA for the simple cubic lattice, Pc = 1/3,
867 is far from the accepted value of 0.2488. In the approach of Sahimi et al., which they call the "Renormalised Effective Medium Approximation" (REMA), the single renormalisation step is used to distance the network from the percolation threshold so that the accuracy of the subsequent EMA calculation is improved. REMA gives an estimate of the percolation threshold, Pc = 0.2673, which is much closer to the accepted value for the simple cubic lattice than either EMA, or pure renormalisation group theory (which gives Pc = 0.2085). In its original formulation, the REMA method was applied to a discrete conductance distribution, in which case the renormalisation step may be carried out analytically. Zhang and Seaton (1992) extended the REMA approach to a continuous distribution of conductances (reflecting a continuous PSD); in this method, called the "Monte Carlo Renormalised Effective Medium Approximation" (MC-REMA) an NMC simulation is carried out to evaluate the conductance distribution of the renormalisation cell, as outlined above. In presenting the working equations of MC-REMA, we follow the analysis of Zhang and Seaton (1992), adapting their equations (which were derived for cylindrical pores) to slit geometry. It is convenient to extract the constant geometrical factors from the diffusional conductance, gj, and define a scaled conductance, aj, as follows:
b gj = a j - -
(4.12)
so that, by comparison with Equation (4.9), aj = hDM(h)H(h )
(4.13)
M realisations of the renormalisation cell are carried out. (In the work reported here, M = 2x104.) For each realisation, an NMC calculation is carried out. As the cell shown in Figure 4.1c has only twelve bonds (some of which may be absent), the mass balance equations (Equation 4.11) are readily solved analytically. The output of the NMC calculation for realisation k is the scaled conductance of the renormalised bond replacing that realisation of the cell, ak'. The NMC results then form the input to the EMA calculation [Kirkpatrick, 1973]. M i'^, ^, 1 S " t~__k--!~EMA_______J.= 0 M- k=l[ak + 2aEMA] Z_.
t
t
(4.14)
where M is the number of realisations of the renormalisation cell, and a'EMA is the "effective" diffusional conductance of the renormalised bonds, a'EMA is related to the effective diffusion coefficient by V ai~MA D~lff = 12 ((hH}}
(4.15)
868 where, as before <> is the arithmetic mean value of hH. Note that the pore breadth and length do not appear explicitly in these equations. In fact, the length is fixed by the values of Z, b, W, and <> via Equation 2.2. The value of the pore breadth b is arbitrary, except that in carrying out the MD simulations in effectively infinite slits we have assumed b >> h.
4.3. Results and Comparison with Experiment Figure 4.2 shows MC-REMA results for the network diffusion coefficients as a function of the coordination number of the network, Z, with the mean pore size as a parameter. For all these simulations, ~ = 0.5 and a lognormal pore size distribution function was employed in all cases. The standard deviation of the PSD is fixed at 0.01 nm and we present the results in terms of an "effective" pore width, <<w>> = <> - 3A. As for individual pores in this size range, oxygen diffuses more rapidly than nitrogen, and both diffusion coefficients are strong functions of Z. Using the best current value for the percolation threshold of the undiluted simple-cubic lattice, Pc = 0.2488 [Sahimi, 1994], and employing the dimensional invariance of the percolation threshold (Section 2.2), we obtain Z c ,~ 1.5. Above this value, the effective diffusion coefficients increase rapidly from zero. (There is an exception to this behaviour. The effective nitrogen diffusivity in a network with a mean pore size of 0.21 nm is non-zero only above Z = 3; this is because a pore of this size is the smallest pore to allow any measurable (in the MD simulations) diffusion of nitrogen. Thus, about half the pores are effectively closed to nitrogen and the "effective" coordination number for nitrogen at Z = 3 is only about 1.5, i.e. ~ Zc. ) Figure 4.3 shows the diffusivity ratio corresponding to the data of Figure 4.2. These are narrow PSDs so the networks would be expected to show a degree of kinetic selectivity similar to that of individual pores of the same dimension as the mean pore size, and this is confirmed by the data. Chihara et al. (1978) measured effective diffusion coefficients for nitrogen and oxygen in Takeda MSC-5A, over a range of temperatures. At 300 K (the temperature of our simulations), they report a nitrogen diffusivity equal to 2.1x10 -7 cm2/s and an oxygen diffusivity equal to 6.7x10 -7 cm2/s; the diffusivity ratio, R = 3.1. These diffusivities are about two orders of magnitude smaller than the MC-REMA results. However, the experimental diffusivity ratio is well matched by a network with a mean pore size of a little less than 0.25 nm (Figure 4.3). Chihara and Suzuki (1979) have measured diffusion coefficients in a different carbon, Takeda MSC 4A. For the unmodified carbon they report R = 4.5; after carbon deposition, they report R = 3.7. These values are matched by networks with mean pore sizes between 0.21 nm and 0.25 nm. Diffusion coefficients have been measured for oxygen and nitrogen in the BergbauForschung CMS, most recently by Chen et al. (1994) who report the following results (r is the characteristic microparticle size): DM,NJ eft' r2 = 9.5x10 -6 S"1 and DM,02/ eft r 2 = 3.5x10 "4 s "l, giving R = 36. This diffusivity ratio is matched by a network with a mean pore size in the region of 0.21 nm, provided Z > 3. Using the value r = 4.3 l~m, measured by Chen et al. using scanning electron microscopy, the effective diffusion coefficients are O(10 -10 - 10-12 cma/s), at least five orders of magnitude smaller than the MC-REMA results. Other results have been presented for the Bergbau-Forschung CMS [Kikkinides and Yang, 1993; Knoblauch, 1978; Ruthven et al., 1986; Farooq and Ruthven, 1991; Kuthven, 1992]. The reported diffusion coefficients vary widely (this is presumably at least partly due to the
869
o ,o <<w>> = 0.2 lnm [] j <<w>> = 0.25nm o , o <<w>> = 0.29nm
eff
(10 -5)
y
.
r.~.-m~''~r" 0
4.0 4.5 5.0 5.5 6.0 Z Figure 4.2. The effective network diffusivity (in cm 2 Is) as a function of the network coordination number Z. The effective pore width is <<w>> = <> - 3A and the standard deviation of the effective pore width distribution is 0.01 nm. The open symbols and the f'dled symbols represent the data for nitrogen and oxygen, respectively. 1.5
2.0
2.5
3.0
3.5
100 0
<<w>> = 0.21 nm [] <<w>> = 0.25 nm <> <<w>> = 0.29 nm
eft DM'~ ,eft
10
./
l
2
El
I-I
A
A
i
I
v
v
..,
m
m
v
v
A
i
3
1 !
n
4
I
5
I
|
6
Z Figure 4.3.
The diffusivity ratio
eft eft DM, o2/DM,N2 as a function of the network coordination
number for the results provided in Figure 4.2. The horizontal arrows indicate the respective diffusivity ratios for the individual pores of width h = w + 3A in which w = 0.21, 0.25, and 0.29 nm.
870 differing pretreatment regimes used in these studies), and are all at least four orders of magnitude smaller than our simulation results. However, the diffusion coefficient ratios are broadly similar to the value obtained by Chen et al. (1994) (23 < R < 38) and are within the range of the simulation results. We return in Section 5 to the discrepancy between the absolute diffusion rates predicted by our simulations and measured experimentally. Figures 4.4 and 4.5 show diffusivity data for the same mean pore sizes and coordination numbers as in Figures 4.2 and 4.3, but for much wider PSDs, with a standard deviation of 0.25 nm. For these PSDs, a proportion of the pores are so small that they do not permit diffusion of nitrogen, and this proportion increases as the mean pore size decreases. The effective percolation threshold for nitrogen is thus larger than Z c ~ 1.5, and increases with decreasing mean pore size. Unlike in the case of narrow PSDs, the diffusivity ratio is a strong function of the coordination number. One of the most important conclusions implied by these results is that networks with a wide distribution of pore widths are able to display the same range of kinetic selectivities as individual pores, and are capable of reproducing the diffusivity ratios observed experimentally. Thus, the functioning of our model CMS does not depend on a narrow PSD; we see no reason why this observation should not apply also to real CMSs. The effect of the width of the PSD on the diffusion coefficients (for fixed mean pore width and porosity) is investigated further in Figure 4.6. Figure 4.7 shows the corresponding data for the ratio of diffusion coefficients. As expected, the increasing width of the PSD reduces the absolute values of the diffusion coefficients. The diffusivity ratio, on the other hand, goes through a minimum as the pore width is increased, so that a network with a very narrow PSD and a network with a wide PSD have similar selectivities. This behaviour is unexpected. The simulation results for single REG pores (see Figure 3.4) show that only pores in a very narrow size range offer a significant selectivity to oxygen and nitrogen. As the width of the PSD increases, the proportion of pores outside this narrow size range increases. These pores are either so large that they show little selectivity, or so small that nitrogen is unable to pass. So, one might expect the selectivity to be "washed out" by increasing the width of the PSD. An elegant analysis due to Ambegaokar et al. (1971) explains why this does not happen. Their analysis, which is not quantitative in three dimensions, involves the following thought experiment. Consider a network with an infinitely wide PSD. First, remove all the bonds from the network. Then, replace the bonds, in order of decreasing diffusional conductance, monitoring the diffusion coefficients. While the first few bonds are replaced, the diffusion coefficients remain zero. This is so until a percolating cluster is formed. As we are considering a network with an infinitely wide PSD, diffusion through the network is controlled by the last "critical" pore to be replaced. All the pores that were replaced earlier are much wider than the critical pore, and they are in series with it. Therefore these pores exert little additional resistance to diffusion. Now, add the remaining pores. These pores are all much smaller than the critical pore and, because they provide alternative diffusion paths, they are effectively in parallel with it. Therefore these smaller pores contribute little to diffusion. Thus, according to this "critical path" analysis, a single pore size controls diffusion in both the limits of zero and infinite width of the PSD. The isosteric heat of adsorption and the activation energy for diffusion offer another point of comparison between simulation and experiment and data reported in the literature for these properties are reproduced in Table 4.1. The theoretical results for qst provided in Figure 3. lb
871
o
9 <<w>> = 0.21 nm
n , I <<w>> = 0.25nm
~/~
<>, 9 <<w>> = 0.29nm
(10 -5 )
2.0
1.5
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
Z Figure 4.4. The effective network diffusivity (in cm 2/s) as a function of the network coordination number Z. The standard deviation of the pore width distribution is 0.25 nm and all symbols are as def-med in Figure 4.2.
10
3
10
2
Deft --M,02 1 eff 10
DM,N2
~.. 0
10
-1 10
Figure 4.5.
v
nm [] <<w>> = 0.25 nm o <<w>> = 0.29 nm
o
<<w>>
,
=
-
-
0.21
I
....
4
The diffusivity ratio
,
t
,
5 eft" eft DM, O2/DM, N2as a function of the network coordination
number Z for the results provided in Figure 4.4.
872 2.5 o Oxygen 2.0
eff
~
9 Nitrogen
1.5
-5 ( 1 0 ) 1.0
o.5 0.0 0.0
,
r
,
0.05
t
0.10
,
I
0.15
~
l
0.20
,
0.25
Standard Deviation of the Slit Width Distribution Figure 4.6. The effective network diffusivity (in cm 2/s) as a function of the standard deviation of the slit width distribution. The average slit width is <<w>> = <> - 3A = 0.25rim and the coordination number of the network is Z = 4.5.
D eft 3 -M,O 2 eft DM,N2 2
0
I
I
l
I
i
I
z
I
t
0.0 0.05 0.10 0.15 0.20 0.25 Standard Deviation of the Slit Width Distribution Figure 4.7.
The diffusivity ratio
eft"
eft
DM,o2/DM,N~ as
a function of the standard deviation of
the slit width distribution for the results provided in Figure 4.6.
873 Table 4.1.
Reported Isosteric Heats of Adsorption and Activation Energies for Diffusion within CMS Gas Oxygen
Nitrogen
CMS Takeda MSC 5A Unmodified Takeda MSC 4A Modified Takeda MSC 4A Bergbau-Forschung Unspecified Origin Takeda MSC 5A Unmodified Takeda MSC 4A Modified Takeda MSC 4A Bergbau-Forschung Bergbau-Forschung Unspecified Origin
qst (kJ/mol) 17.1 15.9
E a (kJ/mol) 10.04 5.86
11.7
27.6
17.8 - 19.5 18.8 15.9
23.4 16.3 10.9
11.7
30.9
20.0 - 21.1
27.2 28.7 10.0 - 11.1
Source Chihara et al. (1978a) Chihara and Suzuki (1979) Chihara and Suzuki (1979) Kikkinides et al. (1993) Fitch et al. (1994) Chihara et al. (1978a, b) Chihara and Suzuki (1979) Chihara and Suzuki (1979) Ruthven et al. (1986) Kikkinides et al. (1993) Fitch et al. (1994)
suggest that the isosteric heat of adsorption for CMS should lie between the asymptotic value 13.9 kJ/mol for the wide pore limit and maximum values of approximately 19 kJ/mol for nitrogen and 24 kJ/mol for oxygen. For Takeda MSC 5A [Chihara et al. (1978)] agreement between model and experimental values of qst is fair; the value for nitrogen is close to our largest value for a single REG pore for nitrogen, and is within the range of single-pore results for oxygen. This would appear to suggest that the controlling pore size for this CMS lies in the range 0.21 nm - 0.25 nm (h = 3.625A to 3.75A) as observed above for the diffusion coefficient ratio. For unmodified Takeda MSC 4A, Chihara and Suzuki (1979) report qst = 15.9 kJ/mol for both species which are also in good agreement with the model results. However, for the Takeda MSC 4A modified by carbon deposition, qst = 11.7 kJ/mol for both species which is significantly below the minimum value reported in Figure 3. lb. The activation energy for diffusion as defined by the Equation (1.1) may also be estimated from MD simulations over a range of temperatures (albeit expensively if the required statistical sampling of the particle trajectories is high) and the results shown in Figure 3.9 suggest that E a for oxygen and nitrogen diffusion within a REG pore of width h = 3.75 A at 300 K ( T* = 6.524 for 0 2 and T* = 7.296 for N2) are 3.9 kJ/mol (02) and 3.8 kJ/mol (N2). Both of these estimates are much smaller than the experimental values cited in the fourth column of Table 4.1 and this suggests that pore sizes of this magnitude (or larger) do not play a significant role in the experimentally observed kinetic selectivity of CMS. On the basis of our earlier observations for the diffusivity ratio and qst, we believe that the rate limiting paths for diffusion within CMS correspond to pores which are very close to the single pore percolation threshold for the given gas and to demonstrate this additional MD simulations were conducted over a range of thermal energies for nitrogen diffusing in pores of width
874 h = 3.625A and 4.0A. The diffusion coefficients determined using the MD simulation technique described in Section 3.2 are plotted in Figure 4.8 as a function of l/T* (-=5e~2c/2<X>) and the activation energies obtained from each of these sets of data are 13.0 kJ/mol (h = 3.625A), 3.8 kJ/mol (h = 3.75A), and 4.4 kJ/mol (h = 4.0A). These results suggest that an REG model subject to 50% etching may provide a reasonable description of the rate limiting pore structure in Takeda MSC 5A, unmodified Takeda MSC 4A, and in the carbon molecular sieves of unspecified origin which were investigated by Fitch et al. (1994). It is anticipated that by varying the extent of etching (and pore width) it should be possible to generate pore structures which accurately model the behaviour of the other CMS media cited in Table 4.1, particularly in view of the high sensitivity of the CMS kinetic selectivity to small changes in the degree of carbon deposition [see for example, Moore and Trimm (1977) and Chihara and Suzuki (1979)].
101
100
DIvI(T)10-1
10-2
9 h=3 9 h 3.75A
I I
1 03 - , -
,
0.0
l
0.05
0.10
0.15
0.20
,
0.25
1/T*
Figure 4.8. The diffusion coefficient for nitrogen in s~ngle REG pores as a function of the inverse reduced temperature. DM(T) is in units of g cc~/(eic/mi) and T* is in units of eic/kB.
5. SUMMARY AND CONCLUSIONS We summarise the comparison of our model with experimental data reported in the literature by the following observations. 1. The model reproduces the relative diffusion rates observed experimentally. 2. The absolute diffusivities predicted by the model are always larger than the experimental values, by at least two orders of magnitude. 3. The calculated activation energies are in agreement with experimental results for Takeda MSC 5A [Chihara et al., 1978], for the unmodified Takeda MSC 4A [Chihara and Suzuki, 1979], and for the carbons studied by Fitch et al. (1994). For
875 these samples, the model isosteric heats are a little too low. The activation energies measured on the other carbons are much higher than the model predictions. The inability of the model to give good absolute values for the effective diffusion coefficients is, on the face of it, a significant failing. However the calculation of diffusion coefficients from dynamic diffusion measurements requires the input of a typical linear dimension of the microporous regions in the adsorbent particle. In the analyses of experimental diffusion rates discussed here, this dimension is identified with the mean microparticle radius [Chihara et al., 1978; Ruthven, 1992; Chen et al., 1994], but this is not necessarily the most appropriate choice. This definition assumes that all the macropores surrounding the microparticles are part of a percolating network. It is at least plausible to suppose that when the microparticles are bound to form the adsorbent particle some of the macropores become disconnected from the percolating macropore network, so that not all microparticles are directly accessible from the macropore network. In this case, the molecules would have to diffuse through microporous regions larger than the microparticles themselves. If these regions were O(10) microparticles in diameter, our diffusion coefficient results would be brought into good agreement with the results of Chihara et al. (1978) for Takeda MSC 5A; as we noted above, there is good agreement between experimental and model results for the activation energy for nitrogen diffusion for this carbon. This is no more than a possible explanation for the discrepancy between our model predictions and some of the experimental results; we have no direct evidence that this occurs in practice. As we pointed out above, our model values for qst are a little lower than the experimental values, even where there is good agreement on activation energies. This suggests that the KEG model lacks a sufficient density of high energy sites; this could be remedied (without significantly changing the activation energy) by reducing the amount of etching. This hypothesis cannot explain the large discrepancy in diffusion rates between the model and the results for the Bergbau-Forschung carbon [Kikkinides and Yang, 1993; Knoblauch, 1978; Ruthven et al., 1986; Farooq and Ruthven, 1991; Ruthven, 1992], or for the modified Takeda MSC 4A [Chihara and Suzuki, 1979]. Our model fails to reproduce the low qst values (modified Takeda MSC 4A) and both the high activation energies and the low diffusion coefficients observed experimentally. This suggests that an acceptable model for these carbons should have fighter restrictions in the pore network and while it should be possible to achieve this with lower etching conditions and small pores it may still be difficult to reconcile the model and experimental values for qst- One possible avenue of investigation involves relaxation of the etched graphitic surface. This could result in locally fight restrictions with large cavities (low qst values on average) within the REG pores. Finally, we note that our model is consistent with the existence of a surface barrier to diffusion, as observed by Dominguez (1988) and LaCava et al. (1989). In our model, a surface barrier to diffusion would be observed experimentally if the portion of the pore network near the surface of the microparticles offered a greater resistance to diffusion than the pores in the interior, due for example to preferential carbon deposition. It is not necessary to assume that the surface resistance is due to pores actually at the surface of the microparticles.
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881
SUBJECT INDEX
Absolute Rate Theory approach 335 Activated carbon 679, 777 Activation energy for adsorption (desorption) 335 Active carbons 715 Adsorption 153, 715,745,837 Adsorption at low temperature 679 Adsorption/desorption isotherm 625 Adsorption-desorption processes 201 Adsorption energy distribution 1 Adsorption equilibria 777 Adsorption in pores 105 Adsorption isotherms 285 Adsorption kinetics 777 Adsorption potential distribution 715 Adsorption rate 285 Adsorptive energy surface 373 Allosteric surfaces 1 Argon 573 Aromatics adsorption in MFI zeolites 519 Arrhenius law 373 Asymptotically correct approximation 1
Barriers surface 373 BET isotherm 1 Bimodal diffusion model 777 Binary systems 777
Cage configurational integral 519 Calorimetric studies of adsorption 519 Capillary condensation 625,745 Capillary-energy ratio 625 Capillary wetting 625 Carbon dioxide adsorption 573 Carbon molecular sieves 837 Carbon monoxide 285 Carbons 745 Chemical diffusion coefficient 373
Chemical surface structure 679 Chemisorption 153 Chlorite 573 Clays 573 Cluster method 201 CO-Ni(111) 285 Collision model 201 Comparison plot 625 Complementary site-matching correlation Computer simulation 451 Condensation approximation I Construction principle 373 Controlled rate thermal analysis 573 Correlated heterogeneous surface 373 Cumulative adsorption energy distribution D Darken's equation 373 Density functional theory 1,745 Derivative isotherms 573 Desorption 153 Desorption kinetics 285,777 Differential adsorption bed 777 Diffusion 153,837 Diffusion in zeolites ---, concentration dependence of 487 ---, dependence on bond energies for 487 ---, energetics of 487 ---, hopping model for 487 ---, kinetic theory of 487 --, Molecular Dynamics simulation of 487 ---, Monte Carlo simulation of 487 ---, stochastic theory of 487 Displacement kinetics 777 Dual diffusion model 777 Dubinin-Astakhov isotherm 519 Dubinin-Radushkevich isotherm 1,625
Effective medium approximation 373 Effective potential 625 Electron exchange reactions 285
882 Electronic structure 679 Elovich equation 335 Empirical isotherm equations 1 Energetic/geometric heterogeneity 625 Energetic heterogeneity 715 Energetic topography 373 Energy correlations in mixed-gas adsorption Energy distribution 777 Enhanced wetting 625 Entropy increase 285 Entropy production 285 Equidistant interface 625 Equilibrium isotherms 285 Equilibrium properties 153 Equilibrium surfaces 1 Exact methods 1 Excluded-volume interactions 519 External surfaces 573
FHH isotherm I Fick's law 373 Fluctuations 625 Fowler-Guggenheim isotherm 1 Fractal dimensions 625, 715 Fractal surfaces 1,625 Frenkel-Halsey-Hill isotherm, FHH exponent 625 Frankel-Halsey-Hill equation 715 Freundlich isotherm 1 FT IR spectroscopy 679 Full isotherm 105
Gas-solid systems 105,201 Generalized gaussian model 373 Geometrical heterogeneity 715 Gibbs equation 1 Gibbs Monte Carlo 745 Grand potential 625 Graphite 451 H
Heat of adsorption 519 Heat of immersion 715 Heterogeneous ideal adsorbed solution 1 Heterogeneous models 777 Heterogeneous pore network model 837
Heterogeneous solid 777 Heterogeneous surfaces 105,201 High ordered structure of porous materials 679 High resolution alpha s-plot 679 Hill-deBoer isotherm 1 Homogeneous models 777 Horvath-Kawazoe method 715 Hydrophobicity 573 Hysteresis 625, 745
Ideal Adsorbed Solution theory 1 Immersion calorimetry 573 Induced heterogeneity approach 519 Inner/outer cutoff 625 Integral equation 1 Integral equation of adsorption 715 Inverse problem 1 Irreversibility 285 Isothermal desorption 285
Jagiello method 1 Jaroniec-Choma equation 715 Jump correlation factor 373
Kaolinite 573 Kelvin equation 625, 715, 745 Kinetic lattice gas 153 Kinetic selectivity 837 Kinetics 153 Kubo-Green's equation 373
Landman-Montroll method 1 Langrnuir-Freundlich isotherm I Langmuir isotherm 1 Langmuirian kinetics 335 Latent pore characterization 679 Lateral interactions 105,201 Lattice gas 153,373 Law of corresponding states 625 Layering transition 745 Light paraffins adsorption 777 Linearized plots for adsorption isotherms 519
883 Liquid-gas interface 625 Localized adsorption 1 Low-pressure adsorption, virial approach
1
M
Mean field approximation 105, 519 Medium approximation 837 Mesopores 745 Metals 153 MFI zeolites 519 Mica 573 Micropores 745 Micropores, diffusion in 487 Micropores, multicomponent diffusion in 487 Micropore filling 679 Micropore size induced heterogeneity 777 Micropore size distribution 679 Microporosity 5 73, 715 Mixed adsorption 105 Mixed-gas adsorption 1 Mobile adsorption 1 Mobile surfaces 1 Molecular dynamics 451 Molecular reorientation 451 Molecular sieves, multicomponent diffusion in 487 Molecular simulation 745,837 Monolayers 451 Monolayer coverages 105, 201 Monte Carlo renormalised effective 837 Montmorillonite 573 Multicomponent adsorption 777 Multicomponent diffusion 487 Multicomponent diffusion in zeolites 487 ---, comparison of models for 487 ---, models for 487 Multicomponent diffusivities, prediction from pure component diffusivities 487 Multicomponent surface diffusion 487 Multicomponent surface diffusion in zeolites 487 Multilayers 153,625 Multilayer adsorption I, 105 Multilayer adsorption, diffusion in 487 Multi-site-occupancy adsorption 1 N Networked pores 745 Nickel 285
Nitrogen 573 Nitrogen isotherm 745 Nonequilibrium thermodynamics 153 Nonideal competitive adsorption 1 Nuclear magnetic resonance method 679 O Order of adsorption-desorption kinetics 335 Oxides 745
Palygorskite 573 Partial isotherms 105 Patchwise topography 1,777 Peak splitting 201 Percolation model 373 Perfect positive correlations of adsorption energies 1 Permanganate electron exchange 285 Phase diagram 625 Phase transition 625 Phase transitions in lattice gas 519 Phase transitions in zeolites structure 519 Physical adsorption 679 Physisorption 153 Pore 625 Pore blocking 745 Pore characterization 679 Pore origin 679 Pore shape 745 Pore size 745 Pore structure 679 Porosity 715 Porous adsorbents 745 Porous solids 373 Potential Theory 1 Power Law equation 335 Precursors 153 Pre-exponential factor 285 Primary salt effect 285
Q Quantum mechanical pertubation theory 285 Quasichemical approximation 105, 201 Quasi-equilibrium 153,573 Queer surfaces 1
884
Random topography 1 Random walk 373 Reaction kinetics 285 Reconstructable surfaces 1 Redistribution of molecules adsorbed on different sites 519 Reduced isotherm 625 Reed-Ehrlich's equation 373 Rough surfaces 1 Rudzinski-Iagiello method 1 Ruthven's model for adsorption in zeolites 519
Self-diffusion 451 Self-similar 625 Separation coefficient 105 Sepiolite 573 Silica 745 Silver electron exchange 285 Sips' method I Site-bond model 373 Small angle X-ray scattering 679 Sorption in pores 451 Standard adsorption 715 Statistical mechanics 153 Statistical Rate Theory of interracial transport 335 Stepped surfaces 451 Sticking 153 Stoeckli's extension of DR equation 1 Submonolayer 625 Substrate potential 625 Sulfur dioxide adsorption 777 Surface diffusion 451,487, 777 Surface forces 745 Surface modification 679 Surface tension 625 Swelling 573
Talc 573 Temkin isotherm 1 Temperature dependence of adsorption 519 Temary systems 777 Thermodynamics of adsorption 715 Thermo-programmed desorption 201 Time-correlation-functions 451 Topography 625 Toth isotherm 1 t-plot 625 Tracer diffusion coefficient 373 Transfer matrix 153 Transition state model 201 Traps surface 373
Vacancy Solution Theory 1 Vanadium electron exchange 285 Van der Waals wetting 625 Volmer isotherm 1 W Water vapour adsorption 573 Wetting transition 625 X XPS-ratio method 679 X-ray diffraction 573,679 X-ray photoelectron spectroscopy 679
Zeolites structure and imperfections 519 Zero-time exchange 285
885
STUDIES IN SURFACE SCIENCE A N D CATALYSIS Advisory Editors: B. Delmon, Universit6 Catholique de Louvain, Louvain-la-Neuve, Belgium J.T. Yates, University of Pittsburgh, Pittsburgh, PA, U.S.A. Volume 1
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Preparation of Catalysts I.Scientific Bases for the Preparation of Heterogeneous Catalysts. Proceedings of the First International Symposium, Brussels, October 14-17,1975 edited by B. Delmon, P.A.Jacobs and G. Poncelet The Control ofthe Reactivity of Solids. A Critical Survey of the Factors that Influence the Reactivity of Solids, with Special Emphasis on the Control of the Chemical Processes in Relation to Practical Applications by V.V. Boldyrev, M. Bulens and B. Delmon Preparation of Catalysts I1. Scientific Bases for the Preparation of Heterogeneous Catalysts. Proceedings of the Second International Symposium, Louvain-la-Neuve, September 4-7, 1978 edited by B. Delmon, P.Grange, P.Jacobs and G. Poncelet Growth and Properties of Metal Clusters. Applications to Catalysis and the Photographic Process. Proceedings ofthe 32nd International Meeting ofthe Societe de Chimie Physique, Villeurbanne, September 24-28, 1979 edited by J. Bourdon Catalysis by Zeolites. Proceedings of an International Symposium, Ecully (Lyon), Septernber 9-11, 1980 edited by B. Imelik, C. Naccache, u Ben Taarit, J.C. Vedrine, G. Coudurier and H. Praliaud Catalyst Deactivation. Proceedings of an International Symposium, Antwerp, October 13-15,1980 edited by B. Delmon and G.F. Froment New Horizons in Catalysis. Proceedings of the 7th International Congress on Catalysis, Tokyo, June 30-July4, 1980. Parts A and B edited by T. Seiyama and K. Tanabe Catalysis by Supported Complexes by Yu.l. Yermakov, B.N. Kuznetsov and V.A. Zakharov Physics of Solid Surfaces. Proceedings of a Symposium, Bechyhe, September 29-October 3,1980 edited by M. Lazni~ka Adsorption at the Gas-Solid and Liquid-Solid Interface. Proceedings of an International Symposium, Aix-en-Provence, September 21-23, 1981 edited by J. Rouquerol and K.S.W. Sing Metal-Support and Metal-Additive Effects in Catalysis. Proceedings of an International Symposium, Ecully (Lyon), September 14-16, 1982 edited by B. Imelik, C. Naccache, G. Coudurier, H. Praliaud, P. Meriaudeau, P. Gallezot, G.A. Martin and J.C. Vedrine Metal Microstructures in Zeolites. Preparation - Properties- Applications. Proceedings of a Workshop, Bremen, September 22-24, 1982 edited by P.A. Jacobs, N.I. Jaeger, P.Jin3 and G. Schulz-Ekloff Adsorption on Metal Surfaces. An Integrated Approach edited by J. Benard Vibrations at Surfaces. Proceedings of the Third International Conference, Asilomar, CA, September 1-4, 1982 edited by C.R. Brundle and H. Morawitz
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Heterogeneous Catalytic Reactions Involving Molecular Oxygen by G.I. Golodets Preparation of Catalysts III. Scientific Bases for the Preparation of Heterogeneous Catalysts. Proceedings of the Third International Symposium, I_ouvain-la-Neuve, September 6-9, 1982 edited by G. Poncelet, P. Grange and P.A. Jacobs Spillover of Adsorbed Species. Proceedings of an International Symposium, Lyon-Villeurbanne, September 12-16, 1983 edited by G.M. Pajonk, S.J. Teichner and J.E. Germain Structure and Reactivity of Modified Zeolites. Proceedings of an International Conference, Prague, July 9-13, 1984 edited by P.A. Jacobs, N.I. Jaeger, P.Jin3, V.B. Kazansky and G. Schulz-Ekloff Catalysis on the Energy Scene. Proceedings of the 9th Canadian Symposium on Catalysis, Quebec, P.Q., September 30-October 3, 1984 edited by S. Kaliaguine and A. Mahay Catalysis by Acids and Bases. Proceedings of an International Symposium, Villeurbanne (Lyon), September 25-27, 1984 edited by B. Imelik, C. Naccache, G. Coudurier, Y. Ben Taarit and J.C. Vedrine Adsorption and Catalysis on Oxide Surfaces. Proceedings of a Symposium, Uxbridge, June 28-29, 1984 edited by M. Che and G.C. Bond Unsteady Processes in Catalytic Reactors by Yu.Sh. Matros Physics of Solid Surfaces 1984 edited by J. Koukal Zeolites: Synthesis, Structure, Technology and Application. Proceedings of an International Symposium, Portoro~-Portorose, September 3-8, 1984 edited by B. Dr:~aj, S. Ho~:evarand S. Pejovnik Catalytic Polymerization of Olefins. Proceedings of the International Symposium on Future Aspects of Olefin Polymerization, Tokyo, July 4-6, 1985 edited by T. Keii and K. Soga Vibrations at Surfaces 1985. Proceedings of the Fourth International Conference, Bowness-on-Windermere, September 15-19, 1985 edited by D.A. King, N.V. Richardson and S. Holloway Catalytic Hydrogenation edited by L. Cerveny New Developments in Zeolite Science and Technology. Proceedings of the 7th International Zeolite Conference, Tokyo, August 17-22, 1986 edited by YoMurakami, A. lijima and J.W. Ward Metal Clusters in Catalysis edited by B.C. Gates, L. Guczi and H. Kn6zinger Catalysis and Automotive Pollution Control. Proceedings of the First International Symposium, Brussels, September 8-11, 1986 edited by A. Crucq and A. Frennet Preparation of Catalysts IV. Scientific Bases for the Preparation of Heterogeneous Catalysts. Proceedings of the Fourth International Symposium, Louvain-la-Neuve, September 1-4, 1986 edited by B. Delmon, P. Grange, P.A. Jacobs and G. Poncelet Thin Metal Films and Gas Chemisorption edited by P. Wissmann Synthesis of High-silica Aluminosilicate Zeolites edited by P.A. Jacobs and J.A. Martens Catalyst Deactivation 1987. Proceedings of the 4th International Symposium, Antwerp, September 29-October 1, 1987 edited by B. Delmon and G.F. Froment
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Keynotes in Energy-Related Catalysis edited by S. Kaliaguine Methane Conversion. Proceedings of a Symposium on the Production of Fuels and Chemicals from Natural Gas, Auckland, April 27-30, 1987 edited by D.M. Bibby, C.D. Chang, R.F. Howe and S. Yurchak Innovation in Zeolite Materials Science. Proceedings of an International Symposium, Nieuwpoort, September 13-17, 1987 edited by P.J. Grobet, W.J. Mortier, E.F. Vansant and G. Schulz-Ekloff Catalysis 1987. Proceedings ofthe 10th North American Meeting ofthe Catalysis Society, San Diego, CA, May 17-22, 1987 edited by J.W. Ward Characterization of Porous Solids. Proceedings of the IUPAC Symposium (COPS I), Bad Soden a. Ts., April 26-29,1987 edited by K.K. Unger, J. Rouquerol, K.S.W. Sing and H. Kral Physics of Solid Surfaces 1987. Proceedings of the Fourth Symposium on Surface Physics, Bechyne Castle, September 7-11, 1987 edited by J. Koukal Heterogeneous Catalysis and Fine Chemicals. Proceedings of an International Symposium, Poitiers, March 15-17, 1988 edited by M. Guisnet, J. Barrault, C. Bouchoule, D. Duprez, C. Montassier and G. Perot Laboratory Studies of Heterogeneous Catalytic Processes by E.G. Christoffel, revised and edited by Z. Paal Catalytic Processes under Unsteady-State Conditions by Yu. Sh. Matros Successful Design of Catalysts. Future Requirements and Development. Proceedings ofthe Worldwide Catalysis Seminars, July, 1988, on the Occasion of the 30th Anniversary of the Catalysis Society of Japan edited by T. Inui Transition Metal Oxides. Surface Chemistry and Catalysis by H.H. Kung Zeolites as Catalysts, Sorbents and Detergent Builders. Applications and Innovations. Proceedings of an International Symposium, WLirzburg, September 4-8,1988 edited by H.G. Karge and J. Weitkamp Photochemistry on Solid Surfaces edited by M. Anpo and T. Matsuura Structure and Reactivity of Surfaces. Proceedingsof a European Conference, Trieste, September 13-16, 1988 edited by C. Morterra, A. Zecchina and G. Costa Zeolites: Facts, Figures, Future. Proceedings of the 8th International Zeolite Conference, Amsterdam, July 10-14, 1989. Parts A and B edited by P.A. Jacobs and R.A. van Santen Hydrotreating Catalysts. Preparation, Characterization and Performance. Proceedings ofthe Annual International AIChE Meeting, Washington, DC, November 27-December 2, 1988 edited by M.L. Occelli and R.G. Anthony New Solid Acids and Bases. Their Catalytic Properties by K. Tanabe, M. Misono, Y. Ono and H. Hattori Recent Advances in Zeolite Science. Proceedings of the 1989 Meeting of the British Zeolite Association, Cambridge, April 17-19, 1989 edited by J. Klinowsky and P.J. Barrie Catalyst in Petroleum Refining 1989. Proceedings of the First International Conference on Catalysts in Petroleum Refining, Kuwait, March 5-8, 1989 edited by D.L. Trimm, S. Akashah, M. Absi-Halabi and A. Bishara
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Future Opportunities in Catalytic and Separation Technology ~ edited by M. Misono, Y. Moro-oka and S. Kimura New Developments in Selective Oxidation. Proceedings of an International Volume 55 Symposium, Rimini, Italy, September 18-22, 1989 edited by G. Centi and F. Trifiro Olefin Polymerization Catalysts. Proceedings of the International Symposium Volume 56 on Recent Developments in Olefin Polymerization Catalysts, Tokyo, October 23-25, 1989 edited by T. Keii and K. Soga Volume 57A Spectroscopic Analysis of Heterogeneous Catalysts. Part A: Methods of Surface Analysis edited by J.L.G. Fierro Volume 57B Spectroscopic Analysis of Heterogeneous Catalysts. Part B: Chemisorption of Probe Molecules edited by J.L.G. Fierro Introduction to Zeolite Science and Practice Volume 58 edited by H. van Bekkum, E.M. Flanigen and J.C. Jansen Heterogeneous Catalysis and Fine Chemicals II. Proceedingsof the 2nd Volume 59 International Symposium, Poitiers, October 2-6, 1990 edited by M. Guisnet, J. Barrault, C. Bouchoule, D. Duprez, G. Perot, R. Maurel and C. Montassier Chemistry of Microporous Crystals. Proceedings of the International Symposium Volume 60 on Chemistry of Microporous Crystals, Tokyo, June 26-29, 1990 edited by T. Inui, S. Namba and T. Tatsumi Natural Gas Conversion. Proceedings of the Symposium on Natural Gas Volume 61 Conversion, Oslo, August 12-17, 1990 edited by A. Holmen, K.-J. Jens and S. Kolboe Characterization of Porous Solids II. Proceedings of the IUPAC Symposium Volume 62 (COPS II), Alicante, May 6-9, 1990 edited by F. Rodriguez-Reinoso, J. Rouquerol, K.S.W. Sing and K.K. Unger Preparation of Catalysts V. Scientific Bases for the Preparation of Heterogeneous Volume 63 Catalysts. Proceedings of the Fifth International Symposium, Louvain-la-Neuve, September 3-6, 1990 edited by G. Poncelet, P.A. Jacobs, P. Grange and B. Delmon Volume 64 New Trends in CO Activation edited by L. Guczi Catalysis and Adsorption by Zeolites. Proceedings of ZEOCAT 90, Leipzig, Volume 65 August 20-23, 1990 edited by G. (~hlmann, H. Pfeifer and R. Fricke Dioxygen Activation and Homogeneous Catalytic Oxidation. Proceedings of the Volume 66 Fourth International Symposium on Dioxygen Activation and Homogeneous Catalytic Oxidation, BalatonfCired, September 10-14, 1990 edited by L.I. Simandi Structure-Activity and Selectivity Relationships in Heterogeneous Catalysis. Volume 67 Proceedings of the ACS Symposium on Structure-Activity Relationships in Heterogeneous Catalysis, Boston, MA, April 22-27, 1990 edited by R.K. Grasselli and A.W. Sleight Catalyst Deactivation 1991. Proceedings of the Fifth International Symposium, Volume 68 Evanston, IL, June 24-26, 1991 edited by C.H. Bartholomew and J.B. Butt Zeolite Chemistry and Catalysis. Proceedings of an International Symposium, Volume 69 Prague, Czechoslovakia, September 8-13, 1991 edited by P.A. Jacobs, N.I. Jaeger, L. Kubelkova and B. Wichterlova Volume 54
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Poisoning and Promotion in Catalysis based on Surface Science Concepts and Experiments by M. Kiskinova Catalysis and Automotive Pollution Control II. Proceedings of the 2nd International Symposium (CAPoC 2), Brussels, Belgium, September 10-13, 1990 edited by A. Crucq New Developments in Selective Oxidation by Heterogeneous Catalysis. Proceedings of the 3rd European Workshop Meeting on New Developments in Selective Oxidation by Heterogeneous Catalysis, Louvain-la-Neuve, Belgium, April 8-10, 1991 edited by P. Ruiz and B. Delmon Progress in Catalysis. Proceedings of the 12th Canadian Symposium on Catalysis, Banff, Alberta, Canada, May 25-28, 1992 edited by K.J. Smith and E.C. Sanford Angle-Resolved Photoemission. Theory and Current Applications edited by S.D. Kevan New Frontiers in Catalysis, Parts A-C. Proceedings of the 10th International Congress on Catalysis, Budapest, Hungary, 19-24 July, 1992 edited by L. Guczi, F. Solymosi and P.Tetenyi Fluid Catalytic Cracking: Science and Technology edited by J.S. Magee and M.M. Mitchell, Jr. New Aspects of Spillover Effect in Catalysis. For Development of Highly Active Catalysts. Proceedings ofthe Third International Conference on Spillover, Kyoto, Japan, August 17-20, 1993 edited by T. Inui, K. Fujimoto, T. Uchijima and M. Masai Heterogeneous Catalysis and Fine Chemicals III. Proceedings ofthe 3rd International Symposium, Poitiers, April 5- 8, 1993 edited by M. Guisnet, J. Barbier, J. Barrault, C. Bouchoule, D. Duprez, G. Perot and C. Montassier Catalysis: An Integrated Approach to Homogeneous, Heterogeneous and Industrial Catalysis edited by J.A. Moulijn, P.W.N.M. van Leeuwen and R.A. van Santen Fundamentals of Adsorption. Proceedings of the Fourth International Conference on Fundamentals of Adsorption, Kyoto, Japan, May 17-22, 1992 edited by M. Suzuki Natural Gas Conversion II. Proceedings of the Third Natural Gas Conversion Symposium, Sydney, July 4-9, 1993 edited by H.E. Curry-Hyde and R.F. Howe New Developments in Selective Oxidation II. Proceedings of the Second World Congress and Fourth European Workshop Meeting, Benalmadena, Spain, September 20-24, 1993 edited by V. Cortes Corberan and S. Vic Bellon Zeolites and Microporous Crystals. Proceedings of the International Symposium on Zeolites and Microporous Crystals, Nagoya, Japan, August 22-25, 1993 edited by T. Hattori and T. Yashima Zeolites and Related Microporous Materials: State of the Art 1994. Proceedings ofthe 10th International Zeolite Conference, Garmisch-Partenkirchen, Germany, July 17-22, 1994 edited by J. Weitkamp, H.G. Karge, H. Pfeifer and W. H61derich Advanced Zeolite Science and Applications edited by J.C. Jansen, M. St6cker, HoG. Karge and J.Weitkamp Oscillating Heterogeneous Catalytic Systems by M.M. Slin'ko and N.I. Jaeger
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Characterization of Porous Solids III. Proceedings of the IUPAC Symposium (COPS III), Marseille, France, May 9-12, 1993 edited by J.Rouquerol, F. Rodriguez-Reinoso, K.S.W. Sing and K.K. Unger Catalyst Deactivation 1994. Proceedings of the 6th International Symposium, Ostend, Belgium, October 3-5, 1994 edited by B. Delmon and G.F. Froment Catalyst Design for Tailor-made Polyolefins. Proceedings of the International Symposium on Catalyst Design for Tailor-made Polyolefins, Kanazawa, Japan, March 10-12, 1994 edited by K. Soga and M. Terano Acid-Base Catalysis I1. Proceedings of the International Symposium on Acid-Base Catalysis II, Sapporo, Japan, December 2-4, 1993 edited by H. Hattori, M. Misono and Y. Ono Preparation of Catalysts VI. Scientific Bases for the Preparation of Heterogeneous Catalysts. Proceedings of the Sixth International Symposium, Louvain-La-Neuve, September 5-8, 1994 edited by G. Poncelet, J. Martens, B. Delmon, P.A. Jacobs and P. Grange Science and Technology in Catalysis 1994. Proceedings of the Second Tokyo Conference on Advanced Catalytic Science and Technology, Tokyo, August 21-26, 1994 edited by Y. Izumi, H. Arai and M. Iwamoto Characterization and Chemical Modification of the Silica Surface by E.F. Vansant, P.Van Der Voort and K.C. Vrancken Catalysis by Microporous Materials. Proceedings of ZEOCAT'95, Szombathely, Hungary, July 9-13, 1995 edited by H.K. Beyer, H.G.Karge, I. Kiricsi and J.B. Nagy Catalysis by Metals and Alloys by V. Ponec and G.C. Bond Catalysis and Automotive Pollution Control II1. Proceedings of the Third International Symposium (CAPoC3), Brussels, Belgium, April 20-22, 1994 edited by A. Frennet and J.-M. Bastin Zeolites: A Refined Tool for Designing Catalytic Sites. Proceedings of the International Symposium, Quebec, Canada, October 15-20, 1995 edited by L. Bonneviot and S. Kaliaguine Zeolite Science 1994: Recent Progress and Discussions. Supplementary Materials to the 10th International Zeolite Conference, Garmisch-Partenkirchen, Germany, July 17-22, 1994 edited by H.G. Karge and J. Weitkamp Adsorption on New and Modified Inorganic Sorbents edited by A. Dajbrowski and V.A. Tertykh Catalysts in Petroleum Refining and Petrochemicals Industries 1995. Proceedings of the 2nd International Conference on Catalysts in Petroleum Refining and Petrochemical Industries, Kuwait, April 22-26, 1995 edited by M. Absi-Halabi, J. Beshara, H. Qabazard and A. Stanislaus 1lth International Congress on Catalysis - 40th Anniversary. Proceedings ofthe 1lth ICC, Baltimore, MD, USA, June 30-July 5, 1996 edited by J. W. Hightower, W.N. Delgass, E. Iglesia and A.T. Bell Recent Advances and New Horizons in Zeolite Science and Technology edited by H. Chon, S.I. Woo and S. -E. Park Semiconductor Nanoclusters - Physical, Chemical, and Catalytic Aspects edited by P.V. Kamat and D. Meisel Equilibria and Dynamics of Gas Adsorption on Heterogeneous Solid Surfaces edited by W. Rudzinski, W. A. Steele and G. Zgrablich
