New Developments and Applications in Sensing Technology

Lecture Notes in Electrical Engineering Volume 83

Subhas Chandra Mukhopadhyay, Aimé Lay-Ekuakille, and Anton Fuchs (Eds.)

New Developments and Applications in Sensing Technology

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Subhas Chandra Mukhopadhyay School of Engineering and Advanced Technology (SEAT) Massey University (Manawatu Campus) Palmerston North, New Zealand E-mail: [email protected] Aime Lay-Ekuakille Dipartimento d’Ingegneria dell’innovazione University of Salento Lecce, Italy E-mail: [email protected] Anton Fuchs Institute of Electrical Measurement and Measurement Signal Processing Graz University of Technology Graz, Austria E-mail: [email protected]

ISBN 978-3-642-17942-6

e-ISBN 978-3-642-17943-3

DOI 10.1007/978-3-642-17943-3 c 2011 Springer-Verlag Berlin Heidelberg  This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typeset & Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper 987654321 springer.com

Guest Editorial

This special issue titled “New Developments and Applications in Sensing Technology” in the book series of “Lecture Notes in Electrical Engineering” contains invited papers from renowned experts working in the field of sensing technology. A total of 17 chapters describe the advancement in the area of smart sensors and sensor networks design, measurement techniques, signal processing, and efficient algorithms in recent times. The 17 carefully selected chapters are extended versions of the conference papers presented at the 4th International Conference on Sensing Technology (ICST 2010), held at University of Salento, Leece, Italy from June 3 to 5, 2010. This special issue has focussed on different aspects of modern sensing technology, i.e. intelligent measurement, information processing, adaptability, recalibration, data fusion, validation, high reliability and integration of novel and high performance sensors. The aspects and methods are used for applications in material testing and analysis, communication, quality inspection, biomedical and environmental measurements. The selection of the chapters in this special issue reflects the range of requirements and suitable approaches for current challenges in sensing technology. While future interest in this field is ensured by the constant supply of emerging modalities, techniques, and engineering solutions, many of the basic concepts and strategies have already matured and now offer opportunities to build upon. We are very happy to be able to offer the readers of ”Lecture Notes in Electrical Engineering” such a diverse special issue both in terms of its topical coverage and geographic representation. We hope that the readers will find it interesting, thought provoking, and useful in their research and practical engineering work. We would like to extend our wholehearted thanks to all the authors who have contributed their work to this special issue. Subhas Chandra Mukhopadhyay, Guest Editor School of Engineering and Advanced Technology (SEAT), Massey University (Manawatu Campus) Palmerston North, New Zealand [email protected] Aime Lay-Ekuakille, Guest Editor Dipartimento d’Ingegneria dell’innovazione University of Salento Lecce, Italy [email protected] Anton Fuchs, Guest Editor Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology Graz, Austria [email protected]

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Guest Editorial

Dr. Subhas Chandra Mukhopadhyay graduated from the Department of Electrical Engineering, Jadavpur University, Calcutta, India in 1987 with a Gold medal and received the Master of Electrical Engineering degree from Indian Institute of Science, Bangalore, India in 1989. He obtained the PhD (Eng.) degree from Jadavpur University, India in 1994 and Doctor of Engineering degree from Kanazawa University, Japan in 2000. During 1989-90 he worked almost 2 years in the research and development department of Crompton Greaves Ltd., India. In 1990 he joined as a Lecturer in the Electrical Engineering department, Jadavpur University, India and was promoted to Senior Lecturer of the same department in 1995. Obtaining Monbusho fellowship he went to Japan in 1995. He worked with Kanazawa University, Japan as researcher and Assistant professor till September 2000. In September 2000 he joined as Senior Lecturer in the Institute of Information Sciences and Technology, Massey University, New Zealand. He is working currently as an Associate professor with the School of Engineering and Advanced Technology of Massey University, New Zealand. His fields of interest include Sensors and Sensing Technology, Electromagnetics, control, electrical machines and numerical field calculation etc. He has authored over 200 papers in different international journals and conferences, edited nine conference proceedings. He has also edited seven special issues of international journals as guest editor and seven books with Springer-Verlag. He is a Fellow of IET (UK), a senior member of IEEE (USA), an associate editor of IEEE Transactions on Instrumentation and Measurements. He is in the editorial board of e-Journal on Non-Destructive Testing, Sensors and Transducers, Transactions on Systems, Signals and Devices (TSSD), Journal on the Patents on Electrical Engineering, Journal of Sensors. He is in the technical programme committee of IEEE Sensors conference, IEEE IMTC conference and IEEE DELTA conference. He was the Technical Programme Chair of ICARA 2004, ICARA 2006 and ICARA 2009. He was the General chair and co-chair of ICST 2005, ICST 2007, ICST 2008, IEEE Sensors 2008, ICST 2010. He has organized the IEEE Sensors conference 2009 at Christchurch, New Zealand as General Chair. He is co-editor in chief of the International Journal on Smart Sensing and Intelligent Systems (www.s2is.org). Aimé Lay-Ekuakille has a Master Degree in Electronic Engineering, a Master Degree in Clinical Engineering, a Ph.D in Electronic Engineering from Polytechnic of Bari, Italy. He has been technical manager of different private companies in the field of: Industrial plants, Environment Measurements, Nuclear and Biomedical Measurements; he was director of a Health & Environment municipal Department. He has been a technical advisor of Italian government for high risk plants. From 1993 up to 2001, he was adjunct professor of Measurements and control systems in the University of Calabria, University of Basilicata and Polytechnic of Bari. He joined the Department of Innovation Engineering, University of

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Salento, in September 2000 in the Measurement & Instrumentation Group. Since 2003, he became the leader of the scientific group; hence, he is the co-ordinator of Measurement and Instrumentation Lab in Lecce. He has been appointed as UE Commission senior expert for FP-VI (2005-2010). He is still: chair of IEEE-sponsored SCI/SSD Conference, member of Transactions on SSD and Sensors & Transducers Journal editorial board. He is Associate Editor of the International Journal on Smart Sensing and Intelligent Systems. He is currently organizing the next ICST2010 in Lecce, Italy. He is a member of the following boards and TCs: Association of the Italian Group of Electrical and Electronic Measurements (GMEE), SPIE, IMEKO TC19 Environmental Measurements, IEEE, IEEE TC-25 Medical and Biological Measurements Subcommittee on Objective Blood Pressure Measurement, IEEE-EMBS TC on Wearable Biomedical Sensors & System and included in different IPCs of conferences. Aimé Lay-Ekuakille is a scientific co-ordinator of different international projects. He has authored and co-authored more than 95 papers published in international journals, books and conference proceedings. His main researches are on Environmental and Biomedical instrumentation & measurements and, measurements for renewable energy. Anton Fuchs was born in Graz, Austria, in 1977. He received the Dipl.Eng. degree in telematics from Graz University of Technology in 2001 and joint the Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology in 2002. He worked as a researcher, project manager, and lecturer and received the Doctoral degree in technical science in 2006 from Graz University of Technology. He was Visiting Researcher and Research Fellow at the Centre for Bulk Solids and Particulate Technologies, University of Wollongong, Australia in 2004 and in 2007/2008 respectively. In 2009 he received the venia docendi for “Process Instrumentation and Sensor Technology” from Graz University of Technology and became Associate Professor. Anton Fuchs is now with the Virtual Vehicle Competence Center in Graz, Austria. He is still Associate Professor and Distinguished Lecturer at Graz University of Technology. His main research interests include capacitive sensors and the measurement of transported material in industrial conveying processes. Anton Fuchs is author and co-author of more than 90 scientific papers and patents.

Table of Contents

Detection of Micro-cracks on Metal Surfaces Using Near-Field Microwave Dual-Behaviour Resonators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Julien Kerouedan, Patrick Qu´eff´elec, Philippe Talbot, C´edric Quendo, Alain Le Brun Improving the Energy Efficiency of Wireless Sensors through Smart Antenna Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Mason, A. Shaw, A.I. Al-Shamma’a Planar Electromagnetic Sensor for the Detection of Nitrate and Contamination in Natural Water Sources Using Electrochemical Impedance Spectroscopy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M.A. Md Yunus, S.C. Mukhopadhyay Current Reconstruction Algorithms in Electrical Capacitance Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Neumayer, H. Zangl, D. Watzenig, A. Fuchs Non-destructive Control of Metallic Plate with Magnetic Techniques . . . . L. Battaglini, P. Burrascano, A. Canova, F. Ficili, M. Ricci, D. Rossi, F. Sciacca Gas Sensing Characteristics of Pure and ZnO-Modified Fe2 O3 Thick Films . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N.K. Pawar, D.D. Kajale, G.E. Patil, S.D. Shinde, V.B. Gaikwad, Gotan H. Jain Design and Construction of a Configurable Full-Field Range Imaging System for Mobile Robotic Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.A. Carnegie, J.R.K. McClymont, A.P.P. Jongenelen, B. Drayton, A.A. Dorrington, A.D. Payne Cr2 O3 -doped BaTiO3 as an Ammonia Gas Sensor . . . . . . . . . . . . . . . . . . . . Gotan H. Jain, S.B. Nahire, D.D. Kajale, G.E. Patil, S.D. Shinde, D.N. Chavan, V.B. Gaikwad

1

15

39

65 107

123

133

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Physical and Electrical Modeling of Interdigitated Electrode Arrays for Bioimpedance Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Ibrahim, J. Claudel, D. Kourtiche, B. Assouar, M. Nadi

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Water Quality Assessment through Smart Sensing and Computational Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . O. Postolache, P. Silva Gir˜ ao, J.M. Dias Pereira

191

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Multi-spectral Analytical Systems Using LIBS and LII Techniques . . . . . Satoshi Ikezawa, Muneaki Wakamatsu, Yury L’vovich Zimin, Joanna Pawlat, Toshitsugu Ueda

207

Electromechanical Sensors Based on Carbon Nanotube Networks and Their Polymer Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Slobodian, P. Riha, R. Olejnik

233

Novel Planar Interdigital Sensors for Detection of Domoic Acid in Seafood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.R. Mohd Syaifudin, K.P. Jayasundera, S.C. Mukhopadhyay

253

Nano-Biosensor Development for Biomedical and Environmental Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.M.G. Preethichandra, E.M.I. Mala Ekanayake

279

Nondestructive Evaluations of Iron-Based Materials by Using AC and DC Electromagnetic Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Koji Yamada, Jiaoliang Luo, Masato Enokizono

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STACK: Sparse Timing of Algorithms Using Computational Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vasanth Iyer, S. Sitharama Iyengar, Garmiela Rama Murthy, Kannan Srinathan, Mandalika B. Srinivas, Regeti Govindarajulu A New Approach to Estimation of Protein Networks for Cell Cycle Based on Least-Squares Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takehito Azuma, Masachika Kurata, Noriko Takahashi, Shuichi Adachi Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Detection of Micro-cracks on Metal Surfaces Using Near-Field Microwave Dual-Behaviour Resonators Julien Kerouedan1,2, Patrick Quéffélec1, Philippe Talbot1, Cédric Quendo1, and Alain Le Brun2 1

Lab-STICC (UMR 3192), Université de Bretagne Occidentale, CS 93836, 6 avenue Le Gorgeu, 29238 Brest Cedex 3, France 2 EDF R&D / STEP, 6 quai Watier, BP 49, 78401 Chatou Cedex, France [email protected]

Abstract. The aim of this paper is to demonstrate that micro-cracks at the surface of metals can be detected and imaged by using near-field micro-wave resonators. It deals with two novel sensors: a first-order dual-behaviour resonator (DBR) filter and a first-order DBR filter with an open-ing in the ground plane. Measurements were mainly carried out on a stainless steel mock-up with several EDM (i.e. manufactured by Electron Discharge Machining) rectangular surface notches presenting widths between 0.1 and 0.3 mm and depths between 0.5 and 3 mm. The results presented here show the high sensitivity of the DBR probes and their ability to differentiate between notches of different depths and notches of different widths. Keywords: electromagnetic sensors, non-destructive testing (NDT), microwaves, near-field resonator.

1 Introduction The fatigue and ageing of metal materials under operation conditions are major concerns in energy production plants. An early and non-destructive diagnostic of surface defects would allow one to carry out relevant preven-tive maintenance operations avoiding unnecessary replacements or early repairing of healthy components. Today, most of the automated non-destructive testing (NDT) solutions available to detect the surface-breaking defects are based on ultrasonic [1] or Eddy current techniques [2]. Despite their high sensitivity and resolution, they are unable to meet all the requirements of every real situation. Eddy current testing sensitivity to different external parameters sometimes makes signal analysis difficult, and ultrasonic techniques are not always suitable for the detection of small depth defects located near the inspection surface. Consequently, it sounded us relevant to evaluate the potential of microwavebased techniques to detect the surface defects with a depth shallower than 3 mm. Over the last years, microwave far- and near-field approaches were investigated to detect surface defects. With a far-field characterization [3], the spatial resolution is of the order of a half wavelength (λ/2). So, to detect micro-cracks it is necessary to work at very high frequencies, which causes high measurement equipment costs. In addition, at very high fre-quencies, signal-to-noise ratio (SNR) issues can appear. As a result, because of their poor spatial resolution, the far-field methods are un-suitable for S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 1–13. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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detecting small depth defects. On the other hand, the use of near-field techniques permits to significantly improve the spatial resolution. Indeed, with a near-field characterization, the spatial resolution primarily depends on two parameters: the size of the probe end and the lift-off distance (the lift-off distance is the distance between the probe end and the sample under test). Consequently, many near-field NDT methods have been recently developed to detect surface and subsurface defects in various materials over a wide range of frequencies. Two main categories of near-field techniques are reported in the literature: the methods based on reflection coefficient measurements and the methods relying on the use of resonators. The first type of near-field techniques consists in measuring the reflec-tion coefficient at the end of either an open-ended rectangular waveguide [4-6] or an open-ended coaxial line [7, 8]. The presence of a crack near the aperture of these open-ended structures induces changes in the reflection coefficient’s magnitude and phase. Due to the use of wellknown open structures of propagation, these methods are easily implemented. However, the detection of a micro-crack requires the measurement of magnitude variations of about the hundredth of dB and of phase variations of a few degrees. Thus, the detection of a surface micro-crack with this first near-field technique is achievable but it requires high levels of accuracy and re-liability for the measurement equipment to detect the very small magnitude and phase variations caused by the micro-crack. The second type of near-field techniques relies on the use of resonators to measure the changes in the resonance frequency and quality factor induced by the interaction with the surface flaws. These variations can be detected using a network analyzer. The resonators presented in the literature are terminated with either a tip [9, 10] or an electric or magnetic dipole [11] in order to guide the radiation issued from the resonator toward the sample under characterization. Many studies have highlighted the link between the spatial resolution of near-field resonators, the size of the probe end and the lift-off distance, and have showed that methods of material characterization resting on the use of resonators make it possible to obtain a spatial resolution lower than the micron at microwave frequencies [9]. These resonant techniques have proven to successfully image defects and non-uniformities in various metals [9-11]. The two major advantages of using near-field resonators to detect surface defects are the dependence of the spatial resolution on the probe end size and the lift-off distance, and the direct detection of variations in the resonance frequency and quality factor by using a network analyzer. The aim of our study was the detection of several rectangular EDM (i.e. manufactured by Electron Discharge Machining) notches with widths between 0.1 and 0.3 mm and depths shallower than 3 mm on the surface of a 20 mm thick austenitic stainless steel plate. After the analysis of the existing microwave-based techniques, it appeared to be interesting to investigate how well a near-field resonant probe could detect the rectangular EDM notches. In order to minimize the costs of the probe fabrication and measurement equipment, we focused our research on the realization of microstrip sensors with resonance frequencies of about 10 GHz. In addition, for practical reasons, we imposed a lift-off distance greater than or equal to 50 μm. To detect and image the surface defects, we developed a novel detection technique using the reflection (S11) and transmission coefficients (S21) of a dual-behaviour resonator (DBR) band-pass filter [12-14]. The main feature of this device is the high selectivity of the DBR resonator, which permits an easy measurement of the frequency shift with a network analyzer. In addition, the high sensitivity of the DBR probe makes it possible to detect micro-cracks at operating frequencies near 10 GHz.

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This paper presents the design and the results obtained with two original near-field resonant probes based on dual-behaviour resonators with and without an opening in the ground plane. Measurements carried out on different notches (with widths between 0.1 and 0.3 mm and depths between 0.5 and 3 mm) are presented, showing the influence of the depth and width of the notch on the behaviour of the DBR sensors. The sensitivity of the two probes is discussed.

2 First-Order DBR Filter Probe 2.1 Description of the Probe A first-order DBR band-pass filter results from the association of two different parallel band-stop structures [12, 13]. Fig. 1 shows (a) the design and (b) the reflection (S11) and transmission parameters (S21) of this first-order DBR band-pass filter. The filter that we studied was realized on an alumina substrate. It included two openended stubs. The low-frequency stub (LF stub for low-frequency stub) of length, l1, and characteristic impedance, Zs1, brings a transmission zero in the lower attenuated band, whereas the high-frequency stub (HF stub for high-frequency stub), of length, l2, and characteristic impedance, Zs2, brings a transmission zero in the upper attenuated band. A band-pass response between the lower and upper rejected bands is created by constructive recombination (Fig. 1.b). (a)

Zs1, l1 Z0

Z0

Zs2, l2

S11 (dB) and S21 (dB)

(b)

0 -10 -20 -30 -40

S21

S11

-50 5 6 7 8 9 10 11 12 13 14 15

Frequency (GHz)

Fig. 1. First-order DBR band-pass filter: (a) design, (b) reflection (S11) and transmission (S21) parameters

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The characterization of a metallic component is realized by moving the DBR filter probe at a constant lift-off distance d above the surface (the lift-off distance d is defined as the distance between the probe end and the surface). Fig. 2 shows (a) a schematic and (b) a photograph of the first-order DBR probe over a notch. (a) LF stub Metallic strip Port 1

Substrate Port 2 HF stub

Z WHF 0 Y

X

h W

d Notch Metal

(b)

Fig. 2. (a) Schematic and (b) photograph of the first-order DBR band-pass filter probe over a notch; W and h represent the width and depth of the notch, d is the lift-off distance, and the length L of the notch is in the y-direction

The principle of micro-crack detection consists in measuring the changes in the capacitive coupling created between the HF stub (of width WHF) and the metal sample under test. The probe-metal coupling can be described by a coupling capacitance [14, 15] that decreases when a defect is located below the HF stub of the DBR probe. This variation of capacitive coupling induces an increase in the frequency of the transmission zero associated with the high attenuated band, fHF, but also an increase in the central frequency, f0, i.e., the frequency associated with the minimum of the filter reflection parameter S11. On the other hand, the frequency of the transmission zero associated with the low attenuated band, fLF, remains unchanged due to a lack of LF stub-defect interaction. The influence of the probe-metal coupling on the behaviour of the first-order DBR filter sensor was studied in detail in [14]. This study highlighted that for a given coupling capacitance the shifts in fHF are more important than the shifts in f0. The frequency fHF obtained by measuring the transmission parameter S21 of the DBR filter is thus the best indicator of the changes in the probe-metal interaction.

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2.2 Experimental Results 2.2.1 Measurements Carried Out on a Stainless Steel Mock-Up Measurements were carried out on a 20 mm thick austenitic stainless steel plate (conductivity σ = 1.4 × 106 S.m-1) with several EDM rectangular notches. The experimental setup is shown in Fig. 3. It consists of an Agilent PNA E8364A (45 MHz – 50 GHz) network analyzer and a motorized three-axis displacement device holding the probe.

Computer

Network analyzer

Probe

3-axes displacement device Steel plate with EDM notches Fig. 3. Photograph of the experimental setup

0 Notch Faultless metal

S21 (dB)

-5 Stainless steel -10 -15

Notch

-20 -25

Faultless metal -30 12,5 12,6 12,7 12,8 12,9 13,0 13,1 13,2

Frequency (GHz) Fig. 4. Measured transmission parameter S21 of the 50 μm wide HF stub filter set at d = 50 μm over a faultless metal plate and over a 200 μm wide, 3 mm deep and 10 mm long notch

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ΔfHF (MHz)

The first experiments were focused on the detection of a 200 μm wide, 3 mm deep and 10 mm long EDM notch. Fig. 4 shows the transmission parameter (S21) responses measured with a 50 μm wide HF stub filter, for a lift-off distance d = 50 μm, when the probe was set over a faultless metal plate and over the middle of the notch. The presence of the notch causes an increase in fHF (ΔfHF = 51 MHz) and f0 (Δf0 = 26 MHz) (data not shown). In order to image the notch, the variations of the HF stub frequency (ΔfHF) were measured during several 1D scans over the notch. Fig. 5 shows four 1D scans in the x-direction with a 10 μm incrementing step measured with the 50 μm wide HF stub DBR filter for four different values of d between 50 and 150 μm. This figure highlights that ΔfHF is the highest in the middle of the notch, and that the DBR filter sensitivity decreases when the distance d increases. Additional measurements were carried out over a 200 μm wide, 1 mm deep and 10 mm long notch and over a 200 μm wide, 0.5 mm deep and 10 mm long notch in order to examine the influence of the notch depth on the response of the first-order DBR probe. Fig. 6 shows the values of ΔfHF measured with the 50 μm wide HF stub filter when 1D scans are performed in the x-direction over the 0.5, 1 and 3 mm deep notches (probe incrementing step = 10 μm, and lift-off distance d = 50 μm). The three plots presented in Fig. 6 differ by their height i.e. by their maximum ΔfHF value. The maximum ΔfHF values associated with the 0.5, 1 and 3 mm deep notches are 42, 45, and 51 MHz, respectively. These experimental results highlight the influence of the notch depth on the ΔfHF variations and show that the first-order DBR probe enables us to differentiate between notches of different depths in the stainless steel mock-up. In order to examine the influence of the notch width on the response of the firstorder DBR probe, other experiments were performed over notches of 100 μm and 300 μm width while maintaining the other dimensions unchanged: 1 mm depth and 10 mm length. 60 55 50 45 40 35 30 25 20 15 10 5 0

Stainless steel

-500 -400 -300 -200 -100 0

d = 50 μ m d = 75 μ m d = 100 μ m d = 150 μ m

100 200 300 400 500

x- position (μm)

Fig. 5. 1D scans (x-direction) over a stainless steel plate with a 200 μm wide, 3 mm deep and 10 mm long notch measured with the 50 μm wide HF stub DBR filter for four different lift-off distances d. Variations of the HF stub frequency (ΔfHF) as a function of the x-position; x = 0 corresponds to the middle of the notch in the stainless steel mock-up.

ΔfHF (MHz)

Detection of Micro-cracks on Metal Surfaces

60 55 50 45 40 35 30 25 20 15 10 5 0

Stainless steel

7

h = 3 mm h = 1 mm h = 0.5 mm

-500 -400 -300 -200 -100 0 100 200 300 400 500

x- position (μm)

Fig. 6. 1D scans (x-direction) over three 200 μm wide and 10 mm long notches differing by their depth h measured with the 50 μm wide HF stub DBR filter for a lift-off distance d = 50 μm. Variations of the HF stub frequency (ΔfHF) as a function of the x-position; x = 0 corresponds to the middle of the notches in the stainless steel mock-up.

ΔfHF (MHz)

The experimental results associated with these two notches were compared to those obtained during the characterization of the 200 μm wide, 1 mm deep and 10 mm long notch. Fig. 7 shows the values of ΔfHF measured with the 50 μm wide HF stub filter when 1D scans are performed in the x-direction over the 0.1, 0.2 and 0.3 mm wide notches (probe incrementing step = 10 μm, and lift-off distance d = 50 μm). 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

Stainless steel

w = 0.3 mm w = 0.2 mm w = 0.1 mm

-500 -400 -300 -200 -100 0 100 200 300 400 500

x- position (μm)

Fig. 7. 1D scans (x-direction) over three 1 mm deep and 10 mm long notches differing by their width W measured with the 50 μm wide HF stub DBR filter for a lift-off distance d = 50 μm. Variations of the HF stub frequency (ΔfHF) as a function of the x-position; x = 0 corresponds to the middle of the notches in the stainless steel mock-up.

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The three bell shape curves presented in Fig. 7 differ by their height and by their width. The maximum ΔfHF values associated with the 0.1, 0.2 and 0.3 mm wide notches are 22, 45, and 71 MHz, respectively, and the widths of the curves associated with the 0.1, 0.2 and 0.3 mm wide notches are about 330, 430 and 540 μm, respectively. These experimental results show the ability of the first-order DBR probe to differentiate between notches of different widths in the stainless steel mock-up. 2.2.2 Measurements Carried Out on an Aluminium Mock-Up Measurements were carried out on a 20 mm thick aluminium plate (conductivity σ = 37.5 × 106 S.m-1) with several EDM rectangular notches, using the same experimental setup as in the study with the stainless steel mock-up (§ 2.2.1, Fig. 3). Fig. 8 shows the values of ΔfHF measured with the 50 μm wide HF stub filter when 1D scans are performed in the x-direction over a 100 μm wide, 1 mm deep and 1 mm long notch and over a 100 μm wide, 0.5 mm deep and 1 mm long notch located on the aluminium mock-up (probe incre- menting step = 10 μm, and lift-off distance d = 50 μm). 30

Aluminium

ΔfHF (MHz)

25

h = 1 mm h = 0.5 mm

20 15 10 5 0 -500 -400 -300 -200 -100 0 100 200 300 400 500

x- position (μm)

Fig. 8. 1D scans (x-direction) over two 100 μm wide and 1 mm long notches differing by their depth h measured with the 50 μm wide HF stub DBR filter for a lift-off distance d = 50 μm. Variations of the HF stub frequency (ΔfHF) as a function of the x-position; x = 0 corresponds to the middle of the notches in the aluminium mock-up.

The two plots presented in Fig. 8 differ by their height i.e. by their maximum ΔfHF value. The maximum ΔfHF values associated with the 0.5 and 1 mm deep notches are 14 and 21 MHz, respectively. These experimental results and those obtained with the austenitic stainless steel mock-up (§ 2.2.1, Fig. 6) highlight the ability of the firstorder DBR probe to differentiate between notches of different depths. Fig. 9 shows the values of ΔfHF measured with the 50 μm wide HF stub filter when 1D scans are performed in the x-direction over a set of three notches of the same

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length and depth (1 mm), but of different widths (0.1, 0.2 and 0.5 mm). The lift-off distance between the probe and the surface of the aluminium mock-up is d = 50 µm (probe incrementing step = 10 μm). 160

ΔfHF (MHz)

140

Aluminium

120

w = 0.5 mm w = 0.2 mm w = 0.1 mm

100 80 60 40 20 0 -500 -400 -300 -200 -100 0 100 200 300 400 500

x- position (μm)

Fig. 9. 1D scans (x-direction) over three 1 mm deep and 1 mm long notches differing by their width W measured with the 50 μm wide HF stub DBR filter for a lift-off distance d = 50 μm. Variations of the HF stub frequency (ΔfHF) as a function of the x-position; x = 0 corresponds to the middle of the notches in the aluminium mock-up.

The three curves presented in Fig. 9 differ by their height and by their width. The maximum ΔfHF values associated with the 0.1, 0.2 and 0.5 mm wide notches are 21, 37, and 110 MHz, respectively, and the widths of the curves associated with the 0.1, 0.2 and 0.5 mm wide notches are about 370, 460 and 780 μm, respectively. These measurement results and those obtained with the stainless steel mock-up (§ 2.2.1, Fig. 7) highlight the ability of the first-order DBR probe to differentiate between notches of different widths.

3 First-Order DBR Filter Probe with Open Ground Plane 3.1 Description of the Probe This second probe was developed to increase the lift-off distance d. In a previous work [14], we have demonstrated the feasibility of increasing the lift-off distance by increasing the HF stub width of the DBR filter. For example, we have shown that a 100 μm wide HF stub first-order DBR filter allows one to detect a 200 μm wide, 3 mm deep and 10 mm long EDM notch on the surface of a stainless steel plate until d = 150 μm [14]. To allow the notch detection for d > 150 μm, our idea was to create an opening in the ground plane below the HF stub of the first-order DBR filter so as to increase the radiation of the sensor. Simulations were performed with the finite element (FEM)-based commercial software package HFSSTM in order to determine the optimal shape and size of this

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opening. For a 100 μm wide HF stub filter, the best compromise between sensitivity and resolution was obtained for a rectangular opening with a width Wo = 1 mm. Figs. 10 and 11 show respectively a schematic and two photographs of the DBR probe with open ground plane.

Fig. 10. Schematic of the first-order DBR filter probe with open ground plane

(a) BF stub

HF stub (b) Ground plane

Opening below the HF stub

Fig. 11. (a) Top view photograph and (b) bottom view photograph of the first-order DBR filter probe with open ground plane

3.2 Experimental Results All the measurements were carried out with the same experimental setup (Fig. 3) and the same stainless steel mock-up as in the study with the first-order DBR filter probe. Fig. 12 shows the transmission parameter (S21) responses measured using a 100 μm wide HF stub filter with open ground plane, for a lift-off distance d = 300 μm, when the probe was set over a faultless metal plate and over the middle of a 200 μm wide, 3 mm deep and 10 mm long EDM notch. This figure highlights an increase in fHF (ΔfHF = 33 MHz) induced by the presence of the notch.

Detection of Micro-cracks on Metal Surfaces

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Fig. 13 gives the variations ΔfHF measured using the 100 μm wide HF stub filter with open ground plane when 1D scans are performed in the x-direction over the 200 μm wide, 3 mm deep and 10 mm long notch with a 50 μm incrementing step and for six different values of d between 100 and 650 μm. This figure shows that the DBR probe with open ground plane can easily detect the notch until d = 400 μm while it is impossible with the classic DBR filter probe (§ 2.2.1, Fig. 5).

0 Stainless steel

Notch Faultless metal

S21 (dB)

-5 -10 -15

Notch

-20 -25 Faultless metal -30 13.6 13.7 13.8 13.9 14.0 14.1 14.2

Frequency (GHz) Fig. 12. Measured transmission parameter S21 of the 100 μm m wide HF stub filter with open ground plane set at d = 300 μm m over a faultless metal plate and over a 200 μm m wide, 3 mm deep and 10 mm long notch

120

d = 100 μm d = 180 μm d = 250 μm d = 300 μm d = 400 μm d = 650 μm

Stainless steel

ΔfHF (MHz)

100 80 60 40 20 0 -2000 -1500 -1000 -500

0

500 1000 1500 2000

x- position (μm)

Fig. 13. 1D scans (x-direction) over a stainless steel plate with a 200 μm wide, 3 mm deep and 10 mm long notch measured using the 100 μm wide HF stub DBR filter with open ground plane for six different lift-off distances d. Variations of the HF stub frequency (ΔfHF) as a function of the x-position; x = 0 corresponds to the middle of the notch in the stainless steel mock-up.

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At last, Fig. 14 shows the values of ΔfHF measured using the 100 μm wide HF stub filter with open ground plane when 1D scans are performed in the x-direction over a 200 μm wide, 3 mm deep and 10 mm long notch and over a 200 μm wide, 1 mm deep and 10 mm long notch, with a 50 μm incrementing step and d = 300 μm. The difference between the maximum ΔfHF values associated with the 1 mm deep notch (ΔfHF = 22 MHz) and the 3 mm deep notch (ΔfHF = 33 MHz) illustrates the influence of the notch depth on the ΔfHF variations. 40

ΔfHF (MHz)

35

Stainless steel

h = 3 mm h = 1 mm

30 25 20 15 10 5 0 -2000 -1500 -1000 -500

0

500 1000 1500 2000

x- position (μm)

Fig. 14. 1D scans (x-direction) over two 200 μm wide and 10 mm long notches differing by their depth h measured using the 100 μm wide HF stub DBR filter with open ground plane, for a lift-off distance d = 300 μm. Variations of the HF stub frequency (ΔfHF) as a function of the xposition; x = 0 corresponds to the middle of the notches in the stainless steel mock-up.

4 Conclusion A near-field microwave NDT method using dual-behaviour resonator filters was investigated in order to detect surface defects in metals. The detection principle was validated theoretically and experimentally. The experimental results obtained with the stainless steel mock-up and the aluminium mock-up showed the good spatial resolution and the high sensitivity of the DBR filter probes. In addition, the measurements carried out on EDM notches highlighted: 1) the influence of the width and depth of the notch on the HF stub frequency variations (ΔfHF) and 2) the enhancement of the detection sensitivity by using a first-order DBR filter with open ground plane. Further investigations will be aimed at studying the dependence of the ΔfHF variations on the dimensions (depth, width and length) of the notch. We also plan to evaluate the potential of the DBR probes to detect fatigue cracks and stress corrosion flaws.

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References 1. Krautkrämer, J., Krautkrämer, H.: Ultrasonic Testing of Materials, 4th edn. Springer, Berlin (1990) 2. Moore, P.O.: Nondestructive Testing Handbook (Electromagnetic Testing), 3rd edn., vol. 5. American Society for Nondestructive Testing (ASNT), Colombus (2004); Udpa, S.S. (technical ed.) 3. Shirai, H., Sehiguchi, H.: A simple crack depth estimation method from backscattering response. IEEE Transactions on Instrumentation and Measurement 53, 1249–1253 (2004) 4. Yeh, C.-Y., Zoughi, R.: A novel microwave method for detection of long surface cracks in metals. IEEE Transactions on Instrumentation and Measurement 43, 719–725 (1994) 5. Ghasr, M.T., Carroll, B., Kharkovsky, S., Austin, R., Zoughi, R.: Millimeter-wave differential probe for nondestructive detection of corrosion precursor pitting. IEEE Transactions on Instrumentation and Measurement 55, 1620–1627 (2006) 6. McClanahan, A., Kharkovshy, S., Maxon, A.R., Zoughi, R., Palmer, D.D.: Depth evaluation of shallow surface cracks in metals using rectangular waveguides at millimeter-wave frequencies. IEEE Transactions on Instrumentation and Measurement 59, 1693–1704 (2010) 7. Wang, Y., Zoughi, R.: Interaction of surface cracks in metals with open-ended coaxial probes at microwave frequencies. Materials Evaluation 58, 1228–1234 (2000) 8. Ju, Y., Saka, M., Uchimura, Y.: Evaluation of the shape and size of 3D cracks using microwaves. NDT&E International 38, 726–731 (2005) 9. Tabib-Azar, M., Su, D.-P., Pohar, A., Leclair, S.R., Ponchak, G.: 0.4 μm spatial resolution with 1 GHz (λ = 30 cm) evanescent microwave probe. Review of Scientific Instruments 70, 1725–1729 (1999) 10. Wei, T., Xiang, X.-D., Wallace-Freedman, W.G., Schultz, P.G.: Scanning tip microwave near-field microscope. Applied Physics Letters 68, 3506–3508 (1996) 11. Wang, R., Li, F., Tabib-Azar, M.: Calibration methods of a 2 GHz evanescent microwave magnetic probe for noncontact and nondestructive metal characterization for corrosion, defects, conductivity and thickness nonuniformities. Review of Scientific Instruments 76, 54701 (2005) 12. Quendo, C., Rius, E., Person, C.: Narrow bandpass filters using dual-behavior resonators. IEEE Transactions on Microwave Theory and Techniques 51, 734–743 (2003) 13. Quendo, C., Rius, E., Person, C.: Narrow bandpass filters using dual-behavior resonators based on stepped-impedance stubs and different-length stubs. IEEE Transactions on Microwave Theory and Techniques 52, 1034–1044 (2004) 14. Kerouedan, J., Quéffélec, P., Talbot, P., Quendo, C., De Blasi, S., Le Brun, A.: Detection of micro-cracks on metal surfaces using near-field microwave dual-behavior resonator filters. Measurement Science and Technology 19, 105701 (2008) 15. Kleismit, R.A., Kazimierczuk, M.K., Kozlowski, G.: Sensitivity and resolution of evanescent microwave microscope. IEEE Transactions on Microwave Theory and Techniques 54, 639–647 (2006)

Improving the Energy Efficiency of Wireless Sensors through Smart Antenna Design A. Mason, A. Shaw, and A.I. Al-Shamma’a Liverpool John Moores University, Liverpool, United Kingdom

Abstract. There is a growing trend in the use of intelligent Wireless Sensor Networks (WSNs) for a wide range of applications. In the early part of the decade the underlying hardware was largely in prototype form and used for small scale demonstration systems, but there is now growing interest in applications which are commercially viable. This work began on the premise that the sensor hardware has gradually become smaller, yet there are still a few peripheral components which are lagging behind; namely the battery and antenna. Here, a novel antenna design is presented; this antenna is of a practical size for use in WSNs, whilst also offering improved energy consumption over commonly used monopole antennas.

1 Introduction Antennas are critical to the operation of wireless communication systems such as those used for radio, television, and mobile phones. They are often taken for granted by an end user of such a product – many consumers are blissfully unaware of how much antenna design can impact on device performance, size and energy consumption. Antenna design is often forgotten in WSNs since the devices are deployed in close proximity to one another (i.e. with a separation of 10m or less). As a result, device communication with simple wire antennas is a simple and affordable solution, but is not necessarily efficient. This leads to data corruption during wireless transmission which can result in three possible scenarios: • Loss; the data is irreparable and is lost forever – in this case the energy put into capturing, processing and transmitting the data is wasted. • Recovery; some protocols may allow data recovery, implying that there is a permanent data overhead which incurs additional energy consumption. • Retransmission; important data may be repeatedly transmitted until the intended message is correctly received – this ensures reliability, but leads to wasted energy. Therefore, it is desirable to have a system which minimises data corruption in order to improve efficiency, particularly when one takes into account the fact that data transmission from a typical sensor node consumes three times more energy than data processing alone [1]. In addition to the energy problem, the physical form of the standard monopole antenna is considered to be unsuitable for many applications. In particular the authors S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 15–37. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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have been involved in the use of WSNs for inventory management [2, 3] and spot weld monitoring [4]. In these situations it is undesirable for the sensor node, or mote, antenna to protrude from the object to which it is attached, since both applications involve high speed movement which could damage or destroy the antenna should it be snagged. The devices used as part of the authors work are the commonly known MicaZ motes [1], one of which is shown in Figure 1. One can clearly see here that the monopole antenna protrudes some way from the mote; the length of the protrusion is linked to the operating frequency of the mote, and therefore its wavelength (λ) [5]. In this case the operating frequency is 2.45GHz, and λ = 122mm. Monopole antennas are typically λ/4 in length [6] and there is no exception here as the MicaZ antenna measures approximately 40mm1. The initial thought in this situation was to simply flatten the antenna and effectively shield it from damage. This scenario is shown clearly in Figure 2.

Fig. 1. Berkeley MicaZ mote with monopole antenna attached

Fig. 2. Mote antenna held in place with a nylon cable tie to prevent snagging 1

Note that λ/4 = 31mm at 2.45GHz, but the MicaZ antenna includes a sheath which is slightly longer than the antenna so as to offer limited protection.

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A simple experiment, using one mote as a transmitter and another as a receiver, demonstrated that flattening the antenna caused significant signal strength degradation. When the transmitter and receiver are separated by 1m of air, the signal strength was found to be 10dBm less if the antenna was flattened, compared to if the antenna was in its normal position (see Figure 1). As a result of these findings it was thought that designing a new antenna for the motes might be a more effective solution, since significant losses in signal strength ultimately lead to data loss.

2 Designing a New Antenna 2.1 Antenna Requirements Since industry disliked the idea of the existing MicaZ monopole antenna alternative types were considered. Wire antennas such as dipoles are typically in the order of λ/2 in length, and loop antennas often have a circumference equal to λ. We can see that dipole and loop antennas would be larger than the standard monopole antenna supplied with the MicaZ and therefore likely to be an even greater concern for industrial use. Smaller sizes are possible for loops and dipoles, but they do not make effective radiators [7, 8]. The best remaining option was a PCB antenna. With size and practicality being major concerns of industry it seemed that a low profile PCB antenna would be ideal. It was thought that such an antenna would be suitable for retrofitting to the current MicaZ motes, and in the future could possibly be integrated with the mote circuitry in a combined PCB design. In order to facilitate this, an aim was set of creating an antenna no greater than the size of MicaZ PCB (i.e. - 57mm × 32mm). Although PCB antennas do have their advantages, it is noted in literature that they tend to suffer from a narrow impedance bandwidth, quite often in the order of just a few percent [5]. The impedance bandwidth [9] refers to the ability of an object to absorb or transmit energy into its surroundings; in the case of antennas, the later is desirable. Impedance bandwidth can be calculated using Equation 1, where fu is the upper operating frequency, fl is the lower operating frequency and f0 is the centre frequency. fu and fl refer to the points where the energy transmitted by an antenna is ≥ 88.9%. In some texts this is also referred to as the point where the voltage standing wave ratio (VSWR) is ≤ 2 [10], and describes the range over which antennas are effective radiators.

⎛ f − fl ⎞ ⎟⎟ × 100 Bandwidth (%) = ⎜⎜ u ⎝ f0 ⎠

(Equation 1)

For the MicaZ mote, fl = 2.485GHz, fu = 2.400GHz and f0 = 2.443GHz [1], since the devices support multiple frequency channels for reduced interference. These figures lead to a minimum impedance bandwidth requirement of 3.48%. In addition to these requirements it was also thought that the antenna should have good directional properties (i.e. seek to radiate equally well in all directions). In literature this is often better defined as directivity, but this parameter is often difficult to quantify accurately, therefore this work takes a qualitative approach. Low directivity is critical for WSNs since it is often impossible to control the orientation of nodes during

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deployment – in mobile applications (e.g. inventory management) orientation may also vary significantly with time. Antennas with only a single plane of polarisation cannot communicate well (if at all) with those orientated perpendicular to themselves [11]; monopole and dipole antennas suffer noticeably from this issue. To summarise before continuing, the new antenna was required to: • • • •

operate at the centre frequency (f0) 2.443GHz. have an impedance bandwidth greater than 3.48%. be no larger than 57mm×32mm×1.6mm. have a low directivity.

2.2 Coplanar Waveguide (CPW) Antenna During a review of literature relating to antenna design, it was discovered that Nithisopa et al [12] had designed a broadband co-planar waveguide (CPW) fed slot antenna which, in simulations, had proven suitable for use over the range of approximately 2.35-2.70GHz, resulting in an impedance bandwidth of 14%. The term CPW refers to the way in which the antenna is fed; two parallel slots are cut into a copper surface to act as a transmission line feed to the radiating elements of the antenna itself – this is illustrated in Figure 3. The radiating elements come in many different forms, although it appears that the slot type is popular. The copper surrounding the feed slots acts as a ground plane which promotes more uniform radiation than one would experience with similar structures such as patch antennas [5, 13, 14]. Based upon the work conducted by Nithisopa, an Ansoft HFSS [15] model was created as shown in Figure 3. The model was set up by following strict guidelines [16] provided by the developer of HFSS for the creation of CPW models. Table 1 gives information relating to the dimensions illustrated in Figure 3. Dimensions W1 and W2 are of particular importance in impedance matching the antenna to a typical 50Ω transmission line. Impedance matching is vital in antenna design in order to ensure that as much power as possible from the radio transceiver is transferred to the propagation medium via the antenna [17]. Poor matching leads to power being reflected by the antenna back toward the transceiver, resulting in reduced transmission range, wasted energy and potential damage to the transceiver itself. Figure 4 shows the difference in simulated performance as a result of using FR4 instead of Duroid substrate, as in Nithisopa’s work. The reason for changing substrate was simply a case of using materials to hand at the time for prototype manufacture, but one can see that the increase in dielectric constant (εr) reduces the impedance bandwidth. For Duroid εr ≈ 2, but for FR4 εr ≈ 4. Despite the decrease in impedance bandwidth FR4 still resulted in an impedance bandwidth – calculated to be 10% – far exceeding the requirements for this application. Table 1. CPW dimensions (mm) h

pcbX

pcbY

W1

W2

H1

H2

L1

1.6

90.0

45.0

0.5

2.4

23.0

10.5

39.0

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Fig. 3. CPW antenna structure and dimensions

Fig. 4. Simulated reflected power for FR4 and Duriod substrates

Another interesting property of Nithisopa’s CPW antenna was its low directivity which is a significant advantage of CPW design when one considers other types of PCB antenna. Balanis [18] gives a guide to constructing a simple patch antenna, a structure mentioned in passing earlier. This structure consists of a rectangular conductive patch above a larger conductive ground plane. The two conductive layers are separated by a dielectric substrate, as shown in Figure 5. A comparison of the CPW antenna and a patch antenna created using the Balanis guide shows that the CPW antenna has a favourable radiation pattern for applications requiring low directivity; this is evidenced in Figure 6. The xz and yz planes are of particular interest at the 180° position where the patch antenna experiences attenuation in the order of 20dB – this is a direct result of the ground plane preventing transmission in this direction. The downside for the CPW antenna is increased attenuation in the plane of the PCB when compared with the patch antenna. However this is a reasonable compromise as the loss is much smaller than that caused by the patch antenna ground plane.

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Fig. 5. A simulated patch antenna, constructed using a guide by Balanis [18]. Note that the model is transparent so that the electric vector field is visible across the entire structure.

Fig. 6. Simulated radiation patterns of the (a) patch and (b) CPW antennas

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Despite the CPW antenna providing a bandwidth much greater than that required and having low directivity, it was still too large. Therefore further investigation ensued with CPW antennas remaining the focus of attention. 2.3 Folded Coplanar Waveguide (FCPW) Antenna In order to solve the issue of size with the CPW antenna, the largest dimension (pcbX) was considered. This dimension had to accommodate the radiating slots of length L1, which were ultimately responsible for radiating power into the transmission medium (i.e. the surrounding air). Thinking of the slots as being analogous to the two arms of a standard wire dipole antenna, some thought was given to what might happen if the slots were folded, therefore allowing them to be accommodated by a shorter pcbX dimension. Folded wire dipole [19] antennas are created by taking the two radiating elements of a standard λ/2 dipole and folding them to form a closed loop; this transition is shown in Figure 7. By applying a similar train of thought to the CPW antenna a new folded co-planar waveguide (FCPW) antenna was created. The simulation model for this antenna is shown in Figure 8, and Table 2 shows the dimensions used for this structure after applying parametric analysis to the model in order to optimise its radiation characteristics. The simulated radiation pattern is shown in Figure 9, and is not too far removed from that of Nithisopa’s CPW antenna. The simulated impedance bandwidth was calculated to be 61% - this is discussed further later.

Fig. 7. Converting the CPW antenna to a folded CPW antenna, with new dimensions also labelled Table 2. FCPW dimensions (mm) h

pcbX

pcbY

W1

W2

W3

H1

H2

H3

H4

L1

1.6

40.0

27.5

0.5

4.0

1.5

2.0

4.0

0.5

8.5

39.0

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Fig. 8. FCPW antenna simulation model, showing the electric vector field surrounding the antenna

Fig. 9. FCPW antenna simulated radiation pattern

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Given the promising impedance bandwidth and directivity results, the largest improvement with the FCPW antenna, in terms of achieving the initial goals, was size reduction. The initial requirements, stated in Section 2.1, were 57mm×32mm×1.6mm. The FCPW antenna is 40mm×27.5mm×1.6mm. This gives a total surface area saving of 39.7%, indicating that the antenna could be suitable for WSN nodes smaller than the Berkeley MicaZ. 2.4 Validating the Design The prototype FCPW antenna is shown in Figure 10. It has a bulkhead type SMA connector attached so as to allow connection to various devices (including the Berkeley MicaZ via an inter-series MMCX to SMA adaptor). Use of an SMA connector was convenient for experimentation purposes, the results of which are presented in the next section. It is imagined that this connector would not be necessary if the antenna were used in practise – instead the antenna could be connected directly to the radio transceiver of a wireless sensor node. The centre conductor of the SMA connector is soldered to the centre copper strip, whilst the outer conductor is connected to the antenna ground plane on either side of the centre strip. The simulated results for reflected power and those measured using the Anritsu VNA show a reasonable agreement (see Figure 11). At 2.45GHz the reflected power is just 2.6% - this means that 97.4% of the power incident to the antenna should be radiated. The impedance bandwidth during simulation was found to be 61%, whilst the measured bandwidth is 53%. This is more than adequate for the successful operation of the MicaZ mote over all of its selectable frequencies.

Fig. 10. Prototype FCPW antenna post construction

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Fig. 11. Simulated vs. measured reflected power for the FCPW antenna

3 Radiation Pattern Measurement 3.1 Methodology Simulations gave an indication of the FCPW antenna radiation pattern, however physical confirmation of this was required in order to validate the design. Testing antennas is not a trivial task as one must take a number of steps to ensure that measurements are not subject to interference from surrounding sources; this is particularly relevant here due to the wide range of uses for the ISM 2.4GHz frequency band (e.g. WLAN infrastructure).

Fig. 12. Inside a typical anechoic chamber

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Small antennas are typically tested in almost ideal conditions inside a structure known as an anechoic chamber [20]. Such chambers are usually shielded with a thick conducting metal, thus utilising the skin effect [21] to prevent signals from the outside world entering the chamber. An example of such a chamber is shown in Figure 12. Unfortunately, an anechoic chamber was not available for this work since they are costly to rent, and even more costly to build. Therefore an alternative method for testing was put into operation. It was thought that since WLAN infrastructure is typically localised to urban areas in the UK, tests could be carried out in a rural scenario where interference would be reduced. In addition, it was thought that a rural location, such as a large open field, would allow transmitted signals to simply travel away from the antenna and into space. This avoids issues with electromagnetic phenomena such as multipath fading [22-24].

Fig. 13. Antennas used for experimentation, including orientation indicators; (a) monopole (b) FCPW and (c) horn

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In addition to the FCPW antenna two further antennas were required; a reference (for comparison purposes), and a receiver. The reference antenna constructed was a λ/4 monopole with a ground plane, designed to approximate the performance of the default MicaZ monopole antenna. A horn antenna was chosen as the receiver since they are well known to have a high directivity [25] and are therefore unlikely to be affected by signals which originate from anywhere but directly in front of the horn aperture. In addition, horn antennas are highly orientation sensitive and only accept radiation in a single plane when they are used at their fundamental operating frequency. The chosen horn had dimensions appropriate for use at 2.4GHz. All three antennas are shown in Figure 13. Both the receiver (horn) and transmitter (monopole or FCPW) needed to be suspended 1.5m above ground level in order to prevent reflections from the floor causing multipath interference. In order to do this, tripods were employed, as shown in Figure 14. The receiving horn antenna was bolted to the top of a large surveyor’s tripod. Since it was assumed that the ground in a rural setting would not be particularly level, a base was made for this tripod with a large bolt at each corner allowing the base to be levelled – a plumb line hanging from the centre of the tripod was used for levelling. A standard camera tripod with full height adjustment was used to mount the transmitter. Much of the tripod was made out of plastic rather than metal which was considered to be useful in terms of reducing its impact on the experimental results (e.g. due to coupling). The mounting here was moveable through 360°, allowing rotation of the transmitting antenna in order to obtain radiation pattern measurements.

Fig. 14. Experimental setup in an open field

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The two antennas were placed 1.5m apart to ensure that the measurements were in the antenna far field (as opposed to the near field). The far field of the monopole antenna is 0.276m, but Balanis [26] notes that this is not a fixed rule and so it seemed appropriate to allow additional range. An Anritsu MS2024A vector network analyser (VNA) was used – the horn antenna was connected to its input port, and the transmitter to its output. The VNA was then used to measure the return loss2, in dB, at 2.45GHz. All measurements were repeated three times, and the averages used in the following sections; further repetitions were not practical because of changeable weather conditions and life time of the Anritsu VNA’s battery. 3.2 Measured Radiation Patterns The results of these experiments represent the measured return loss arbitrarily normalised to -50dB. The measurements shown are relative to the antenna orientations indicated in Figure 13. For the monopole antenna the electric field is polarised in the z direction, whilst for the FCPW it is taken to be in the y direction. With the pyramidal horn being a flared waveguide, it is possible to assume [27] that the electric field is parallel to the z axis of the waveguide (see Figure 13). This means that turning the horn antenna through 90° allows one to consider how the antennas perform when there is a polarisation mismatch. The measurements shown in Figures 15 and 16 include the situation where the antennas polarisations are matched and mismatched respectively. This is important for this work since we cannot often guarantee the relative orientation of the antennas of sensor nodes in a WSN.

Fig. 15. Monopole antenna radiation patterns with (a) matched and (b) mismatched polarisation

2

The ratio of received power (input) to transmitted power (output).

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Fig. 16. FCPW antenna radiation patterns with (a) matched and (b) mismatched polarisation

3.3 Discussion Looking first at the results of the monopole antenna (Figure 15), we see good correlation of the measured radiation patterns with those from known and respected literature [28, 29]. This is an excellent indication of the validity of the measurement method used in this work. When polarisation between the monopole and horn antennas is matched, as in Figure 15(a), we see in the xy plane that the monopole antenna acts isotropically; this is indicated by the reasonably uniform distribution of measured radiated power. However, in the xz and yz planes significant attenuation occurs in the z axis. Unlike infinite ground planes, their finite counterparts do not completely block transmission [13] but there is definite attenuation (in the order of 5-10dB) due to the presence of the monopole ground plane between 90° and 270° (not inclusive). The maximum measured power occurs in the yz plane at 310°, and is 8.07dB. The patterns are also generally quite symmetrical in the z axis, again as one would expect, so a similar peak can be found at 50°. A wire antenna is said to have a polarisation which is parallel to the wire [11]. Therefore it is not surprising that, as shown in Figure 15(b), there is a significant loss experienced with the monopole antenna when polarisation is mismatched. This is particularly evident in the xy plane, where the radiated power reaches -40dB. There is some improvement in the xz and yz planes, although it is suspected that this is a result of reflections incident to the ground plane. It is possible that the ground plane is acting as a poor parabolic dish antenna in these cases. Let us now consider the case of the FCPW antenna beginning with the radiation pattern results when polarisation is matched, as shown in Figure 16(a). Radiation is weakest in the plane of the PCB since electric field lines formed as a result of opposing charges on the FCPW ground plane converge at the PCB edges but cannot join. This is further highlighted in Figure 17. A drop in the radiated power of approximately 10dB is also present at the 90° and 270° degree positions due to the lack radiating elements covering these positions. Whilst a 10dB loss is significant, it is not nearly as significant as the loss that HFSS reported (shown as a 35dB loss in Figure 9). Another noteworthy feature is that the FCPW antenna, whilst not

Improving the Energy Efficiency of Wireless Sensors through Smart Antenna Design

29

outputting peak power near that of the monopole antenna, does not drop much below 10dB, whilst the monopole antenna drops to almost -20dB along the z axis. Looking finally at the most exciting results from the FCPW antenna, shown in Figure 16(b), this antenna is much more tolerant of polarisation mismatch than the monopole antenna. In the xy plane there is typically a 2-3dB loss when compared with the monopole measurements shown in Figure 15(b); the same reductions in radiated power are present at the 90° and 270° positions. These reductions apply to the yz plane also. The most notable feature, however, is the xz plane which displays peaks of up to 4.05dB which is more than experienced by the antenna in the Figure 16(a). So, the question is, why does the FCPW antenna exhibit such results? Looking at Figure 18 one can see that the vector fields radiate perpendicular to the PCB in most cases. A result of this is the drop in radiated power shown in Figure 16(b) in the xz plane at 0°, because directly above the antenna there is a polarisation mismatch, whilst there is an increase shown nearer the edges of the board. At the edges of the PCB the electric field appears to change direction so that it is nearly orthogonal to the field in the centre. This indicates that the polarisation of the electric field is not strictly linear, as is the case with the monopole antenna. Figure 19 serves to reinforce this argument. Here the electric field is shown to be parallel to the PCB; in the centre of the antenna the field is parallel to the y axis, but near the edges this changes and the field becomes almost parallel to the x axis. The reason for this occurring is most likely due to the fold introduced to the antenna. Normally the electric field would form across the narrowest dimension of the radiating slots, but at the fold the electric field can form between the centre strip and the ground plane, opposing the direction of the central electric field. Since the results in Figure 16(a) and 16(b) are not identical (i.e. the two radiation patterns are not of the same magnitude) this indicates that the antenna has an elliptical rather than circular polarisation. This means that the antenna is not truly omni-directional, but does exhibit properties which are far more favourable than a simple monopole antenna. The next section of this chapter looks at the practical implications of these properties.

Fig. 17. FCPW antenna simulated electric field vector plot showing the formation of fields at the edges of the PCB

30

A. Mason, A. Shaw, and A.I. Al-Shamma’a

Fig. 18. HFSS simulated vector field pattern for the FCPW antenna showing the formation of multiple polarisations (side view, xz plane)

Fig. 19. HFSS simulated vector field pattern for the FCPW antenna showing the formation of multiple polarisations (top view, xy plane)

4 Energy Efficiency Measurements 4.1 Experimental Setup Whilst a measurement of the antenna’s radiation patterns was useful to understand how power was distributed around the FCPW antenna, it is important to highlight the

Improving the Energy Efficiency of Wireless Sensors through Smart Antenna Design

31

real world implications of these measurements. Since the objective is to replace the MicaZ antenna, it was thought that this could involve a direct comparison of the existing monopole antenna and the FCPW antenna. Therefore experimental results were obtained which compared the two antennas in operation when attached to a MicaZ mote. A PC application was written which utilised the Received Signal Strength Indicator (RSSI) feature of the motes to calculate a ten second average for signal strength, along with the number of data packets received (out of a maximum of 1000). It was decided to record both of these items of data to ensure not only a good signal strength, but also that data transmission was taking place. Figure 20 shows a screenshot of this application.

Fig. 20. Bespoke application for recording mote RSSI and packet loss information

Tests were conducted outdoors, and the setup as shown in Figure 21. The base station and a moveable MicaZ node were placed 1.5m above ground level, and the distance between the two was then increased in 1m increments, starting at 0.1m and ending at 25m3. The transmission power level of both the base station and the moveable node were set to 0dBm (i.e. 1mW). Various orientations of both the standard monopole and FCPW antenna were investigated, including what happened when a polarisation mismatch occurred.

3

The first interval was 0.9m in order to accommodate the 0.1m initial spacing.

32

A. Mason, A. Shaw, and A.I. Al-Shamma’a

Fig. 21. RSSI and packet loss measurement setup

4.2 Measured Results The results obtained for the standard MicaZ monopole antenna are shown in Figures 22 and 23, whilst those for the FCPW antenna are shown in Figures 24 and 25. Each figure includes six sets of results. The associated orientation of each set of results is given in terms of the plane which is parallel to the base station monopole antenna.

Improving the Energy Efficiency of Wireless Sensors through Smart Antenna Design

33

The orientations are relative to those shown in Figures 13. For a polarisation mismatch the base station antenna was moved from being vertical to being horizontal with respect to the Earth.

Fig. 22. MicaZ monopole antenna RSSI as a function of distance

Fig. 23. MicaZ monopole antenna packet loss as a function of distance

34

A. Mason, A. Shaw, and A.I. Al-Shamma’a

Fig. 24. MicaZ FCPW antenna RSSI as a function of distance

Fig. 25. MicaZ FCPW antenna packet loss as a function of distance

4.3 Discussion Comparing the RSSI results (Figures 22 and 24), the monopole antenna performs best when it is vertical with respect to Earth (i.e. with the xz and yz planes) and there is no polarisation mismatch. For the FCPW antenna, the best performance is achieved when

Improving the Energy Efficiency of Wireless Sensors through Smart Antenna Design

35

the copper face of the antenna is facing the base station (i.e. the xy plane) and there is no polarisation mismatch. It is notable that whilst the monopole antenna gives the highest RSSI result (yz plane), it displays the largest variation in results also. Comparing the results in the yz plane with no polarisation mismatch and those in the xy plane with a polarisation mismatch there is a 20-25dBm difference. Looking at the best and worst results for the FCPW, the difference is limited to a maximum of almost 10dBm. The RSSI results do not tell the whole story however, which is why the packet loss results (Figures 23 and 25) are also included. These results show that the monopole antenna experiences heavy packet loss when it is orientated in the xy plane, even at a distance of just 2m. As the distance increases this packet loss varies greatly, and on numerous occasions peaks at over 50% loss. For the FCPW antenna however, packet loss does not appear to occur at all until a distance of 11m, and even then it is not as pronounced as that experienced by the monopole antenna. It is thought that these results are due to the FCPW antenna having an elliptical polarisation, as discussed in Section 4. This is a significant finding, since it is likely that WSN nodes will be deployed in close proximity to one another in many applications, and therefore often it is short range communications (<10m) which are of most interest. These results indicate that the FCPW antenna would be far more efficient than the typical monopole in this situation. It is difficult to quantify precisely how much more efficient the FCPW antenna would be, as differing environments and data recovery mechanisms would result in differing levels of wasted energy. Using the data present in Figures 23 and 25, we can say that 6000 data packets are transmitted between the transmitter and receiver for every 1m increment, beginning at 0m (i.e. there are 1000 packets per orientation plane, and two polarisations). Table 3 attempts to give a quantitative evaluation of how much more energy is used effectively when using the FCPW antenna in this particular scenario. The energy consumed per packet transmitted was calculated using oscilloscope measurements shown in Figure 26. This calculated figure is 6.08mW per packet, assuming average power consumption during processing and transmitting of 37.5mW and 60.5mW respectively. As one can see from Table 3, a significant amount of energy is put to more effective use by utilising the FCPW antenna in both the 0-10 and 0-25m ranges. Whilst these experiments took place outdoors in line-of-sight conditions, it is not unreasonable to expect similar performance differences in real world applications.

Table 3. Energy efficiency comparison of the monopole and FCPW antennas

Distance

Packets transmitted

Energy wasted due to Increase in energy lost packets (J) used effectively (J) FCPW Monopole FCPW

Packets lost Monopole

0-10m

72000

2946

0

17.9

0

17.9

0-25m

156000

15784

6315

95.9

38.4

57.5

36

A. Mason, A. Shaw, and A.I. Al-Shamma’a

Fig. 26. Measured time period and current for a single packet transmission from the MicaZ mote

5 Conclusions This work presents an alternative antenna design appropriate for use at 2.45GHz. It is cheap to produce and provides more than adequate bandwidth for Zigbee devices such as the Berkeley MicaZ mote whilst remaining relatively small (40mm×27.5mm×1.6mm). It also has good omni-directional characteristics which makes it extremely well suited to applications where node orientation is not known and cannot be controlled. This leads to greater energy efficiency in these applications, since it means data loss is less likely. For WSNs this is an important outcome as energy constraints are often foremost in the minds of those planning sensor node deployments. It is also noteworthy that the useable impedance bandwidth makes the FCPW antenna appropriate for use in other wireless communications standards such as IEEE802.11 and IEEE802.16 (Wi-Fi and WiMAX, respectively). The former has a similar frequency range to IEEE802.15.4 compliant devices (such as the Berkeley MicaZ mote), whilst the later operates at 2.3, 2.5 and 3.5GHz.

References [1] Crossbow (2004). MPR/MIB Users Manual, http://www.xbow.com/ (April 6, 2010) [2] Mason, A., et al.: Inventory Management in The Packaged Gas Industry Using Wireless Sensor Networks. In: Mukhopadhyay, S.C., Leung, H. (eds.) Advances in Wireless Sensors and Sensor Networks, 1st edn., vol. 64, pp. 75–100. Springer, Heidelberg (2010) [3] Mason, et al.: Asset Tracking: Beyond RFID. Presented at the 7th Annual Postgraduate Symposium on the Convergence of Telecommunications (PGNET), Liverpool John Moores University, Liverpool, UK (2006)

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[4] Mason, A.: Wireless Sensor Networks and their Industrial Applications, PhD, General Engineering Research Institute, Liverpool John Moores University, Liverpool (2008) [5] Balanis, C.: Microstrip Antennas. In: Antennas: Theory, Analysis and Design, 2nd edn., pp. 727–730. Wiley, Chichester (1996) [6] Ulaby, F.: Quarter wave monopole antenna. In: Fundamentals of Applied Electromagnetics, p. 358. Prentice-Hall, Englewood Cliffs (1999) [7] Balanis, C.: Loop Antennas. In: Antennas: Theory, Analysis and Design, 2nd edn., p. 203. Wiley, Chichester (1996) [8] Blake, L.: Radiation Resistance and Efficiency. In: Antennas, pp. 138–139. John Wiley & Sons, Chichester (1966) [9] Balanis, C.: Bandwidth. In: Antennas: Theory, Analysis and Design, 2nd edn., pp. 63–64. Wiley, Chichester (1996) [10] Garg, R., et al.: Broadbanding of Microstrip Antennas. In: Microstrip Antenna Design Handbook, pp. 534–535. Artech House, Norwood (2000) [11] Balanis, C.: Long Wire Antenna Polarisation. In: Antennas: Theory, Analysis and Design, 2nd edn., p. 496. Wiley, Chichester (1996) [12] Nithisopa, K., et al.: Design CPW Fed Slot Antenna for Wideband Applications. Piers Online 3, 1124–1127 (2007) [13] Garg, R., et al.: Effects of Finite Size Ground Plane. In: Microstrip Antenna Design Handbook, pp. 293–296. Artech House, Norwood (2000) [14] Ansoft. Probe Feed Patch Antenna Example. HFSS Users Guide, http://www.ansoft.com/ [15] Ansoft. Ansoft HFSS, http://www.ansoft.com/products/hf/hfss [16] Ansoft, Port tutorial series: Coplanar waveguide (CPW). HFSS v8 Training (2005), http://web.doe.carleton.ca/~mmariani/Thesis/ port_tutorial_CPW.ppt (22/05/2008) [17] Carr, J.: Transmission Lines Characteristic Impedance. In: Practical Antenna Handbook, 4th edn., p. 67. Tab Books (2001) [18] Balanis, C.: Microstrip Antennas: Patch Antenna Example. In: Antennas: Theory, Analysis and Design, 2nd edn., pp. 727–730. Wiley, Chichester (1996) [19] Kraus, J., Marhefka, R.: Folded Dipole Antennas. In: Antennas for All Applications, pp. 593–597. McGraw Hill Science, New York (2002) [20] Kraus, J., Marhefka, R.: Anechoic Chambers and Absorbing Materials. In: Antennas for All Applications, pp. 841–844. McGraw Hill Science, New York (2002) [21] Ulaby, F.: Plane Wave Propagation in Lossy Media. In: Fundamentals of Applied Electromagnetics, pp. 277–279. Prentice Hall, Englewood Cliffs (1999) [22] Blake, L.: Interference. In: Antennas, pp. 26–27. John Wiley & Sons, Chichester (1966) [23] Carr, J.: Fading Mechanisms. In: Practical Antenna Handbook, 4th edn., p. 35. Tab Books (2001) [24] Basagni, S., et al.: Multipath Fading and Shadowing. In: Mobile Adhoc Networking, pp. 235–236. Wiley-Blackwell (2004) [25] Balanis, C.: Horn Antennas. In: Antennas: Theory, Analysis and Design, 2nd edn., pp. 651–711. Wiley, Chichester (1996) [26] Balanis, C.: Field Regions. In: Antennas: Theory, Analysis and Design, 2nd edn., p. 33. Wiley, Chichester (1996) [27] Blake, L.: Waveguides. In: Antennas, p. 94. John Wiley & Sons, Chichester (1966) [28] Balanis, C.: Antennas: Theory, Analysis and Design, 2nd edn. Wiley, Chichester (1996) [29] Kraus, J., Marhefka, R.: Antennas for All Applications. McGraw Hill Science, New York (2002)

Planar Electromagnetic Sensor for the Detection of Nitrate and Contamination in Natural Water Sources Using Electrochemical Impedance Spectroscopy Approach M.A. Md Yunus1,2 and S.C. Mukhopadhyay1 1

School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand 2 Control and Instrumentation Engineering Department, Universiti Teknologi Malaysia, Skudai, Malaysia

Abstract. This paper highlights the progress of developing a low-cost system for detection of nitrate and contamination in natural water resources based on a planar electromagnetic sensor which consists of meander and interdigital structure. The sensor has been operated and evaluated using electrochemical impedance spectroscopy (EIS) approach, based on estimated electrical model; the results obtained from the experiments were interpreted. The objectives of the present work are to conduct simulation, experiments and analysis of a new nitrate detection method using novel planar electromagnetic sensors by means of electrochemical spectroscopy analysis. The sensor was tested with two aqueous solutions of nitrates forms namely, sodium nitrates (NaNO3) and ammonium nitrates (NH4NO3), each of different concentration between 5 mg and 20 mg dissolved in 1 litre of distilled water to observe their response. Furthermore, the sensor was tested with various kinds of prepared samples and natural water samples taken from natural sources around New Zealand. The simulation results using COMSOL have assisted in understanding the characteristic and response of the sensor to the change of properties in the aqueous solution. The experimental results have authenticated the results obtained from the simulation and show the sensor can well detect the presence of nitrate added in distilled water and distinguish the concentration level from the calculated sensitivities. The experiment results with the water sample taken from various places around New Zealand show a very good correlation of contamination level, translated from the qualitative and quantitative results. The work and improvement for future consideration are also discussed in this chapter.

1 Introduction The field of non-destructive testing or inspections is a very broad, interdisciplinary field that produces reliable information of the material under investigation so that proper actions can be taken for the benefits of animal, environment, and human. As has been reported [1], non-destructive testing and evaluation based on electromagnetic approaches are gaining worldwide attention since it was introduced due to simplicity, S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 39–63. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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M.A. Md Yunus and S.C. Mukhopadhyay

fast response, convenience, and low cost. In this research, particular electromagnetic approach of interest is the non-destructive testing or inspections based on the inductive and capacitive or electromagnetic effects. Such devices or sensors can be seen in the industrial areas [2], agriculture [3], and engineering/scientific applications e.g. land mine detection [4], health and food monitoring [5], manufacturing [6, 7], automation [8], structure inspection [9]etc. Towards the development of a planar electromagnetic device, the sensing or/and exciting elements are commonly flat and separated by substrates. Several examples of planar electromagnetic systems are discussed in this section. Inductive planar electromagnetic devices have been reported in [10-19] and extension used as sensors for testing (nondestructive testing) the integrity of materials (conductive and magnetic material) [20-29]. Other applications of inductive planar sensors are such proximity and displacement sensors [17, 30]. A method for inspecting integrity of different coins and successfully discerns between coins is introduced in [31]. Capacitive planar electromagnetic sensors or commonly known as co-planar interdigital sensors have been used for many applications, some of the examples are moisture measurement in pulp [32], monitoring the change impedance caused by the growth of immobilized bacteria [33], human health confirmation based on the content of water in human skin [34], humidity sensors [35], food inspection for human safety [36], and estimation of material dielectric properties such as food, saxophone reeds and leather [37-40]. Schlicker et.al. [4] have reviewed the advantages of applying electromagnetic sensors array at multiple positions and different orientations for detecting landmine and unexploded ordnance to overcome the weakness of existing technologies in discriminating between harmful object and harmless clutters. The inductive and capacitive sensor array were constructed and used separately, each array has a unique single drive structures that forms an electromagnetic field which penetrates deeper into the ground. The images obtained through scans over the ground surface and then enhanced using spatial filtering. It is shown that the inductive sensor array and capacitive sensor array are clearly responsive to metal and no-metal object, respectively. A serially closed circuit inductor-capacitor (LC) element which forms a passive sensor, and operates on wireless and remote query basis has been applied for environmental monitoring [41], bacteria growth monitoring [42, 43], monitoring of electrical properties of biological cell solutions [44], quantifying packaged food quality [45], real-time monitoring of water content in civil engineering materials [46, 47]. Despites of offering a good performance, this system is quite complex, considering the material properties estimation was achieved from impedance spectrum of the sensor measured using a remotely located antenna. The material properties (e.g. complex permittivity) are calculated from the impedance spectrum at resonant frequency with the inductance and capacitance of the sensor values based on calculation of analytical model. Moreover, looking at the current technologies, the potential of this system to be a portable or home appliance is hindered as it requires either an expensive impedance analyzer or lock in amplifier. Application of metal inspection is also not suitable as the presence of metal will disturb the responses of the transmitter and the receiver. In this research, our attention and interest have been drawn into to develop an electromagnetic sensor consisting both inductive and

Planar Electromagnetic Sensor for the Detection of Nitrate

41

capacitive element which can be integrated as a low cost, convenient, and suitable for in-situ measurement system for water quality monitoring, particularly nitrate detection.

2 Motivation The freshwater reserves make out almost 2.53% of the total water resources and from the freshwater reserves, we consume water for daily use that sourcing from groundwater (29.9% of freshwater reserves) and surface water (0.29% of freshwater reserve) [48]. We need clean water, as clean water is vital to our body, to our food, to the environment and human health, and also it is essential for water sports and recreation, and essential for fish and other wildlife species. Water is continuously absorbed to the land, flow into the sea from the river, evaporate into the sky and fall back into land, this process repeats over and over again. Under normal situation, the organic pollutants are biodegraded and converted into beneficial nutrient to the aquatic life. As for the inorganic pollutants, the hazards are limited because of their even dispersion in the water. Today, we have to face issues such as fulfilling the growing water demands while keeping it clean in the same time. The human civilizations have been contaminating the water supplies with foreign matters (whether in liquid or solid forms) that deteriorate the quality of the water and deliberately or not, increase the amount of inorganic pollution. Our research is mainly concerned with the amount of nitrate contamination in natural water sources as it is one of the much discussed issues in recent times. However, we are also looking into the detection of contamination level caused by any substances in natural water sources. Nitrate is a natural compound present in all ecosystems. It is a form of nitrogen that is used to create vital structural proteins and enzymes which help the plant to grow and develop. However, too much of it can be a problem and is difficult to remove from water due to its high solubility. Figure 1(a) illustrates the data for all the rivers throughout the world. It is seen little changes in nitrate mg (NO3)--N/L concentrations between the two decades under comparison can be observed from figure 1(a), while changes in the median value were not statistically significant [49]. Several parts in Europe, particularly in regions with intensive livestock production, concentrations higher than 50 mg/L were observed frequently in 39 groundwater bodies (14%). Around 20% of EU measure stations had concentrations in excess of the limit concentration, and 40% locations were in excess of the standard (25 mg/l) between 1996 and 1998. France and Sweden are showing an overall increase in nitrate concentrations in groundwater [50]. In New Zealand, the peak level of nitrate concentration in the rivers, taken as average value, has increased between 1990 and 2007 by approximately 66% [51]. Illness caused by nitrate poisoning is extremely fatal to infants; they are vulnerable to methemoglobinemia due to haemoglobin oxidation and can cause fatal consequences. It can affect the baby in very little time, sometime even in couple of days [52]. Livestock especially ruminants also exposed to the same problem, they will start to how signs like lack of coordination, labored breathing, blue membranes, vomiting, and abortions [53]. The high amount of nitrate in surface water and groundwater because of over fertilizing, resulting in accelerated growth of algae and

42

M.A. Md Yunus and S.C. Mukhopadhyay

weed (eutrophication). When the algae and weeds completely died and decomposed, the process remove oxygen from the water thus killing the fish and other aquatic life [54]. According to reports from experiments, there is still little information to come to a conclusion about the relationship of high nitrate intake and other human cancer like gastric cancer, thyroid Cancer and thyroid disease. However, the link cannot be ruled out due to inadequacy of data available [55, 56]. Figure 2 illustrates the maximum

(a)

(b)

Fig. 1. (a) Nitrate contamination of surface water in the world compared over two decades (b) Nitrate contamination of groundwater in the EU in 2003

Fig. 2. Recommended nitrate limit by government agencies in several counties in the world (all the values are presented in mg·(NO3)-1-N/L unit, to convert the value into mg·(NO3)-1/L unit, multiply by 4.33)

Planar Electromagnetic Sensor for the Detection of Nitrate

43

concentration in drinking water as recommended by agencies in several countries where it was mainly established for protection from methemoglobinemia [55]. The main sources of nitrate contamination are agricultural fertilizers and animal wastes, industrial wastes related to food processing, munitions, and septic tanks; especially in densely populated areas [57-59]. Other factors are application of nitrogen-rich fertilizers to turf grass. This occurs on golf courses and in residential areas, sites where accidental spills of nitrogenous materials may accumulate, and finally, manure storage by the farmers [60]. Nitrate detection methods can simply be divided into indirect and direct methods. Such applications of indirect methods, but not limited to, are the detection of results of nitrate reduction reaction such as nitric oxide gas followed by geometry or potentiometry [61], reaction to diphenylamine [62], reaction to salicylic acid followed by spectrophotometer[63], detection of ammonium ion resulting from the reduction of nitrate by trichloride in hydrochloric acid [64], spectrophotometric [65], polarography and voltmetric [66]. A recent indirect and in-situ detection of nitrate as reported in [67], measure the change of LED intensity emitted through a nitrate sensitive membrane using photo-detectors. This system has a good sensitivity with range of 0.002 and 1000 mM and a precision of 4%. However, one major drawback is the optical path has to be properly guided and aligned, requiring a regular repositioning to maintain the accuracy. In New Zealand, the measurement of nitrate level in water sources conducted by the government agencies which the data then made public has been conducted using absorption spectrophotometry using a specific reagent since over three decades [68, 69]. Most of the indirect methods were often developed and established for laboratory standards thus making it expensive due to many components requirement. It also often involved laborious measuring steps and inevitably consumes a lot of time. They also require controlled working condition, and preparation of extra reagent or chemical. The direct methods have been proven to provide much more simpler solution; such applications are chromatography [70] and biosensors incorporating enzymes, antibodies, and whole cells[16]. The most popular direct method which is also considered as low cost is potentiometry based on ion selective electrodes (ISE) [71, 72]. The application of the method had been reported in [61]. The advantages of this technology are such as unsophisticated sensor fabrication process, simple monitoring instrumentation, fast and rapid response, accurate, compact and acceptably responsive even to limited amount of sample, and suitable for continuous measurement. However, the strength of the output signal depends on the strength or concentration of the targeted ions and often requires amplification. Moreover, the output signal also susceptible to the interference from not targeted ions thus a large number of reagents were necessary for the purpose of neutralization of the noise and adversely might be harmful to the environment [64, 73]. Looking at the importance of nitrate detection and the challenges arise, this research motivates to develop a sensor which can be integrated as a low cost, convenient, and suitable for in-situ measurement system for water quality monitoring, particularly nitrate detection.

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M.A. Md Yunus and S.C. Mukhopadhyay

3 Construction and Operating Principle of Planar Electromagnetic Sensor A novel electromagnetic sensor has been designed and fabricated for the detection of nitrates and contamination in natural water sources [74]. The sensor was designed using Altium Designer 6 and it has been fabricated using simple printed circuit board (PCB) fabrication technology where the thickness of the PCB is around 0.25 mm. The sensor is then sprayed with Wattyl Incralac Killrust spray to form a coating layer in order to prevent any direct contact with water sample. The layer was applied at least twice, giving a protection layer with 10 micron width. Figure 3 shows the top and bottom layers of the planar electromagnetic sensor. The meander type of coil is connected in series with the interdigital coil and an ac voltage is applied across the combination of the coils. The meander coil produces a magnetic field and the interdigital coil produces an electric field. The combination of meander and interdigital coils produce electromagnetic field which interacts with the material under test. The purpose of providing a grounded backplane is to minimize the effect of background noise [75]. The total impedance is used as the characterization parameter for the parallel combination sensor as described in [75], however the calculation of the impedance is shown again here as it will be related to other section

Fig. 3. Left hand side (a) schematic diagram of the top layer, (b) Schematic diagram of the bottom layer. Right hand side (a) a picture of the top layer taken using a camera, (b) a picture of the top layer taken using a camera

Planar Electromagnetic Sensor for the Detection of Nitrate

45

(section 7). The electrical equivalent circuit of the series combination sensor is shown in figure 4. The sensor is connected to a function generator where Rg is the output resistance with nominal value of 50 Ω, R1 denotes the series surface mount resistor connected to the sensor as shown in figure 4. Hence, the real part, Rtotal_s and the imaginary part, Xtotal_s can be calculated from: I 1 = V3∠0 D R1

(1)

where I1 is the rms value of current through the sensor. The total impedance Ztotal_s is given by, Z total _ s = V1∠θ 1 I 1∠0 D

(2)

θ1 is the phase difference between v1(t) with v3(t) in degree, taking v3(t) as reference. Therefore, Rtotal_s and Xtotal_s are given by: Rtotal _ s = Z tota _ s cos (θ 1 ) − R1

(3)

X total _ s = Z total _ s sin (θ1 )

(4)

Fig. 4. Electrical equivalent circuit of SCS3

4 Simulation Using Comsol Multiphysics The COMSOL Multiphysics has been used to model of the sensor [75]. This section will discuss the improvement of the model which taking into account the factor and effect of coating film. 4.1 Description of the Model The sensor has been described in section 3 and the geometry configuration of the selected sensor is shown in figure 5. All sensors are drawn based on the dimensions taken from section 3. The quasi-statics electromagnetic equation set of the COMSOL

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M.A. Md Yunus and S.C. Mukhopadhyay

3.5a electromagnetic module (electric and induction currents) is used for the model in time-harmonic analysis type. Both of magnetic (induction current) and electric (electric current) properties are considered and set meaning that vector potential à and the scalar electric potential Ṽ are the physical unknown to solve which derived after using a gauge transformation [76]. à and Ṽ are set to be vector and linear elements, respectively. This equation is expressed as following:

( jωσ − ω ε ε )A~ + ∇ × (μ 2

0

r

−1 0

)

(

)

~ ~ μ r−1 × A − σv × ∇ × A = J e

(5)

where ε0 is the permittivity of vacuum equals to 8.854×10-12 F/m, μ0 is the permeability of vacuum which sets to be 4π×10-7 H/m, εr is the relative permittivity, μr is the relative permeability, Je is the external current density, σ is the electric conductivity, v is the velocity of the conductor and ω is the angular velocity. 4.2 Subdomain Equation There are four physical variables: the scalar electrical potential Ṽ and the three components of magnetic vector potentials (Ã) Ãx, Ãy, and Ãz. The sensor models can be divided into four subdomains which are PCB copper tracks, insulating layer (Polytetrafluoroethylene or Teflon), environment (air/ pure water) [77, 78], and incralac coating film. Incralac is an acrylic coating for copper and bronze. It consists of a solution of methyl methacrylate copolymer incorporating benzotriazole [77, 79, 80]. All the subdomains are shown in figure 5 and use the same and unique system, equation (5), at this point. The domain properties for the sensor models are given in table 1.

Fig. 5. 3D-drawing and the boundary condition of the sensor

Planar Electromagnetic Sensor for the Detection of Nitrate

47

Table 1. Subdomain parameters of planar electromagnetic sensors

Subdomain name σ εr µr Je v

1 2 3 4 PCB copper tracks PCB insulating layer Air/Water Incralac coating film 5.998×107 0.004 0.8 1.45×10-12 1 4.5 1/82 9 1 1 1 1 0 0 0 0 0 0 0 0

4.3 Boundary Condition The PCB copper tracks are subjected with impedance boundary condition, given by: ε− j

n×H +

μ

σ ω ⋅ n × (E × n ) = 0

(6)

where H is the magnetic field intensity and E is the electric filed intensity. Insulating dielectric layers (pcb and incralac layers) are treated with continuity boundary equations given by: n × (H 1 − H 2 ) = 0

(7)

n ⋅ (J 1 − J 2 ) = 0

(8)

where J is the current density, the outer surface boundaries need the magnetic insulation and electric insulation, the equations are given by: n× A=0

(9)

n⋅J =0

(10)

The other boundaries are the input and output boundaries as shown in inset picture of figure 5. The input boundary requires magnetic insulation equation and port boundary. 4.4 Impedance Equations The induced current in the sensor is calculated by the integration of normal current density on the surface area of the sensor near the ground point which can be written as:

∫

I = J .ds

(11)

S

The induced voltage across the sensor is calculated over a number of finite elements. The general approximation is given by: Vinduced =

∑ M

i =1

(E

x

⋅ i x + E y ⋅ i y + E z ⋅ i z )i .N Ai

(12)

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M.A. Md Yunus and S.C. Mukhopadhyay

where N stands for the number of turn in the conductor (not the number of meander spiral turn). Ex, Ey, and Ez are the electric field intensity for x, y, and, z component, respectively given in V/m unit, at the ith element. ix, ix, and iz are the current directions at x, y, and, z, respectively, at the ith element Ai is the cross section area at the ith element, given in m2.The current port boundary condition gives the total impedance of the sensor which derived from equations (11) and (12), the total impedance is given by: Z total _ s =

1 Vinduced = Y11 I

(13)

4.5 Simulation Results At this stage, the purpose of the simulation is to serve three objectives. The first objective is to calculate the sensor characteristic from the total impedance response when the environment is set to air. The second objective is to observe the sensor response when the environment is set to water condition while the rest of the properties remain the same as the first one. The third objective is to as compare the simulation results with the experimental results. For the first simulation (σ=0.8, εr=1and frequency range between 1 kHz and 100 MHz), the impedance values of the sensor decrease from 1 kHz to 10 MHz showing a capacitive behaviour as shown in figure 6. The inductive effect starts to appear after 10 MHz.

Fig. 6. Impedance characteristic (air) obtained from simulation results

For the second simulation, the environment was set as pure water (σ=0.8 and εr=82) and was simulated for frequency range between 1 kHz and 100 MHz. The Nyquist (complex impedance plane) plot shows that the real part (Z’) decreases with the increasing frequency and the imaginary part (Z’’) having a peak at 30 kHz. The simulation to calculate the response of the sensor to the decrease of electrical conductivity while the relative permittivity remains the same and to the increase of relative permittivity while the electrical permittivity remains the same have been conducted using the above model. However, it was difficult to get any good response

Planar Electromagnetic Sensor for the Detection of Nitrate

49

Fig. 7. Impedance response of the sensor to water using electric and induction currents model on a Nyquist plot

because of the dominancy of the inductive part. Therefore, a step has been taken to reduce the model by taking only the effect of electric field (electric current). As a result, the electric field is curl free and can be assign a scalar potential V when induction is disregarded. The equation of the subdomains then becomes: − ∇ ⋅ ((σ + jωε r ε 0 )∇V ) = 0

(14)

The PCB copper tracks, insulating layer and coating layer are subjected to continuity boundary condition of equation (8). The input boundary requires port boundary and the output boundary was set to ground. The impedance is calculated as discussed in section 4.6. Addition of aqueous solution into the environment results in changes of permittivity and conductivity of the environment. The aqueous solution will respond to the applied electric field by redistributing its electrons and protons, positive charges being attracted towards the negative electrode and vice-versa, thus changing the total impedance of the sensor. Therefore, the total impedance of the sensor will be determined by the content of aqueous solution involved. Figure 8 depicts the Nyquist plot when the environment set to pure water, a capacitive semicircle can be seen from the figure. When εr is fixed at 82 and σ is decreased between 1.45×10-12 (set to be the lowest conductivity, i.e. coating film) and 145, the complex spectra is shrinking to a smaller semicircle as illustrated in figure 9. The change of the electrical conductivity value seems to have a significant effect to the sensor’s response. Under the situation when σ is fixed at 1.45×10-12 and εr is decreased between 82 and 16, there is a slight deviation of the rest of the spectra as comparison to the initial value (σ=1.45×10-12 and εr=82). There is a significant change when the relative permittivity value was changed from 82 to 42 and lower. However, the changes between εr=41 and εr=16 are not proportional.

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M.A. Md Yunus and S.C. Mukhopadhyay

Fig. 8. Nyquist plot of impedance curve of pure water using electric current model

Fig. 9. Nyquist plot of impedance curves with different water different σ values

Fig. 10. Nyquist plot of impedance curves with different water different εr values

Planar Electromagnetic Sensor for the Detection of Nitrate

51

5 Experimental Setup The sensor characteristic of the sensor was determined by measuring the total impedance (absolute) of the sensor at different frequencies between 10 kHz and 100 MHz when no material is placed near the sensor (air). Then, the next experiment involved the response of the sensors with distilled water, different concentration of nitrates samples in the form of Sodium Nitrate (NaNO3) diluted in 1 litre of distilled water and different concentration of Nitrates samples in the form of Ammonium Nitrate (NH4NO3) diluted in 1 litre of distilled water. The operating frequency range was between 25 Hz and 1 MHz. The experimental setup is shown in figure 11, the setup has a waveform generator where standard sinusoidal waveform with 10 Volts peak-to-peak value was set as the input signal for the sensor. An old microscope was used as a platform for the sample container and the sensor was immersed into the water sample. The Agilent 54622D mixed signal oscilloscope was interfaced to a PC where the output signals and the sensor’s impedance was recorded and calculated consecutively using developed program using LabView software.

Fig. 11. Experimental setup for the experiments

6 Experimental Characteristic of the Sensor Impedance curve in the simulation (figure 7) is steeper compared to the impedance curves obtained from experiment in figure 12, although showing a good similarities. The actual resonance frequency from the experiment is approximately at 30 MHz, about 20 MHz difference from the simulation (figure 7) under comparison. The combination of inductive and capacitive effects is presented between 3 MHz to 10 MHz.

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M.A. Md Yunus and S.C. Mukhopadhyay

Fig. 12. Impedance characteristic obtained from experimental results

7 Experiment with Distilled Water, Different Concentration of NaNO3 and NH4NO3 Diluted in 1 Litre Distilled Water and Results Electrochemical impedance spectroscopy approach was used to estimate qualitatively the reaction of the sensor with water samples. The response in the frequency domain of an electrochemical reaction can be used to estimate the contamination in the water. Figure 13(a) describes a system for coated electrodes response to aqueous solution as has been described in [79, 81]. The circuit composed of the solution resistance (Rs), coating resistance (Rc), coating capacitance (Cc), charge transfer resistance (Rct), and double layer capacitance (Cdl). In this research, an additional meander inductance (L) has been added to represent the system as shown in figure 13(b).

Fig. 13. Equivalent circuit to describe the system

Figures 14 and 15 show the response of the sensor to the different concentration of Sodium Nitrate (NaNO3) diluted in 1 litre of distilled water and different concentration of Ammonium Nitrate (NH4NO3) diluted in 1 litre of distilled water, respectively. Both figures contain the response of the sensor to distilled water. The

Planar Electromagnetic Sensor for the Detection of Nitrate

53

simulated response of the sensor with distilled water in figure 7 as comparison to figure 14 or 15 (0 mg/L) showing an obvious peak. This is postulated that the total inductance as produced in the model is much larger than is actually existed as shown in the experiment result. In general, the sensor is giving capacitive loop and inductive loop at low frequency and high frequency, respectively. A typical electrochemical spectroscopy response with aqueous solution often show both capacitive and inductive semicircle [82]. However, our sensor shows significant deviation of the loops as can be seen in figure 14 and 15, this is caused by the presence of inductive characteristic [81] of the meander sensor, especially at high frequency. The impedance curve is shrinking and smaller as the concentration is increased, the simulation results in figure 9 suggests that it caused by the reduction of σ value. All the spectrums can be fitted using nonlinear least square fit (NLLS-fit) and simulation computer program to estimate the values of the circuit components in figure 13 in future.

Fig. 14. Nyquist plot for experiment involving different concentration of dissolved NaNO3

Fig. 15. Nyquist plot for experiment involving different concentration of dissolved NH4NO3

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For quantitative results, the sensitivities of the sensors are calculated from the real part as taking the value from the distilled value as reference in the following equation: %Real_part =

⎡ (Rtotal )sample − (Rtotal )distilled

∑ ⎢⎣ f

(R ) total

distilled

⎤ × 100⎥ ⎦

(14)

In the simulation results above (figure 10), the imaginary part showed a response other than linear, therefore the absolute difference value is used to calculate the imaginary part sensitivity as shown in equation: % Imaginary_part =

⎡ ( X total )sample − ( X total )distilled

∑ ⎢⎢ f

⎣

(X ) total

distilled

⎤ × 100⎥ ⎥ ⎦

(15)

Where (Rtotal)distilled is the real part of the impedance value when the sensor is immersed in the distilled water and (Rtotal)sample is the real part of the impedance value when the sensor is immersed in the water sample. (Xtotal)distilled is the imaginary part of the impedance value when the sensor is immersed in the distilled water and (Xtotal)sample is the imaginary part of the impedance value when the sensor is immersed in the water sample. The symbol f represents the frequency. Figures 16 shows the sensitivities value when tested with solution based on NaNO3 and NH4NO3. Similar response can be observed for both solution types, except the real part sensitivity and the imaginary part sensitivity of NH4NO3 are much higher than NaNO3.

Fig. 16. Sensitivity of the sensors to the aqueous solution

The real part negatives values progressively decreases with the total concentration of the chemicals. The linear equation y1 and y3 represent the real part sensitivity of NaNO3 and NH4NO3, respectively where x1 and x2 signify the total concentration of NaNO3 and NH4NO3, respectively. This indicates that the conductivity of the water

Planar Electromagnetic Sensor for the Detection of Nitrate

55

has increased as can be seen from the simulation result (figure 9). Good linear correlation with R2=0.965 and R2=0.982, between the real part sensitivities with the chemical concentration of NaNO3 and NH4NO3, respectively. The sensitivity of the imaginary part for both type of aqueous solution is increasing when the concentrations were increased in polynomial order of two as can be seen in figure 16. The linear equation y2 and y4 represent the imaginary part sensitivity of NaNO3 and NH4NO3, respectively where x2 and x4 signify the total concentration of NaNO3 and NH4NO3, respectively. The total sensitivities give good polynomial order of 2 correlation with R2=0.996 and R2=0.990. Double layer capacitance, Cdl (figure 13) is formed as ions from the solution accumulate around the electrodes surface [83]. The value of Cdl is usually higher than the coating, Cc [79, 81], so the small change of Cdl will be apparent in the imaginary part of the impedance response. The value of the double layer capacitance depends on many variables such as temperature, ionic concentrations, types of ions, oxide layers, electrode roughness, dielectric properties/relative permittivity [84, 85], etc. The change of the imaginary sensitivity is caused by the decrease in Cdl, resulting from a decrease in the relative permittivity of the water samples.

8 Experiment with Water Samples The sensor has been used to observe the response of the sensor with natural water mixed with contaminants. From the Nyquist plot and calculated sensitivities, the qualitative and qualitative responses are studied. The samples of different bottles were taken from previous research by Karunanayaka in [86]. Figure 17 shows the water samples collected from different places in the respective containers. The water samples were analysed using Nuclear Magnetic Resonance (NMR) technique to check the amount of organic material in the sample and summarized in table 2. Table 2 shows the amount of mineral in g·m-3 including the pH value.

Fig. 17. Water samples in containers, arranged in ascending order from left to right

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M.A. Md Yunus and S.C. Mukhopadhyay

In this experiment mili-Q water was used as reference, milli-Q refers to water that has been purified and deionized to a high degree by a water purification systems where the operating frequency is between 20 Hz and 1 MHz. Figure 18 illustrates that similar curve obtained when the sensor tested with mili-Q water when compared with the curve for distilled water in figure 18 but with larger curve. This proves that mili-Q water is purer than distilled water. As a whole, all the water samples produced a significantly smaller complex plane curves compared to the mili-Q curve. Table 2. Content of water samples from NMR test results Sample

pH

570-1,Matakana raw 570-2,Matakana magnetic circle tank 570-3,IPL Mixed water UV 570-4, Edgecumbe raw 570-5, McCains D119 raw 570-6, Matakana raw2 570-7, McCains treated 570-8, Tauranga tapwater 570-9, Technical water system potable 570-10, Edgecumbe magnetic treatment

Mg Fe Ca (gm-3-Ca) (gm-3-Mg) (gm-3-Fe)

CaCO3 (gm-3CaCO3)

Sulphate (gm-3-SO4)

Total mineral (gm-3)

8.69

3.3

0.54

0.036

8

0.63

12.506

8.06

3

0.51

0.039

10

0.24

13.789

8.38

1.7

0.32

0.027

4

0.09

6.137

7.529

11.6

11.8

3.33

77

0.02

103.75

6.62

17.3

24.8

2.62

150

0.23

194.95

7.8 8.2

3 4.6

0.51 3

0.036 0.334

8 24

0.63 606

12.176 637.934

7.27

2.8

0.56

0.005

8

1.8

13.165

7.16

5.3

0.56

0.11

14

2.17

22.14

8.58

14.4

8.1

0.014

70

0.02

92.534

Fig. 18. Nyquist plot for experiment involving water samples

Typically, pure water has relative permittivity of 81 and electrical conductivity of 0.8 at room temperature. Any addition of other substance will reduce the relative

Planar Electromagnetic Sensor for the Detection of Nitrate

57

permittivity of the aqueous solution and increase the electrical conductivity. Therefore, we can say that the curves that deviate further away inside the mili-Q curve have more contamination. From figure 19, the most deviated curves are 570-1, 570-2, 570-3, 570-4, 570-5, 570-7, and 570-10. The sensitivities were calculated using equations (14) and (15), taking the values from mili-Q as reference. The sensitivity values calculated for the water samples as shown in figure 20 suggested that the water samples to be divided into three groups of unsafe to consume, use with caution and safe to consume. The unsafe to consume water sources are of 570-5 and 570-7. This can be explained from the highest imaginary sensitivities over 10000% and this condition is caused by high the total amount of mineral in 570-5 and 570-7 as given in table 2.

Fig. 19. Nyquist plot for experiment involving water samples excluding the response from mili-Q water and distilled water

Fig. 20. Sensitivity of the sensors to the aqueous solution arranged in order of increasing total mineral (gm-3) in table 2

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As for the safe to consume water samples, 570-8 and 570-9 are included giving the imaginary sensitivities just above 4000% where the total amount of mineral for each sample in Table 1 confirms the selection. Finally, 570-1, 570-2, 570-3, 570-4, 570-6 and 570-10 are all group together in consume-with-caution group as the total sensitivity has just passed 6000 % and just above 8000 %. Although some of the samples in the cautious group appear to be in low total amount of mineral, there are some discolorations caused by certain kind of algae or living organism inside the water samples, thus changing the electrical properties of the water samples as shown in figure 17. This may have cause the imaginary sensitivity to be significantly high as compared to the total amount of mineral given in Table 2. As conclusion, if we combine the qualitative data interpreted form the impedance spectra and the quantitative data from the total sensitivities, only water samples of 570-8 and 570-9 are acceptable high quality standard drinking water and safe to be consumed. This is indeed a better way to describe the contamination rather than single frequency result as has been described in [75].

9 Future Consideration and Conclusion For further improvements, the sensor will be miniaturized to reduce the overall cost. Furthermore, the concept of ion selective electrode (ISE) polymerised with pyrolle doped with nitrate will be developed which will increase the selectivity [72]. The process involves the replacement of copper track with graphite and polymerization of pyrolle doped with nitrate on the graphite through electrolysis to form polypyrolle. The concept of low cost system is also considered where the function generator will be replaced with a voltage controlled oscillator, auxiliary circuits will be built to measure the sensor impedance and a microcontroller will be used for signal analysis. The present study shows that a very distinct detection of nitrates and contamination in water can be achieved using the sensor using electrochemical data analysis approach. It can be used as a tool for water sources monitoring in farm where the nitrate level should not exceed 10 mg/L in New Zealand. The sensor has also been tested with drinking water samples and has shown very promising results to be a sensing tool of contamination detection in drinking water when electrochemical impedance spectroscopy was used in understanding the response between the sensors and aqueous solution.

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[62] Bartzatt, R., Donigan, L.: The colorimetric determination of nitrate anion in aqueous and solid samples utilizing an aromatic derivative in acidic solvent. Toxicological & Environmental Chemistry 86(2), 75–85 (2004) [63] Monteiro, M.I.C., Ferreira, F.N., de Oliveira, N.M.M., et al.: Simplified version of the sodium salicylate method for analysis of nitrate in drinking waters. Analytica Chimica Acta 477(1), 125–129 (2003) [64] Cho, S.-J., Sasaki, S., Ikebukuro, K., et al.: A simple nitrate sensor system using titanium trichloride and an ammonium electrode. Sensors and Actuators B: Chemical 85(1-2), 120–125 (2002) [65] Ferree, M.A., Shannon, R.D.: Evaluation of a second derivative UV/visible spectroscopy technique for nitrate and total nitrogen analysis of wastewater samples. Water Research 35(1), 327–332 (2001) [66] Gumede, N.J.: Harmonization of internal quality tasks in analytical laboratories case studies: water analysis methods using polarographic and voltammetric techniques. Faculty of Applied Sciences, Durban University of Technology, Durban (2008) [67] Maria Dolores, F.-R., et al.: The use of one-shot sensors with a dedicated portable electronic radiometer for nitrate measurements in aqueous solutions. Measurement Science and Technology 19(9), 95204 (2008) [68] Downes, M.T.: An improved hydrazine reduction method for the automated determination of low nitrate levels in freshwater. Water Research 12(9), 673–675 (1978) [69] http://www.mfe.govt.nz/publications/ser/technical-guide-newzealand-environmental-indicators/html/page4-12.html [70] Connolly, D., Paull, B.: Rapid determination of nitrate and nitrite in drinking water samples using ion-interaction liquid chromatography. Analytica Chimica Acta 441(1), 53–62 (2001) [71] Kjær, T., Hauer Larsen, L., Revsbech, N.P.: Sensitivity control of ion-selective biosensors by electrophoretically mediated analyte transport. Analytica Chimica Acta 391(1), 57–63 (1999) [72] Bendikov, T.A., Harmon, T.C.: A Sensitive Nitrate Ion-Selective Electrode from a Pencil Lead. An Analytical Laboratory Experiment. Journal of Chemical Education 82(3), 439– 441 (2005) [73] Moorcroft, M.J., Davis, J., Compton, R.G.: Detection and determination of nitrate and nitrite: a review. Talanta 54(5), 785–803 (2001) [74] Md Yunus, M.A., Mukhopadhyay, S.C.: Novel planar electromagnetic sensors for detection of nitrates and contamination in natural water sources. IEEE Sensors Journal (accepted for publication September 24, 2010) [75] Md Yunus, M.A., Mukhopadhyay, S.C., Sen Gupta, G.: A new planar electromagnetic sensor for quality monitoring of water from natural sources. In: 4th International Conference on Sensing Technology, ICST 2010, June 3-5, pp. 554–559 (2010) [76] COMSOL AC/DC Module, User’s Guide, Version 3.5a (November 2008) [77] Stafford, O.A., Hinderliter, B.R., Croll, S.G.: Electrochemical impedance spectroscopy response of water uptake in organic coatings by finite element methods. Electrochimica Acta 52(3), 1339–1348 (2006) [78] Light, T.S., Licht, S., Bevilacqua, A.C., et al.: The fundamental conductivity and resistivity of water. Electrochemical and Solid State Letters 8(1), E16–E19 (2005) [79] McNamara, C.J., Breuker, M., Helms, M., et al.: Biodeterioration of Incralac used for the protection of bronze monuments. Journal of Cultural Heritage 5(4), 361–364 (2004)

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[80] Cano, E., Lafuente, D., Bastidas, D.M.: Use of EIS for the evaluation of the protective properties of coatings for metallic cultural heritage: a review. Journal of Solid State Electrochemistry 14(3), 381–391 (2010) [81] Tan, Y.J., Bailey, S., Kinsella, B.: An investigation of the formation and destruction of corrosion inhibitor films using electrochemical impedance spectroscopy (EIS). Corrosion Science 38(9), 1545–1561 (1996) [82] Itagaki, M., Taya, A., Watanabe, K., et al.: Deviations of Capacitive and Inductive Loops in the Electrochemical Impedance of a Dissolving Iron Electrode. Analytical Sciences 18(6), 641–644 (2002) [83] Grahame, D.C.: The Electrical Double Layer and the Theory of Electrocapillarity. Chemical Reviews 41(3), 441–501 (1947) [84] Jeyaprabha, C., Sathiyanarayanan, S., Muralidharan, S., et al.: Corrosion inhibition of iron in 0.5 mol L-1 H2SO4 by halide ions. Journal of the Brazilian Chemical Society 17, 61–67 (2006) [85] Ravichandran, R., Nanjudan, S., Rajendran, N.: Corrosion inhibition of brass by benzotriazole derivatives in NaCl solution, Bradford. ROYAUME-UNI, Emerald (2005) [86] Karunanayaka, D.: Studies of Magnetic Filtration Techniques to Purify Potable Water and Waste Water. School of Engineering and Advanced Technology, Massey University, New Zealand, Palmerston North (2007)

Current Reconstruction Algorithms in Electrical Capacitance Tomography M. Neumayer, H. Zangl, D. Watzenig, and A. Fuchs Institute of Electrical Measurement and Measurement Signal Processing Graz University of Technology Kopernikusgasse 24/IV A-8010 Graz [email protected]

1

Electrical Capacitance Tomography in General

Controller LAN

Front End Circuitry

Figure 1 depicts a scheme of an electrical capacitance tomography (ECT) sensor. A number of electrodes are mounted on the exterior of a nonconductive process pipe. By measurements of the capacitances between certain electrode, it is aim to compute an image of the material distribution. Electrical capacitance tomography is an inverse problem which uses capacitance measurements for determining information about specifics of a certain domain. The inverse problem is given by the task of determining the material distribution, which caused the measured capacitances. ECT is well-suited for industrial processes since materials typically involved in industrial processes like multiphase flows show good contrast in terms of the permittivity. Knowledge about the spatial distribution of the components is usually necessary to directly characterize the state of such processes and consequently be able to reach the aforementioned goals through process control [34]. Examples of processes including multiphase flow can be found in almost every industry, e.g. food production,

Shield

Fig. 1. Schematic of the ECT system and the reconstruction task. By measurements of the capacitances beteween the electrodes, the material distribution inside the pipe should be determined. S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 65–106. c Springer-Verlag Berlin Heidelberg 2011 springerlink.com 

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separation columns, fluidized beds, chemical reactors, oil production, waste water treatment, mining operations, and pneumatic conveying. The fact that the electrodes can be non-invasively mounted outside of the nonconducting process pipe makes ECT applicable to processes containing aggressive fluids. The inverse problem of ECT is an ill-posed inverse problem. Hadamard [3] gave a definition for well-posed inverse problems by the claims on • A solution exists. • The solution is unique. • The solution depends continuously on the data. If one of this definitions is hurt, which is truly the case in ECT, the inverse problem is of ill-posed nature. In this case a stable solution can only be found by incorporating available prior knowledge about the solution.

Fig. 2. 3D scheme of the electrodes and two inclusions.

A main difference between ECT and other tomographic methods like i.e. the computer tomography is, that ECT belongs to the class of so called soft field tomography systems. For every tomography assignment an appropriate physical value has to be chosen to gather information about the spatial parameters within the expanded object. A basic classification of tomography systems can be made by separating by the way of interaction between the object and the physical value used for the measurement. If the physical value is not influenced in its direction the method is called hard field tomography. An example for this is x-ray tomography. The beam pierces straight through the object. Only its intense is reduced - the effect is described by Lambert-Beers law. If the propagation of the physical value is influenced by the object the method is called soft field tomography. This is given in electrical tomography systems like that of ECT, where the distribution of the displacement currents (and therefore the capacitances between the electrodes) are influenced. Because of this soft field character electrical tomography systems have drawbacks in resolution compared to hard field systems.

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The interaction of the field by the object further causes a spatial dependence of the sensitivity. This ends up in different achievable resolutions for the same object in different regions within the problem domain. Capacitive measurement systems generally provide high sensitivity in the region near the electrodes but provide only low sensitivity for distant objects. Thus, the sensitivity in the center of the pipe is low. Hence, the reconstruction of inclusions placed in the center of the pipe is harder than reconstructing the same inclusion when placed near the electrodes. All these facts make the inverse problem of ECT a comparatively hard inverse problem to solve and the obtainable resolutions are comparatively low with respect to methods like x-ray. However, the main advantage of electrical tomography systems is still the simple hardware concept. Compared to the necessary hardware of other tomographic methods like x-ray, γ-ray, microwaves, neutron tomography, positron emission tomography, electrical methods like ECT only require some attached electrodes and a measurement circuitry. Hence, ECT requires less space to mount the sensor. In contrast to the mentioned methods also no ionizing radiation is produced, which makes ECT a very save sensing technology. Although real ECT systems are always 3D systems like schematically depicted in figure 2, the inverse problem is mostly solved for a two dimensional problem. Thus, typical ECT systems provide cross sectional images. Of course, this simplification can only be made if the electrodes have a certain length compared to the diameter of the process pipe. Otherwise, 3D effects dominate the behavior of the coupling capacitances and the 2D reconstruction will fail. Typically, if the length of the electrodes is in the range of the diameter of the process pipe, the simplification is assumed to be allowed [28]. The number of electrodes is a critical parameter in the design of ECT systems, as the number of electrodes effects the number of measurements which can be taken. Typically the number of electrodes is between 8 and 16 [28]. Also ECT systems with an increased number of smaller electrodes were build, but these systems suffer from an decreased signal to noise ratio (SNR), as also the coupling capacitance between the electrodes decrease due to the smaller area of the electrodes. Also systems with (electronically) rotating electrodes [2] were proposed to increase the number of electrodes. However the effective number of measurements becomes only slightly higher compared to a system with 16 electrodes. 1.1

Physics within an ECT-Sensor

Like for any other electrical system the governing partial differential equations that describe the physical phenomena within the ECT-sensor are given by the Maxwell’s equations [1]: dB , dt dD ∇×H =J + , dt ∇×E =−

(1) (2)

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∇ · B = 0, ∇ · D = ρ.

(3) (4)

−1

−1

B = μH,

(5)

D = εE,

(6)

E denotes the electric field in Vm , H denotes the magnetic field in Am . B denotes the magnetic flux in Vsm−2 and D denotes the electric displacement in Cm−2 . Equation (1) is called Faraday’s law of induction and describe the interaction between the electric field and a time varying magnetic field. Equation (2) is called Ampere’s circuital law and describes that the sources of magnetic fields are electric currents (given by the the current density J in Am−2 ) and by time varying displacement currents. Equation (3) is sometimes referred as Gauss’s law of magnetism and has the physical interpretation that no magnetic monopoles exist. Equation (4) is called Gauss’s law and means that charges ρ in C are the source term for the electric field. Equation (1) to (4) are extended by the two material equations

where μ denotes the magnetic permeability and ε denotes the dielectric permittivity. The permeability μ is given by the product μ = μ0 μr where μ0 is the absolute permeability given by 4π × 10−7 VsA−1 m−1 and the dimensionless relative permeability μr . Similarly, ε0 denotes the absolute permittivity given by 8.854 × 10−12 AsV−1 m−1 and εr denotes the dimensionless relative permittivity. In addition J = σE,

(7)

describes the interaction between the current density J and the electric field E in a conductive media, where σ is the conductivity in Ω−1 m−1 . Equation (7) is referred as Ohm’s law. ECT specific simplifications Although several electric effects that can occur in the ECT-sensor are described by the Maxwell’s equations, typically some simplifications are made in order to get a more simple partial differential equation. The main assumptions are: • The interaction between the electric field and the magnetic field is neglected. • The conductivity of the materials is small. • No charges are present. The first assumption leads to the most drastic simplification as it reduces the type of differential equation from a hyperbolic (Helmholz type) to an elliptic type (Laplace type). From a physical point of view the reduction means that wave propagation effects within the ECT-sensor are neglected. For the typical frequency range and the typical dimensions of ECT-sensors this is a valid assumption. This can be verified by the fact, that the wavelength λ 1 λ= √ f με

(8)

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is typical a several multiples of the sensor dimensions. In the case of conductive materials the wavelength is given by √ 2 2π λ =  √ (9)  ωμ σ 2 + ω 2 ε2 + ωε and thus is also dependent on the conductivity. It had been reported in [8] that in this case even for small conductivities the wave length can decrease to values in the range of the dimensions of the sensor. Another rule of thumb to estimate the occurrence of wave propagation effects is given by  ωε  ωμσLc 1 + 1 (10) σ where Lc is defined as characteristic distance over which E varies significantly. Although (10) is more a heuristic it also indicates the appearance of wave propagation effects within the ECT-sensor when conductive materials are present. The benefits of a full implementation of the Maxwell’s equations have been investigated in [4]. However, this assumptions were made for free space propagation. Due to the large cover angle of the electrodes, they act as shield. Hence, wave propagation effects have only a small impact and appropriate calibration techniques can further decrease their impact. Starting with (1) the partial differential equation for the ECT-sensor can be derived as follows: ∇ × E = 0,

(11)

which means that the curl of the electric field is zero. Hence, E can be expressed by the gradient of a scalar potential. Thus E is expressed by E = −∇V,

(12)

where V is the electric scalar potential in V. Taking the divergence of Faradays law (2) leads to   dD ∇ · (∇ × H) = ∇ · J + = 0, (13) dt as the ∇ · (∇ × A) = 0 for any vector field A. By using the scalar potential V and taking the Fourier transform (13) becomes ∇ · (jωε∇V ) = 0,

(14)

where J has been set to zero. In ECT it is common to replace the term jω by 1 as it has no effect on the solution V . Hence, the quasistatic differential equation (14) becomes − ∇ · (ε∇V ) = 0,

(15)

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which is the well known partial differential equation from electrostatics. The same result can be obtained by using Gauss’s law ∇·D =ρ =0 ∇ · (εE) = 0

(16) (17)

and substituting (12). In the occurance of conductivity J in equation (2) can be expressed by J = σE, which turns the potential equation (14) to − ∇ · ((σ + jωε)∇V ) = 0

(18)

for the quasistatic case. Thus, the electrical behavior depends on the ratio of σ rσ,ωε = . (19) ωε In the case rσ,ωε  1 the conductivity is small compared to ωε. Hence, the behavior is determined by the permittivity distribution, which is the ideal case for applying ECT. In the case rσ,ωε  1 the effects are mainly determined by the conductivity. In this case electrical impedance tomography is in general a more suitable tool. For noninvasive ECT senor designs, the equivalent circuit becomes a serial circuit consisting of a resistor and a capacitance. Depending on the measurement circuitry the application of ECT results in reconstructed material distributions with high permittivities for the conductive objects. The effect of reconstructing objects with increased permittivity can already be observed for objects with a low conductivity. For rσ,ωε ≈ 1, the effects of the conductivity and the permittivity are in the same order of magnitude. Using an I/Q demodulator to determine the real and the imaginary part of the capacitance, it is possible to reconstruct both quantities [9]. In the presence of conductivity one might argue about the appropriateness of the name electrical capacitance tomography. However, in the noninvasive case the electrical coupling through the nonconductive pipe is always of capacitive nature. Hence the name has its justification. 1.2

Measurement Modi

This subsection refers to the different measurement modi which can be applied in electrical capacitance tomography systems. Although the name electrical capacitance tomography suggests that the capacitance between the electrodes is measured (or at least a quantity which is proportional to the capacitance like the displacement current) not all ECT systems measure the capacitance. As the difference lies in the measurement circuitry we will classify the different measurement modi by the used hardware terms • Low-Z measurements • High-Z measurements Figure 3 depicts the two schemes. Basically the classification is done through the input impedance of the measurement circuitry. A short summary about the main difference will be presented in the following. Detailed informations about the benefits of each circuit can be found in [5].

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A

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A

(a) Low-Z measurement. Not all am- (b) High-Z measurement. Not all caperemeters are depicted. pacitances are depicted. Fig. 3. Different measurement schemes for Low-Z and High-Z measurements.

Low-Z Measurement The low-Z measurement modi is depicted in figure 3(a). The measurement steps are the following • An AC-voltage is applied to one electrode which is referred as transmitter electrode. • The other electrodes are kept on ground potential and displacement currents are measured. These electrodes are referred as receiver electrodes. It should be noted that not all amperemeters are depicted in figure 3(a). As the dielectric currents is proportional to the capacitance by u i = 1 = jωCu,

(20)

jωC

the low-Z method may appear as the more natural method to obtain measurement data from the ECT sensor. A typical capacitance measurement pattern, which forms the input data of the reconstruction algorithm, is depicted in figure 4. The circuitry for Low-Z measurements are typically transimpedance amplifiers, where the electrode is connected with the virtual ground of the circuitry. Also circuits using resonant circuits in the input stage are used. In this configuration the receiver electrode is not connected to virtual ground but as the impedance of the resonant circuit is much lower than the impedance of the capacitance to measure, this configuration is also referred as low-Z circuitry. Additionally the resonant circuit builds a first filter stage against external disturbers.

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For the partial differential equation (15) the boundary conditions for the low-Z measurement modi are given as Dirchlet type boundary conditions given by VΓT ransmitter = VT , VΓReceiver = 0.

(21) (22)

The capacitances between the electrodes are computed using Gauss’s law given by 1 C=− n · ε∇V dΓ, (23) VT Γelec where n denotes the surface normal vector on the surface of the electrode. Hence the computations are sometimes referred as Dirchlet to Neumann map. Typical ECT systems are equipped with up to Nelec = 16 electrodes. Thus the number of independent measurements (the coupling capacitances between the electrodes) is given by Nelec (Nelec − 1) = 120 2

(24)

measurements. High-Z Measurement The high-Z measurement modi is depicted in figure 3(b). For the high-Z measurement modi the measurement steps are given by • An AC-voltage is applied between two different electrodes. • The floating potentials of the other electrodes are measured. Thus, not the capacitances are measured, but the floating potentials of the electrodes depends on the capacitances between the electrodes. Compared to the Low-Z measurement modi now all capacitances have an influence on the measured quantity. This is indicated by the capacitance between the two electrodes where the voltmeters are attached. Hence, in terms of an equivalent circuit, the system can be described by a large capacitive voltage divider. Due to the ground potential of the receiver electrodes in the other Low-Z measurement modi, the effect of the coupling capacitances between the receiver electrodes is eliminated. Thus, the equivalent circuit in the Low-Z case is the single coupling capacitance. The High-Z measurement modi seems unnatural for ECT. However, the scheme it is often used in electrical impedance tomography, where currents are impressed into the problem domain and the potential on some boundary points is measured. The circuitry for high-Z measurements are typically amplifiers with high input impedance. The boundary conditions to complete the partial differential equation (15) are given by VΓelec,i = V1 , VΓelec,j = V2

(25) i = j.

(26)

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0.6

0.5

C (pF)

0.4

0.3

0.2

0.1

0

20

40

60

80

100 120 140 # of measurement

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180

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Fig. 4. Typical measurement pattern.

As the measured quantities are the potentials of the electrodes, the solution is directly obtained from the solution of V . 1.3

Prototype ECT-System at Graz University of Technology

Figure 5 depicts a photo of the ECT-system prototype which was developed at the Institute of Electrical Measurement and Measurement Signal Processing at

Fig. 5. Photo of a prototype ECT system.

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Graz University of Technology [6]. For commercial sensors we refer to [17]. To meet the demands of industrial tomography systems the ECT system was built for a non-invasively use. Center of the system is a plastic process pipe with an exterior diameter of 110 mm and a wall thickness of 3.5 mm. For the electrical access of the problem domain 16 electrodes are mounted in a symmetric arrangement at the exterior of the pipe. The cover angle of each electrode is 17.5 o , the length of the electrodes is 5 cm. Each electrode is equipped with it’s own front end circuitry which is mounted in a modular way on an installation board around the pipe. The front end circuitry includes an output driver stage, a receiver stage, an analog-to-digital-converter (ADC) and a digital logic for the transmission of the measured quantities to a host micro-controller, which is also mounted on the installation board. Thus, every electrode can act either as transmitter or as receiver. The input stage is build to measure the displacement currents. Hence, the senor is build on a low-Z principle. The measurement frequency used by the system is 40 MHz. The host-controller coordinates the measurement process and transmits the measured values to a personal computer (PC) where the reconstruction algorithms are implemented. For the communication a local-areanetwork (LAN) is used. 1.4

Calibration of ECT Systems

Like in nearly any other measurement system, also the capacitance measurements of an ECT system suffer from offset and gain variations. These measurement errors come to hand due to • • • • •

unmodeled physical effects, parasitic effects, different geometries, discretization errors, hardware imperfections.

Also, the fact that the ECT sensor is a 3D system but the reconstruction task is performed in 2D make a suitable calibration necessary. To minimize their impact, appropriate calibration strategies have to be applied. A common calibration scheme for a ECT-sensor is based on a simple Offset-Gain-calibration, i.e. C = CM,air + K · (CS − CS,air ),

(27)

where the index S labels measurement values from the sensor and M labels computed values from the forward model. The text in the index notes the filling of the sensor. The gain value K permits a degree of freedom for a problem dependent calibration. One possible choice for K is for example K=

CM,water − CM,air , CS,water − CS,air

(28)

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which is useful for reconstruction tasks with a large range of permittivity values. For reconstruction tasks of material distributions with low permittivities a possible approach is given by K=

2

CM,air . CS,air

(29)

Reconstruction Algorithms in ECT – A Classification

Although accurate and reliable hardware and measurement circuitry plays - like in every other measurement system - an important role in the measurement chain much more effort concerning the topic of ECT has always been been put on reconstruction algorithms. As mentioned, the number of independent measurements is in general low, e.g. 120 independent measurements for a system with 16 electrodes. Imagine an image with 10 × 10 independent pixels, the number of independent values is already 100. An representation of the cross section using 10 × 10 pixel would indeed be very insufficient. As we will demonstrate in section 5, by the use of prior knowledge one is able to obtain far better results. In this book chapter we will present a selection of 5 different reconstruction algorithms/methods which can be applied to solve the inverse problem of ECT and describe them in more detail. This is done in section 4. To give 5 catchwords for our selection we decided for • • • • •

Linear backprojection LBP Optimal approximation OA Nonlinear iterative Kalman Filters Markov Chain Monte Carlo MCMC

Each algorithm has its certain properties concerning typical criterions like the speed or the quality of the result. We explicitly don’t want to make a classification of the methods concerning quantities like the computation time in this section. The computational load and thus the amount of the different methods will be obvious when the algorithms are explained. Instead, in this section we want to give a more generalized overview about the applicability of the five reconstruction methods and want to give some grading for the five algorithms in the concern of data representation. 2.1

Classification by the Type of Data Representation

By the term tomography people associate the representation of the result by an image. This is of course due to the increased ability of visual perception of human compared to other human senses. However, for further processing steps or process control, images are in general not the quantity of interest. Moreover process parameters like void fractions, areas of inclusions or mixture states are of interest. Hence, we first want to make a classification by

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True distribution

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Parameter estimation

Data Extraction

Reconstruciton

Tomography

Determine parameter set q1, e.g. shape model

Determine parameter set q2, e.g. void fraction

LBP OA Nonlinear iterative Kalman filters MCMC Fig. 6. Illustration concerning the difference between tomography and parameter estimation.

• Tomography: algorithms that directly provide an image . • Parameter estimation: algorithms that estimate certain parameters θ. Figure 6 depicts this mentioned classification. The left path depicts the tomographic approach where the result of the reconstruction directly is an image. Out of this image certain parameters of interest can be computed. The right

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path in figure 6 is referred as parameter estimation task. In this approach, the reconstruction result is a parameter set θ. As depicted in figure 6, it can be possible to generate a tomographic image out of the estimated parameters. This for example will be the case if θ presents the parameters of a shape model. Figure 7 exemplary depicts a shape representation of a closed contour, where for example the coordinates of the corner points are collected in θ. Hence, the application of

Fig. 7. Exemplary shape of a closed contour.

parameter estimation does not mean an abandonment regarding the quality of the result - it more means the use of a more appropriate material description assigned to the wanted quantity. Although tomography can be seen as a large scale parameter estimation problem, we explicitly want to use this term due to the fact, that the result is an image. By this principle classification between tomography and parameter estimation a first quantification about the properties and the applicability of the five methods between tomography and parameter estimation can be made. The bar chart in the lower part of Figure 6 tries to depict the range of the deployment of the methods. Full tomography implies a large scale parameter estimation problem. All of the five methods can be used for this. Reducing the optimization task toward a parameter estimation problem, methods like LBP or OA will become non applicable. Optimal approximation methods allow a slightly increased application towards the direction of parameter estimation problems. This comes by the design of the algorithms out of this method which will be explained later. The fact that the reduction of the parameter space leads to harder estimation problems comes due to the growing nonlinearity of the model when specific descriptions θ are used. The last three methods cover the whole range of inverse problems. For nonlinear iterative methods and Kalman filters this is because of the fact that these methods linearize the model in the current model state. This means that the computation of gradients becomes necessary. MCMC methods do not need gradient information. Instead they use sampling techniques to obtain the solution. Beside MCMC all algorithms in this book chapter are explained for tomographic applications. We decided for this because of the reasons that the

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produced results are comparable and because of the fact that the tomographic application leads to simpler algorithms. The following overview should provide an idea about the application of the five methods. • Linear backprojection: − Tomography: Linear backprojection [47], Landweber iteration [18], Offline iteration online reconstruction [26] • Optimal approximation − Tomography: OFOA and OSOA [7] [10] • Nonlinear iterative − Tomography: Gauss-Newton [32], [33] − Parameter estimation: Boundary element/ Level set [41], [12]. • Kalman Filters − Tomography: Kalman Filer [50], H∞ Filter [11] − Parameter estimation: Particle Filter [13] • MCMC − Tomography: [23] − Parameter estimation: by shape models [22], [14] 2.2

Classification Concerning the Type of Algorithm

For a second classification we want a distinction between deterministic methods and statistical methods. We define a deterministic algorithm, as an algorithm that computes the same result when calling it with the same input data twice. By this definition the first four algorithms are determinist algorithms. However, algorithms out of the methods of optimal approximation and Bayesian recursive algorithms are truly statistical methods. For optimal approximation techniques this decision comes by the fact, that the prior knowledge is incorporated out of statistical knowledge about the expected true material distribution. Although the resulting algorithm is a deterministic algorithm, its whole design is based on statistical assumptions and prior knowledge given by probability density functions (pdf). For deterministic approaches like nonlinear iterative methods the prior knowledge is incorporated in form of a regularization term. The design of regularization terms is often based on knowledge or assumptions like smoothness. By this no knowledge in the form of pdf’s is used. We will later mention that in certain cases a close connection between statistical methods using prior pdf’s and deterministic methods with regularized solutions exist in a way, that a regularization term can be formed out of the probability density function of the prior knowledge [23]. In the case of Kalman filters the inverse problem is treated by the way, that the parameter to estimate is a random variable. Hence, the Kalman filter is truly a statistical methods, although the algorithm is a deterministic algorithms. Finally, MCMC methods are fully statistical methods. The previous reconstruction algorithms provide a single reconstruction result, or a point estimate, for the input data. MCMC method evaluate Bayes law of probability, i.g. p(εr |d) =

p(d|εr )p(εr ) ∝ p(d|εr )p(εr ), p(d)

(30)

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where p(εr ) is termed the prior, p(d|εr ) denotes the likelihood and p(d) denotes the evidence. p(εr |d) is known as posterior distribution. Thus, rather than providing a point estimate, MCMC methods provide a probability density function of the result.

3

Mathematical Tools in ECT

This section provides information about some mathematical tools needed to solve the inverse problem of electrical capacitance tomography. Basically two tools are needed for the later treatment of the inverse problem. • A method to compute the forward problem. • A method to compute derivatives. To solve the forward problem we shortly want to explain the idea of the finite element method as a method to solve equation (15). Further, some of the later introduced reconstruction algorithms require derivatives, i.e. how changes the measurement on some electrode, if some permittivity value is changed. As the numerical computation of such derivatives may become too time consuming, we will explain an analytical method to compute derivatives. 3.1

Finite Element Method

In the following we shortly want to explain the idea and the employment of the finite element method (FEM) to solve the governing partial differential equation within an ECT sensor. For detailed information about the finite element method we refer to [38]. A very readable introduction into the FEM is given by [39]. Numerical methods for solving partial differential equations have an approximative character. This means, that for example for solving (15) such methods seek for a solution Vˆ such that V ≈ Vˆ =

N

vj Nj .

(31)

j=1

Hence, the approximate solution is given in form of a series representation of linear independent functions Nj , which are weighted by vj . Thus, a method has to be found to determine the weights vj . A way to access this problem is given by method of weighted residuals. Given a (partial) differential equation of type Lu = f , where L denotes the differential operator, u denotes the solution and f is the right hand side term, the equation Lˆ u−f = 

(32)

can be interpreted as the error of the approximate solution compared to the exact solution. As the exact solution ( = 0) can in general not be found, the method of weighted residuals tries to find u ˆ in such a way, that (Lˆ u − f )wdΩ = 0 (33) Ω

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holds. Hereby, w denotes a weighting function of the error . Again, the function w is given in form of a series representation w=

N

βi wi .

(34)

i=1

One can also say that with (33) the error  is orthogonal to the functions w. It becomes clear that (33) is a weaker claim than  = 0. Therefore, this formulation is called the weak formulation. The most used approach is to set wi = Ni , which is called the Ritz-Galerkin method. Applying the Ritz-Galerkin method on equation (15) ends up in ε∇Ni · ∇Vˆ dΩ = 0,

(35)

(36)

Ω

which is called the Ritz-Galerkin equation. Evaluating (36) for all functions Ni ends up in a linear equation system of form Kv = 0.

(37)

The matrix K is called stiffness matrix, the vector v is the solution vector, which contains the weights vj of equation (31). However, equation (37) is yet still free of boundary conditions. The incorporation of Dirichlet type boundary conditions should be shown by example on a 3 × 3 matrix. Given the equation system ⎡ ⎤⎡ ⎤ ⎡ ⎤ k11 k12 k13 v1 0 ⎣ k21 k21 k22 ⎦ ⎣ v2 ⎦ = ⎣ 0 ⎦ , (38) k31 k32 k33 v3 0 the value v3 should be set to the value v30 . This is done by the operation ⎤⎡ ⎤ ⎡ ⎤ ⎡ v1 −k13 v30 k11 k12 0 ⎣ k21 k22 0 ⎦ ⎣ v2 ⎦ = ⎣ −k23 v30 ⎦ , (39) 0 0 1 v3 v30 wherewith the boundary condition is applied. Applying this procedure to the equation system (37) leads to the equation system ˆ = b, Kv

(40)

whose solution v forms the approximated solution Vˆ of V . With the approach (33) and the Ritz-Galerkin method a way is given to find an approximate solution for differential equations. Nevertheless it is in most

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cases impossible to find appropriate basis functions Ni for the whole problem domain Ω. Only in a very limited number of cases, i.e. for a plate capacitor, such functions can be reconstructed easily. For complex geometries this will in general not be possible. The way out of this problem is the finite element method. The problem domain is discretized in small subdomains, the so called finite elements. For those subdomains it is allowed to use simple basis functions Ni . Assuming that the discretization is fine enough the error made with this method is small. To give an example for a finite element, figure 8 depicts a triangular finite element and the basis function Ni (x, y). The basis function Ni has the attribute of beeing 1 in the node i and beeing 0 in the node j and k. The weights vi , vj and vk are elements of the vector v. In the finite element procedure, the vkNk

viNi

xk;yk

xi;yi vjNj xj;yj

Fig. 8. Triangular element with linear basis function.

Ritz Galerkin equation (36) is applied for each finite element, resulting in an equation systems Kelem velem = 0,

(41)

for each element. This so called element matrices Kelem and the element solution vectors velem are then assembled to build the equation system (37). After applying the boundary conditions the equation system can be solved to obtain the solution v. Charge computation In the case of low-Z measurements the dielectric currents between the electrodes are measured. As these currents correspond to the charge on the electrodes in the electrostatic case, the charges have to be computed out of the solution V . The relation between the capacitances and the charges is given by Q = CVT . Two methods for the computation of the charges are possible.

(42)

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Charge integration The first method is simply given by the integration of the inflowing displacement current over the boundary of the electrode. Hence n · ε∇V dΓ. (43) Qelec = − Γelec

Thereby, the permittivity ε denotes the electric material values of the surrounding area of the electrode and n is the normal vector on the surface of the electrode. The method is quite easy but requires the computation of the gradient. Charge method Alternative, a method named ”charge method” [19] can be used can be used to compute the charges. The method was introduced in electrical capacitance tomography but it can be used in the same manner for other problems with a Laplacian differential equation. For the use of the charge method only the stiffness matrix K and the solution vector v are used. The charges can be computed by evaluating

Qelec = (Kv)nelec . (44) nelec

Hence, the computation of a gradient is not necessary. (Kv)nelec is the scalar product between the vector v and the rows of the matrix K that correspond to nodes on the surface of the specific electrode. 3.2

Analytic Computation of Derivatives

After providing a method to solve the governing differential equations, we now want to present a method to compute derivatives. As some of reconstruction methods require gradient information, the computation of derivatives is of high demand. Although, the computations of a 2D forward problem can be carried within a fraction of a second (i.e. 50 ms), the numerical computation of derivatives is in general not preferred. E.g. for an inverse problem with 500 unknowns, the computation of the gradient would take up to 50 s when a difference quotient scheme is used. Hence, fast analytic methods are required. One analytic method to compute the derivatives from an underlying differential equation problem is the adjoint variable approach [48]. The method can be applied for scalar functions Ψ = Ψ (x, v(x)), (45) (e.g. the computed capacitance) where v(x) is the solution of an underlying system of the form Kv = b.

(46)

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In the general case K, v and b also depend on x. Then, the total derivative of the i-th component of the gradient of Ψ is given by  T   ∂Ψ ∂Ψ ∂v dΨ = dxi + dxi . (47) ∂xi ∂v ∂xi The problem in (47) is the last derivative, where the solution vector v is derived to xi . Taking the derivative of (46) using the chain rule for the left hand side results in       ∂K ∂v ∂b v+K = . (48) ∂xi ∂xi ∂xi   ∂v With this ∂x can be expressed as the solution of a linear equation system i       ∂v ∂b ∂K K = − v. (49) ∂xi ∂xi ∂xi However, the number of operations is still not reduced. Now by defining an adjoint variable vector γ with   ∂Ψ T K γ= , (50) ∂v the inner product of (47) can be written as  T     ∂Ψ ∂v ∂v T =γ K . ∂v ∂xi ∂xi Using the right hand side of (49) for (51), (47) can be written as      ∂Ψ ∂b ∂K dΨ = dxi + γ T − v dxi . ∂xi ∂xi ∂xi

(51)

(52)

Since by the fact that with (50) only one equation system more is to solve, the adjoint variable approach convinces with a dramatically reduced amount of computational effort. Applying the adjoint variable approach to the finite element method implementation (40) and the charge method (44) to compute the derivative of the charge measured on the j th electrode (the ith electrode is active) with respect to the permittivity of domain r, results in evaluating      ˆi ∂Q ∂bi ∂K T dQi,j = ∂εi,j dε + γ − vi dεk , (53) k j ∂εk ∂εk k where the adjoint problem is given by    ∂  nelecj (Kvi )nelecj ∂Q i,j T ˆ i γj = K = . ∂vi ∂vi

(54)

Hence, a method is provided to compute derivatives with high accuracy at the computational cost of solving an additional forward problem.

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Reconstruction Algorithms

In this section five reconstruction algorithms mentioned in 2 are explained in more detail. As not all aspects about their details can be explained in this book chapter, we will refer to the original publications. Other overviews about reconstruction methods can be found in and [36] and [37]. The first four algorithms aim on the reconstruction of the permittivity values of the finite elements inside the pipe. The permittivity values of the elements are collected inside the vector ε. Hence, this algorithms can be referred to the class of algorithms that perform a fully tomographic reconstruction. The fifth reconstruction algorithm uses a shape model to reconstruct the contour of an inclusion. Thus, the algorithm performs a parameter estimation. Figure 9 depicts the finite element mesh used for the algorithms. A more detailed view on the electrode (black region) is depicted in figure 9(b). The material values of the finite elements in the interior of the pipe form the elements of the vector ε.

(a) Finite element mesh of the ECT sensor.

(b) Detailed view on one electrode.

Fig. 9. Finite element mesh and detailed view on one electrode.

4.1

Linear Back Projection

To start, we first want to present the maybe most simple class of reconstruction algorithms, which are referred as linear back projection (LBP) methods. A common attribute of all algorithms out of this class is the extensive use of so called sensitivity information and linearisation. As the capacitances C(ε) depend nonlinearly on the permittivity distribution ε, the results of LBP methods can be expected to be in general of lower quality. In fact the reconstruction results of LBP methods are images where one can see the presence of materials. The color value may allow a distinction between different materials but it does not correspond to the permittivity of the material.

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LBP methods are based on the simplification, that C(ε) can be approximated by [47] C = Sε, (55) where S is referred as sensitivity matrix. The elements of the sensitivity matrix can be computed by Si,j (k) = μ(k)

Ci,j (k) − Ci,j (ε1 ) ΔCi,j Δε

(56)

where the indices i and j indicate the capacitance for the specific transmitter electrode and the specific receiver electrode and k indices the specific finite element. Ci,j (ε1 ) is the capacitance when the pipe is filled with a material with permittivity ε1 , e.g. the pipe is filled with air. The terms ΔCi,j and Δε are given by ΔCi,j = Ci,j (ε2 ) − Ci,j (ε1 ), (57) Δε = ε2 − ε1 ,

(58)

where Ci,j (ε2 ) denotes the capacitance when the sensor is filled with a material with a permittivity of ε2 , e.g. oil. The capacitance Ci,j (k) denotes the capacitance, when only the material value of pixel (or finite element) k is set to ε2 , while the rest of the material values is set to ε1 . μ(k) in equation 56 is a correction factor which is related to the area of the element k. Hence, the elements of S are similar to normalized derivatives. It becomes clear, that the quality of the reconstruction depends strongly on ε1 and ε2 , as this material values are used for the linearization. I.e. if the linearization is performed for low contrast materials, the reconstruction will fail for high contrast material distributions. The now most simple LBP algorithm is given by ε = S T C,

(59)

where only the transposed of the sensitivity matrix is used to compute ε out of the measured capacitances C. One can imagine, that due to all simplifications, the reconstruction result will be of low quality. Moreover, from a physical point of view the use the transposed of the matrix S is an incorrect approach, as the physical units do not conform. In order to increase the quality of the reconstruction results, schemes like Landweber iteration [27] can be applied. Using the solution of (59), the iteration scheme εk+1 = εk + αk S T (C − SC)

(60)

can be applied, to increase the quality of the reconstruction result, where αk denote a step width. However, as the same sensitivity matrix S is used during the Landweber iterations, the inherent drawback of the the localness of S remains.

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To overcome the iterative behavior of the Landweber method, offline iteration online reconstruction (OIOR) was developed. OIOR is based on the propositon, that the systems xk+1 = Ak xk + B k v

(61)

D k+1 = Ak Dk + B k

(62)

and

xk+1 = Dk v

(63)

will produce the same time series for x, if x0 = D 0 v holds. This can be simply proven by applying complete induction. Based on this proposition, the iterative scheme (60) can be rewritten as εk+1 = εk − αk S T Sεk + αk S T C,   = I − αk S T S εk + αk S T C.

(64) (65)

Applying the scheme of equation (62) to equation (65), leads to Ak =  T I − αk S S and B k = αk S T . Hence, by (61) the Landweber iteration can be reexpressed by εk+1 = Ak εk + B k C,

(66)

where C equals v and εk = xk . Now using the properties of (63) and setting D0 = S T , a matrix Dk can be found by running equation (62) till convergence occurs. By this, the iteration procedure of the Landweber iteration can be done prior and the OIOR reconstruction algorithm is given by ε = DC,

(67)

where D is the result of (62). Concluding this subsection it can be said that by linear back projection a simple and fast reconstruction algorithm is given, as the reconstruction is performed by a matrix vector multiplication. The algorithm is obtained by using sensitivity information. Thus, for the design of the algorithm only some evaluations (the number of pixels) of the forward problem are necessary. Beside the material values ε1 and ε2 no explicit prior knowledge is used. Hence, the result can be assumed to be of lower quality. 4.2

Optimal Approximations

The reconstruction methods termed optimal approximation (OA) was developed at the Institute of Electrical Measurement and Measurement and Measurement Signal Processing (EMT) in 2007 [7]. In this section we will present optimal first order approximation (OFOA) and optimal second order approximation (OFOA)

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techniques. Linear back projection methods take use of sensitivity information to design a function f for determining the material distribution by ε = f (C).

(68)

Now explicit prior knowledge is incorporated. However, one might argue about the appropriateness of using sensitivity information to design the function f . In fact, the question should be: ”What is the optimal function f ?”. An intuitive approach of formulating the problem is by seeking for a function f , that minimizes the difference between the reconstructed material distribution and the real material distribution in a quadratic sense, i.e. f ∗ = arg min ||f (C(ε)) − ε||22 . f

(69)

As it will in general be not possible to find an analytic solution for (69), two steps are necessary, to treat the problem. First, an appropriate function set for f has to be determined. In order to keep the computational complexity of the algorithm at the same level as back projection methods, a logical choice for f is given by ε = P h,

(70)

where the vector h contains the measured capacitances. For the OFOA and the OSOA, h is given by T  hOF OA = 1 C1 C2 . . . CN , (71)   T 2 . (72) hOSOA = 1 C1 C2 . . . CN C12 C22 . . . CN By the first entry in both vectors at least an affine algorithm is given. In the case of the OSOA the algorithm uses quadratic information. Hence, the algorithm is a nonlinear algorithm. The second step concerns the choice of ε in equation (69). The crux is now given by the fact, that the true material distribution is unknown. However, it is still possible to define a meaningful prior distribution π(ε) for ε. Thus, the specific permittivity distribution is a realization of π(ε). By this two steps it is possible the formulate (69) as a least squares problem of form HP T = E

(73)

where the rows of E are given by the samples of the prior distribution εTi of the prior distribution π(ε) and the rows of H are the corresponding vectors hTi . Thus, P can be computed by  −1 P T = HT H HT E = H† E, (74) where H† is referred as Pseudo inverse of the matrix H. The computation of H† is done by singular value decomposition (SVD) for numerical reasons. In addition

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also the prior knowledge about the measurement noise can be incorporated, by taking multiple use of a material pattern εi in E and applying noisy realizations of hi in H. For the generation of samples εi from the distribution π(ε) several ways are possible. A possible way is to generate samples of π(ε) by means of Markov Chain Monte Carlo methods. However, a more simple way to generate samples is given by directly drawing realizations of the assumed material distribution. I.e. for material distributions mostly consisting of circular objects, a simple method is to arbitrarily place circular objects inside the domain Ω. Algorithm 1 lists the computational steps for the sampling procedure to generate a pattern. Algorithm 1. Algorithm to obtain samples εi and hi . 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11:

Define a grid and set all elements to one Draw #incl ∼ U(1, #max incl ) for k = 1 to #incl do Draw [xcenter , ycenter ] ∼ U (in ROI) Draw ε ∼ U(εmin , εmax ) Draw rincl ∼ U(rmin , rmax ) Create an inclusion out of the parameters and place it on the grid end for Set all grid elements outside ROI to zero Map the distribution from the grid onto the finite element mesh (gives ε) Compute the forward problem to obtain the capacitances

(a) Rod like data.

(b) Gaussian data.

Fig. 10. Possible realizations of the sample data obtained by algorithm 1.

The procedure listed in algorithm 1 produces rod like prior data. Hence, sharp material boundaries are assumed in the training data. If one is interested in

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smooth material transitions, one can replace the rods by Gaussian bumps. A possible way is to use the sampled radius as for the full with half peak (FWHP) value of the Gaussian bump to characterize the material distribution. Figure 10 depicts two realizations of the sampling algorithm for rod like data and for Gaussian bumps. By this, the matrices H and E can be assembled and by solving (73) for P the reconstruction matrix is found. To further increase the image quality, (70) can be modified to ε = P h − (P h0 − 1),

(75)

where h0 contains the measurements of the capacitances for an empty pipe. Thus, an offset correction is performed. Finally, local non feasible solutions (i.e. εr < 1) can be replaced to feasible material values. Concluding this subsection it can be stated that optimal approximation techniques are a statistical approach to obtain a reconstruction algorithm of the same computational cost as linear back projection methods. The computation of the projection matrix P is of higher computational cost, as sampling techniques are necessary. However, by this design prior knowledge can be incorporated. Hence, the quality of the results should be increased. In section 2 we stated that optimal approximation techniques are in general not suitable for parameter estimation tasks. This of course will be true for higher dimensional representations like shape models. However, if for example the fill level of a fluid in a horizontal mounted pipe should be determined, optimal approximation techniques would be a very suitable approach. 4.3

Nonlinear Iterative Methods

As the forward problem of ECT is of nonlinear nature, nonlinear reconstruction methods are in general better suited to provide reconstruction results of high quality compared to linear algorithms. A common reconstruction approach for nonlinear methods is to formulate the inverse problem as an optimization problem of form [32], [33]   ε∗ = arg min ||C(ε) − C meas ||22 + αR(ε) . (76) ε

The first part in equation (76) is referred as misfit, as it describes the misfit between the current model output C(ε) and the measured data C meas . The second term is a so called regularization term. As the inverse problem has an illposed nature, the direct minimization of the misfit term would lead to completely wrong results. Hence, the regularization term acts as an penalization term in (76). The design of the regularization term R(ε) depends on the available prior knowledge about the material distribution. An often used approach is referred as Tikhonov regularization [35], where R(ε) is of form R(ε) = ||Lε||22 .

(77)

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In the most simple case, the regularization matrix L is chosen to be the identity matrix. In this case, the regularization penalizes large permittivity values. An other approach is to apply some smoothness assumption on the solution [32]. In this case the matrix L is a discrete version of the Laplace operator. If one is interested in reconstructing sharp material boundaries, methods like total variation (TV) can be used. However, they have the disadvantage of changing (76) to become a nonsmooth optimization problem. The weight between minimizing the measurement term and the regularization term is controlled by the regularization parameter α. The choice of α is critical, as a too large value of α means, that the result is mostly affected by the regularization, whereas a too low value of α means an unstable solution due to the ill-posedness. A method to determine an appropriate value for α is the so called L-curve criterion [29]. Hereby, log(||C(ε) − C meas ||22 ) is plotted versus log(||Lε||22 ) for different values of α. The resulting curve has sometimes a distinct corner called L-curve corner. Regularization parameters in the region of the L-curve corner are known to be good candidates for the regularization. Also adaptive methods of the form α = α0

||C(εr ) − Cmeas ||22 ||Lεr ||22

(78)

have been suggested [42]. By this, the regularization parameter is adapted by the ratio between the misfit term and the regularization term. α0 in equation (78) denotes a weighting factor that is set for one time. In general any optimization algorithm can be used to minimize (76). A very powerful class of optimization algorithms is given by second order methods or Newton methods. The term ”Newton method” is assigned to a class of optimization algorithms which take use of the second derivative of the objective function. Quadratic functions can be formulated as 1 Ψ (x + p) = Ψ (x) + gT p + pT Gp, 2

(79)

where x is the vector containing the optimization variables and p is any arbitrary vector. g is the gradient and G the Hessian [20]. To minimize Ψ a descent direction s can be found by applying the first optimality condition ∂Ψ = g + Gp = 0. ∂p

(80)

p = −G−1 g,

(81)

s = p,

(82)

Therefore, with

the search direction

is found, which leads to a fast decrease of (76) compared to steepest descent methods, where s = −g is used.

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A special variant of Newton Methods forms the Gauss-Newton method, which offers some distinct advantages with respect to (76). The Gauss-Newton method is a Newton method for objective functions of type Ψ=

m

ri (x)2 = rT r.

(83)

i=1

Defining the Jacobian matrix J as   J = ∇r1 ∇r2 . . . ∇rm ,

(84)

the gradient and Hessian can be written as [20] g = 2Jr,

(85)

and G = 2JJT + 2

m

ri ∇2 ri .

(86)

i=1

Since the residuals are minimized the second term in (86) can be neglected and a reasonable approximation for G is given by G ≈ 2JJT .

(87)

With this an iteration step of the Gauss-Newton method is given by the operations  −1 Jr, (88) x(k+1) = x(k) − JJT where k denotes the iteration index. Applying the Gauss-Newton scheme to (76), ends up in  −1   εk+1 = εk − s JJT + αLT L J r k + LT Lεk ,

(89)

where the residual vector r is defined as r k = C(εk ) − C meas .

(90)

s in equation (89) is the step width of a line search parameter [20]. A line search is in general necessary, as the approximation of the Hessian and the gradient are only local approximations of the derivatives of (76). A further improvement of the reconstruction result can be achieved by taking care to physical constraints of the permittivity values. The relative permittivity can never become smaller than one and in most cases knowledge about the upper value of ε is available. Hence, the unconstrained optimization problem (76) becomes an optimization problem with inequality constraints. A method to take care to this constraints is called Active Set Method [20]. Another approach

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which allows to take care to the lower bound of the permittivity, is to replace ε by [15]   ε = eε˜ + 1 , (91) and perform the optimization problem for the new variable ε˜. Summarizing the properties of nonlinear iterative methods it can be stated that the used approach forms a very flexible method to solve inverse problems of different kind and/or for different descriptions. E.g. also shape models in combination with boundary element methods have been implemented to solve the inverse problem of ECT by this method [41]. As the minimization of the cost functional is in general done by iterative method, the computation time for the solution is high compared to the previous methods. However, by the use of second order optimization methods in general only a few number of iterations are necessary to obtain appropriate results. Due to the linearization, nonlinear iterative methods offer the probability to reconstruct material distributions with high contrast ratios (e.g. oil bubbles in water), where linear algorithms generally fail. 4.4

Kalman Filters

Another class of algorithms which can be used to solve the inverse problem of ECT belongs to the class of recursive Bayesian algorithms. In particular the Kalman filter and Particle filter [16] methods belong to this class of algorithms. In principle this class of algorithms uses temporal information taken from measurements and a process model for estimating the desired quantity, or to be more precise the state of the model. Hence, mostly state space models are used as process models and the referred algorithms treat the state variables as random variables. Although the inverse problem of ECT is a nonlinear problem, the estimation of states was traditionally developed on linear models. Therefore, we will also start with linear systems of form xk+1 = Fk xk + wk ,

(92)

yk = Hk xk + vk .

(93)

Equation (92) and (93) form a linear time variant state space model for an autonomous system. Fk is refereed as system matrix and describes the evolution of the current state vector xk towards its new value xk+1 . wk is the so called process noise, which can be seen as a random input for the system. While equation (92) describes the dynamic evolution of the process, equation (93) describes the relation between the measurements yk and the current state vector xk by the matrix Hk . Hence, equation (93) is referred as measurement equation. vk describes additional additive measurement noise. ˆ k for the true State estimation refers now to the task to find an estimate x state xk using temporal information from measurements and the prior knowledge about the evolution of the state. In the following a very short introduction about

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the Kalman [46] filter is presented. For detailed informations we refer to [49]. In 1960 R. Kalman presented a recursive filter scheme that provides an estimator that was designed by the claim   ˆ )2 , min E (x − x (94) ˆ x

where E denotes the expectation. The resulting filter is known as the Kalman Filter. By (94) the Kalman filter minimizes the expectation of the quadratic error. The filter equations are given by ˆ k−1 x∗k = Fk x P∗k = Fk Pk−1 FTk + Qk  −1 Kk = P∗k HTk Hk P∗k HTk + Rk ˆ k = x∗k + Kk (yk − Hk x∗k ) x Pk = (I −

Kk Hk ) P∗k .

(95) (96) (97) (98) (99)

For the derivations, Kalman assumed that all noise processes and the state vector are Gaussian distributed random variables, which means that they can be described by the mean and the covariance matrix. For the measurement noise vk and the process noise wk zero mean is assumed. Thus, measurement noise and the process noise are fully determined by the covariance matrix Rk and Qk . For example, π(v k ) is given by   1 T −1 π(v k ) ∝ exp − v k Rk v k . (100) 2 The matrix P∗k in equation (96) is the covariance matrix of the predicted state x∗k . ˆ k denotes the (final) estimation of the state using the actual measurement yk x ˆ k is denoted by Pk (equation (99)). (equation (98)). The covariance matrix of x As the covariance matrix Pk provides information about the uncertainty of the estimation result, a benefit the Kalman filter is, that it provides information about the quality of the result. Thus, although the filter scheme is an deterministic algorithm by the definition made in subsection 2.2, the Kalman filter provides statistical information. The Kalman filter given by the equations (95) to (99) estimates the state x of a linear dynamic system. To apply the Kalman filter to the nonlinear and ill posed-inverse problem of ECT some extensions to the vector y k and the matrices Rk and H k have to be made [50]. These extensions are mainly to incorporate a regularization and to deal with the nonlinearity. With εr replacing the state vector x the augmented measurement vector is given by   C meas,k yk = . (101) 0

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Thus, the approach first seeks for an estimate, which minimizes the misfit between the measurement and the actual model output. With the augmented measurement equation   J k (ε∗k ) , (102) H= αL the augmentation of (101) by 0 affects the claim to minimize the regularization term. Hence, the approach is similar to the nonlinear approach given by equation (76). J k denotes the Jacobian. By the augmentation of the measurement vector, also the covariance matrix Rk has to be adapted. This is typically done by   Rv,k 0 , (103) Rk = 0 σ2 I where Rv,k is the covariance matrix of the measurements C meas,k and σ2 is typically chosen as the mean of the variances of the measurements [30]. To overcome the nonlinearity, an extended form of the Kalman filter is implemented [49]. Thus, the update equation (98) is replaced by    C(ε∗r,k ) ˆr,k = ε∗r,k + Kk yk − ε , (104) αLε∗r,k forming an extended version of the Kalman filter. For the system matrix F often the identity matrix I is used. Thus, the system equation (92) is set to a random walk model. One might argue about the appropriateness of the use of Kalman filters, as this type of state estimators were designed for estimation tasks when dealing with dynamical systems. However, we applied the algorithm in order to demonstrate its applicability. A possible method to obtain a system matrix F which provides information about the dynamic behavior, is to use the search direction given by equation (82) for its design. As the extended Kalman filter does not incorporate any state constraints, the results produced by it may be infeasible. The incorporation of the physical constraints of the relative permittivity can be done by a method called maximum probability method [45]. The maximum probability method seeks for a state x by maximizing its probability to certain constraints. In the case of Kalman filtering the probability of the estimated state is given by the a-posteriori covariance matrix P . The correct state is found by solving minx (x − x∗ )T P −1 (x − x∗ ) s.t. bl ≤ x ≤ bu

(105) (106)

As x∗ is constant the problem can be written as minx xT P −1 x − 2x∗ T P −1 x s.t. bl ≤ x ≤ bu which is a simple quadratic programming problem.

(107) (108)

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To conclude this section it has to be said, that the behavior of the Kalman filter is typically very similar to the nonlinear iterative methods. Basically, the minimization problem is formulated as a state estimation problem, to apply the Kalman filter. Hence, the Kalman filter can also be used for different representations. However, as the first of the presented algorithms, the Kalman filter provides information about the quality of the estimation result. This of course has to be treated carefully, as the inverse problem of ECT is a nonlinear problem and the Kalman filter was developed for linear models. 4.5

Markov Chain Monte Carlo

The previous presented algorithms provide a single point estimate for the material distribution. As a first algorithm, the Kalman filter has in principle the possibility to quantify the quality of the result. However, the Kalman filter is based on the assumption, that all processes are Gaussian. One can imagine, that this assumption will not correspond to the true distribution. In contrast to this point estimators, the now presented algorithm does not provide a single point estimate. It rather presents the result in form of a full probability density function (pdf). Statistical inversion theory is based on the evaluation of Bayes law of probability, which was given by equation (30) as p(εr |d) =

p(d|εr )p(εr ) ∝ p(d|εr )p(εr ). p(d)

The attractiveness of statistical inversion lies in several facts: • • • •

The approach is applicable to any forward problem. No derivatives have to be computed. Available prior knowledge can be incorporated in a natural way. Information about the measurement noise can be added in the same natural way. • The result is a probability density function. Hence, complete information about the uncertainty of the result is available. As we only want to demonstrate the aspects and the easy applicability of statistical inversion theory we keep our explanations more on an application level to use statistical inversion theory. For detailed informations we refer to [23]. In order to switch to the nomenclature used in the previous sections we replace the data d by the measurements C meas . Further we replace the material distribution εr by a more general representation which we term θ. Hence, equation (30) becomes p(θ|C meas ) =

p(C meas |θ)p(θ) ∝ p(C meas |θ)p(θ), p(C meas )

(109)

where again p(θ) is termed the prior, p(C meas |θ) denotes the likelihood, p(C meas ) denotes the evidence and p(θ|C meas ) is the posteriori distribution.

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Thus, rather than providing a single point estimate, it is possible to obtain a probability density function (pdf) of the posteriori distribution. Hence statistical inversion theory provides more information about the solution. The drawback of this information gain, is the drastically increased computational burden. As an analytic evaluation of (109) can become uncalculable in the case of complex forward problems or higher dimensional spaces, a sampling scheme has to be applied to obtain a sampled solution of the posteriori distribution. Thus, in general a large number of evaluations of the forward problem is necessary. As in higher dimensional spaces also the visualization and interpretation of the posteriori distribution becomes impossible, characteristics or point estimates have to be computed out of the density function. Such point estimates are for example the maximum a posteriori (MAP) estimate θMAP = arg max π(θ|dm ),

(110)



or the conditional mean θCM =

θπ(θ|dm )dθ.

(111)

It should be mentioned, that for Gaussian processes, the MAP estimate equals the result of a least squares approach [23]. Thus, a certain link between statistical and deterministic methods exists. For further explanations, the terms of Bayes law have to be discussed in more detail. Evidence: The evidence p(C meas ) is the pdf of the measured data and thus representing the marginal density function of the likelihood. Thus the computation of the evidence is only possible in low dimensional spaces. For higher dimensional spaces the computation becomes untraceable. However, the evidence has only the role of a normalization constant, which normalizes the posteriori distribution. Hence, the evidence can be skipped resulting in the reduced formula in equation (30) and (109). Likelihood: The likelihood p(C meas |θ) is assigned to be a probability density function, which expresses the probability that the measurements C meas are caused by the material distribution represented by θ. In other words, the likelihood answers the question ”How likely is it, that θ causes the measured capacitance values?”. Thus, the likelihood quantifies the misfit e = C meas − C(θ), which can be understood as the noise (compare with equation (93)). Hence, the likelihood is in essential the pdf of the measurement noise. I.e. in the case of additive Gaussian measurement noise, the likelihood is given by   1 π(C meas |θ) ∝ exp − eT Σ −1 e , (112) 2 where Σ denotes the covariance of the measurement noise. Estimators which maximize the likelihood are called maximum likelihood (ML) estimators. However, the ML estimate may lead to infeasible solutions, i.e. θ represents

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a material distribution with relative permittivity values smaller 1, or material boundaries which lie outside the problem domain. Prior: The prior distribution p(θ) forms a key issue for the success full application of statistical inversion theory. By the prior it is possible to incorporate statistical knowledge about θ. The most simple prior is given by only evaluating the permissibility of θ, i.e. p(θ) = 0 for distributions θ which are infeasible and p(θ) = 1 for any valid distribution. By this, infeasible solutions are effectively neglected. An estimator which maximizes the posteriori, is called a maximum a posteriori (MAP) estimator. For the simple 0/1 prior the MAP estimator is in essential an ML estimator which avoids infeasible solutions. Using this simple prior the quality of the estimator is increased. However, by the choice of more specific priors it is possible to build highly effective estimators, which can be used to reconstruct complex material distributions θ. More information about the choice of prior distributions will be given later. Posteriori: The distribution p(θ|C meas ) is called the posteriori distribution. In essential the posteriori distribution gives answer to the question ”Given the prior π(θ), how likely is it, that θ causes the measured capacitance values?”. Hence, the posterior distribution provides statistical information about the probability of θ. Summarizing the explanation of the terms in Bayes law, it provides a systematic approach to quantify the probability for θ being the solution of the reconstruction task. However, so far Bayes law only provides the possibility to compute the posteriori probability p(θ|C meas ) of a, now called candidate, θ. To obtain a posteriori distribution Bayes law has to be evaluated for a large number of candidates. For lower dimensional spaces sometimes a grid based exploration of the posteriori distribution is possible. In the case of higher dimensional spaces this approach may become impossible or leads to unacceptable high computation times. Thus, the efficient exploration of the posteriori distribution π(dm |θ) becomes the major computational aspect when dealing with Bayesian methods. An efficient class of algorithms to solve Bayesian inference problems are so called Markov Chain Monte Carlo (MCMC) methods. The basic idea of MCMC can be found in a number of text books, e.g. [23]. The maybe most prominent algorithm out of the class of MCMC type algorithms is the Metropolis Hastings (MH) algorithm [40]. The basic idea is presented in algorithm 2. As by line 5 in algorithm 2 the evaluation of the forward problem becomes necessary, the high computational costs of statistical methods become clear. The principle of Bayesian inversion theory has been explained so far. However, for practical implementations in ECT some details about the the representation θ, the generation of proposal candidates θ and the design of priors π(θ) are

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Algorithm 2. Basic Structure of MH MCMC 1: Pick a valid initial candidate θ and compute the prior π(θ) and the likelihood π(C meas |θ) 2: for i = 1 to NM CM C do 3: Generate a proposal candidate θ 4: if π(θ ) > 0 then     meas |θ )π(θ ) 5: Evaluate the MH acceptence ratio α(θ, θ ) = min 1, π(C π(C meas |θ)π(θ)

6: Draw u ∼ U(0, 1) 7: if u < α(θ, θ ) then 8: θ = θ 9: π(θ) = π(θ  ) 10: π(C meas |θ) = π(C meas |θ  ) 11: end if 12: end if 13: end for

necessary. The previous explained reconstruction algorithms aimed on reconstructing the material values of the individual finite elements within the pipe. For the statistical approach presented in this subsection, we want to demonstrate an approach using a shape representation. Hence, the approach is able to reconstruct sharp material transitions. In the further we will give a short introduction about the three missing topics. Shape models: For the first a decision for an appropriate shape model θ has to be made. Figure 7 already depicted an exemplary shape to represent the closed contour of an inclusion. In principles, it would be sufficient to store the coordinates of the corner points to build a shape description. However, a number of methods were developed, which can be used to represent closed contours, e.g. • • • •

Fourier models [44]. Spline representations. Radial basis functions [21]. Level set methods [25].

Level set functions may be a less appropriate shape description in concern with statistical inversion theory, as they were initially invented to describe the evolution of shapes. However, we stated them for completeness. When using the finite element method for solving the forward problem, the contour has to be mapped onto the permittivity values of the finite elements. This mapping causes an additional approximation error, as the obtained permittivity distribution on the finite elements will in general never correspond exactly to the contour. In the case of a boundary element method, the contour can be directly used. Generation of candidates: The generation of a proposal candidate θ out of the current candidate θ becomes one major issue when implementing

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a sufficient MCMC methods. Concerning with shape reconstructions, the generation of a proposal candidate is refereed as move [22]. Figure 11 depicts the so called corner move. The idea of the corner move is to randomly

Fig. 11. Exemplary move to generate a proposal candidate θ  .

pick a corner point of the shape and then to change the position randomly. In essential, this is the only move necessary to obtain a Markov Chain which reaches an equilibriums distribution. This comes by the fact, that by the corner move every possible shape can be generated out of the initial shape. However, to increase the performance additional moves like rotation, transition or scaling [22] are mandatory. Prior distributions: The last point to explain more in detail concern the choice of the prior distribution π(θ). As already mentioned, the most simple prior is given by only rejecting infeasible solutions. We only want to explain here one more specific prior given by    1 c(θ)  π(θ) ∝ exp − 2 −1 I(θ) (113) 2σpr 2 Γ (θ)π where c(θ) denotes the circumference and Γ (θ) denotes the area of the inclusion. I(θ) forms the prior proving the feasibility of θ. Hence, this prior aims on the ratio of the circumference of the inclusion with respect to the circum2 ference of a circle with the same area as the inclusion. σpr is the variance, by which this ratio can can be controlled. To conclude this subsection it has again to be mentioned, that MCMC methods form a simple way to solve inverse problems of any kind. The major advantage of the method is the fact, that by the posterior distribution an evaluation of the quality of the result becomes possible. Also the fact that available prior knowledge can be incorporated in a natural way make the method attractive.

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The major drawback of the approach is the increased computation time due to the necessity of using a sampling technique to obtain the posterior distribution.

5

Reconstruction Results

This section provides reconstruction results for some of the presented algorithms. Figure 12 depicts a photography of the real material distribution. Several plastic rods and pipes have been placed inside the sensor. The gray colored objects are PVC rods, with a relative permittivity of εr = 3.5. The white colored rod is of another material, which has a slightly lower permittivity. The pipe is out of teflon which has a permittivity of about 2.2. As the material distribution is of complex nature, the tomographic algorithms using volumetric descriptions are preferable for the reconstruction. An algorithm for shape reconstruction would not be suitable for this material distribution. Hence, to demonstrate the ability of statistical inversion theory, we will demonstrate the reconstruction of a single inclusion.

Fig. 12. Photography of the real distribution.

Figure 13 depicts the reconstruction result obtained by the OIOR algorithm. Figure 14 depicts the result obtained by OFOA and OSOA. As both algorithms are out of the class of back projection methods and thus having the same computational costs, it makes sense to compare this results. The result obtained by the OIOR is a strongly blurred image. One can imagine that inclusions are situated in the lower part of the pipe. However, it is not possible to say something more about the specifics of the material distribution. Also the drawback of the decreased sensitivity in the center of the pipe and the fact, that for the design of the OIOR no prior knowledge was used comes fully to hand. In comparison

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Fig. 13. Reconstruction result obtained by the OIOR algorithm.

the four results obtained by the OFOA and the OSOA offer results of increased quality. Although the OFOA is not able to reconstruct the interior of the teflon pipe. However, the air region on the right side of the half circle formed PVC rod is visible. Compared to the OIOR algorithm, the images firstly allow to interpret the distribution. The two results obtained by the OSOA clearly demonstrate the increased quality due to the nonlinear approach. The teflon pipe is now clearly visible. One can also see, that the choice of the prior distribution (rod like or Gaussian). The reconstructed permittivity distributions for the OFOA and the OSOA are higher compared to the real material values. Summarizing one can say, that the use of prior knowledge can help to highly increasing the quality of the reconstruction result. Figure 15 depicts the reconstruction result obtained by the nonlinear method. Compared to the results obtained by the OSOA, the quality is slightly increased. The result contains no artifacts in the upper region of the pipe as they can be seen in the results obtained by the OFOA and the OSOA. However, as the nonlinear method is based on an iterative algorithm, the reconstruction time increase by a factor of about 105 . The resluts obtained by the Kalman Filter are very similar to that of the nonlinear method. Hence, they are not depicted. Figure 16 depicts the result obtained by applying statistical inversion to reconstruct an elliptic object. Figure 16(a) depicts the conditional mean and the MAP-estimate as point estimates of the reconstruction result. One can see good accordance to the real distribution. As mentioned, a least squares estimator would also provide the MAP estimate for Gaussian processes. Hence, with an deterministic approach using a suitable shape model the same result could be obtained. The big advantage of statistical inversion theory is offered in figure 16(b). The so called scatter plot depicts randomly chosen points of the shape model. Hence, out of the variance of the scatter plot one can quantify the uncertainty of the point estimates.

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1

(a) OFOA with rod like data.

(b) OFOA with Gaussian data.

(c) OSOA with rod like data.

(d) OSOA with Gaussian data.

1.5

2

2.5

3

3.5

4

Fig. 14. Reconstruction result obtained by the OFOA and OSOA algorithm.

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Fig. 15. Reconstruction result obtained by the nonlinear algorithm.

True Object MAP CM

(a) Conditional mean and MAP estimate.

(b) Scatter plot.

Fig. 16. Reconstruction result obtained by statistical inversion.

6

Conclusion

In this book chapter several reconstruction algorithms for ECT have been presented. The palette reached from linear methods which are suitable for real time applications, to nonlinear methods which offer increased possibilities, to fully statistical methods, which not only provide the single reconstruction result but a statistic about the solution of the problem. The chapter started with an introduction about ECT, containing physical aspects about the electrical effects within the sensor, different measurement principles as well as calibration schemes. In section 2 we tried to define a classification

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scheme for reconstruction algorithm based on the representation of the reconstructed data. By this we wanted to demonstrate that, although the presented algorithms perform a full tomographic reconstructions, some of the presented methods offer the probability for parameter reconstruction tasks. Section 3 presented some numerical tools. In section 4 five algorithms are presented in more detail. Finally some reconstruction results are presented for real measurement data.

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Non-destructive Control of Metallic Plate with Magnetic Techniques L. Battaglini1, P. Burrascano1, A. Canova2, F. Ficili2, M. Ricci1, D. Rossi3, and F. Sciacca3 1

Università degli Studi di Perugia, Dip. di Ingegneria Industriale 2 Politecnico di Torino, Dip. Di Ingegneria Elettrica 3 AMC Instruments srl, spin-off del Politecnico di Torino

Abstract. Magnetic inspection techniques are nowadays widely used in system for Non-destructive Test (NDT) of various type of application. One of the most important application is the magnetic inspection of metallic object, that use family of sensor (Hall, GMR and coils) sensible to the magnetic flux (Hall and GMR are sensible to intensity of flux, and coils are sensible to the variation of the flux). System based on this techniques, in general, are able to read the sensor and, applying a proper software processing to the data collected, give information about the external an internal state (damage, presence of inclusion) of the object under test. An example of this method is the magneto-inductive inspection of metallic rope, widely used for the inspection of cableways. In this paper we will present a different application of this techniques, were the object that we want to test is a metallic plate (or sheet) and the output of a sensor designed ad-hoc for the application is combined to numerical techniques that can give a visual representation of the state of the object. Keywords: Non-destructive techniques, Magnetic Inspection, Electronic Sensor Design, Software Processing.

1 Introduction The various techniques for Non Destructive Testing of Materials (NDT) (based on electromagnetic sensors, ultrasound, X-ray, thermal, ...) are technologies that still continue their development today, started from the 50s. There is a growing requirement of both manufacturers and end-consumers for an increase in the quality of products, even in the case of large scale productions. The objective is that of obtaining a production which is able to have a complete control of possible defects; this result calls for an exhaustive verification: random inspections, performed by sampling the lot, show to be not adequate for the highest quality levels –even if are adopted sophisticated sampling plans-. The manufacturers move thus to the verification of the entire production by making use of “Online Non Destructive Testing” (Online NDT) techniques. Applying this latter approach to product inspection calls for accuracy, reliability, and the ability to process very large amount of raw data in each time unit (high throughput), in order to be effectively introduced in mass productions. S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 107–122. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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It is possible to apply this approach of Online NDT to a larger and larger variety of types of industrial productions due to the impressive growth, developed in the recent years, of the signal processing capacity. This increased processing power has improved greatly the potential of some of these techniques, enabling them to respond to requests for a constant improvement of efficiency of production processes. In particular this increase in the processing power has allowed the evolution in the direction of a control of the entire production, directly performed into the production line. To obtain useful information from the raw data detected by the sensors, highresolution imaging systems are needed. Moreover systems often have to operate in real time, with limited acquisition times and adverse conditions of signal to noise ratio. A typical example of this approach can be observed in metal industries where Online NDT techniques begins to be adopted even in the case of automotive products or for household appliances: inspection procedures at the end of the line and non subjective, which are capable of detecting both included and surface defects, are currently introduced; they rely on a set of measurements and computer aided decisions. The great complexity of an entire production line monitoring is necessary to consider carefully the project is the system hardware and software processing, optimizing the overall performance of the system. The report presented here, we propose the results of a collaborative work between 1) the Faculty of Engineering, University of Perugia – Campus of Terni, 2) the Faculty of Engineering at Politecnico of Torino and 3) AMC Instruments SrL, a spin-off of Politecnico of Torino. All the skills were complemented at their best: those needed for designing the system architecture, those needed for processing multidimensional data and the skills needed for the realization of a product already engineered for its possible use in a production system. This cooperative work enabled us to develop an applied research study that appears to have excellent potential application. The paper presented here describes the different aspects of this project, motivates the different choices and shows the results that we get by applying the resulting system to a number of particularly significant benchmarks. The paper is organized as follows: in the first part the concept of the system and the realization of the sensor are described. Then are described the interface and the software developed for the processing of the information. Finally, in the last section, are reported and commented on the results of some laboratory measurements for some cases of particular interest. The last section draws some conclusions and indicates the possible development prospects.

2 Concept The basic idea of this work is to develop an electronic system able to detect (and, if possible, characterize) fault present in an manufactured product (in this case is speaking about metallic sheet), using magnetic techniques. A system like this is made by three fundamental block: • • •

Sensing Head (or Sensor) Acquisition System Software (and processing platform)

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The sensor has the purpose of get the basic information, in the analog domain. This is an hardware block, that has mechanics and electronics components. The electronic components are sensors and other mixed-signal blocks (filters, amplifiers, muxs…). The acquisition system is made basically by an acquisition board, that has the task of convert the information from analog to digital domain, under some specification, in order to make the results usable. This is a digital block, that use programmable logic and A/D converters. Then we have the software, that use a standard support (desktop PC) in order to connect with the previous block. This logic is widely used in automatic measurement system, and is commonly known as PC-based measurement system. The use of a PC-based system reduce the complexity of the acquisition block (acquisition board and software), that in the other case must be designed ad-hoc. The software block has the purpose of use the digital information obtained form the two previous block, in order to provide the final output. This block make an extensive use of numerical algorithim, in order to correctly process the data and obtain easy readable information. Figure 1 shows a basic diagram of the measurement chain.

Fig. 1. Basic block diagram of the system

3 Sensor Topology The firs problem to solve is, of course, how to get the right initial information, or more specifically, how to get a signal (possibly an electrical signal in voltage) that is, somehow, proportional with the presence of a discontinuity in the metallic sheet. The idea is to create a constant magnetic flux orthogonal to the sheet itself. In this condition the presence of a discontinuity (given from a fault or an inclusion) in the material, generate a variation in the flux, displaying the presence of a fault. In order to make the information readable form a standard acquisition system, the signal must be converted from magnet to electrical domain. In order to do this, some Hall effect sensor can be used (the use of Hall sensor instead GMR or Coils will be clarified later on). So the typical setup of this system, use a permanent magnet, with its north pole orthogonal with the sheet and an array of sensor between the magnet and the sheet (see figure 2). This type of configuration allow to reveal this kind of lack: • Discontinuity (holes, abrasions, scratch…) of various type on magnetic plate • Non-magnetic inclusions on magnetic plate • Magnetic inclusions on non-magnetic plate

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Fig. 2. Setup of the sensor. Hall probe are placed between metallic sheet and PM.

Obviously isn’t possible to reveal discontinuity in non-magnetic plate (ex: aluminium plate). Magnetic inclusion in magnetic material can generate little flux variation. The possibility of reveal such variation depends on factor like resolution of sensor and background noise. It’s important to notice that this kind of system born modular, so it can be adapted to sheet of different dimension. So, in order to monitor a sheet of a given dimension (where the dimension is a multiple of the dimension of the sensing head) more sensor can be mounted in modular way (see figure 3).

Fig. 3. Example of a modular system

This is a fundamental aspect for system that, like this, are intended to be used for monitoring object during production cycle.

4 Technological Choiches and Specifications Starting from the idea discussed before, the following step was a proper design of the electronic board of sensing head.

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Fig. 4. Position of the Array in the reference frame

The sensing head should have the following specification (see figure 4): 1. 2. 3.

Should have a resolution of 1mm in the X direction (refer to figure 4) Should work with a sliding speed of the sheet up to 2m/s (typical on-field speed <= 1m/s) The output should be the “magnetic” image of the total length of the sheet, that is known, so no position sensor are needed

In order to follow this specification the sensor head must have some particular characteristics. First, to obtain a resolution of 1mm in X, each sensor must be read individually, so, since every channel of the acquisition system should read one sensing head, that is made by N sensor (256 in the typical configuration), a multiplexing stage is required. More, every “scan” should be performed as faster as we can, due to the sliding speed of the object in the real application, otherwise the resolution in the Y (refer again to figure 4) direction decrease. This condition can be achieved using an high performance acquisition board, able to operate up to the GS/s. The specification for the output means that this output should be easily readable, so printing a magnetic “image” of the object, where the intensity and colors give immediate information about presence of lack, can be an interesting choice. Based on the specification discussed before, the choice is to develop a sensing head made by two electronic board. The first board is the sensor board, that is populated with the sensor array and a firs multiplexing stage. The second board is the conditioning board that contains the second multiplexing stage and some conditioning electronics. The choice of use two multiplexing stage depend on the fact that the number of sensor per array achieve the hundreds of units, and become difficult to find a device with the proper number of input on the market. For the sensor array we used hall

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effect sensor because this kind of devices are less dependent on speed than coils and have advantages in cost, packaging and range with respect to GMRs that are suitable for different applications.

5 Schematic The prototype made for laboratory test use a sensing head with an array of 256 hall sensor. A group of suitable Hall sensors for this application is the A139x family form Allegro Microsystem. This devices are 3,3V powered hall sensor in a high integrated MLP/DFN-6 package. The use of an high integration package allow to build a sensing head that can reveal down to 1mm fault using just a double array of probe. The family include device of several sensitivity and Bsat (see table below): - A1391, sensitivity = 1.5mV/G, Vpp, Cbypass= 20 mV, Bsat=1050G - A1392 sensitivity = 2.5 mV/G, Vpp, Cbypass= 40 mV, Bsat=700G - A1393 sensitivity = 5 mV/G, Vpp, Cbypass= 40 mV, Bsat=350G - A1395 sensitivity = 10 mV/G, Vpp, Cbypass= 80 mV, Bsat=175G For the first prototype we used the A1391. Figure 5 shows the sensor schematic. Every sensor has its proper low-pass filter, that is a single pole resistive network with a cut frequency of 450 Hz.

Fig. 5. Sensor schematic. The sensor used is an Allegro 1391, in DFN-6 package.

The sensing area of the head is made by a double array of sensor, placed in the middle of the board. The choice of duplicate the array depends on the specification for the resolution on X: to achieve this resolution the only way is to use two shifted parallel array of probe. The output of the filter is directly connected to the mux network. The sensor board is populated with 8 ADG732 analog mux (every mux has 16+16 input channels and 2 output, so every device can be considerate like a 2 separate mux), that can collect all the 256 signal. Every mux of the sensor board is driven by the real clock frequency of the system (that depends on the sliding speed of the plate). The conditioning board collect the output of all the mux of the sensor board, and all the signal are routed to the final multiplexing stage (another ADG732), that output 2 signal corresponding to the relative probe at time. This mux is driven by a frequency that is 1/16 of the acquisition clock (the frequency division in achieved by a commercial timer/counter). Figure 6 illustrates the complete multiplexing procedure. Figure 7 show the connection between sensor and conditioning board.

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Fig. 6. Multiplexing stage

Fig. 7. Prototype and connection of sensor (up) and conditioning (down) board.

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6 Sensor Interface and Preliminary Tests Although the overall sensor is quite complex, the architecture adopted allows to easily interface it by means of few signals. Figure 9 sketches the sensor inputs and the outputs: beside the +5V required to supply all the active elements, the sensor is completely driven by two digital signals: a single pulse that resets the internal counter followed by a square wave that acts as a clock. The output voltages from the probes are collected into two output analog signals as illustrated in the previous sections. All the signals are processed by a single National Instruments Data Acquisition Card NIUSB 6259 while the hardware management and the data acquisition tasks have been carried out by exploiting the LabVIEW software.

Fig. 8. Schematic diagram of the sensor connections

In general, in order to faithfully retrieve information from all the probes, the ADC sampling rate fADC must be commensurable with the clock frequency fClk. To accomplish this aim in this work we have employed the Clock signal also as External Clock for the ADC conversion assuring the synchronization between switching of the multiplexers and sampling, fADC =fClk. This choice also implies the collection of only one sample for each probe during the sweep of the arrays. Furthermore the Clock square wave contextually acts as enable signal for the conversion process: indeed the number of samples acquired by the DAQ card for any analog input channel is equal to the number of cycles of the square wave. Of course by varying fClk also the resulting sample rate of the single probe fS varies linearly. In particular we had fClk =128×fS. At the same time, fS can not exceeds values that lead to a clock period comparable with the characteristic switching time of the muxes. We have carried out some preliminary tests to check the working conditions of the hardware and software components, some results are reported below. Figure 9 illustrates a typical turn-on time of the sensor reporting the time diagram of the all signals involved.. As expected, at the arrive of the reset pulse there is a discontinuity on the amplitude of the arrays output signals corresponding to the jump of the counter from a random to “0” value. Then, when the clock signal reaches the sensor, the muxes start to interrogate the probes.

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Fig. 9. Test on synchronization of the input and output signals of the sensor.

Fig. 10. Test on maximum achievable sampling rate with actual multiplexers

Figure 10 illustrates the limitations in the maximum sampling rate achievable by comparing the analog signal from one array for three different fS values. We note that for fS equal or greater than 15 kHz the settling time of the muxes is comparable with the clock period, thus hampering the right collection of the probes signals. We have also measured the effective magnetic field impinging on the different probes: in Figure 11 are reported the steady values of the probe voltages with the permanet magnet adopted in the experiments reported. Since the zero field value of the outputs is of about 1.65 V and the probe sensitivity is of 1.25mV/Gauss, we estimate a mean Bz component near to 400 Gauss at the probes positions.

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Once performed the functional tests of the sensor, we have connected it to a XY motorized scanner, also computer controlled by the Labview software and by the NIUSB6259 card. In this way we have been able to simulate the relative sliding of the metal sheet under the sensor at velocities comparable with the ones typical of the production and we have carried out some resolution tests on artificial defects. Special attention has been paid to synchronize the scanner motion with the clock generation, in order to have a constant resolution during each scan. Moreover the duration of the clock signal was automatically calculated in order to cover a desired sample length. By fixing the scanner velocity vSCAN at vSCAN =0.1 m/s we have performed different tests by varying the Clock rates in order to have fS in the range 0.5-2 kHz corresponding to a spatial sampling between 5 and 20 samples/mm.

Fig. 11. Steady state voltage value of the probes with the permanent magnet adopted (1st array Black, 2nd array - Grey)

Before discussing the results attained on artificial and real samples, let us firstly introduce some details on the signal processing steps implemented in order to reconstruct and enhance the images of the samples investigated.

7 Image Processing The sensor has been designed to work with both ferritic and austenitc steel sheets, i.e. ferromagnetic and paramagnetic materials respectively. In the latter case, the presence of defects consisting of ferromagnetic inclusions a well as locally changes in the crystallographic characteristic of the steels leads to very low variations of the magnetic field (δB/B < 10-3) thus leading to a voltage signal comparable with the noise introduced by electric devices and by the environment. (K. Szielasko, et al 2006, W. S. Singh et al 2008) At the same time, hardware processing on the multiplexer output signal is strongly limited since the information content coming from each probe must be preserved. Moreover the active filtering of each probe is quite onerous and with poor scaling

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properties. It is therefore necessary to perform some digital processing procedure to enhance the signal to noise ratio of the collected data. To gain insight into the image processing protocol developed, in this section we illustrate all the processing steps by referring to a benchmark sample. In particular, to test the resolution of the sensor we have realized a collection of holes with different diameters D into a ferromagnetic plate 4mm thick. The set consists of six through-holes with D=7,6,5,4,3,2 mm and a single blind-hole with D=1.5mm and 1mm of depth. Adopting the configuration previously described (sensor + XY scanner) , by specifying a scan length L, we attain at the end of the ADC process two rows of data, one for each array, containing a number of samples NS given by the expression NS=128×L×ρ where ρ=fS/vSCAN is the resolution, expressed in samples per millimetres, along the scan direction. By reshaping the two vectors we obtains two matrices, IM1 and IM2, corresponding to the magnetic images collected by the 1st and the 2nd array respectively. These images have a number of pixels, Nx×Ny, equal to: [Nx,Ny]=size(IM1,IM2)= NS=128×(L×ρ). For example, referring to the reference sample, we have chosen the following values: L=250mm, vSCAN=100mm/s, fS=2kHz → ρ= 20 samples/mm, Ny=5000 pixel while the effective lift-off between sample and probes was about 4-5mm.

Fig. 12. a) Photo of the benchmark sample; b) direct acquired “magnetic” image

Due to the different “steady-state” value of each probe (see Fig.11), the images IM1 and IM2 are not directly readable, as shown in Fig.12-b. An AC “software” coupling implemented for any probes separately allows to remove the lines pattern. Once carried out the AC coupling, the new images, IMAC1 and IMAC2, exhibit a more desirable aspect (Fig.13). Nevertheless, as clearly evidenced in the 3D surface plot, they yet suffer the contextual presence of a “Salt&Pepper” noisy component and of a slow background intensity variation. In the present case, for the larger through-holes, such noisy level is quite lower than the signal amplitude but it becomes comparable with the signal of the smaller holes, thus hampering its detection. In order to attenuate such components, we have implemented a 2D band-pass filter in the frequency domain. Indeed the background drift exhibits a spatial frequency spectrum characterized by low wave vector values while the “Salt&Pepper” noise is characterized by highfrequency oscillations.

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Fig. 13. Colormap and 3D surface of the AC-coupled image.

Fig. 14. Colormap and 3D surface of the image form one array after the 2D band-pass filter.

Fig. 15. Colormap and 3D surface of the overall image attained at the end of the processing.

By filtering out a proper range of wave-vectors values, we have significantly reduced these unwanted effects. The effect of the band-pass 2D filter is illustrated in Figure 14. At this step the images of both arrays have been successfully enhanced. The final processing task implemented concerns the increase of the image resolution. This goal is accomplished by :1) merging IMBP1 and IMBP2; 2) performing on the extended

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image the deconvolution respect than the 2D impulse response of a point-wise defect previously measured. In particular we have exploited the Wiener deconvolution filter in the frequency domain combined with the total variation regularization.(N. Wiener, 1949, Rudin 1992). Figure 15 summarizes the result of the overall processing protocol. We note that: 1) the resolution of the final image, IMTOT was effectively increased respect than the original images; 2) there is a good agreement between photographic and magnetic images; 3) even the blind-hole is clearly detected.

8 Experimental Results Once defined the processing protocol, we have applied it to several images attained from artificial and real samples, both ferritic and austenitic. In the following we report the result regarding few relevant cases. As first testing sample, we have replicated the sequences of through- and blindholes in a austenitic plate 4mm thick. Beside the sequences of aligned hole, we have added some small mechanical punchings near the holes, see Fig.16-a. The result of the scan is reported in Fig.16-b.

Fig. 16. Magnetic image attained for an austenitic plate with through-holes, blind-holes and mechanical punchings.

Due the paramagnetic feature of austenite, the field variations associated to the through-holes are some order of magnitude lower than for the ferritic case. This implied the reduction of the lift-off value as well as a higher attenuation of highfrequency image components. Nevertheless the sensor resolution also allows to detect

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the presence of holes. At the same the blind-holes and the mechanical punchings are clearly detected and visualized. We retain that such quite surprisingly behaviour should be due to the the local variation of the sheet magnetic properties induced by mechanical stress. (Vincent et al. 2005, Wilson et al. 2007)

Fig. 17. Magnetic image attained for a ferritic plate with scratches.

Fig. 18. Magnetic image attained for an austenitic plate with oxide-scales.

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Fig. 19. Magnetic image attained for an austenitic plate with marks.

9 Conclusions We have designed and realized a sensor able to scan the surface of steel sheets by performing a 2D magnetic image of the sample surface. This image is carried out by exploiting two arrays of Hall probes. An electronic circuit has been realized to easily interface and drive the arrays. Quite generally the data can be affected by several unwanted noises that could require a combination of different filtering and denoising approaches to be effective. In the present application, we combined 2D frequency domain filtering with Wiener deconvolution and Total Variation deblurring. Several alternative strategies can be pursued like wavelet denoising, principal component analysis, etc. (Truchetet and Laligant 2008) .The protocol has been applied to samples having defects with different geometric extension and metallurgic nature. In all the tested cases the algorithm have leaded to a good identification of the defects. In particular, also defects with extension of few mm2 can be detected with a satisfactory signal to noise ratio. Moreover the results obtained evidenced a good correspondence between “magnetic” and “true” image of the samples, therefore such technique could be a valid integration/alternative to standard vision inspection techniques, especially in applications where the environmental noise degrades the vision approach.

Acknowledgment L. Battaglini, P.Burrascano and M. Ricci acknowledge financial supporting by Fondazione Cassa di Risparmio di Terni e Narni.

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References Szielasko, K., et al.: High-Speed, High-Resolution Magnetic Flux Leakage Inspection of Large Flat Surfaces. In: Proceedings of the European Conference on NDT 2006 (2006) Singh, W.S., et al.: Detection of leakage magnetic flux from near-side and far-side defects in carbon steel plates using a giant magneto-resistive sensor. Meas. Sci. Technol. 19, 15702 (2008) Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Wiley, New York (1949) Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992) Vincent, A., et al.: Magnetic Barkhausen noise from strain-induced martensiteduring low cycle fatigue of 304L austenitic stainless steel. Acta Materialia 53, 4579–4591 (2005) Wilson, J.W., Tian, G.Y., Barrans, S.: Residual magnetic field sensing for stress measurement. Sensors and Actuators A 135, 381–387 (2007) Truchetet, F., Laligant, O.: Review of industrial applications of wavelet and multiresolutionbased signal and image processing. J. Electron. Imaging 17, 31102 (2008)

Gas Sensing Characteristics of Pure and ZnO-Modified Fe2O3 Thick Films N.K. Pawar1, D.D. Kajale2, G.E. Patil2, S.D. Shinde3, V.B. Gaikwad3, and Gotan H. Jain2,* 1

K.A.A.N.M.S. Arts, Commerce and Science College, Satana, India 2 Materials Research Lab., Arts, Commerce and Science College, Nandgaon 423 106, India [email protected] 3 Materials Research Lab., K.T.H.M. College, Nashik 422 005, India

Abstract. Thick films of AR grade Fe2O3 were prepared by screen-printing technique. The gas sensing performances of thick films were tested for various gases. It showed maximum response to H2S gas at 300oC for 200ppm.The films were surface modified by dipping them in a solution of 0.1M zinc acetate for different intervals of time. The modified Fe2O3 films showed enhancement in response to H2S gas at 100ppm than the response to pure Fe2O3 film at comparatively low temperature (200oC). The adsorption-desorption phenomenon is the function of surface misfits and surface morphology of the film. Zinc oxide increases the reactivity of film to H2S gas, suppressing the response to other gases. A systematic study of sensing performance indicates the key role-played by Zinc oxide on the surface. The sensitivity, selectivity, response and recovery time of the sensor were measured and presented. Keywords: Fe2O3 gas sensor, thick film, dipping technique, H2S gas sensor, Sensitivity, Selectivity.

1 Introduction Hydrogen sulphide gas is harmful to human body and environment. The threshold limit value (TLV) defined for H2S is 10 ppm. Human exposure to H2S gas at level higher than 250 ppm are likely to result in neurobehavioral toxicity and may even cause death[1]. Metal oxide semiconductor gas sensors have advantageous features such as high sensitivity under ambient conditions, low cost and simplicity in fabrication [2, 3]. The sensor performance can be improved by increasing the porosity .The fundamental sensing mechanism of metal oxide based gas sensors relies on change in electrical conductivity due to the interaction process between the surface complex such as O-, O2- , H+ , OH+ ,reactive chemical species and the gas molecules to be detected[4]. Semiconductor ZnO is sensitive to many sets of gases and has high satisfactory stability, but it has some disadvantages, such as high working temperature of 4000C–5000C; poor gas selectivity and comparatively low sensitivity. Furthermore, it has also been reported that the gas sensitivity and selectivity of semi conductive complex oxides can be *

Corresponding author.

S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 123–132. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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influenced by dipping or doping. Some workers discovered that complex oxides exhibit good sensitivity to reducing gases [5, 6]. α- Fe2O3 has non stoichiometric structure and free electron originating from oxygen vacancies contribute to electronic conductivity change when the composition of the surrounding atmosphere is attended. Metal oxide gas sensors are stable at high temperature [7]. Sensing principal is based on the change in the electrical resistance of semiconductor oxide films when specific gas interacts with its surface [8]. Nano Composite of SnO2/ Fe2O3 showed sensitivity to CO, H2S, NO2 and Ethanol [9]. Some reports say α- Fe2O3 form has been recognized as having minimal gas sensing response. ZnO is a very attractive material having a wide band gap. It is one of the most versatile and useful oxide because its typical properties e.g. resistivity control over the range 10-3 – 10–5 ohm cm, direct band gap, absence of toxicity, abundance in nature, high electrochemical stability. Recently, Sun et al. have found that Fe2O3 exhibited is sensitive to H2S based on the catalytic chemiluminescence at 360o C [10, 11]. However, the application of these sensors is limited by some disadvantages, for instance, poor selectivity, long response time, high operating temperature or the limited detection range. Recently, Fe2O3 has been proved to be an important solid state gas sensor. γ- Fe2O3 doped with Zn showed good response to acetone and ethanol (C2H5OH) [12]. αFe2O3 is an n-type semiconductor oxide and has been widely used as gas sensors. However, most of the researches on α- Fe2O3 sensors were focused on the sensing properties towards alcohol. In the present paper the Fe2O3 film was surface modified by ZnO and its performance for different gases was studied. The ZnO- Fe2O3 surface modified film enhanced the sensitivity to H2S gas at comparatively low temperature as compared to sensitivity to pure α-Fe2O3 film. The surface modified film showed maximum response to H2S gas at 2000C, where as the pure film showed maximum response to H2S at 3000C, as well as the selectivity was observed to be increased. Scanning electron microscopy was used to study the microstructure and surface morphology of the films. TGA/DTA was used to characterize the material.

2 Experimental 2.1 Preparation of Fe2O3 Thick Films The AR grade powder of Fe2O3 was calcined at 1000oC for 6h and further milled for 2h. The thixotropic paste was formulated by mixing the fine powder of Fe2O3 with a solution of ethyl cellulose (a temporary binder) in a mixture of organic solvents such as butyl cellulose, butyl carbitol acetate and terpinol etc. The ratio of the inorganic to organic part was kept at 75:25 in formulating the paste. This paste was screen printed on a glass substrate in a desired pattern [13]. The films were fired at 550oC for 30min. Silver contacts were made for electrical measurements. 2.2 Preparation of ZnO Surface Modified Fe2O3 Thick Films The modified Fe2O3 thick films were obtained by dipping pure Fe2O3 thick films in a 0.1M aqueous solution of Zinc acetate for different intervals of dipping time of 5, 10,

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20, 30 and 45min. These films were dried at 80oC, followed by firing at 550oC for 30min. These films so prepared are termed as ‘surface modified films’. 2.3 Details of Gas Sensing System Specially designed gas sensing system was used for sensing of various toxic gases [14]. The sensing setup consisted of glass dome vessel of 25 liter volume. The dome vessel was kept on circular metallic base. Inside the dome the film was mounted where the heating coil was fitted to maintain the temperature. There were electrical feeds through the base plate. The heater was fixed on the base plate to heat the sample under test up to required operating temperatures. A sample under test can be mounted on the heater. The Cr–Al thermocouple was mounted to measure the operating temperature. The output of the thermocouple was connected to a digital temperature indicator. A gas inlet valve was fitted at one of the ports of the base plate. The required gas concentration inside the static system was achieved by injecting a known volume of a test gas with a gas-injecting syringe. A constant voltage was applied to the sensor and the current was measured by digital piccoammeter. Air was allowed to pass into the glass chamber after every gas exposure cycle.

3 Characterization Results 3.1 Microstructural Analysis Scanning electron microscopic (SEM) studies were carried out by using JEOL 6300 (LA) Japan. Figure 1(a-b) depicts the SEM images of an unmodified and ZnOmodified (30min) Fe2O3 thick films fired at 550oC.

(a) Fig. 1. SEM images of: (a) Unmodified, and (b) ZnO-modified (30min) Fe2O3 thick films.

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(b) Fig. 1. (continued)

3.2 Elemental Analysis The elemental analysis of the films was carried out to know the constituent elements such as Fe, O and Zn associated with various films. It is clear from the Table 1 that the weight percentage of Zn increased with dipping time, reached to a maximum at dipping time 30min and decreased further increase in dipping time. The film with the dipping time of 30min was observed to be more oxygen deficient (22.07). This oxygen deficiency may make the sample possible to adsorb a large amount of oxygen species. Table 1. Elemental analysis of unmodified and ZnO modified Fe2O3 films.

Sample Unmodified Fe2O3 Dipping time (modified) 5 min 10 min 30 min 45 min

Fe (wt %) 77.36

O (wt %) 22.64

Zn (wt %) -

75.32 74.88 69.59 74.30

22.77 22.46 22.07 22.27

1.91 3.05 7.94 3.60

3.3 Thermal Stability of Pure and ZnO Modified Fe2O3 Thick Films Thermo gravimetric analysis (TGA) of pure and surface modified samples was carried out and their profiles are represented in figure 2. Thermo gravimetric analysis (TGA) of the films was conducted in air using Mettler Toledo Star System 851 at a heating rate of 10oCmin-1 in the temperature range from30oC to 900oC. Fig. 2 shows the TGA

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profiles of unmodified (pure) and surface modified (30min) ZnO-Fe2O3 thick films. Table 2 lists losses or gains in weight of these films observed during TGA in the different temperature ranges. It can be concluded from the profiles that the weight of pure sample continuously decreased up to temperature 550oC and then remained constant in the temperature range 550o C to 678oC. It shows smallest weight gain in the temperature range 678oC to 853oC. Surface modified Fe2O3 film was more stable than the pure Fe2O3. The variation in weight with temperature was comparatively a less weight loss and a more weight gain in the surface modified sample which can be attributed to the adsorbed oxygen content. The film with the high amount content of Zn (7.94 wt%) was observed to contain the small amount of oxygen (22.07 wt %, Table 1), which could be attributed to the largest deficiency of oxygen in the film. It is therefore quite possible that the material would adsorb the largest possible amount of oxygen, showing relatively larger gain in weight (7.79 wt%) in the temperature range of 375oC to 900oC. The zinc oxide on the surface modified sample would form misfit regions between the grains of Fe2O3 and could act as efficient catalyst for oxygenation.

Fig. 2. TGA profile of: pure, and ZnO -modified (30min) Fe2O3 thick films. Table 2. Thermal analysis of Unmodified Fe2O3 and surface modified Fe2O3.

Temperature

Unmodified Fe2O3 Loss

Gain

(wt %)

(wt %)

25 - 525

6.25

--

525-765

---

765- 875

--

oC

Temperature

Modified Fe2O3 Loss

Gain (wt

(wt %)

%)

25 - 95

---

0.4

---

95-145

---

---

1.2

145 - 875

--

15.6

oC

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3.4 Dependence of Electrical Conductivity on Temperature Fig. 3 represents the variation of conductivity with temperature for the pure and ZnO surface modified Fe2O3 samples. The conductivity varied nonlinearly with temperature for all samples, showing negative temperature coefficient.

1000/T(1/oK)

log(conductivity)mho/m

-2.3 1.38

1.58

1.78

-3.3 -4.3

1.98

2.18

2.38

2.58

pure

5min

10min

20min

30min

45min

-5.3 -6.3 -7.3

Fig. 3. Electrical profile of pure and modified Fe2O3 thick films

3.5 Measurement of Sensor Response Gas response of a sensor is defined as the ratio of the conductive change upon exposure to a test gas to the conductance in air

Where Gg and Ga are conductance of a sample in the presence and absence of a test gas respectively and ∆G is the change in conductance. Figure 4 (a) shows that the optimum operating temperature for pure Fe2O3 film is 300oC and Figure 4(b) shows histograms indicating the response values (to H2S gas) of the ZnO surface modified Fe2O3 films treated for different intervals of times. The maximum response was for the sample dipped for 30min. table 1 depicts that the 30 min. dipped sample has the highest 7.94 wt% of Zn. The response to H2S goes on increasing with the amount of Zn, attains its maximum at 7.94 wt% and decreases further. The largest sensitivity in case of the sample with 7.94 wt% of Zn (for 30min.) may be because of more available sites for the oxygen to be adsorbed and in turn to oxidize the test gas. However, the decrease in response may be due to the insufficient number of misfits available in the surface that acts as a catalyst.

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250

sensitivity

200 150 100 50 0 150

200

250

300

350

400

450

Temp. (0C)

(a)

sensitivity

500 pure 10min 30min

450 400 350 300

5min 20min 45min

250 200 150 100 50 0 150

200

250

300 350 Temp.(oC)

400

450

(b) Fig. 4. (a). Variation of H2S gas response to pure Fe2O3 film with operating temperature; (b). Variation in response to H2S gas with temperature for pure and modified Fe2O3 films

3.6 Microstructural Analysis Fig 5 shows the bar diagram indicating the selectivity of the ZnO surface modified Fe2O3 sensor operated at 200oC to H2S gas against CO, LPG, NH3, CO2, Cl2, O2, ethanol and H2 gases. It is evident from the figure that the sensor was highly selective to H2S gas against other gases. The high selectivity to H2S can be attributed to the surface modification of Fe2O3 films. 3.7 Response and Recovery Time Response and recovery times are basic parameters of gas sensors. The response time has been defined as the time taken to attain 90% of final value, and the recovery time as time taken to recover 90% of the original value. the response time and recovery time of surface modified ZnO-Fe2O3 thick film at 200oC are found 4sec. and 46 sec

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respectively i.e. the 90% response and recovery time were attained within 4sec and 46sec respectively. The very short response and moderate recovery times are the important features of the surface modified ZnO-Fe2O3 sensor.

500 400 300 200 100 0 H2 pure fe2o3

CO

CO2

NH3

LPG

O2

Cl2

Ethan H2S ol

0.023 -0.053 0.141 -0.139 6.692 0.21 0.125 1.389 21.34

ZnO- fe2O3 0.576 -0.006 0.262 0.185 0.189 0.433 0.214 0.272 441.3

Fig. 5. Comparison of gas response of ZnO surface modified film (30min) to different gases at 200OC.

4 Discussions The gas sensing properties of the pure and ZnO surface modified Fe2O3 were studied. It is well known that the gas sensing property is greatly influenced by the temperature. The TGA makes it clear that as the temperature changes the weight percentage of ZnO also changes. This change in the weight percentage may cause the change in the structure of the film that causes the corresponding change in sensitivity. In order to determine optimum operating temperature the additive amount of ZnO on the surface, selectivity to the various gases and the response of all the modified films at different dipping time interval were tested to various gases at different temperature. It is observed that the response of the sensors to the various gases varies with the change in temperature, dipping time, as well as concentration of ZnO on the surface of the film. The pure film showed maximum sensitivity at 3000C to H2S along with response to Ethanol. The surface modified film (dipping time 30min) enhanced the sensitivity (441) to H2S gas with suppressing the response to Ethanol at 2000C. The selectivity of the gas sensor is an important parameter for practical application of gas sensor. As compare to pure Fe2O3 sensor the surface modified film showed better selectivity and sensitivity at comparatively low temperature. 4.1 Adsorption of Oxygen When oxygen is adsorbed on the zinc zones of strong localization at elevated temperatures, the potential barrier between the Fe2O3 grains may be raised further and as a result the total resistance increases in comparison with the sample without zinc. It is known that abstraction of electrons from bulk Fe2O3 by the adsorbed oxygen results in

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the formation of surface states. The amount of oxygen adsorbed on the surface of ZnO-Fe2O3 film would be larger since zinc oxide would form misfit regions between the grains of Fe2O3 and would act as an efficient catalyst for oxygenation. 4.2 Desorption of Oxygen When reducing gas such as H2S adsorb between the grains of Fe2O3, the potential barrier would be decreased as a result of oxidative conversion of the H2S gas and desorption of oxygen. Due to desorption of adsorbed oxygen ions and conversion of zinc oxide into sulphides the resistance of the film would change abruptly leading to high response to H2S. 4.3 The Amount of Surface ZnO and Response to H2S The gas-sensing mechanism of α-Fe2O3 -based sensors belongs to the surfacecontrolled type, which is based on the change in conductance of the semiconductor. The oxygen adsorbed on the surface directly influences the conductance of the α-Fe2O3 based sensors. The amount of oxygen adsorbed on sensor surface depends on the operating temperature, particle size, and specific surface area of the sensor [15]. As mentioned in the earlier work of the semiconductor gas sensor gas sensing mechanism the state of oxygen on the surface of Fe2O3 sensor undergoes the following reaction O2 (gas) → O2 (ads)

(1)

O2 (ads) + e−→ O2− (ads)

(2)

O2− (ads) + e−→ 2O− (ads)

(3)

O− (ads) + e−→ O2− (ads)

(4)

The oxygen species capture electrons from the material, which results in the concentration changes of holes or electrons in the Fe2O3 semiconductor. When the sensor is exposed to H2S, the reductive gas reacts with the oxygen adsorbed on the sensor surface. Then the electrons are released back into the semiconductor, resulting in the change in electrical conductance of the Fe2O3 sensor. It can be expressed in the following reaction H2S + 3O2−→ H2O + SO2 +6e−

(5)

For the Fe2O3 sensors, the low response at low operating temperature can be attributed to the low thermal energy of the gas molecules, which is not enough to react with the surface adsorbed oxygen species. As a result, the reaction rate between them is essentially low and low response is observed [15, 16].

5 Conclusions Pure Fe2O3 and surface modified ZnO- Fe2O3 thick film gas sensors were prepared by screen printing technique. The pure Fe2O3 film showed maximum selectivity (189) to H2S gas at 300OC along with the selectivity (13.15) to Ethanol at same temperature. The surface modified film showed enhancement in gas response. The ZnO- Fe2O3

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(30min dipped) film showed enhancement in response to H2S gas. The selectivity to H2S gas was 441 at 200oC. The notable change in selectivity response to H2S is enhancement in selectivity to H2S gas at comparatively lower temperature 200oC. The modified film completely suppressed the response to ethanol. Compared to pure Fe2O3 gas sensor the ZnO surface modified Fe2O3 gas sensor seems to be a more promising gas sensor at lower temperature 200oC for detecting H2S gas due to its higher selectivity and sensitivity, as well as shorter response and recovery time.

References 1. Struve, M.F., Brisbios, J.N., James, R.A., Marshall, M.W., Dorman, D.C.: Neurotoxicological Effects Associated with Short-Term Exposure of Sprague-Dawley Rats to Hydrogen Sulfide. Neurotoxicology 22(3), 375–385 (2001) 2. Bendahan, M., Boulamani, R., Seguin, J.L., Aguir, K.: Characterization of ozone sensors based on WO3 reactively sputtered films: influence of O2 concentration in the sputtering gas, and working temperature. Sensors and Actuators B 100, 320–324 (2004) 3. Hofer, U., Frank, J., Fleisscher, M.: High temperature Ga2O3-gas sensors and SnO2-gas sensors: a comparison. Sensors and Actuators B 72, 6–11 (2001) 4. Yamazoe, N.: New approaches for improving semiconductor gas sensors. Sensors and Actuators B 5, 7 (1991) 5. Tianshu, Z., Ruifang, Z., Yusheng, S., Xinquin, L.: Synthesis and gas-sensing characteristics of high thermostability -Fe2O3 powder. Sensors and Actuators B 32, 181–184 (1996) 6. Li, B., Yue, Z., Li, L., Zhou, J.: Low-fired microwave dielectrics in ZnO-TiO2 ceramics doped with CuO and B2O3. Journal of Material Sci. 13(7), 415–418 (2002) 7. Fleischer, M., Meixner, H.: Fast gas sensors based on metal oxides which are stable at high temperatures. Sensors and Actuators B 43, 1 (1997) 8. Madou, M.J., Morrison, S.R.: Chemical sensing with solid state devices, p. 431. Acedemic Press, San Diego (1999) 9. Rumyantseva, M., Kovalenko, V., Gaskov, A., Makshina, E., Yuschenko, V., Ivanova, I., Ponzoni, A., et al.: Nanocomposites SnO2/Fe2O3: Sensor and catalytic properties. Sensors and Actuators B 118, 208–214 (2005) 10. Zhang, Z.Y., Jiang, H.J., Xing, Z., Zhang, X.R.: A highly selective chemiluminescent H2S sensor. Sensors and Actuators B 1021, 55–161 (2004) 11. Sun, Z.Y., Yuan, H.Q., Liu, Z.M., Han, B.X., Zhang, X.R.: A highly efficient chemical sensor material for H2S: α-Fe2O3 nanotubes fabricated using carbon nanotube template. Adv. Mater. 17, 2993–2997 (2005) 12. Jing, Z.: Synthesis, characterization and gas sensing properties of undoped and Zn doped γ-Fe2O3 based gas sensors. Material Science and Engineering A 441, 176–180 (2006) 13. Jain, G.H., Gaikwad, V.B., Patil, L.A.: Studies on gas sensing performance of (Ba0.8Sr0.2)(Sn0.8Ti0.2)O3 thick film resistors. Sensors and Actuators B 122, 605–612 (2007) 14. Jain, G.H., Patil, L.A., Wagh, M.S., Patil, D.R., Patil, S.A., Amalnerkar, D.P.: Surface modified BaTiO3 thick film resistors as H2S gas sensors. Sens. Actuators B 117, 159–165 (2006) 15. Aroutiounian, V.M., Aghababian, G.S.: The theory of semiconductor gas sensors. Sens. Actuators B 50, 80–84 (1998) 16. Chang, J.F., Kuo, H.H., Leu, I.C., Hon, M.H.: The effects of thickness and operation temperature on ZnO:Al thin film CO gas sensor. Sens. Actuators B 84, 258–264 (2002)

Design and Construction of a Configurable Full-Field Range Imaging System for Mobile Robotic Applications D.A. Carnegie1, J.R.K. McClymont1, A.P.P. Jongenelen1, B. Drayton1, A.A. Dorrington2, and A.D. Payne2 1 School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand 2 Department of Engineering, University of Waikato, Hamilton, New Zealand

Abstract. Mobile robotic devices rely critically on extrospection sensors to determine the range to objects in the robot’s operating environment. This provides the robot with the ability both to navigate safely around obstacles and to map its environment and hence facilitate path planning and navigation. There is a requirement for a full-field range imaging system that can determine the range to any obstacle in a camera lens’ field of view accurately and in real-time. This paper details the development of a portable full-field ranging system whose bench-top version has demonstrated sub-millimetre precision. However, this precision required non-real-time acquisition rates and expensive hardware. By iterative replacement of components, a portable, modular and inexpensive version of this full-field ranger has been constructed, capable of real-time operation with some (user-defined) trade-off with precision.

1 Introduction Mobile robotic devices are critically dependent upon extrospective sensors in order to be able to operate in an environment. Specifically, accurate range finding is required in order to be able to negotiate around obstacles, map the environment and implement efficient path planning. Such ranging needs to not only be accurate, but also able to be acquired in real-time with a high field-of-view coverage. Infrared ranging is often used for close-range obstacle avoidance and has become increasingly popular with the development of the inexpensive Sharp GP2 range of analogue and digital detectors [1]. However, such sensors are limited in range and are seldom reliable past 3 metres or so. Ultrasonic techniques are often employed when ranging to ~10 metres (dependent upon the carrier frequency) however they suffer significantly from the broadness of the transmitted beam, exacerbated by the comparative difficulty in focussing an acoustic rather than an EM wave. Subsequently for this technique to be of use in mobile robotic ranging and mapping, considerable software processing is often required to counter the multiple path reflections. Furthermore, because of the inherent dead-band, ultrasound must often be augmented by an IR sensor for close range obstacle detection. The limited range of these techniques is a significant impediment to mobile operation in a large open environment, and hence laser-based ranging using a projected S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 133–155. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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dot or line is often employed. Sick [2] (for example) are a major supplier of such laser based systems and produce a variety of devices with fields of view ranging from 70° through to 360°. The wider fields of view are achieved at the expense of lower acquisition rates or significantly greater cost. Whilst precision to the millimetre level is obtainable, this is normally only achievable if range is significantly compromised. Consequently, laser scanning systems that provide an acceptable compromise between long range, good precision, wide field of few and low acquisition times are typically extremely expensive and outside the budget of many mobile robot projects. Certainly this expense often precludes their incorporation into commercial devices. A niche exists for the development of a ranging system that provides a wide field of view, high precision, and a short acquisition time. Furthermore, such a system would ideally be able to alter its field of view simply by changing the lens or incorporating a telephoto system adjustable by the user (or autonomously by the robot) during range acquisition. These requirements can be satisfied by a Full-Field Range Imaging System (FFRIS), whereby the range to every object in a field of view (determined by an optical lens) can be obtained simultaneously. A specific requirement by the authors is to obtain a FFRIS that can be mounted on robots in their mobile robotic fleet, three of which are pictured in Figure 1. Ideally this FFRIS could be used in both an indoor and an outdoor environment.

Fig. 1. Robotic platforms requiring extrospective sensors, (a) MARVIN an indoor security robot, (b) two robots designed for urban search and rescue applications

The authors believe it is instructive to relate the development of our successful FFRIS in terms of the successive design iterations that addressed particular problems or limitations that were identified at each stage in the development process. We hope that such a description will aid the reader in appreciating the problems inherent in the development of full-field ranging systems and hence understand the justification of

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the techniques and components selected for this particular implementation. In the process, we hope that the reader gains an added appreciation of full-field ranging techniques, image capture devices, high frequency modulatable illumination sources and drivers, FPGAs as embedded control systems, external memory requirements and interfacing, and power supply issues. This list is too exhaustive to cover any one topic in much detail given these space constraints, but the integration of these into a complete functional system forms the basis of this work. To begin the discussion, the next section will outline the theory behind FFRIS operation. Section 3 details the requirements of a FFRIS that would make such a device useful for mobile robotics. Section 4 discusses an initial design solution that successfully implemented full-field ranging but could not be employed on mobile robots because of its cost, bulk, power requirements and long acquisition times. Section 5 provides a solution to many of these problems, but still resulted in a system that had to be bench-mounted. The successful device is presented in detail in section 6, where a modular, robust, compact and inexpensive FFRIS is constructed. The chapter concludes with the results of this new device.

2 Principles of Full-Field Image Ranging Many techniques exist for determining the distance to an object using an optical source, including: • • • •

passive stereo and photogrammetry, active triangulation and structured light, interferometry, direct and indirect time-of-flight (ToF)

Space does not permit a full discussion of the merits of each of these techniques and the reader is referred to numerous books and articles which review these techniques. The FFRIS utilises an indirect time-of-flight measurement technique over a field of view determined by the optics of a standard camera lens and therefore the following discussion is limited to an introduction of the ToF method. The images presented in Figures 2 to 4 below are extracted from [4]. A direct time-of-flight system records the time for a pulse of light to be emitted, reflected off an object and returned to a receiver placed adjacent to the illumination source as illustrated in Figure 2. These systems can be point, line, or full-field scanning or full-field non-scanning depending upon the configuration of the source and sensor. Distance d is simply determined by

d=

ct 2

Equation 1

where t is the total flight time and c is the speed of light. A significant problem with such systems is that the distance resolution is critically dependent on the resolution of the timer. For example, to obtain 1 cm distance

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resolution the timer must be able to resolve the returned pulse to within 66 ps. The expense and complexity of the electronics required to achieve this resolution renders such systems of questionable value for most range determination applications.

Fig. 2. Principle of distance determination using direct time-of-flight.

To overcome the high bandwidth requirements of the direct ToF method, a number of techniques exist that encode the distance information indirectly, often through amplitude or phase. For example in [4] a light source and a high-speed shutter are pulsed at the same frequency as illustrated in Figure 3. There will be a delay in the light reaching the shutter that is proportional to the object’s distance. As indicated in Figure 4, objects that are nearer to the illumination/shutter system will have a higher proportion of the reflected pulse pass into the image sensor than objects that are more distant. The intensity of the received light can therefore be used as an indicator of object distance. However this intensity may also be affected by changes in object orientation, reflectivity and ambient lighting, and these effects need to be eliminated by appropriate system normalisation. An alternative technique is a heterodyne approach where intensity modulated light at frequency fm is projected onto a scene and the receiving camera shutter is modulated at a slightly different frequency fs = fm + δ. The modulation frequency fm is in the range 10 MHz –100 MHz, and δ is typically 1 – 5 Hz. These signals are effectively mixed and integrated by the camera system to produce a beat signal of frequency δ. The light received at the camera has a phase offset of the modulation envelope introduced due to the time taken for the light to travel twice the distance from the imaging system to the object. Calculating distance then becomes an issue of accurately determining the phase offset φ of the signal at the beat frequency δ. Distance is related to the phase offset by:

Design and Construction of a Configurable Full-Field Range Imaging System

d=

φ c 4π f m

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Equation 2 .

The phase offset φ can be determined by a variety of techniques including the Discrete Fourier Transform and an inner-product method. Space constraints restrict a more detailed review of the theory and implementation of full-field ranging systems. The interested reader is referred to reviews of such systems in [5] or the more detailed descriptions of the techniques we employ in [6-10] or further theoretical treatment in [11].

Fig. 3. Principle of distance determination using direct time-of-flight.

Fig. 4. Principle of distance determination using direct time-of-flight.

FFRIS devices currently on the market include systems developed by PMDTechnologies [12], Mesa Imaging [13] and Cantesa [14], with two typical examples illustrated in Figure 5. Our bench-top system has demonstrated several advantages over the offerings from either of these manufacturers particularly in terms of configurability and versatility. This will be explained in subsequent sections.

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Fig. 5. (Left) Cam Cube 3-D Range Imaging Camera from PMDTechnologies [12] (Right) SR4000 Range Imaging Camera from Mesa Imaging [13]

3 Full Field Range Imaging System Requirements As discussed in the introduction, a full-field ranging system is of significant potential use as an extrospective sensor for mobile robots. In order for such a system to implement active ranging using either a homodyne or heterodyne approach, the system must possess the following: • • • • • •

The ability to precisely modulate an illumination source (preferably at frequencies in excess of 10 MHz) A sensor to image the intensity of the received illumination The ability to precisely modulate a “shutter”. This will control the timing (and hence the intensity) of light entering the sensor The ability to precisely frequency lock the shutter and illumination modulation The ability to process the received frames to determine the phase (that encodes the distance) An intelligible output of the range to objects within the device’s field of view

Furthermore, it is desirable that the FFRIS also contains: •

•

The ability to alter the modulation frequencies (both shutter and illumination). This provides the user with the ability to experiment with different configurations. The authors have found this to be invaluable for determining the frequencies that produce the best precision results [15,16]. It would be additionally useful if these frequencies could be changed “on the fly”, in other words be configurable without requiring the downloading of new code. The ability to alter the illumination intensity. This permits the range of the system to be increased if required (assuming the user has taken precautions regarding eye safety).

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• •

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An embedded processor so that the device can be operated independent of a host PC. Circuitry to protect the illumination source from over-current or overheating (which would shorten the lifetime of the illumination source).

Finally, for the device to be useful as an extrospective sensor on a mobile robot, the device should preferably be all of: • • • •

Inexpensive. Due to the low quantity of production this requires use of offthe-shelf components Portable. We require the system to be able to run without the need of mains power or attached PC Low – or at least variable – acquisition time. The system must be able to operate in real-time even if this incurs a loss of precision. Configurable. The system should be configurable for use over a variety of ranges, lighting conditions, object reflectivities and required spatial and range resolutions

Our solution, as mentioned in the introduction, is the result of several development iterations. We initially demonstrated the proof-of-concept of full-field range imaging using a heterodyne technique, and then iteratively solved limitations and problems until arriving at our final design solution that is suitable for mounting on a mobile robot.

4 Initial Design Proof of concept of the principle of operation was first obtained using a heterodyne approach [6]. This system utilised AD9852 direct digital synthesizer (DDS) ICs interfaced to a reprogrammable microcontroller to provide the shutter and illumination modulation frequencies. This DDS is a 300 Mega-Samples Per Second (MSPS) device with high frequency resolution provided by a software programmable 48 bit tuning word. The illumination was provided by red Agilent HLMP EL series LEDs that could be successfully driven at up to 20 MHz, and the shutter modulation was provided by a photo-cathode modulated Photek MCP125 Image Intensifier connected to a standard 80 – 200 mm focal length Nikon F-mount zoom lens and an 8-bit monochrome Pulnix TM9701 camera. This system successfully produced images oscillating at the beat frequency (the difference of the shutter and illumination modulation frequencies, i.e. a heterodyne technique), and a DFT in time was applied to each pixel to provide an estimate of the phase that represents time-of-flight from which the range was determined. This device, operating at 10 MHz and beat frequencies of 1, 2 and 5 Hz, was successful in producing a highly linear response with a standard deviation of ~ 1 centimetre for objects ranging from 1 metre to 5 metre distance. However, there was a systemic offset error, and the 8-bit limitation of the camera system dramatically

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depreciated the system’s performance. Furthermore, the low modulation frequency also impacted performance as (from Equation 2) the range precision is proportional to the modulation wavelength. 4.1 Revised Design For the next iteration of the design [7], the Pulnix camera was replaced by a 12 bit Pantera TF 1M60 capable of 100 Hz operation at 512 × 512 resolution (i.e. 2 × 2 binning mode) or 200 Hz operation at 128 × 128 resolution (i.e. 8 × 8 binning mode). The image intensifier was replaced by a custom ordered Photek model that had a response almost an order of magnitude faster – the photocathode being capable of being gated down to 1 ns. The slow response LEDs were replaced with four 80 mW Mitsubishi ML120G21 laser-diodes that have a potential modulation frequency of the order of 100 MHz, and the higher speed (400 MSPS) DDS chips, AD9952 were employed to generate the modulation signals up to 200 MHz with a resolution of 0.093 Hz (from the 32 bit tuning word – lower than the AD9852 but still adequate for this application). In practice, the maximum useable modulation frequency of this improved system was limited to 90 MHz. Sub millimetre precision was obtained at a modulation frequency of 35 MHz, with precision peaking at approximately 0.6 mm at 65 MHz. This system clearly demonstrated the success of our proof of concept. Subsequent improvements [8] included the introduction of an FPGA to provide laser level control and intensifier gain control. The FPGA was also configured to generate a shuttering signal to gate the photocathode of the intensifier to ensure it turned off during CCD frame transfer in order to limit image smear. For the phase determination, it was now possible to arrange the drive signals so that the beat signal falls completely within a frequency bin. Hence an inner-product approach could be used in place of the previous DFT. This algorithm was almost an order of magnitude faster and provided essentially the same phase resolution. This improved system could obtain 0.5 mm resolution for acquisition times > 22 seconds, but even at 10 seconds, a precision of 0.6 mm could be obtained. Employing the 8 × 8 binning, the system could generate range images between 15.7 and 68.5 Hz with a best precision of 2.5 mm [15]. The penalty for this however, was significantly reduced spatial resolution. The primary remaining acquisition time limitation was the employment of the digital camera attached to the image intensifier. Affordable cameras at the time possessed a slow frames-per-second acquisition rate, typically 50 Hz to 100 Hz, with higher rates only possible with pixel binning. This system was also severely limited by the utilisation of the image intensifier. This device was costly (~6000 euro) and bulky as can be seen in Figure 6, where the image intensifier is the tube-like device attached to the camera. Furthermore, the intensifier requires three independent voltages, one of the order of -50 to +10 V, another ~700 V, and the third at a level of approximately 6 kV (note the presence of the high voltage power supplies in the foreground of Figure 6). Finally, the range processing was performed off-line by an externally interfaced PC. So whilst successful, this form of the FFRIS was limited to being a bench-top device.

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Fig. 6. Original FFRIS configuration featuring image intensifier and high-voltage external power supply

5 Real-Time Capable Design As mentioned, the primary limitations of the previous systems were the bulk and expense of the image intensifier and the limitations in frame acquisition imposed by the requirement for a commercial digital camera. The next iteration in the system development [9] replaced the Image Intensifier and the digital camera with a PMDTechnologies PMD19k-2 image sensor chip. This CMOS based PMD sensor permits the gain modulation of the imaging pixels to be controlled on-chip, effectively replacing the shuttering function previously performed by the image intensifier and the image acquisition function of the camera. This vastly reduced the bulk and power requirements of the FFRIS, rendering the system potentially portable. Furthermore the component costs of the system were reduced by over 80%! One disadvantage however, is that the pixel array sizes of these chips are currently limited – although significant improvements are expected in the near future. At this point, the decision was made to produce a semi-modular system comprising • • • •

an FPGA development board (based on the Stratix III), an Illumination board containing the laser diodes and driver circuitry, an Imaging Sensor board (with the sensor and lens attached as a daughter board), a VGA/Ethernet board for VGA output display and interfacing to a PC.

This is illustrated in Figure 7 [9]. Note the circular arrangement of the illumination diodes on the right. This semi-modular system provided the capability to upgrade or change one of the boards without necessitating a full system redesign, for example the sensor or illumination diodes could be exchanged with little effect on the remaining hardware.

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Fig. 7. System architecture of modular bench-top full-field imaging system

In the previous design iteration an FPGA controlled the gain of the laser diodes and produced a shuttering signal. Now that an FPGA was embedded in the system, the capability existed to utilise it to generate the modulation frequencies and hence eliminate the requirement of employing the DDS chips. This significantly simplified the FFRIS board design. Furthermore, by choosing an FPGA with sufficient capacity, the received frames could be stored and processed on-chip to determine the phase and hence the range to objects in those frames. Eliminating the requirement for an external PC is a significant improvement for mobile robotic applications. A Stratix III Development kit hosting an Altera Stratix III EP3SL150 FPGA was employed. This FPGA was chosen primarily for its easily reconfigurable phase-locked-loop resources which provide the ability to reconfigure the phase, frequency and duty cycle of the output channels in real time. This FPGA also has enough on-chip logic and memory resources to buffer the frames and hence calculate the full-field range images in real-time. The FPGA now incorporated [9] (again change with previous xxx) the tasks of: • • • • •

Driving the modulation signals Controlling data retrieval from the sensor Calculating range images Timing and control of signals for VGA display of range image data Handling JTAG interface and Ethernet connections to a PC in order to receive and process user commands or transferring data for long-term storage

The Illumination board comprised independently driven infrared (808 nm) and visible red (658 nm) laser diodes employed in two independent banks of 8. These diodes were driven in a controlled current configuration (by the FPGA) with a continuous

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total optical output power of 800 mW. Lasers of different wavelength were employed to investigate how their different reflectivities affected the performance of the FFRIS. Extra care is required in using the IR lasers however, since eye damage can easily result due to the beam being undetectable by the unassisted retina, preventing a natural blink reflex. Parallel work by the authors (for example [15,17]) provided a theoretical framework to select optimum frequencies within the achievable system bandwidth. A problem encountered is that range ambiguity occurs if the phase shift exceeds 2π and is a significant issue as the modulation frequency is increased. The maximum unambiguous range du is inversely related to the modulation frequency fm as:

du =

c 2 fm

Equation 3

Hence for a 10 MHz modulation frequency ranges up to 15 metres can be resolved unambiguously, but at 60 MHz, this reduces to 2.5 metres. In other words, at this modulation frequency the system would not be able to resolve the difference between an object located at 3 metres distance from one located at 5.5 metres. This problem has been resolved [16,18] by employing two modulation frequencies simultaneously, however a discussion of this is outside the scope of this chapter. The PMD 19K-2 3D Video Sensor Array from PhotonICs is employed as the imaging sensor. This sensor features a 160 × 120 array of pixels grouped into four independently modulated blocks of 40 × 120 pixels. Each modulation block presents a capacitive load of 250 pF at the driver interface. To drive these blocks at modulation frequencies of 10 MHz and above, ultra high current pin drivers (EL7158 from Intersil) are employed.

Fig. 8. Assembled FFRIS utilising Stratix III FPGA development board

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For this particular sensor, a rise and fall time of 12 ns can be achieved, limiting the maximum modulation frequency (without skew) to 41 MHz. Maximum contrast is achieved with a modulation input of +2.5 V. At a modulation frequency of 41 MHz, the imaging sensor’s modulation inputs have an impedance of approximately 15.5 Ω each and so the current draw per modulation input is 160 mA, with a worst case total current draw of 640 mA. The complete assembled system is illustrated in Figure 8.

6 Portable Version The system described so far demonstrates many of the requirements that we wish for our final system, however its size is constrained by the large physical dimensions (210×180×70 mm) of the Stratix III development board as can be appreciated in Figure 8. For the portable version, suitable for mounting on a mobile robot, the modular construction of the previous design is retained to allow a versatile system since it can be expected that the FPGA will be replaced in the future with a more powerful version, the illumination diodes may be exchanged for different wavelengths, bandwidth or intensity, and certainly advances are expected in sensor capacity. The new system has the arrangement of Figure 9 [19] which illustrates how the four boards sandwich against each other to minimise physical dimensions. The boards are separated and attached to each other using M3 PCB standoffs. Physical sizing is determined in the first instance by the circular cut-out for the Illumination board. This must have a minimum diameter of 35 mm to accommodate the optical lens (diameter 30 mm). The laser diodes of the Illumination board have an 8 mm diameter, and to accommodate 16 requires them to be centred on a circle with a minimum diameter of 43 mm. To accommodate the on-board voltage regulation and laser driving circuitry, and the 6 mm diameter of the standoffs, a board design of 100 mm square is required (considerably smaller than the dimensions of the Stratix III development board). The system is designed to be generic and flexible in terms of the components it will tolerate. The device is designed to meet the processing requirements of a 1 Mpixel image although we expect it to be some time before such a sensor becomes inexpensively available. Similarly, the current ratings for the components are overspecified which has obvious implications for the power source. Again this is done deliberately to accommodate circuit additions in the future if required. The following sections detail the function of each board and a brief description of how this was implemented. Communication between the boards is facilitated by a generic I/O interface that carries 17 differential transmit lines, 17 differential receive lines, a +3.3 V rail spread across 20 pins, a +12 V rail spread across 19 pins, 4 single ended I/O lines, and 4 JTAG lines (TDO, TDI, TMS, TCK). This is implemented with a 172 pin high speed mezzanine male connector. The form of this connector is modelled on the external interfaces provided on the Stratix III development kit.

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Fig. 9. System architecture of portable modular full-field imaging system

6.1 FPGA Board This board is the “heart” and “brains” of the entire system. It is configured to internally contain the NIOS processor which performs many of the system control function. The FPGA board connects to the image sensing/capture board via one of the two generic digital I/O ports and via these ports controls the image capture and illumination processes. The FPGA board also connects to the External Interface board to facilitate the FPGA sending captured images to peripheral devices such as a PC or VGA monitor. The FPGA selected has to have sufficient I/O lines to access the generic I/O ports and to interface to external RAM. As discussed, the prototype version utilised the Altera Stratix III family of FPGAs. For this portable implementation, the Altera Cyclone III EP3C40 is preferred due to the substantially lower cost. This version of the Cyclone family contains 39,600 logic elements, 4 PLLs, ~ 1 Mbit RAM, and 535 User I/Os. This is deemed sufficient for our initial purposes, but should a future iteration require additional processing of the raw images, then the EP3C120 contains three times the number of logic elements and RAM and provides for an easy upgrade path (pins are mostly identical). A 50 MHz oscillator is employed as the system clock. The design of the full-field range imaging system utilises four discrete blocks of memory for processing and storage of images. The prototype system implements the RAM required for image processing on the FPGA’s internal 5499 Kbit static random access memory. This is sufficient for storage and processing of image frames from the 160 × 120 (19K2) pixel PMD sensor but not for higher resolution sensors and so provision for external memory must be provided.

Fig. 10. Block diagram of FPGA board

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6.2 Memory Requirements Five different types of memory are required for this system: • • • • •

Accumulator – required for the storage of images during image processing Output buffer – stores the images to be accessed by the NIOS processor, the VGA output and/or the Ethernet output NIOS program – stores the firmware of the NIOS processor NIOS Ethernet frame buffer – required to store image frames to output to a peripheral computer Flash FPGA configuration memory – stores the FPGA configuration to be loaded at start up.

In determining the choice of memory to satisfy the above requirements, it is important to note that the Cyclone III family supports several high speed external memory interfaces to allow external memory to be connected to the FPGA with little customisation of hardware or software. These supported memory devices include DDR, DDR2 and SDR SDRAM. DDR2 SDRAM is preferred over the older memory types for this application. The accumulator is used to store the accumulated real and imaginary terms (up to 16 bits each) for each pixel needed to calculate the phase [20]. Hence for a 1 Mpixel image this would require 4 MB. For an image processing routine operating at a clock frequency of 10 MHz, the accumulator must operate at a data rate of 320 MBit/s (32 bits/pixel × 10 MHz). The accumulator must read and write a pixel value each time it performs a mathematical operation. This increases the required data rate of the accumulator to at least 640 MBit/s. Depending upon the application, the output buffer may be required to hold between 1 and 4 frames at 16 bits per frame. An application that only requires the distance information would simply require 1 frame, if amplitude was also required then 2 frames would be necessary and if the raw pixel data is needed then all 4 frames would need to be stored. Hence for a 1 Mpixel frame, in the worst case, 8 Mbytes are required. This output buffer is accessed by the NIOS processor, the VGA output process and the image processing process. All three accessing processes are clocked at 10 MHz and so the memory must be able to transfer data to each process at a speed of 320 Mbit/s. Time multiplexing is utilised to handle the three accessing processes. This increases the required data transfer speed to 960 Mbit/s (320 Mbit/s by each process). For the accumulator and output buffer, the Micron MT47H64M8 DDR2 SDRAM is selected. This device has a memory size of 512 Mbit and operates at a clock frequency of 333 MHz. It has an 8-bit wide data bus allowing data transfer rates of 2.7 Gbit/s. For the NIOS program, the Micron MT47H32M16 DDR2 SDRAM is selected, effectively being a 16 bit variant of the H64M8 above. The NIOS Ethernet buffer RAM stores frames that are to be output to a peripheral computer via the Ethernet connection. We have designed 1 Gbyte of memory, sufficient to buffer 256 1 Mpixel output frames. This is implemented using the M47H512M8 DDR2 SDRAM which has a memory size of 4 Gbit and operates at a frequency of 333 MHz. It has an 8-bit wide data bus allowing data transfer rates of

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2.7 Gbit/s. Two of these devices operating in parallel increase the memory density to 8 Gbit (1 Gbyte) and double the data transfer rate to 5.3 Gbit/s. The Flash configuration is 64 Mbyte which is deemed to be easily sufficient for any current or future sized configuration files. 6.3 Illumination Circuit Board As explained previously, 16 laser diodes are incorporated into the Illumination board and are chosen to be the 130 mW CWML101J27 Mitsubishi devices, operating at 660 nm. These are driven by the iC-HK 155 MHz laser switches to provide up to 150 mA continuous current or 700 mA maximum pulsed current. A block diagram representation of this board is provided in Figure 10. It is important at start-up that the diode current be gradually increased to allow the laser to reach a steady operating temperature since at low temperatures the high currents could potentially cause catastrophic optical damage. The diode current in earlier iterations of the board was controlled by an on-board microcontroller. This functionality is now undertaken by the FPGA, and the diodes are modulated from the FPGA via a control signal marked in Figure 11 in the form of a Two-Wire Interface TWI bus. This control signal feeds into a digital to analogue convertor (DAC) (AD5311 from Analog Devices) and then to the laser switch. A protection circuit has also been included to switch off the laser diodes if the control or modulation signals from FPGA board become disconnected from the Laser Illumination board.

Fig. 11. Block diagram for Illuminator Board

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A DIP switch allows the user to select between running the laser diodes at maximum power or half power. The half power option operates the laser diodes at a safer optical intensity when output power is not a critical consideration for image ranging a scene. 6.4 Image Sensing and Capture Board This board connects to the FPGA via the generic digital interface. This board handles all the digitising of the captured images from the sensor and all the signal modifications to drive the image sensor. It also busses power and modulation control signals from the FPGA board to the illuminator board. It is illustrated in block diagram form in Figure 12.

Fig. 12. Block diagram for the Image Sensing and Capture Board

The FFRIS requires control over the modulation of the sensor and must be able to access the raw pixel values. These requirements eliminate many of the image sensors currently on the market. We chose not to wait for an improvement over the PMD 19k sensor to become available, and elected instead to design the Image Capture board so that it could immediately host the PMD Daughter board from the previous system incarnation. As this image board is only a sub-system of the complete FFRIS, it will be straight-forward to design a modified board in the future as appropriate sensors become available (this is the motivation behind the modular design – to easily implement hardware changes without incurring a system redesign). The PMD daughter board connects to this board so that the imaging sensor is centred on the board, ensuring that the attached optical lens will be aligned with the circular cut-out of the Laser Illumination board.

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The image sensor has two analogue video outputs. A 16-bit Imaging Signal processor ADC (AD9826 from Analog Devices) is provided to convert the two analogue video streams from the image sensor into digital frames to be processed by the FPGA. This ADC can sample the video streams at a maximum rate of 15 million samples per second. Each 16-bit data word is transferred to the FPGA in two sequential bytes on an 8-bit data bus via the generic I/O interface. The generic I/O interface between the FPGA and Image Capture board utilises LVDS signals to improve the quality of the transmitted signals. LVDS receivers and transmitters convert the LVDS signals to the single-ended CMOS signals required by the image sensor, ADC and high current pin drivers. The LVDS receiver/transmitters are the SN65LVDT388/389 8-way high speed drivers, and the repeater is the SNLVDS100. The Image Capture board busses the modulation and control signals to the Illumination board via Serial Advanced Technology Attachment (SATA) connections. 6.5 External Interface Board This board provides all the necessary communication drivers to allow the system to connect with peripheral devices such as an external computer or VGA monitor. It also provides communication interfaces to allow configuration and management of the imaging system by an external control computer. Figure 13 presents a block diagram of the main sub-systems of the External Interface board.

Fig. 13. Block diagram of the External Interface board

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Image frames can be displayed on an external standard 640 × 480 resolution VGA monitor over a VGA interface allowing users to examine four frames of raw and processed data simultaneously in real time. The VGA output is driven by a triple 10bit high speed video DAC (ADV7123, from Analog Devices). This DAC has three separate 10-bit input ports that drive three complementary outputs to produce the analogue red, green and blue video streams to display images on a VGA monitor. All the VGA data and control signals are sent over the generic I/O interface from the FPGA board to the External Interface board. The External Interface board uses a standard VGA jack to allow the connection of any standard VGA monitor. An 8-bit microcontroller (ATMega32U4 from Atmel) incorporating a USB controller is employed to provide the communication interface between the control computer and the range imaging system (which contains a Mini–B USB connector). The MCU provides full-speed and low-speed USB connections and allows the range imaging system to appear as a USB device to any USB host running on an external computer. The External Interface board contains a RJ-45 jack with an integrated Ethernet interface to allow long term storage of processed and raw image frames on an external computer. A stand-alone Ethernet controller (DM9000A from Davicom) provides both the MAC and PHY transceiver and is interfaced to the FPGA via a 16-bit data bus over the generic I/O interface. The NIOS processor is responsible for writing image frames to the Ethernet controller chip.

7 Power Supply As can be seen from Figures 10 – 13, the component boards require a plethora of voltages, including +1.2, +1.8, +2.5, +3.3, +5.0, +12 V. These are provided on the component boards by an appropriate regulator, but such designs are straight-forward and will not be further described here. The power consumption of the image ranger sub-systems has been calculated based on the maximum ratings of the components utilised on each board plus a future expansion capacity. The resultant maximum rating of each board is (rounded up to the nearest 5 W): • • • •

FPGA Board – 25 W. External Interface Board – 5 W. Image Sensing and Capture Board – 10 W. Laser Illumination Board – 50 W.

The FPGA board power consumption is primarily set by the configuration of the FPGA, specifically the speed and the number of resources utilised, 20 W being the measured output of the working Stratix as configured in the bench-top system. Based on the power consumption calculation the input power supply must be able to deliver 90 W of power. The power supply will typically be from a 12 V source and hence the supply must be capable of providing a maximum of 7.5 A. The External Interface board incorporates protection and filter circuits to provide a clean power supply to the unregulated power bus that runs through the entire system. A 40 A rated Schottky diode (48CTQ060SPBF, from Vishay) provides reverse polarity protection

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on the input voltage and two power-out connectors, fused at 10 A each limit the total current. An inductor-capacitor (LC) filter, rated at 23 A, provides filtering on the input power supply to prevent high frequency noise.

8 Results The completed and assembled unit is illustrated in Figure 14 [19]. With the modular board arrangement, it has dimensions of 120×200×120 mm (excluding protruding connectors) which is approximately 30% of the size of the bench top system of Figure 8. This comparison is illustrated in Figure 15. Different connectors could be employed to further reduce the device’s physical size, but this is not a priority since the current sizing can easily be accommodated on our mobile robots. With the PMD sensor, 19200 simultaneous range measurements at sampling frequencies of up to 24 Hz to centimetre precision have been achieved. Sub-centimetre acquisition requires sampling at rates of 0.1 Hz or lower. Range disambiguation has been implemented [16,17] with little loss of precision.

Fig. 14. Completed portable FFRIS

The field of view of the system is limited only by the optical hardware. Using a 16 mm focal length lens results in a field of view (horizontal × vertical) of 22.2° × 16.5°. Care must be taken with the selection of lens as wider angle lenses in particular will introduce optical distortion. Calibration of the system for different lens types will mitigate this effect. Figure 16 displays a sample (face of a mannequin) output from the system where colour has been used to illustrate the range. This image was taken with a modulation frequency of 36 MHz and a frame integration time of 20 ms. As required, changes in acquisition time, laser intensity, and modulation frequencies can easily be made by a user.

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Fig. 15. Comparison of portable and bench top FFRIS

Fig. 16. Colour enhanced range full-field range image

9 Summary Reviewing the original specifications, this completed FFRIS satisfies all of the essential and desirable requirements. Specifically: • •

The system is able to operate at configurable modulation frequencies up to 40 MHz. All modulation and synchronisation frequencies are generated within the FPGA and are precisely frequency locked with each other.

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• • • • •

•

•

•

The illumination intensity of the laser diodes can be easily changed in software. Full-field range measurements are calculated by the on-board embedded FPGA. This range can be visually displayed, or provided in numerical form for another processor. No external PC is required unless the configuration details are to be altered or the frames transferred for long-term storage or analysis. The lasers are protected from over-current during turn-on or in the event of loss of modulation or control signals. The device can be entirely powered by the 12 V batteries that power most of the fleet’s mobile robots. The system has been currently over-engineered allowing for a maximum power consumption of 90 W. In operation 50 – 60 W is a more typical figure, but this can increase particularly if greater intensity is required from the laser diodes. Regardless, for the mobile robots in our fleet, the robot’s locomotive motors easily dominate power requirements. The device is inexpensive. The largest cost is the sensor (currently 1000 euro), with the next most expensive component being the FPGA which is of the order of 100 euro. Total component cost of the system is approximately 2000 euro. It is compact with the most significant room for improvement being a change of the connectors. Whilst commercial production could further reduce the system size, it is comparable to other systems currently on the market. Acquisition time and modulation frequency can be varied in software to provide versatility between real-time operation where precision may not be so critical through to longer acquisition, high precision measurements when greater environmental detail is required.

Whilst other devices on the market may outperform our system in one or more of spatial resolution, frame rate, depth precision or size, none out perform us on all of these criteria. The most significant advantage of our solution is that it is extremely configurable and provides a solution for a greater range of situations than any other systems we have investigated.

References [1] Sharp GP2Y3A003K and GP2Y2A002K at http://sharp-world.com/products/device/catalog/index.html [2] Sick, http://www.sick.com/ [3] Jongenelen, A.P.P.: Development of a Configurable Range Imaging System for Unambiguous Range Determination, PhD Thesis under examination, Victoria University of Wellington (2010) [4] Christie, S., Hill, S.L., Bury, B., Gray, J.O., Booth, K.M.: Design and Development of a Multi-Detecting Two-Dimensional Ranging Sensor. Measurement Science and Technology 6, 1301–1308 (1995)

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[5] Blais, F.: Review of 20 years of range sensor development. Journal of Electronic Imagine 13(1), 231–243 (2004) [6] Carnegie, D.A., Cree, M.J., Dorrington, A.A.: A high-resolution full-field range imaging device. Review of Scientific Instruments 76(8) (2005) [7] Dorrington, A.A., Carnegie, D.A., Cree, M.J.: Towards 1 mm depth precision with a solid-state full-field range imaging system. In: Proceedings of SPIE: Sensors, Cameras, and Systems for Scientific/Industrial Applications, San Jose, CA, USA, vol. 6068 (2006) [8] Dorrington, A.A., Cree, M.J., Payne, A.D., Conroy, R.M., Carnegie, D.A.: Achieving sub-millimetre precision with a solid-state- full-field heterodyning range imaging camera. Measurement Science and Technology 18, 2809–2816 (2007) [9] Jongenelen, A.P.P., Carnegie, D.A., Payne, A.D., Dorrington, A.A.: Development and characterisation of an easily configurable range imaging system. In: Proceedings of the 24th International Conference Image and Vision Computing (NZ), Wellington, New Zealand, pp. 79–84 (2009) [10] Payne, A.D., Dorrington, A.A., Cree, M.J., Carnegie, D.A.: Characterization of modulated time-of-flight range image sensors. In: SPIE – 7239 3D Imaging Metrology, San Jose, CA, USA (2009) [11] Büttgen, B., Seitz, P.: Robust optical time-of-flight range imaging based on smart pixel structures. IEEE Transactions on Circuits and Systems I: Regular Papers 55, 1512–1525 (2008) [12] PMDTechnologies, http://www.pmdtec.com/ [13] MESA Imaging, http://www.mesa-imaging.ch/ [14] Canesta Inc., http://canesta.com/ [15] Dorrington, A.A., Cree, M.J., Carnegie, D.A., Payne, A.D., Conroy, R.M., Godbaz, J.P., Jongenelen, A.P.P.: Video-rate or High-Precision: A Flexible Range Imaging Camera. In: Proceedings SPIE Image Processing: Machine Vision Applications, San Jose, CA, USA, vol. 6813 (2008) [16] Jongenelen, A.P.P., Carnegie, D.A., Payne, A.D., Dorrington, A.A.: Maximizing precision over extended unambiguous range for TOF range imaging systems. In: Proceedings of the 27th IEEE International Instrumentation and Measurement Technology Conference, Austin, TX, USA, pp. 1575–1580 (2010) [17] Jongenelen, A.P.P., Bailey, D.G., Payne, A.D., Dorrington, A.A., Carnegie, D.A.: Analysis of Errors in ToF Range Imaging with Dual-Frequency Modulation. IEEE Transactions on Instrumentation & Measurement (publication pending) [18] Payne, A.D., Jongenelen, A.P.P., Dorrington, A.A., Cree, M.J., Carnegie, D.A.: Multiple frequency range imaging to remove measurement ambiguity. In: 9th Conference on Optical 3-D Measurement Techniques, Vienna, Austria, pp. 139–148 (2009) [19] McClymont, J.: The Development of Extrospective Systems for Mobile Robots. ME Thesis, Victoria University of Wellington (2010) [20] Jongenelen, A.P.P., Bailey, D.G., Payne, A.D., Carnegie, D.A., Dorrington, A.A.: Efficient FPGA Implementation on Homodyne-Based Time-of-Flight Range Imaging. Journal of Real-Time Image Processing, Special Issue (2010) (publication pending)

Cr2O3-doped BaTiO3 as an Ammonia Gas Sensor Gotan H. Jain1,*, S.B. Nahire2, D.D. Kajale1, G.E. Patil1, S.D. Shinde3, D.N. Chavan4, and V.B. Gaikwad3 1

Materials Research Lab., Arts, Commerce and Science College, Nandgaon 423 106, India [email protected] 2 G.M.D. Arts, B.W. Commerce and Science College, Sinnar, India 3 Materials Research Lab., K.T.H.M. College, Nashik 422 005, India 4 Department of Chemistry, Arts Commerce and Science College, Lasalgaon 422 306, India

Abstract. The thick films of pure BaTiO3 (BT) were prepared by screenprinting technique. The gas sensing performances of these films were tested to various gases by using static gas sensing system at various operating temperatures. The pure film showed maximum response to H2S gas at 350oC but poor selectivity. Different wt% of Cr (0.56, 5.27 and 6.07) was added in BaTiO3, base material, followed by sintering at 550oC for 30min. The thick films of such powder were prepared by screen-printing technique. The thick films of this Crdoped BT were prepared and tested to various gases. The Cr2O3-doped BT film (5.27wt %) showed maximum response to ammonia gas at 350oC and suppresses the response to H2S gas. The response of 5.27wt% film was observed to be the most amongst the 0.56 and 6.07wt. The selectivity of the Cr2O3-doped BT was found to be more against the other gases. The 90% response and recovery levels were attained within 3 and 20 s, respectively for Cr2O3-doped BT (5.27wt %) film. The very short response and recovery time are the important features of this Cr2O3-doped BT film to NH3 gas. Keywords: BaTiO3, thick films, NH3 gas sensor, sensitivity, selectivity.

1 Introduction In the recent, sensors have attracted a great deal of attention from scientists and engineers. Even in the near future, it is expected to gain importance in view of the construction of more or less intelligent ensembles, which integrate actuating, sensing and computing subsystems. Detection of various gases using solid-state chemistry has generated a great deal of interest, both in academia and in industry. Although much research has been focused on sensors based on SnO2 technology, other inorganic oxides are receiving increased attention. These include binary oxides, such as oxides of titanium, tungsten, and gallium, and more complex ternary oxides. Compounds having pervoskite structures are among one of the most important classes of ternary oxides. Barium titanate (BaTiO3) is one of the most intensively investigated *

Corresponding author.

S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 157–167. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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ferroelectrics and has been widely used in electronic industry in applications for capacitors, thermocouples, transducers, sensors and actuators, etc. A number of studies have focused on the chemistry and physics of the response of these materials to gases. Depending on the conditions, these compounds can behave as n- or p-type semiconductors. Because of their structural similarity, similar mechanisms of interaction with gases are expected to occur for these compounds, although the relative importance of the mechanisms for any specific operating condition would depend, in each instance, on the specific compound. It is well known that a number of pervoskite oxides (ABO3) have been used as gas sensor materials because of their stability in thermal and chemical atmospheres. So over the last decade, the pervoskite oxide ceramics such as BaTiO3 (BT) have created and promoted interest in chemical sensors. It is capable of detecting a particular gas in the high temperature region, 175-450oC, near and above the temperature giving the maximum resistivity [1]. Modifications in the microstructure, the processing parameters and also the concentration of acceptor/donor dopants can vary the negative temperature coefficient of the resistance (NTCR) and conductivity of BaTiO3. It is also known in literature [2-6] that PTCR (positive temperature coefficient region) disappears completely when donar-doped BaTiO3 was annealed at high temperatures in atmosphere of low oxygen partial pressure. BaTiO3 is well known for the detection of CO [8, 9], CO2 [10-12], humidity [13], etc. Various attempts have been made to improve the selectivity and sensitivity of BaTiO3 by using dopants and additives [14, 15]. There are a few reports dealing with BaTiO3-based gas sensors. Efforts are, therefore, made to develop BaTiO3 - based gas sensors and for the improvement in its sensing performance by doping and modifying the surface of the thick films. Pure and modified BaTiO3 are observed to be most sensitive to H2S gas. Some well-known materials for H2S gas sensing are SnO2-ZnO-CuO [16], SnO2-Pd [17], SnO2-Al2O3 [18], SnO2CuO [19-24], SnO2-CuO-SnO2 [25], and ZnSb2O6 [26]. Researchers have developed various types of sensors by adding different additives [1,27-29] into semiconducting BaTiO3. The sensing materials modified by incorporating different additives, either by doping or dipping technique. The sensing performance of pure and modified BaTiO3 films was studied in terms of the change in conductance in the presence and absence of gases.

2 Experimental 2.1 Preparation of BaTiO3 Powder Powders of Ba(OH)2.8H2O and TiO2 of analytical reagent grade were ball milled to mix thoroughly at the same molar concentrations. The mixture was sintered at 1000oC for 6h to obtain BaTiO3 [30, 31]. The fine-grain powder of BaTiO3 was obtained by milling in a planetary ball mill for 2h. The sub micron size powder was then used to formulate the paste for printing of thick films. 2.2 Preparation of BaTiO3 Thick Films The thixotropic paste was formulated by mixing the fine powder of BaTiO3 with a solution of ethyl cellulose (a temporary binder) in a mixture of organic solvents such as

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butyl cellulose, butyl carbitol acetate and terpineol, etc. The ratio of the inorganic to organic part was kept at 75:25 in formulating the paste. This paste was screen printed [32, 33] on a glass substrate in a desired pattern. The films were fired at 550oC for 30 min. Silver contacts were made for electrical measurements. 2.3 Preparation of Cr2O3-doped BaTiO3 Thick Films Different wt% of CrO3 was added in BaTiO3, base material, followed by sintering at 550oC for 30min. CrO3 is not thermally stable above its melting temperature (197oC). At higher temperature, it loses oxygen to give stable Cr2O3. In this way, the Cr2O3doped BaTiO3 powder was obtained. The thick films of such powder were prepared by screen-printing technique. 2.4 Thickness Measurements The thickness of the thick films was measured by using the Taylor-Hobson (Talystep, UK) system. The thicknesses of the films were observed in the range from 65 to 70μm. The reproducibility in thickness of the films was possible by maintaining the proper rheology and thixotropy of the paste.

3 Characterization Results 3.1 Structural Properties Fig. 1 shows the X-ray diffractogram of a Cr2O3-doped BaTiO3 thick film, for 5.27wt% of Cr. The observed peaks are matching well with the reported data [34] of BaTiO3 confirming the single phase of the compound. The presence of separate Cr2O3 peaks indicates the composite nature of the material i.e. independent identity of Cr2O3. The average grain size calculated from Scherrer formula was 278 nm.

Fig. 1. X-ray diffractogram of Cr2O3-doped BaTiO3 thick film

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3.2 Microstructural Analysis Fig. 2(a) depicts a SEM image of an unmodified BaTiO3 thick film fired at 550oC. The film consists of voids and a wide range of particles with particle sizes ranging from 200 to 1330nm distributed non-uniformly. Fig. 2(b-d) depictS SEM images of Cr2O3-doped BaTiO3 thick films fired at 550oC with 0.56, 5.27 and 6.07wt% of Cr, respectively. The agglomeration of particles increases as Cr2O3 wt% increases. The change in doping concentration changes the particle sizes. The particle sizes ranging from 0.3 to 1.0μm (Fig. 2(b)), 0.5 to 1.0μm (Fig. 2(c)), and 0.66 to 2µm (Fig. 2(d)) were observed.

(a)

(c)

(b)

(d)

Fig. 2. SEM images of (a) unmodified BaTiO3 film and Cr2O3-doped BaTiO3 films with (b) 0.56wt%, (c) 5.27wt%, and (d) 6.07wt% of Cr.

3.3 Elemental Analysis The constituent elements such as Ba, Ti, O and Cr associated with various films are represented in Table 1. It reveals from the table that the film with 5.27wt% of Cr was observed to be the most oxygen deficient as compared to other samples. This deficiency could be attributed to the larger oxygen adsorption capability of the sample.

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Table 1. Quantitative elemental analysis

Samples Unmodified BaTiO3 Cr2O3-doped BaTiO3 : 0.56wt% Cr : 5.27wt% Cr : 6.07wt% Cr

Ba 80.46 82.08 79.07 76.39

wt% of Ti O 12.63 6.91 13.17 4.19 11.30 4.36 12.28 5.26

Cr 0.56 5.27 6.07

3.4 Electrical Conductivity of Cr2O3-doped BaTiO3 Films Fig. 3 represents the variation of conductivity with temperature for the pure and Cr2O3-doped BaTiO3 (BT) films. The legends suffixed with ‘a’ are the graphs for the conductivities of the films in the air ambient, while legends suffixed ‘g’ are the graphs for conductivities of the films in the NH3 gas ambient. It is clear from the graphs that the conductivity is varying approximately linearly with temperature for all films. The conductivity of Cr2O3-doped BaTiO3 films was observed to be increased.

Log(conductivity)1/Ohm-m

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Fig. 3. Variation of electrical conductivity with temperature

3.5 Gas Sensing Performance 3.5.1 Gas Response of Unmodified BaTiO3 Film with Operating Temperature The variation of H2S gas response with operating temperature ranging from 100 to 450oC is shown in Fig. 4. The response goes on increasing with the temperature, attains its maximum (350oC) and then decreases with further increase in temperature. It is clear from graph that the optimum operating temperature is 350oC.

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Gas response

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Fig. 4. H2S gas (100ppm) response with operating temperature

3.5.2 Selectivity Fig. 5 shows the histogram of the selectivity of pure BaTiO3 film to various gases. The table attached to histogram shows the gas response values to various gases. It reveals that H2S gas is most selective against CO2 and poor selective against NH3 gas.

Gas response

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Fig. 5. Selectivity of pure BaTiO3 films to various gases

3.5.3 Gas Response of Cr2O3-doped BaTiO3 Films with Operating Temperature Fig. 6 depicts the variation of NH3 gas response with operating temperature. The response to NH3 gas goes on increasing with temperature for pure and doped films. The film with 5.27wt% of Cr was observed to be the most sensitive to NH3 gas and its optimum operating temperature was 350oC.

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Fig. 6. Variations in NH3 gas (100ppm) response with operating temperature

3.5.4 Selectivity Fig. 7 shows the bar diagram of the selectivity of pure and Cr2O3-doped BaTiO3 films to various gases at optimum operating temperature. The table attached to bar diagram indicates the gas response values to various gases. It is observed that the pure BaTiO3 film showed highest H2S gas response while Cr2O3-doped BaTiO3 films showed highest response to NH3 gas.

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Fig. 7. Selectivity of Cr2O3-doped BaTiO3 films to various gases

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3.5.5 Response and Recovery Time of Cr2O3-doped BaTiO3 Sensor The transient response of Cr2O3-doped (5.2wt%) BaTiO3 film to NH3 gas is depicted in Fig. 8. The gas response of this film was found to be largest at 350oC. The 90% response and recovery levels were attained within 3 and 20s, respectively for this sample. The very short response and recovery time are the important features of this Cr2O3-doped BaTiO3 film to NH3 gas.

100oC 200oC 300oC 400oC

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24

Fig. 8. Transient response of Cr2O3-doped (5.27wt%) BaTiO3 to NH3 gas

4 Discussions It is known that atmospheric oxygen molecules are adsorbed on the surface of Cr2O3doped BaTiO3 semiconductor oxide in the forms of O- and O2- thereby decreasing the electronic conduction. Atmospheric oxygen molecules take electrons from the conduction band of Cr2O3-doped BaTiO3 to be adsorbed as O-BaTiO3. The reaction is as follows: O2(g) + 2e- → 2O-BaTiO3

(1)

The Cr2O3-doped BaTiO3 is more oxygen deficient as compared to pure BaTiO3. The excess Ba ions (due to oxygen vacancies) act as donors [35]. When reducing gas molecules like NH3 react with negatively charged oxygen adsorbates, the trapped electrons are given back to conduction band of Cr2O3-doped BaTiO3. The energy released during decomposition of adsorbed ammonia molecules would be sufficient for electrons to jump up into conduction band of Cr2O3-doped BaTiO3, causing an increase in the conductivity of sensor. The possible reaction is: 2NH3 + 3O-BaTiO3 → 3H2O + N2 + 3e-

(2)

For this reaction to proceed to the right hand side, some amount of activation energy has to be provided thermally. An increase in operating temperature surely increases the thermal energy so as to stimulate the oxidation of NH3 (equation (2)). The reducing gas

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(NH3) donates electrons to Cr2O3-doped BaTiO3. Therefore, the resistance decreases, or the conductance increases. This is the reason why the gas response increases with operating temperature. The point at which the gas response reaches maximum is the actual thermal energy needed for the reaction to proceed. However, the response decreases at higher operating temperatures, as the oxygen adsorbates are desorbed from the surface of sensor [36]. Also, at high temperatures the carrier concentration increases due to intrinsic thermal excitation, and the Debye length decreases. This may be one of the reasons for the decreased gas response at high temperatures. When the optimum amount of Cr (5.27wt%) is incorporated into the BaTiO3 material, the Cr2O3 species would be uniformly distributed. Due to this, not only the initial resistance of the film is high but this amount would also be sufficient to promote the catalytic reaction effectively and the overall change in the resistance on exposure of ammonia gas leading to high sensitivity. When the amount of Cr2O3 on the surface of base material, BaTiO3, is less than the optimum, the dispersion may be poor and the sensitivity of the film is observed to be decreased since this amount may not be sufficient to promote the reaction effectively. On the other hand, as the amount of Cr2O3 on BaTiO3 surface is more than the optimum, an additive Cr2O3 would be distributed more densely. As a result, base material BaTiO3 would be masked and the overall change in the resistance on the exposure of gas would be smaller leading to lower response to ammonia gas.

5 Conclusions Following statements can be made from the experimental results. 1) 2) 3) 4)

The thick films of unmodified BaTiO3 were sensitive to H2S gas. The Cr2O3-doped BaTiO3 was observed to be semiconducting in nature. The Cr2O3-doped BaTiO3 was most sensitive and selective to NH3 gas. The resistance of the Cr2O3-doped BaTiO3 films in ambient air was observed to be very high. 5) The resistance of the Cr2O3-doped BaTiO3 films was observed to decrease suddenly upon exposure to NH3 gas at optimum operating temperature. 6) The fast recovery of the sensor could be attributed to the larger oxygen deficiency in BaTiO3. The larger oxygen deficiency would enable BaTiO3 to adsorb more oxygen ions, helping the sensor to recover fastly. 7) Cr2O3-doped BaTiO3 was observed to be more sensitive to NH3 gas than unmodified BaTiO3.

References 1. Zhou, Z.G., Tang, Z.L., Zhang, Z.T.: Studies on grain-boundary chemistry of pervoskite ceramics as CO gas sensors. Sens. Actuators B 93, 356–361 (2003) 2. Haayman, P.W., Van Dam, R.W., Klaasens, H.A.: Method of preparation f semiconducting materials. German Patent 929350 (1995) 3. Jaffe, P., Cook Jr., W.R., Jaffe, H.: Piezoelectric Ceramics, p. 94. Academic Press, New York (1971)

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4. Sahuri, O., Wakino, K.: Processing techniques and applications of positive temperature coefficient thermistors. IEEE Trans. Component 10, 53 (1963) 5. Ravi, V., Kutty, T.R.N.: Current limiting action of mixed phase BaTiO3 ceramic semiconductors J. Appl. Phys. 68, 4891 (1990) 6. Kutty, T.R.N., Ravi, V.: Varistor property of n-BaTiO3 based current limiters. Appl. Phys. Lett. 59, 2691 (1991) 7. Zhou, Z.G., Tang, Z.L., Zhang, Z.T.: Studies on grain-boundary chemistry of pervoskite ceramics as CO gas sensors. Sens. Actuators B 93, 356–361 (2003) 8. Tang, Z.T., Zhou, Z.G., Zhang, Z.T.: Experimental studies on the mechanism of BaTiO3 based PTC CO gas sensor. Sens. Actuators B 93, 391–395 (2003) 9. Ishihara, T., Kometani, K., Nishi, Y., Takita, Y.: Improved sensitivity of CuO-BaTiO3 capacitance type CO2 sensor. Sens. Actuators B 28, 49–54 (1995) 10. Liao, B., Wei, Q., Wang, Q.Y., Liu, Y.X.: Study on CuO-BaTiO3 semiconductor CO2 sensor. Sens. Actuators B 80, 208–214 (2001) 11. Haeusler, A., Meyer, J.U.: A novel thick film conductive type CO2 sensor. Sens. Actuators B 34, 388–395 (1996) 12. Wang, J., Xu, B.K., Liu, B.F., Zhang, J.C., Zhang, T.: Improvement of nanocrystaline BaTiO3 humidity sensing properties. Sens. Actuators B 66, 159–160 (2000) 13. Wagh, M.S., Patil, L.A., Seth, T., Amalnerkar, D.P.: Surface cupricated SnO2-ZnO thick films as a H2S gas sensor. Mater. Chem. Phys. 84, 228–233 (1985) 14. Kanefusa, S., Nitta, M., Haradome, M.: High sensitivity H2S gas sensor. J. Electrochem. Soc. 132, 1770–1773 (1985) 15. Lantto, V., Romppainen, P.: Response of some SnO2 gas sensors to H2S after quick cooling. J. Electrochem. Soc. 135, 2550–2556 (1988) 16. Tamaki, J., Maekawa, T., Miura, N., Yamazoe, N.: CuO-SnO2 element for highly sensitive and selective detection of H2S. Sens. Actuators B 9, 197–203 (1992) 17. Manorama, S., Sarala Devi, G., Rao, V.J.: Hydrogen sulfide sensor based on tin oxide deposited by spray pyrolysis and microwave plasma chemical vapor deposition. Appl. Phys. Lett. 64, 3163–3165 (1994) 18. Sarala Devi, G., Manorama, S., Rao, V.J.: Gas sensitivity of SnO2/CuO heterocontacts. J. Electrochem. Soc. 142, 2754–2756 (1995) 19. Tamaki, J., Shimanoe, K., Yamada, Y., Yamamoto, Y., Miura, N., Yamazoe, N.: Dilute hydrogen sulfide sensing properties of thin film prepared by low pressure evaporation method. Sens. Actuators B 49, 125–186 (1998) 20. Vasiliev, R.B., Rumyantseva, M.N., Yakovlev, N.V., Gaskov, A.M.: CuO/SnO2 thin film heterostructures as chemical sensor for H2S. Sens. Actuators B 50, 186–193 (1998) 21. Mangamma, G., Jayaraman, V., Gnanasekaran, T., Periaswami, G.: Effects of silica addition on H2S sensing properties of CuO-SnO2 sensors. Sens. Actuators B 53, 133–139 (1998) 22. Yuanda, W., Maosong, T., Xiuli, H., Yushu, Z., Guorui, D.: Thin film sensors of SnO2CuO-SnO2 sandwich structure to H2S. Sens. Actuators B 79, 187–191 (2001) 23. Tamaki, J., Yamada, Y., Yamamoto, Y., Matsuoka, M., Ota, I.: Sensing properties of dilute hydrogen sulfide of ZnSb2O6 thick film prepared by dip-coating method. Sens. Actuators B 66, 70–73 (2000) 24. Yamazoe, N., Kurokawa, Y., Seiyama, T.: Effects of additives on semiconductor for gas sensor. Sens. Actuators B 4, 283–289 (1983) 25. Lee, M.S., Meyer, J.U.: A new process for fabricating CO2-sensing layers based on BaTiO3 and additives. Sens. Actuators B 68, 293–299 (2000)

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26. Ishihara, T., Kometani, K., Nishi, Y., Takita, Y.: Improved sensitivity of CuOBaTiO3capacitive type CO2 sensor by additives. Sens. Actuators B 28, 49–54 (1995) 27. Lee, J.D.: Concise In-organic Chemistry, 5th edn., p. 698 28. Manku, G.S.: In-organic Chemistry, pp. 465–467 29. Patil, L.A., Wani, P.A., Sainkar, S.R., Mitra, A., Pathak, G.J., Amalnerkar, D.P.: Studies on “fritted” thick films of photoconducting CdS. Mater. Chem. Phys. 55, 79 (1998) 30. Aslam, M., Chaudhary, V.A., Mulla, I.S., Sainkar, S.R., Mandale, A.B., Belhekar, A.A., Vijaymohan, K.: A highly selective ammonia gas sensorusing surface-ruthenated zinc oxide. Sens. Actuators A 75, 162–167 (1999) 31. Chaudhary, V.A., Mulla, I.S., Vijaymohan, K.: Impedance studies of an LPG sensor using surface ruthenated tin oxide. Sens. Actuators B 55, 127–133 (1999) 32. Niranjan, R.S., Chaudhary, V.A., Sainkar, S.R., Patil, K.R., Mulla, I.S., Vijaymohan, K.: Surface ruthenated tin oxide thin-film as a hydrocarbon sensor. Sens. Actuators B 79, 132– 136 (2001) 33. Chaudhary, V.A., Mulla, I.S., Vijaymohan, K.: Comparative studies of doped and surface modified tin oxide towards hydrogen sensing: synergistic effects of Pd and Ru. Sens. Actuators B 50, 45–51 (1998) 34. ASTM Data Manuals, pp. 34–129 35. Ishihara, T., Kometani, K., Hashida, M., Takita, Y.: Application of mixed oxide capacitor to the selective carbon dioxide sensor. J. Electrochem. Soc. 138, 173 (1991) 36. Cotton, F.A., Wilkinson, G.: Advanced Inorganic Chemistry, 2nd edn., p. 828. Interscience Publishers, John Wiley & Sons (1967)

Physical and Electrical Modeling of Interdigitated Electrode Arrays for Bioimpedance Spectroscopy M. Ibrahim1, J. Claudel1, D. Kourtiche1, B. Assouar2, and M. Nadi1 1

Electronic Instrumentation Laboratory of Nancy, Nancy University, France 2 Institute Jean Lamour, Nancy University, France

Abstract. This paper concerns a theoretical and electrical modelling of interdigital sensor in a wide band frequency. A theoretical approach is proposed to optimize the use of the sensor for bioimpedance spectroscopy. CoventorWare® software was used to modelize in three dimensions the interdigital sensor system for measuring electrical impedance of biological medium. Complete system simulation by Finite element method (FEM) was used for sensor sensitivity optimization. The influence of geometric parameters (number of fingers, width of the electrodes, …), on the impedance spectroscopy of biological medium was studied. A high level description of the sensor and the biological medium was also developed under VHDL-AMS with SystemVision® software from mentor graphics. The simulation results are compared with measurements obtained with a true interdigitated sensor illustrating a good correlation. This shows that even the theoretical model is simple, it remains very effective.

1 Introduction Electrical impedance measurement has been demonstrated as a potential useful approach in biomedical applications. This method allows to determine the physiological status of ex vivo or living tissues as well as their electromagnetic characterization [1]. The changes induced by some pathologies could be associated with variations of essential tissue parameters such as the physical structure or the ionic composition that can be reflected as changes in the passive electrical properties. The range of applications derived from this technique is quite wide [2, rigaud et morucci]. Planar interdigitated electrode arrays have become more prominent as a sensor device due to the ongoing miniaturization of electrodes and the low cost of those systems [3]. An important advantage of these sensor devices is the simple and inexpensive mass-fabrication process and the ability to use these devices over a wide range of applications without significant changes in the sensor design [4-5]. Typically these sensors have been used for the detection of capacitance, dielectric constant and bulk conductivity in biological medium [6-7]. Basically, the structure consists on two S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 169–189. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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parallel coplanar electrodes whose design (width, gap between electrodes, length) is repeated periodically [8]. This paper, based on previous work [9], presents a new approach of physical and electrical modelling system of a biological sensor. The electrical and physical modelling of the Interdigital sensor and the medium was developped by using COVENTORWARE® and Systemvision (MENTORGRAPHICS®) sofware respectively. Section two describes the correlation between design parameters and frequency behavior in coplanar impedance sensors. By developing total impedance equations and modeling equivalent circuits we propose a theoretical optimization of the geometrical parameters of the sensor. One objective was to get the optimal ratio between the width of the electrodes and the gap. The third section gives a description of the sensor and medium model with the finite element method FEM using CoventorWare® software. We studied the influence of the medium’s physical properties on the frequency sensor response. We simulated the influence of electrodes number and we found the number 16 as optimized for a cross section 1mm*1mm. In the fourth section, we give a description of electrical model for IDT sensor with VHDL-AMS (SystemVision software®). This software provides an electrical approach that can be readily used in current electronic design flow to include distributed physics effects. VHDL-AMS language permits to simulate the sensor and medium. It allows fast simulations to validate a simplified model and to serve as a reference to power conditioning. The sensor manufacturing is described in the fifth section. A test bench based on a measurement system composed by RCL meter connected with computer was built to test the sensor. Preliminary bioimpedance measurements were done on calibrated ionic solution of NaCl. Section six concludes on the validity of models and presents the perspectives.

2 Theoretical Aspect 2.1 Description of Interdigital Sensors Interdigital sensor is equivalent to a parallel plate capacitor (Figure 1) [10-11]. An electric field is created between the positive and negative electrodes (instantaneous polarity) shown on figure 1 (a) and (b) respectively. When a medium is placed on the sensor, the electric field across the medium under test is also shown on figure 1 (c). The dielectric properties of the material as well as the geometry of the material under test affect the capacitance and conductance between the two electrodes. The variation of the electric field can be used to determine the properties of the material depending on the application. To use them in bioimpedance domain, a potential difference is applied between two electrodes and the electrical impedance between the electrodes is measured. The electrodes of the interdigital sensor are coplanar, so the measured impedance will have a very low signal-to-noise ratio.

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Fig. 1. Funtion principle of an Interdigital sensor.

The main dimensional characteristics parameters of such a pair of electrodes are (figure 2): 1. 2. 3. 4.

The number of digits N. The length of digit L. The digit width W. The distance between a digits S.

Fig. 2. Interdigitated sensor structure and dimensional parameters

2.2 Equivalent Circuit Model Figure 3.a gives the configuration of the planar structure when switched as an interdigitated impedance cell. When such a cell is immersed in an electrolyte, the simplified equivalent electrical circuit is represented by figure 3.b.

Fig. 3. (a) Configuration of interdigitated impedance cell and (b) its equivalent circuit

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The electrical elements in the equivalent circuit modelize the physical phenomena that determine the total electrical impedance (Z) detected in the measurement cell (figure 3.a). Thus, the equivalent model elements could be expressed in terms of physical quantities. The resistance RSol of the resistance is the sensing element and is related to the electrolyte conductivity σSol by the cell constant KCell [12]: RSol =

KCell

(1)

σSol

The cell constant KCell is equal to [13] : KCell =

1

with K (k ) = ∫

0

2

(N - 1)L

1

(1 - t² )(1 - k²t² )

()

K k

. K

( 1 - k² ) ⎛π W ⎞ . ⎟ ⎝2 S+W⎠

dt and k = cos⎜

(2)

Where N is the number of fingers, S the finger spacing, W the finger width and L the finger length. The function K(k) is the incomplete elliptic integral of the first kind [14]. So, the cell constant depends entirely on the geometry of the sensor. The lead resistance RLead is the result of the series resistances of the connecting wires. Direct capacitive coupling between the two electrodes is represented by the cell capacitance CCell given by: CCell =

ε 0. ε r, Sol

(3)

KCell

with εr,sol ≈ εr,water = 80. One model element which is not drawn in figure 1.b is a capacitor representing the direct capacitive coupling between the connecting wires. This capacitor comes in parallel with CCell and will therefore virtually increase the observed cell capacitance. The impedances that explain the interface phenomena occurring at the electrodeelectrolyte interfaces, are simplified to the double layer capacitances CDL. These are depending on the electrode material and the electrolyte solution but, for horizontal electrode surfaces, they can be approximated by: CDL = 0.5.A. CDL, Surface = 0.5.W.L.N.CDL, Surface

(4)

where A is the electrode surface and CDL,Surface the characteristic of the double layer capacitance of the electrode-electrolyte system. One must notice that the factor 0.5 is the result of CDL determined by only half of the electrode surface A. The characteristic of the double layer capacitance CDL, Surface is supposed to be equal to the characteristic capacitance of the Stern layer for electrolytes having a quite high ionic strength. This characteristic capacitance of the Stern layer is approximated by CStern, Surface = 10-20 μF/cm2 [15, 16].

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Based on the equivalent circuit of figure 1.b, the total observed impedance can be expressed as

( )

Z jω = 2RLead +

Z1

(5)

j.ω.CCell. Z1 + 1

Where Z1 = RSol +

2 j.ω.CDL

2.3 Theoretical Optimization of the Sensor

Impedance, Ohm

Figure 4 shows a schematic graph of total impedance of equivalent circuit (Figure 3.b). There are three zones in the impedance spectrum, which correspond to the three kind of elements in equivalent circuit. The frequency dependent property of these zones can be analysed using the equivalent circuit mentioned above. As shown in Figure 3.b, there are two parallel branches (CCell and CDL). When the frequency is not adequatly higher than fHi, the current cannot cross the middle of the dielectric capacitor. That is, the capacitor is inactive, and just acts as an open circuit. Then , the total impedance corresponds to the double layer capacitance and solution resistance in series. Although both CDL and RSol provide to the total impedance below fHi, each of them dominates at different frequencies.

Cdl

Rsol

Ccell

Frequency, Hertz

Fig. 4. Schematic diagram of total impedance–frequency plots

The CDL becomes essentially resistive at the frequency lower than fLow, and it contributes mainly to the total impedance value: Z≈

2 + jω .CDL.RSol j.ω .CDL

(6)

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and fLo ≈

1

(7)

π. RSol CDL

The impedance increases with the decrease in the frequency (double layer region). However, above fLow, double layer capacitance offers no impedance. This is explained by the fact that only the resistance of the solution contributes to the impedance while below fHi the influence of CCell is not yet indicative, the total impedance is independent of the frequency (resistance of the solution zone). This results into a frequency band, restricted by fLo and fHi, in which the results (e.g. the conductivity) can be deduced from the observed impedance using:

( )

Z jω = 2RLead + RSol

(8)

To optimize the impedance cell leads to maximize the plateau width in the curve of figure 4. When the frequency is higher than fHi, the current cross the middle of the dielectric capacitor instead of crossing the electrolyte solution resistance. That is, the branch (CDL + RSol + CDL) is inactive, and the branch (CCell) is active. In this zone, the dielectric capacitance of the medium governs the total impedance, and the double layer capacitance and medium resistance could be neglected. Thus, the total impedance value is inversely proportional to the frequency: Z≈

RSol j.ω .CCell.RSol + 1

(9)

and fHi ≈

1

(10)

2. π .RSol.CCell

Or in terms of conductivity parameters:

σSol

fLo ≈

(11)

0.5.π .W. L. N. CDL, Surface . KCell

and fHi ≈

σSol 2.π . ε 0. ε

(12) r, Sol

Note that the higher boundary frequency, fHi, is not dependent on the geometry, according to the theory, when the wiring capacitance is not present. Obviously, maximising the width of the plateau can only be done by decreasing the lower boundary frequency. In order to make the lower boundary frequency (11) as low as possible, the geometrical term

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(13)

should be maximised. When using a square structure of L*L, one variable can be eliminated since L*L : L = N. (W+S) - S With L in mm and S in microns L+S≈L Finaly L = N. (W+S)

(14)

However, it is more illustrative to introduce a factor a = S/W. Using the substitutions: W=

L N

.

1

and S =

(a + 1)

L N

.

a

(a + 1)

(15)

Which are based on equation (15) together with the ratio a, expression (13) becomes: 2.L

.

1

(N - 1) (a + 1)

()

K k

. K

( 1 - k² )

= X (N, L) * Y(a)

(16)

Where

(

)

X N, L =

2.L

(N - 1)

and Y(a ) =

1

(a + 1)

()

K k

. K

( 1 - k² )

The function K has the same meaning as it had in equation (2). This optimisation expression, which has to be maximised in order to minimise flo, can be split into two parts. The first is the factor X (N, L), showing that the cell size L*L must be as large as possible while the number of fingers must be reduced. Since there is no maximum in the desired cell size, with respect to the optimisation of flo, the value L can be chosen arbitrarily. A cell size of about 1*1 mm² will be used in the modeling. The optimal number of fingers N has a minimum for N = 2 since this is the lowest possible number of fingers. On the other side, the sensitivity of the impedance measurement depends on the number of fingers. Then, the modelling allows us to study the influence of the number of fingers on the impedance measurement. The highest the number of fingers, the highest the sensitivity. The factor X (N, L) is related to the sum W+S according to equation (14), the second factor in expression (16) has only the ratio W/S = a as a parameter. In figure 5 this factor is plotted as a function of a, a varying from 0 to 10. For a = 1, the finger width is equal to the gap between them. It can be seen that this is not the optimal ratio, for a = 0.66 the optimisation function has a maximum which means that the finger width should be approximately 1.5 the gap width W = 3S/2. This maximum for the function denoted in the figure as f (a) is equal to 0.66.

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Therefore, when designing a square structure, the design rule, based on the maximum frequency range criterion becomes according to equation (14): L = (2.5N-1).S. When the minimum number of fingers, N = 2, is also taken into account, the design rule becomes : L = 5S. 0.7

Y(a) 0.6 X: 0.66 Y: 0.51

0.5

Y(a)

0.4

0.3

0.2

0.1

0

0

1

2

3

4

5

6

7

8

9

10

a

Fig. 5. Optimisation of the S/W ratio.

3 COVENTORWARE® Modeling 3.1 Model Description In this section, the model of the sensor loaded by the blood medium is described. This model was developed for simulation with the finite element method FEM using CoventorWare® software. We used the module MEMS electro quasistatic harmonic response proposed by the software. 3.1.1 Sensor Modeling The structure of the impedance sensor used in this simulation is at micron scale. It is composed by layers of glass and platinum and is showed in figure 6. A glass layer of length 1 000 µm and width of 1 000 μm has been defined as a substrate thickness of 1 000 μm to carry the system (gray layer on figure 6). The glass is a good electrical insulator (10-17S.m-1) with a relative permittivity around 5-7. As the glass has a very small permittivity, we do not need to put an insulating layer between the substrate and the electrodes. Next, we define a mask of platinum electrodes (thickness 1 μm) known as a good conductor 9.66*106 S·m-1, using the graphical editor of CoventorWare. This is deposited on the glass (red layer on figure 5). The effective region of electrodes forms a square 1 000 μm*1 000 μm.

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Fig. 6. 3D view of a planar interdigitated electrode arrays.

The characteristic parameters of the electrodes, the length of digit L, the number of digits N, the digit width W and the distance between a digits S, were selected according to formulas of optimization given in section 2: L is fixed at 1000 μm, W=

3.S 2

μm, S =

L

(5.N/2 − 1)

μm

and N is a variable. For example, N = 4 electrodes, S = 1000/(5.4/2-1) = 111 μm and W = 3.111/2 = 167 μm. 3.1.2 Modeling the Medium The medium modeled is composed by two layers: The two layers DL, that describe the interface phenomena occurring at the electrode-electrolyte interfaces, are simplified to a single equivalent layer. This is the first layer shown on figure 7 (green layer). The second layer is the blood (blue layer). The double layer can be formed from interface phenomena platinum electrodemiddle blood, with a thickness about 50 A° [17]. The relative permittivity of the layer DL (thickness 50 A°) for medium blood is about 97 [18]. The 50 A° thickness creates problems for the mesh system, then they were shifted to a thickness of 10 µm (equal to 50 A°*2*1000) and a relative permittivity 194 000 (equal to 97 *2*1000) in order to not change the capacity of this layer. One can notice that DL layer is almost insulating. We tested the blood as a biological medium with 0.7 S/m as conductivity and 80 at a frequency of 1 Ghz as relative permittivity [19]. The layer of the blood was equal to 500µm of thickness.

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Fig. 7. 3D view of a planar interdigitated electrode arrays and the blood medium.

3.2 Results and Discussion A 1 volt sinusoïdal signal between terminals of interdigitated electrodes and a frequency range from 100 Hz to 1000 GHz was applied. The biological impedance was measured for different cases of electrode configuration. For the first three models, we arbitrarily chose N equal 16 electrodes. Figure 8 shows the influence of the double layer DL on biological impedance of the blood medium. Simulation results are obtained with and without interface double layer DL. One can notice that the impedance is constant in the second case (without DL) and does not take into account the cut off frequencies fLo and fHi. The impedance consists of only resistance in parallel with capacitance (negligible). Figure 9 shows the influence of the conductivity of the blood medium. Simulation results are obtained for two different conductivity : 0.7 and 9 m/s. When the conductivity increases, the RSol decreases and the plateau shifts to a lower impedance. In addition, a change in the height of the plateau implies a change in the boundary frequencies fLo and fHi, (equations 11 and 12 of section 2). Figure 10 gives the influence of medium permittivity on biological impedance and the boundary frequencies fLo and fHi. By comparing simulation results for two different permittivities, one can observe that the permittivity does not affect the impedance for small frequencies (less than 107 Hertz). For high frequencies when the permittivity increases, the impedance decreases and the fHi takes a smaller value which is similar to the equations (12) of the second paragraph. Figure 11 gives simulation for N fingers. Six different structures were used with respect to the conditions of optimization explained above in the paragraph sensor modeling. The case N equal 2 is taken as reference since this is the lowest possible number of fingers. In this case we can see that the impedance shows an unpredicted resonance at high frequencies.

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For N equals 8 a resonance is not observed but an unstable plateau occurs for high frequencies. Where N equals 12, there is not resonance or instability, but the distinction of fHi is not clear. The two curves of 16 and 20 electrodes are almost together, and we can distinguish three regions of frequencies between fLow and fHi. For N equal 30, the impedance curve shows a resonance at high frequency.

10

10

Impedance, Ohm

10

10

10

10

10

7

16 electrodes, with interface double layer DL 16 electrodes, out interface double layer DL

6

5

4

3

2

1

0

10 2 10

10

4

flow

10

6

10

8

fhigh1010

10

12

Frequency, Hertz

Fig. 8. Simulated impedance of a blood medium deposited on the structure of the sensor number of fingers 16 electrodes, with and without interface double layer DL.

10

10

Impedance, Ohm

10

10

10

10

10

10

8

16 electrodes, conductivity = 0.7 s/m 16 electrodes, conductivity = 9 s/m

7

6

5

4

3

2

1

0

10 2 10

10

4

flow

flow 10

6

10

8

fhigh 10

10

fhigh

10

12

Frequency, Hertz

Fig. 9. Impedance-frequency characteristics for t conductivities of 0.7 S / m and 9 S/m

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7

10

16 electrodes, permittivity = 80 16 electrodes, permittivity = 5200

6

10

5

Impedance, Ohm

10

4

10

3

10

2

10

1

10

0

10 2 10

4

10

6

10

10

8

10

12

10

10

Frequency, Hertz

Fig. 10. Influence of the blood permittivity on the impedance. 7

10

2 electrodes 8 electrodes 12 electrodes 16 elctrodes 20 electrodes 30 electrodes

6

10

5

10

Impedance, Ohm

180

4

10

3

10

2

10

1

10

0

10 2 10

4

10

6

10

8

10

10

10

12

10

Frequency, Hertz

Fig. 11. Biological impedance of the medium at various the number of digits N.

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4 VHDL-AMS Modeling 4.1 Model Description In this section, we describe the model of the sensor and the medium that we developed and simulated using the high-level behavioral language VHDL-AMS using SystemVision software. The systems libraries was used to describe the model as an electrical circuit. 4.1.1 Electrical Modeling for Sensor and Medium In VHDL-AMS, the sensor and medium are described as an electrical circuit. In this circuit, the impedance of medium depends on the geometry of the sensor; therefore one must model the sensor loaded by the medium. The general model, in figure 12, is obtained by symmetry from the simple model between two classical plane electrodes; that represents the impedance between two fingers.

Fig. 12. General electrical model for an interdigitated sensor with medium

CDL, e represents the double layer capacity per finger, ZMed, e and CCell, e the impedance of medium and the cell capacitance between two fingers. These components are governed by the same equations (1), (3) and (4), but with a different cell constant, which does not contain the term N and (N-1) : the electrodes form factor Ke (equation 17). Ke =

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theoretical part in figure 3.b. This simplified model proves that one can find the conductivity and permittivity of medium by using cell constant.

Fig. 13. Steps to simplify the model.

4.1.2 VHDL-AMS Description The sensor loaded by a blood sample is described, using VHDL-AMS, like a dipole consisting on simple passives components. Their values are calculated, from the geometric characteristics of the sensor and the medium. For example, the electrode constant is calculated with the Euler method in a loop (figure 14). The final circuit is realized by placing basic components with a loop; it is the “Port Map” function (figure 15). constant nu constant ki constant kip

: real : real : real

:= W/(W+G); :=sin(nu*MATH_PI/2.0); :=sqrt(1.0-ki**2);

pure function K(x:real) return real is variable y,nbr,pas,t,result: real; begin result:=0.0; pas:= 1.0/100000.0; for nbr in 1 to 99999 loop t:=real(nbr)/100000.0; y:= result + pas*(1.0/sqrt((1.0-t**2.0)*(1.0-(x**2.0)*(t**2.0)))); result:=y; end loop; return y; end K; constant Fac:real:= L*K(ki)/(2.0*K(kiP)); --------------------------------------------------------------------------------------constant ccel: real:=PHYS_EPS0*(epssub+epsech)*Fac;

Fig. 14. Example of calculation of constants (here: the electrodes form factor and CCell).

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begin proc1 :for i in 1 to (N/2) generate c1:entity WORK.capa(ideal) generic map (cap => cdl) port map ( P1 => P1, P2 => NM(2*i-1)); c2:entity WORK.capa(ideal) generic map (cap => cdl) port map ( P1 => P2, P2 => NM(2*i)); end generate proc1; proc2 :for i in 1 to (N-1) generate c1:entity WORK.res(ideal) generic map ( re => rsol) port map ( P1 => NM(i), P2 => NM(i+1)); c2:entity WORK.capa(ideal) generic map ( cap => ccel)

Fig. 15. Placement of components by Port Map for an electrolytic medium.

4.2 Results and Discussion 4.2.1 Ionic Solution Sample An ionic solution is characterized by its conductivity σSol. So, the medium impedance ZMed,e is a resistance. This model has been simulated in VHDL-AMs using SystemVisionTM with the same parameters used in ConventorWare® model. We choose N=16, L=1mm, W=38µm, S=26µm and σSol=0.7 S/m. The frequency analysis simulation is made by connecting an alternative current source to the sensor. An AC current of 1A was applied at a frequency varying from 100Hz to 1GHz. Figure 15 gives the simulation result of the impedance variation. The central plateau is the resistance of the solution. Impedance (Ohm) 10

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Fig. 16. Simulated impedance of a sensor with 16 electrodes, for an electrolytic medium with a conductivity of 0.7 S/m.

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4.2.2 Blood Sample The electric and dielectric behavior of blood sample present more properties than a simple ionic solution. It is constituted by free cells in an electrolyte : the blood plasma. The cells are composed by an electrolyte which is contained in an insulating membrane. The classical modeling is a resistance for the electrolyte, and a résistance in series with a capacitor is given by Fricke’s model (figure 17). We take for ZMed,e the equation of figure 17 with the electrodes form factor. For this simulation, we keep the same geometric parameters than the previous simulation for ionic solution. The values for blood’s parameters are σP=1.5 S/m, σC=1 S/m, Cm=1.75 µF/cm² and Ø=55%. The surface capacity of membrane is high but less than the capacity of double layer. Its effect appears at higher frequency. The results of the simulation are given in figure 18 and figure 19.

Fig. 17. Fricke’s Model for blood and its equivalent impedance in [Ohm.m]. With rP, rC, Cm, a and Ø the resistivity of plasma in [Ohm.m], the intern resistivity of blood cells in [Ohm.m], the membrane surface capacity in [F/m], the radius of blood cells in [m] and the volume in percentage of blood cells. Impedance (Ohm) 4

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Fig. 19. Conductance and capacity of a sensor with 16 electrodes for blood medium.

The figures 18 represents the impedance; one can see two plateaux which correspond respectively to the plasma resistance and plasma resistance in parallel with blood cell resistance. The value of capacitance is difficult to evaluate in this type of curve, but it is easily find in the figure 19. Figure 19 gives the conductance and the capacity versus the frequency. For the conductivity, each plateau represents respectively the plasma resistance and the plasma resistance in parallel with the blood cell resistance. In capacity, each plateau represents respectively the double layer capacity, the blood cells capacity and CCell.

5 Experimental Validation 5.1 Sensor Manufacturing The sensor was provided by our colleague from the IJL team (Institut Jean Lamour, Henri Poincaré-Nancy 1 University). It was obtained by a deposit of 500 nm platinum on an insulating glass substrate in a 5 steps process: • • • • •

Sensor cleaning with acetone and isopropanol. Deposition of a platinum by ion-beam sputtering. UV lithography: deposit resin, mask application, insolation and development. Ion beam etching. Removal resin with acetone and isopropanol.

Its geometrical parameters are N=100, W=4µm, S=8µm and L=1000µm. This is a first prototype sensor for which we recycled a mask designed for SAW interdigitated sensor previously developped at IJL. A printed circuit board (PCB) was designed and built to connect the sensor with an appropriat measuring instrument. The connections between the sensor and the PCB were realized using a gold wires bonding figure 20.

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Fig. 20. Sensor and PCB connection and its gold wires bonding and partial microscopic view of the fingers

5.2 Measurements The measurement system is composed of an LCR meter HP4284A controlled by © VEE software with a GPIB interface. It allows a fast and automatic measurements between 20Hz and 1 MHz. A photography of this system is given in the figure 20a. The measurements were performed with a calibrated drop of ionic solution. This solution contains 0.9% of NaCl, and has an approximate conductivity of 0.72 S/m. We placed a drop directly on the sensor, as shown on figure 21.b. The sensor connections (bonding) were not isolated, and can cause some errors of measurements. These first measurements were done just to validate the model . Figure 22 shows the measurement results compared to VHDL-AMS simulation results.

(a) Fig. 21. (a): Measurement system using the HP4284A PRECISION LCR METER. (b): Deposit of a drop of calibrated solution on the sensor.

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(b) Fig. 21. (continued) Impedance (Ohm) 5

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Simulation of complex system by the finite element method with CoventorWare®, takes lot of memory for computation. For N equal 100 electrodes, our simulation equipment was not able to model and simulate the whole system. The preliminary experimental measurements agree with the simulation. One can see a plateau at higher frequency, at a level close to that of simulation, but the instrument frequency limitation do not allow to check the precise level. The slope of the curve is slightly lower in measurement, because the real system response is not exactly the same those classical passive electronic components.

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6 Conclusion This paper presents a comparative approach for simulation of biological sensor modeling in physical and electrical domains using two softwares. CoventorWare® software for three dimensional interdigilal sensor simulation techniques to analyse the influence of the physical properties of the medium and the impedance response was used. The simulation results are in agreement with the theoretical equations of optimization. This optimization method used for bioimpedance spectroscopy sensor is obtained from theoretical equations, by developing total impedance equations and modeling equivalent circuits. The equations given relate the cutoff frequencies to the geometric parameters of the sensor and physical properties of the measured medium. A geometric structure of the sensor was proposed. The use of a square cross section permit to eliminate one of the geometric parameters of the sensor, that simplifies the optimization and the analysis of the sensor. Electrical modeling of the interdigital sensor and the medium is carried out with VHDL-AMS software from MENTOR GRAPHICS®. The use of VHDL-AMS language shows the advantage to combine multiphysical domains. The approach can be readily used in current electronic design flow to include distributed physics effects into modelling and simulation process with VHDL-AMS. Simulations results give similar results as physical simulation. However, all the physical properties are not represented, especially at high frequency. The useful properties are correctly simulated. The use of behavioural models in simulation simplify physics and explore interactions between different domains in a reasonable amount of time compared to physics modelling with CoventorWare® software. The simulation results of the impedance obtained with VHDL AMS don’t show any resonance because all the geometric parameters, such as thickness of the medium and the interactions between ions were not include in the model. The experimental results obtained with a sensor, designed by the IJL (Institut Jean Lamour, Nancy University) team, are in agreement with those obtained by simulation. The future goal is to design a specific sensor by optimizing its dimensions for blood measure samples. It will be necessary to design a tank on the active area of the sensor, to avoid measurement errors, and do measurement at higher frequency. The simulation and measured curves present many similarities; the preliminary experiment measures are satisfactory. The next goal is to realise our own sensor by optimizing dimensions to measure blood samples. It will be necessary to design a tank limited to the active area of the sensor, to reduce measurement errors, and allows measurement at higher frequency.

References [1] Faes, T.J., Meij, H.A., de Munck, J.C., Heethaar, R.M.: The electric resistivity of human tissues (100 Hz-10 MHz): a meta-analysis of review studies. Physiol. Meas. 1999 20, R1-10 (1999) [2] Katz, E., Willner, I.: Electroanalysis 15(11), 913–947 (2003) [3] Mukhopadhyay, S.C.: Sensing and Instrumentation for a Low Cost Intelligent Sensing System. In: SICE-ICASE International Joint Conference, pp. 1075–1080 (October 2006)

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[4] Mukhopadhyay, S.C., Gooneratne, C.P., Demidenko, S., Sen Gupta, G.: Low cost sensing system for dairy products quality monitoring. In: Proceedings of 2005 International Instrumentation and Measurement Technology Conference, IEEE Catalog Number 05CH37627C, pp. 244–249 (2005) ISBN 0-7803-8880-1 [5] Van Gerwen, P., Laureyn, W., Laureys, W., Huyberechts, G., Op De Beeck, M., Baert, K., Suls, J., Sansen, W., Jacobs, P., Hermans, L., Mertens, R.: Nanoscaled interdigitated electrode arrays for biochemical sensors. Sens. Actuators, B 49, 73–80 (1999) [6] Timms, S., Colquhoun, K.O., Fricker, C.R.: J. Microbiol. Meth. 26, 125 (1996) [7] Geng, P., Zhang, X., Meng, W., Wang, Q., Jin, L., Feng, Z., Wu, Z.: Electrochim. Acta 53, 4663 (2008) [8] Igreja, R., Dias, C.J.: Sensors and Actuators A 112, 291–301 (2004) [9] Borkholder, D.: Based biosensors using microelectrodes. Ph.D. dissertation, Stanford University, Palo Alto (1998) [10] Mamishev, A., Sundara-Rajan, K., Yang, F., Du, Y., Zahn, M.: Interdigital sensors and transducers. Proceedings of the IEEE 92, 808–845 (2004) [11] Sundara-Rajan, K., Byrd II, L., Mamishev, A.V.: Moisture content estimation in paper pulp using fringing field impedance spectroscopy. IEEE Sensors Journal 4, 378–383 (2003) [12] Olthuis, W., Streekstra, W., Bergveld, P.: Sensors and Actuators B 24-25, 252–256 (1995) [13] Jacobs, P., Varlan, A., Sansen, W.: Design optimisation of planar electrolytic conductivity sensors. Medical & Biological Enfineering & Computing, 802–810 (November 1995) [14] Abramowittz, M., Stegun, I.: Handbook of mathematical functions. Dover Publications Inc., New York (1965) [15] Dahmen, E.A.M.F.: Electroanalysis Theory and applications in aqueous and non aqueous media and in automated chemical control. Elsevier, Amesterdam (1986) [16] Bard, A.J., Faulkner, L.R.: Electrochemical methods, fundamentals and applications. John Wiley and Sons, New York (1980) [17] Kovacs, G.T.A.: Introduction to the theory, design, and modeling of thin-film microelectrodes for neural interfaces. In: Stenger, D.A., McKenna, T.M. (eds.) Enabling Technologies for Cultured Neural Networks, pp. 121–165. Academic, London (1994) [18] Bard, A.J., Faulkner, L.R.: Electrochemical Methods. Willey, New York (2001) [19] Jaspard, F., Nadi, M., Rouane, A.: Dielectric properties of blood: an invetigation of haematocrit dependance. Physiological Measurement 24, 134–147 (2003)

Water Quality Assessment through Smart Sensing and Computational Intelligence O. Postolache1,2, P. Silva Girão1, and J.M. Dias Pereira1,2 1

Instituto de Telecomunicações, IST, Av. Rovisco Pais, 1049-001 Lisboa, Portugal Tel.: +351 21 8417974 [email protected] 2 Escola Superior de Tecnologia de Setúbal (LabIM), Instituto Politécnico de Setúbal, 2910-761 Setúbal, Portugal Tel.: 351 265 790000 [email protected] Abstract. Surface water quality monitoring is one of the important activities in the environmental monitoring domain and implies complex measurement activities in order to obtain physical, chemical and biological characteristics of the water. Some of these characteristics are able to be measured in the field but imply the utilization of specific water quality sensors that are used by operators as individually units or, preferably, are part of distributed water quality monitoring networks particularly when monitoring extensive areas. Two concepts are nowadays associated with environment monitoring networks: smart sensing nodes and computational intelligence algorithms. Thus, different smart sensing nodes deliver data that are used by advanced processing units for different purposes, namely: (1) to evaluate the characteristics of water based on measurement channel indirect modeling; (2) to perform the short time and long term forecasting of these characteristics; (3) to detect pollution events and anomalous functioning; (4) to perform data recovering using intelligent algorithms such as neural network and adaptive neuro-fuzzy. The overall operation of the network is optimized if its nodes are provided with functionalities such as auto-identification, networking plug-and-play, auto-calibration, and fault detection. IEEE 1451 family of standards define all aspects necessary not only to transform a sensor into a smart sensor, but also to interface or integrate sensors in networks. In the paragraphs that will follow, we propose the architecture of a smart sensing node suitable for a distributed water quality monitoring network that is IEEE 1451 compatible. The emphasis is placed on the identification of each sensor – which permits individual addressing - and on the algorithms for multivariable characteristics modeling that prove to be very useful for accurate direct digital readout of water quality parameters. Keywords: IEEE 1451, smart sensor, RFID tag, neural network, adaptive neuro-fuzzy, water quality monitoring.

1 Introduction Quality monitoring of surface waters is an important issue to guarantee that they are adequate to the required uses [1][2]. Different measuring solutions have been S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 191–206. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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proposed by equipment providers and integrators, and by the R&D community. They include, as a rule, expensive equipments that also include proprietary protocols associated with sensing channel data acquisition, data processing and data logging. YSI, Campbell Scientific [3][4] are examples of manufacturarers that provides equipments in the area of field water quality monitoring which assure the measurement of multiple water parameters (e.g. pH, temperature, conductivity, turbidity, etc.) as so as parameters as depth and water flow. Such equipments present wired communication interfaces such as RS485 or SDI12 [5] that permits to develop water quality monitoring networks. The acquired data is usually sent from individual equipments to a central location using RS232 to wireless modems for relatively short distances such tenth of km (e.g RS232 3G/HSDPA modem from SIMCOM, RS232-RF modems from XStream) or even using RS232 Satellite Modem (e.g SLIN 0011AA - NAL Satellite Modem). In order to assure the auto-identification, auto-calibration and compatibility between different devices of and extended water quality monitoring network the smart sensor network technology is considered. Thus a standard for smart sensors, IEEE1451.X [6] that was developed in the late 1990’s was considered as an interesting solution for the water quality monitoring field. The standard permits an easy development of smart transducer manufacturing and an increasing connectivity of smart sensors to networks. Nowadays, the IEEE1451.X family is a set of protocols for wired interfacing (IEEE1451.0, IEEE1451.1, IEEE1451.2, IEEE1451.3, IEEE1451.4, IEEE1451.6) and wireless interfacing (IEEE1451.5 and IEEE1451.7) of smart sensors suitable for distributed applications including environment quality monitoring. The identification of different smart sensors is performed through the utilization of a memory embedded in the smart sensor and includes information about the transducer included in the so-called Transducer Electronic Data Sheet (TEDS). The direct access to the transducer manufacturing and calibration information is available only for IEEE1451 compatible transducers through TEDS [7], which limits the interest and importance of this standard, since many real systems are characterized by analogue outputs (4-20mA). To overcome this problem, IEEE1451.4 [8] protocol represent an interesting solution. Two different implementations of IEEE1451.4 are considered in the present chapter. The first one uses a 1-wire memory while the second uses the memory of an UHF RFID tag to store the Basic TEDS information [9] providing wired and wireless connection capabilities, respectively. As part of distributed water quality monitoring network, the hardware of a water quality monitoring node (sensing elements, conditioning circuits, acquisition, RFID tags and reader, and communication) must usually be complemented with signal processing blocks to perform different tasks such as data linearization, data compensation, short and long term prediction of pollution events (duration and concentration)[10-12]. Considering the nonlinearity of the single or multivariable characteristics associated with water quality measuring channels (e.g. conductivity measurement channel), intelligent data processing algorithms, such as neural networks and adaptive neuro-fuzzy [13] represents good option to improve the accuracy of the measurements through accurate models of measurement channels.

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2 Distributed Smart Sensing Network As mentioned before, the smart sensing node as part of water quality dis-tributed monitoring was the result of the work developed by a research team, including the chapter authors, to develop hardware and software for a system designed to perform the measurement of water quality and the sound monitoring on the Sado Estuary region, a well known place as a habitat for bottlenose dolphin families. The nodes (SSNi) constitute a network and are wireless connected to a base station (B_STAT) based on a personal computer (PC) land installed (Fig.1). Additionally a field measurement station (F_STAT) expressed by a laptop PC can be also considered [14].

Fig. 1. The sensing node distribution on the Sado Estuary (SSN1,SSN2,SSN3 – smart sensing nodes, F_STAT – field measurement station embedded on a ship, B_STAT – base station on the land).

Fig. 1 presents a set of three monitoring nodes that are distributed in three important localizations regarding the dolphin groups daily motion trajectories inside the Sado Estuary. The base station that is located on the land receive the data from the smart nodes and performs additional tasks such as intelligent processing, data logging and web based data publishing. 2.1 Smart Sensing Node’s Architecture Considering the Basic TEDS memory support, two architectures for water quality smart sensing nodes were considered and represented in Fig 2. the differences between the presented architectures being related to the existence of 1-wire network (Fig. 2. a) or RFID-system ( Fig.2. b). Important components of the nodes are: the WQ measurement module connected to low-cost 4-20mA transducers, the 1-wire uLAN or the RFID reader, the GPS module, the Wi-Fi interfacing module connected to a high gain Yagi antenna, and a power supply module including a solar panel.

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More details regarding the hardware and software components of the distributed water quality monitoring system including smart nodes IEEE1451 compatible are described in the following sub-sections. 2.1.1 WQ Parameters Sensing The water quality measurement module is constituted by a set of water quality transducers (WQT1, WQT2..WQTj) having 4-20 mA analog outputs. The measurement of only four parameters was considered: temperature, pH, conductivity and turbidity, which are measured using WQ101, WQ201, WQ301 and WQ770, from Global Water. A multi-channel current to voltage converter module based on RCV420 from Burr-Brown, assures a 0-5V output voltage for 4-20mA input current. The voltage signals are applied to the analog channels (ACH0-ACH3) of a data acquisition module (Ipsil IPu8930) that includes a 16-bit analog-to-voltage converter (ADC) and an Ethernet interface. The acquisition module is connected to the SW&Wi-Fi module that permits the wireless communication with the base PC. The compatibility of the water quality transducers with IEEE1451 standard is assured adding a 1-wire memory [15] to each transducer or an UHF RFID [16]. Based on the information received from sensors’ memories, the selection of previously

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calculated internal parameters of intelligent processing blocks are used to perform the voltage-to-water quality parameter (V-wqp) conversion. 2.1.2 IEEE1451.4 Implementations Identification represents an important feature of the smart sensors. Embedding the RFID technology at the level of the WQ smart sensing node, the transducer tracking can be done during calibration, testing in laboratory but also during on-field operation. As was mentioned above two kinds of memories are attached to the water quality transducers: 1-wire memories and UHF RFID tag memory. Both memory supports are able to store specific information denominated Basic TEDS. Table 1 identifies the Basic TEDS fields that are mandatory to comply with IEEE1451.4 and that are stored in the 1-wire or RFID tag’s memory. Table 1. Implemented Basic TEDS fields.

Components Manufacturer ID Model Number Version Letter Version Number Serial Number

Number of bits 14 15 5 6 24

Allowable range 17-16381 0-32767 A-Z 0-63 0-16777215

The Basic TEDS .Dot4 field is filed in with the values of each transducer (smart sensor) according to the specification given by the manufacturer. Two solution of TEDS’ implementation were considered: Basic TEDS based on 1-wire memory solution The WQ transducers are connected to the WQ measurement module that includes and Ethernet DAQ unit expressed by uP8930 from Ipsil that has a 16-bit ADC. The DAQ Ethernet port is connected to the sensing node Switch and Wireless Communication block (SW&Wi-Fi module). The transducer identification uses the Basic TEDS information codified according with IEEE1451.4 standard. Thus, the information for each transducer of the WQTj, as indicated in the Table 1, is stored in a 1-wire memory (DS2433) associated to each transducer and accessed through a 1-wire MicroLAN Coupler (DS2409) (Fig.3). A 1-wire to RS232 protocol converter is used to assure the connection between the 1-wire MicroLAN and the RS232 port of the SB72-EX (high performance Serial-to-Ethernet device) that is followed by an Ethernet/Wi-Fi bridge (D-LINK DWL-G820). The data from the transducers (WQT1, WQR2 …WQTj) memories (M1, M2 …Mj), that store the TEDS information (see Table 1), are individually read by the land unit (expressed by a PC) using a set of adapters including the MicroLAN coupler, RS232-to-Ethernet bridge, Ethernet-WiFi bridge. The land unit software uses the received data to extract extended information that is stored in water quality transducer database that is denominated Virtual TEDS [17].

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Basic TEDS based on UHF-RFID tag memory solution The architecture of the WQ measurement module is similar to the architecture presented on the Basic TEDS based on 1-wire memory solution. The difference is related to the existence of UHF RFID reader that replaces the elements such as 1-wire MicroLAN and the existence of RFID tags that replaces 1-wire memories. The RFID tags are attached as labels to the WQTj transducers (Fig. 4). WQ transducer Ti

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As it can be observed in Fig. 4, the low cost passive transponders (tagj, j=1…n, where tagj are expressed by ALL-9540-02 World Tag 860-960MHz in the present case) are attached to the water quality transducers WQTj’, as labels attached to the transducer cables the compatibility with IEEE1451.4 standard is assured. The Basic TEDS (Transducer Electronic Data Sheet) is stored in the tag’s memory. The main characteristics of the used tags are: EPC class1, RF communication protocol ISO/IEC18000-6 CEPC Class 1 Gen 2 (generation 2 – read and write many times), EPC memory size: 96 bits, access control: 32 bits, kill code: 32 bits. The typical distance between the reader and tags (read range) is of about 4m, which is enough for the present application. The identification process of the sensor with RFID label begins when the reader (ALR-8800 from Alien) is switched on: it starts emitting a signal in the selected frequency band (860MHz-960MHz). The tags reached by the reader’s field will “wake up” (supplied by the field itself) [18]. In order to discriminate between the received information from the multiple tags, an anti-collision algorithm is implemented at the reader level [19]. Considering the memory size of the used tags (96 bits for ALL-9540), the TEDS information storage is restricted to Basic TEDS. These informations can be written using the capabilities of ALR-8800 RFID reader that is designed to program EPC Class 1 Generation 2 tags [20]. Two external circular polarized antennas, ant1 and ant2 (ALR-8610-AC) operating at 850-875MHz and with 6dBi gain, are used to read the tag fixed on the transducers or to write the memory of the tags, including new elements, during operation. The ant 1 works as the transmitting antenna while ant 2 works as the receiving antenna (Fig.2.b). An important advantage of the UHF RFID tags is that they are easily attached to the transducer cable and they can be read and written wirelessly. However, a drawback is the impossibility to direct perform the identification tag-measuring channel that is done using an additional sense sw module (see Fig. 2). The module works under reader DIO port control, which switches on the measuring channel at the same time the procedure for the identification of the tag-measuring channel is run, thus assuring that the signal acquired on a specified channel (e.g. ACH0) corresponds to the

Fig. 5. System Efficiency, SE, versus antenna – tag distance for different attenuations of the emitted power of transmission RF signal

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identified transducer (e.g tag0). The transducers are connected one by one to the ADC of the uP8930, and at the same time, the corresponding tag is read. A verification procedure implemented by the software installed in the base station detects the existence of a new transducers connected to the WQ measurement module and simultaneously is obtained the information for the attached tag. The correspondence transducer-tag is stored in a file. Based on TEDS stored in the tag’s memory (e.g. TEDS1=1C02 0042 06A5 9000 is connected to ACH0), the processing parameters from intelligent virtual extended TEDS are extracted. Related to RF identification, a practical approach concerning the evolution of system efficiency, SE, defined as the relation between the number of detected tags and total number of tags, for different distances between ant1 and ant2 and the UHF tags (tagj) was carried out. Some of the obtained results are presented in Fig. 5.

3 Intelligent Modelling of Measuring Channels Direct and inverse modeling of sensors characteristics using intelligent algorithms such as artificial neural network and neuro-fuzzy systems are reported in literature [11-13][21][22] and represents one of the field of interest for the chapter authors . The purpose of the direct modeling is to obtain a neural network or an adaptive neurofuzzy designed model of the measuring channels in such way that the outputs of the considered channel and the model match closely. The direct model corresponds to the calibration curve model obtained for a given measuring channel, while inverse model uses the measuring channel output data (acquired voltages) to extract the information related the measured physical value (e.g. water pH). In the particular case of water quality monitoring application, the data received by the base station from the smart sensor node trough Wi-Fi communication are processed using inverse model coefficient that are stored in a database named Intelligent Virtual TEDS (IV-TEDS). The model selection (coefficient selection) is performed using the Basic TEDS information that is stored in the memory associated to each measuring channel (1-wire memory, passive RFID tag memory). Two intelligent algorithms were considered in order to perform a comparison between the modeling accuracy and model implementation complexity for well known multilayer perceptron artificial neural network (MLP-NN) [23][24] and adaptive neuro-fuzzy (ANFIS) [25][26] algorithms. Accurate models conduct to accurate digital reading of the water’s physical characteristics (e.g. temperature, conductivity, pH, turbidity). Thus using as input values the normalized acquired voltages from the corresponding measuring channel and the model coefficients (MLP-NN or ANFIS coefficients) stored on the IV-TEDS database the normalized values of water quality parameters are calculated. In the particular case of the multilayer perceptron neural network (MLP-NN) represented in Fig. 6, the specific weights and biases (model coefficients) are obtained during the training phase based on a training algorithm such as the Backpropagation or the Levenverg Marquardt [23]. Training uses a training set constituted by known values of the WQ parameters (normalized values) and the common influence factors (e.g. temperature) and the corresponding voltage values obtained at the output of the measuring channels. Fig. 6 depicts the overall processing scheme that includes the neural network processing block associated with WQ measuring channel inverse

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modeling with external common factor compensation [27]. Fig. 7 shows the diagram including a set of six layers associated to an ANFIS processing structure, WQ measurement channel inverse model, that includes the fuzzification, the implication and (if needed) defuzzification stages.

Vm

Vi N ViN

VmN OL

IL

HL

wqpNNN N-1 wqpNNN

Fig. 6. Water quality parameter neural processing scheme (Vm – main input voltage, Vi- influence factor voltage, IL – neural network input layer, HL- neural network hidden layer, OLneural network output layer, N, N-1 norm. and denormalization blocks).

IL

Vm

mf-IL RL mf-OL wSO

O

weighted sum

Vi wqc

normalization factor

Fig. 7. A Water quality parameter ANFIS inverse model designed to extract the WQ values with external influences (e.g. temperature influence) correction ( IL-input layer, mf-ILmembership function input layer, RL-rule layer, mf-OL – membership function output layer, wSO-weighted sum output, O-output, wqc-compensated value of measured water quality parameter).

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Regarding the inverse modeling can be mentioned that during the measurement, the Vm and Vi (e.g.Vm=VC, Vi= VT ) acquired voltages are converted into values of water quality parameters (e.g. conductivity C). Referring to the neural network architecture, two single input-single output neural networks, one for VT →T and another for VTU→TU conversion, and two dual inputsingle output neural networks, one for (VpH, VT)→ temperature compensated pH and another (VC, VT)→ temperature compensated C conversion were designed and implemented. The networks have one single hidden layer (HL), the training algorithm in all four cases was Levenberg Marquardt and sum-square errors (SSE) of 1E-4 are the stop condition. The MLP-NNs training and test are performed using a data set of voltage values delivered by the transducers during the calibration phase. The calibration solutions used were formazin {10, 20, 40, 100, 200, 400, 800, 1000}[NTU] for TU transducer, buffer solutions {4, 5, 6, 7, 8, 9} for pH transducer, and {84, 447, 1500, 2764}[uS/cm] for conductivity. Calibrations were performed in a laboratory for different temperatures, TЄ[5; 30]°C. A study concerning the number of neurons in the hidden layer and neural network inverse model accuracy can be done. Good results are normally obtained for tansignoid [28] neurons in the hidden layer expressed by the following numbers: nhidden|MLP-NNT =6, nhidden|MLP-NNpH =7, nhidden|MLP-NNC =12, nhidden|MLP-NNTU =5. After MLP-NNs design, the calculated neuron weights and biases are stored in the intelligent virtual TEDS whose organization is presented in Table 2. On-line neural conversion and compensation blocks that use intelligent virtual TEDS information are based on the following relation:

wqp = W2 ⋅ tanh(W1 ⋅ V + B1) + B2

(1)

where W1 and W2 represent the weights matrices, B1 and B2 the biases matrices of the neural network designed to obtain the WQ parameter value (wqp) using the acquired normalized voltages VTU, VC, VpH and VT included in vector V. Referring to the ANFIS model architecture, the first layer of neurons is denominated input layer (IL) and receives the input from the measuring channels (acquired voltage values). The second layer, membership function input layer (mf-IL), calculates the fuzzy membership degree to which the input voltage values (e.g. Vm=VpH) are mapped from the input voltage intervals to the unit interval through a membership function, mf. This mf can be defined in linguistic terms highlighting the advantage in this sense of the ANFIS models representation. For example, the pH of water under test is not defined in a crispy sense as acid or neutral but rather as 0.5 acid and 0.5 neutral. Each node (neuron in the neural network sense) of the mf layer includes an mf for one of the inputs (e.g Vm=VpH and Vi=VT). Fig. 7 shows the ANFIS architecture with two inputs and four corresponding membership functions. The mf functions used in the present work are of the triangular, trapezoidal and Gaussian type, the last one being defined by:

(

μ Aij x si ,cij , σij

)

⎛ ⎛ x − c ⎞2 ⎞ j ij = exp ⎜ − ⎜ ⎟ ⎟ ⎜⎜ ⎜ 2 ⋅ σ ⎟ ⎟⎟ ij ⎠ ⎝ ⎝ ⎠

(2)

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where the cij, σij internal parameters are adjusted during the adaptive neuro-fuzzy network training phase. The third layer is the rule layer (RL) and represents associations between the input and the output variables. The number of rules (n - number of rules layer neurons) is normally included in the 4 to 100 interval for the models associated with WQ measuring channels taking into account their non-linearity. The rules syntax is characterized by the following structure: If xS1 is A1 and xS2 is B1, then f1 = p1 ⋅ xS1 + q1 ⋅ xS2 + r1 If xS1 is A2 and xS2 isB2, then f2 = p2 ⋅ xS1 + q2 ⋅ xS2 + r2 … If xS1 is An and xS2 is Bn , then fn = pn ⋅ xS1 + qn ⋅ xS2 + rn

(3)

which corresponds to a Sugeno fuzzy inference system [29]. In (3) the Ai and Bi are linguistic terms. For example, in the case of the ANFIS model for pH measurement channel, A1 represents “high acid”, B1 “low temperature”, xS1 and xS2 are model input values, and p1…pn and q1…qn, r1…rn are consequent parameters. Table 2. TEDS for a two input – one output neural network.

ANN (TEDS) Fields Name Number of ANN inputs Number of ANN output Number of ANN layers

Value

N

The number of ANN layers includes the input, the hidden and the output layers

ANN training stop condition

α

The ANN training stop condition is expressed by a sum square error value (SSE)

ANN hidden layer neurons weights matrix

W1

The hidden layer neurons weights are used to calculate the hidden neurons output values

B1

The hidden layer neurons biases are used to calculate the hidden neurons output values

W1

The output layer weights are used to calculate the ANN output

B2

The output layer biases are used to calculate the ANN output

ANN hidden layer neurons biases vector ANN output layer weights transposed vector ANN output layer biases

Comments

ninput

The input variables are the sensors’ output voltages

noutput

The output variable is the temperature corrected value Hy

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The fourth layer calculates the degrees to which output membership functions, oSic are matched by input data:

oSic = w i ⋅ fi

(4)

where wi is the firing strength of rule i. Layer five includes summation of rule outputs and firing strength, the former sum being divided by the latter on the sixth layer to yield the overall output of the system. In the particular case of single input – single output ANFIS model for a WQ measuring channel, the output corresponds to a water quality parameter such as temperature or turbidity while for the measuring channels such as pH, electrical conductivity, or dissolved oxygen the output corresponds to the respective temperature corrected values. The ANFIS model design uses a backpropagation hybrid algorithm [30] as training algorithm. We designed and implemented the ANFIS models using the MATLAB fuzzy toolbox. Referring to the models accuracy, the performance criteria based on the maximum absolute error (emax) and root mean square error (rms) were evaluated. Table 3 highlights the numerical values for pH measuring channel. Table 3. Accuracy of FuNNpH versus FuNN’s architecture and training type – FuNN testing phase.

No. mf 2

4

10

mf type Triangular trapezoidal Gauss Triangular trapezoidal Gauss Triangular trapezoidal Gauss

Bkp Hyb No. of epoch 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Bkp.

Hyb. emax

0.687 0.681 0.688 0.683 0.686 0.685 0.682 0.681 0.684

0.0031 0.0258 0.0071 8.861E-5 0.0064 0.0016 0.0001 0.0001 0.0001

As shown in Table 3, the number of training epoch is independent on the training algorithm (Backpropagation (Bkp) or Hybrid (Hyb)) while the accuracy depends on the number and type of adaptive neuro-fuzzy membership functions. For the presented case the best results are obtained for four triangular membership functions. Comparing the MLP-NN and ANFIS as intelligent algorithm used to obtain the direct and inverse modeling of water quality both algorithm were proven to be universal approximators with good results on direct digital readout of WQ values starting from acquired voltages associated with WQ measurement channels. Thus in particular measurement conditions and for particular measurement ranges both applied methods are characterized by errors lowers than 1%. Thus, a comparison between the obtained results is presented in Table 4.

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Table 4. The evolution of relative errors associated with water quality measurement using the designed intelligent algorithms: multilayer perceptron neural network, adaptive neuro-fuzzy inference system

Intell. algorithm\errors

εT(%)

εpH(%)

εC(%)

εTU(%)

MLP-NN

0.22

0.26

0.23

0.18

ANFIS

0.31

0.59

0.42

0.25

The results presented in Table 5 corresponds to the temperature (T), pH, conductivity (C) and turbidity (TU) variation ranges that corresponds to the particular case of Sado River Estuary (ranges (5°C
4 GUI The GUI associated with the software implemented in the based station permits the remote control of the data acquisition module, 1-wire memory through the 1-wire microLAN as so as the RFID tag reading through the RFID reader. For the particular

Fig. 8. Smart Sensing WQ Node – GUI.

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case of RFID usage for transducer identification the graphical user interface is represented in Fig. 8. The user can activate independently functionalities such voltage acquisition and tag reading through the ACQ and RFID buttons. Imposing the time interval between two readings (e.g. Dt=30s), the voltages from WQ measuring channels (T, pH, C, TU) are received through the wireless communication between the node and base station together the Basic TEDS information that internally select from the Intelligent Virtual TEDS database the type of algorithm and the corresponding values of the intelligent processing coefficients (e.g. weights and biases for MLP-NN). These values are used to convert the acquired voltage in WQ value and represented in WQ time evolution chart.

5 Conclusion In this chapter a smart sensor node architecture prototype, for water quality monitoring, that uses low cost analog WQ sensors (4-20mA output) is presented. 1-wire networking and RFID technology were used to assure the IEEE1451.4 compatibility for the water quality measurement node and at the same time to serve as identification for the intelligent virtual transducer data sheet database. Two intelligent processing algorithms, related with water quality modeling, were presented and their performance evaluated. The referred algorithms includes a multilayer perceptron artificial neural network and an adaptive fuzzy neural network, both were used to perform an universal approximation of transducers’ characteristics. A practical approach regarding the intelligent algorithm design was carried out as so as a novel representation of neural network coefficients representation on the virtual TEDS database where the input identifiers are obtained by communication with individual memories associated with WQ transducers. Optimal modeling of the water quality measurement channel characteristics conducts to accurate digital readout of the water quality parameters. Thus, values of relative errors less than 1% were obtained for the particular variation ranges of water parameters, such as temperature, pH, conductivity and turbidity, in Sado Estuary.

References 1. EPA - Monitoring and Assessing Water Quality: http://water.epa.gov/type/watersheds/monitoring/ monitoring_index.cfm 2. Harmanciogammalu, N.B., Fistikoglu, O., Ozkul, S.D., Singh, V.P., Alpaslan, M.N.: Water Quality Monitoring Network Design. Springer, Heidelberg (1999) 3. YSI Environment - YSI-6600 EDS - YSI Catalog, http://www.ysi.com 4. Campbell Scientific, Stand-alone Water Quality Monitoring and Control, http://www.campbellsci.com/water-quality 5. SDI-12 – SDI-12 Protocol Specifications, http://www.sdi-12.org/ 6. IEEE Standard for a Smart Transducer Interface for Sensors and Actuators - Network Capable Application Processor (NCAP) Information Model, IEEE 1451.1- 1999 Standard (1999), http://standards.ieee.org/catalog/olis/im.html

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7. Viegas, V., Dias Pereira, J.M., Silva Girão, P.: Smart Transducer Block Enables Plug & Play Transducers. In: Proceeding IMEKO World Congress, Lisbon, September 2009, pp. 1452–1455 (2009) 8. IEEE Std 1451.4-2004, Standard for a Smart Transducer Interface for Sensors and Actuators- Mixed-Mode Communication Protocols and Transducer Electronic Data Sheet (TEDS) Formats. IEEE Standards Association, Piscataway, NJ, subclause 5.1.1 (2004) 9. Kim, J., Kim, D.-J., Byun, H.-G., Ham, Y., Jung, W., Han, D.-W., Park, J.-S., Lee, H.-L.: The definition of basic TEDS of IEEE 1451.4 for sensors for an electronic tongue and the proposal of new template TEDS for electrochemical devices. Elsevier Talanta 71(4), 1642–1651 (2007) 10. Patra, J., Kot, A., Panda, G.: An Intelligent Pressure Sensor Using Neural Networks. IEEE Trans. on Inst. and Meas. 49(4), 829–834 (2000) 11. Pereira, J.M.D., Postolache, O., Girão, P.M.B.S., Cretu, M.: Minimizing Temperature Drift Errors of Conditioning Circuits Using Artificial Neural Networks. IEEE Trans. on Inst. and Meas. 49(5), 1122–1127 (2000) 12. Postolache, O., Dias Pereira, J.M., Girão, P.M.: Aplication of Neural Structures in Water Quality Measurements. In: Proc. IMEKO World Congress, Wien, Austria, vol. IX, pp. 353–358 (September 2000) 13. Postolache, O., Girão, P.M., Dias Pereira, J.M., Ramos, H.G.: Intelligent Processing of the Dynamic Response of Sensors for Water Quality Monitoring. In: Proc. IEEE International Conf. on Signals, Systems, Devices- SSD, Tunisia, vol. I (March 2005) 14. Postolache, O., Girão, P., Patricio, G.P., Sacramento, J.S., Macedo, P.M., Dias Pereira, J.M.: Distributed Instrumentation and Geographic Information System for Dolphins. In: Proc. IEEE International Instrumentation and Technology Conf. - I2MTC, Victoria, Canada, vol. I, pp. 1777–1782 (May 2008) 15. Ulivieri, N., Distante, C., Luca, T., Rocchi, S., Siciliano, P.: IEEE1451.4: A way to standardize gas sensor. Sensors and Actuators B: Chemical 114(1), 141–151 (2006) 16. Postolache, O., Girão, P., Dias Pereira, J.M.: An IEEE1451.x and RFID compatibility unit for water quality monitoring. In: Proc IMEKO World Congress, Lisbon, Portugal, vol. 1, pp. 2178–2182 (2009) 17. National Instruments, Upgrading Your System for Virtual TEDS, http://zone.ni.com/devzone/cda/tut/p/id/4470 18. Finkenzeller, K.: RFID Handbook: Radio-Frequency Identification Fundamentals and Applications. Wiley, Chichester (2000) 19. Piramuthu, S.: Adaptive Framework for Collisions in RFID Tag Identification. Journal of Information & Knowledge Management 7(1), 9–14 (2008) 20. Alien, ALN-9540 Squiggle® Inlay – Product overview, http://www.alientechnology.com/docs/products/ DS_ALN_9540_Squiggle.pdf 21. Postolache, O., Girão, P., Dias Pereira, J., Ramos, H.: Intelligent Processing of the Dynamic Response of Sensors for Water Quality Monitoring. Trans. on Systems, Signals and Devices 3(4), 539–550 (2008) 22. Postolache, O., Girão, P., Dias Pereira, J., Ramos, H.: Self-organizing Maps Application in a Remote Water Quality Monitoring System. IEEE Transactions on Instrumentation and Measurement 54(1), 322–329 (2005) 23. Haykin, S.: Neural Networks - A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)

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24. Patra, J.C., Bos, A., Kot, A.C.: An ANN-based smart capacitive pressure sensor in dynamic environment. Sensors and Actuators 86, 26–38 (2000) 25. Jang, J.S.R.: ANFIS: adaptive-network-based-fuzzy-inference-system. IEEE Trans. Syst. Man Cybernet. SMC23, 665–685 (1993) 26. Jang, J.S., Sun, C., Mizutani, E.: Neurofuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs (1997) 27. Patra, J.C., Bos, A., Kot, A.C.: An ANN-based smart capacitive pressure sensor in dynamic environment. Sensors and Actuators 86, 26–38 (2000) 28. Postolache, O., Girão, P., Dias Pereira, J.: Smart Sensors and Intelligent Signal Processing in Water Quality Monitoring Context. In: International Conf. on Sensing Technology ICST, Lecce, Italy, vol. 1 (June 2010) 29. Zhang, H., Liu, D.: Fuzzy Modeling and Fuzzy Control. Springer, Heidelberg (2006) 30. Shoorehdeli, M., Teshnehlab, M., Sedigh, A.: Identification using ANFIS with intelligent hybrid stable learning algorithm approaches. Neural Comput. & Applic. (18), 157–174 (2009)

Multi-spectral Analytical Systems Using LIBS and LII Techniques Satoshi Ikezawa, Muneaki Wakamatsu, Yury L’vovich Zimin, Joanna Pawlat, and Toshitsugu Ueda Graduate School of IPS, Waseda University

Abstract. In this paper, we propose an advanced approach to particle analysis, involving laser-induced breakdown spectroscopy (LIBS) and laser-induced incandescence (LII) temporal analytical techniques. Various technical properties of fine particles are analyzed via LIBS and LII. LIBS is a useful tool for determining the elemental composition and relative concentration of various materials, whereas LII facilitates the measurement of particle size. Both techniques do not require any pre-processing. The combined use of the LIBS and LII techniques enables highly synergistic fine particle measurement. In the LIBS section, we propose spectrometric analysis via a novel ink-jet technique, and we discuss the effectiveness of Ar as a surrounding gas. In the LII section, we compare the calculated particle size prediction with the experimental results. Keywords: LIBS, LII, particle measurement, real-time measurement.

1 Introduction In the laser-induced breakdown spectroscopy (LIBS) technique, a high-energy laser pulse is focused on a sample to create plasma. Emissions from the atoms and ions in the plasma are collected using lenses, guided toward a spectrograph and streak camera, and analyzed using a computer. Some of the well-known analytical techniques that involve the atomic emission spectroscopy (AES) method for vaporization and excitation include arc/spark spectrometry, inductively coupled plasma (ICP) spectrometry, direct-coupled plasma (DCP) spectrometry, microwave-induced plasma (MIP) spectrometry, and laser ablation inductively coupled plasma mass spectrometry (LAICP-MS). These methods typically require laboratory-scale analytical facilities. The advantage of LIBS is that it is a compact and stand-alone system for determining the elemental composition of various materials, regardless of their physical state (solid, liquid, or gas), and it requires no pre-processing. Despite the advantage of qualitative analysis, LIBS is often limited as an elemental analysis technique because of its insufficient sensitivity for quantitative analysis, as compared to other AES methods. Our research group has improved the LIBS system for use in quantitative analysis [1-3]. For quantitative use of the LIBS system, a micro-droplet sub-system for sampling was designed. This system makes it possible to generate a constant volume of the sample liquid, and the droplet size, which is smaller than the laser beam spot diameter, can be obtained by separating the droplet from its surroundings. Using these liquid micronization methods, improved sensitivities and quantitative data were obtained. Although S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 207–232. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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the basic quantitative LIBS measurement was accomplished, there were several uncertainties, especially in the particle size measurement. It was difficult to analyze the particle volume because of the breakdown process of the LIBS system. Although the LIBS measurements for particles of various sizes were taken into account, the same intensity values were obtained in the case of the same mass concentration of elements. In this study, particle size measurement was accomplished using the LII measurement technique for the first time. The LII signal was obtained by recording a time profile of the intensity, depending on the particle size.

2 LIBS Measurement Fig. 1 shows a CG image of the LIBS system with the micro-drop sampling system. The optical layout of the LIBS system consists of four subsystems: a Nd:YAG laser, a spectrograph, a streak camera, and a delay pulse generator. The Nd:YAG laser was operated at 1064 nm to generate a 52.8-mJ Q-switched pulse with a width of 8 ns (full width at half maximum, FWHM). The emissions from the target were guided into the spectrograph and dispersed by a grating with a groove density of 1200 lines/mm, and the resulting electrical signal was recorded using a streak camera. The data were stored in a PC.

Fig. 1. CG image of LIBS system using micro-drop sampling system

Extraction and relaxation of the spectrum of element formation, and the subsequent dissipation of the laser plasma occurred very rapidly. Within 4 μs of plasma initiation, intense continuum radiation was observed, along with ionic lines over a broad wavelength

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range; within 30 μs of atomic emission, the spectrum of the element was observed. The plasma emission within 4 μs was caused by bremsstrahlung spectra and recombination radiation from the plasma as free electrons and ions recombined in the cooling plasma. The intensity of the spectrum emission was optimized with respect to the plasma background. Spectral measurements were carried out after an appropriate time delay to allow for the decay of the continuum radiation. To accomplish rapid observation in under 1 μs, a streak camera was used in this LIBS system. Fig. 2 shows a schematic representation of the measurement concept for temporal analysis using a streak camera. The streak camera was operated by directing the light onto a photocathode. Photons produced electrons via the photoelectric effect. The electrons were accelerated in a cathode ray tube and passed through an electric field produced by a pair of sweep electrode plates, which deflected the electrons sideways. By modulating the electric potential between the plates, the electric field was quickly changed to produce a time-varying deflection of the electrons, sweeping the electrons, amplifying by a micro-channel plate (MCP), across a phosphor screen at the end of the tube. Finally, a CCD array was used to measure the streak pattern on the screen, and thus the temporal profile of the light pulse.

Micro-channel plate

Phosphor screen

Sweep electrode

Slit

Photoelectron surface

Spectrum

Fig. 2. Schematic representation of measurement concept for temporal analysis using streak camera

Fig. 3 shows a typical temporal profile of sodium atomic spectra recorded by a streak camera. The spectrum evolves as the plasma cools. The earliest phase of the plasma emission was dominated by a continuum that cannot be distinguished from the atomic spectrum. This overlapping was caused by bremsstrahlung radiation and recombination radiation from the plasma as free electrons and ions recombined in the plasma cooling process. The plasma expands with time and the excited species relax further. The spectrum became visible approximately 4 μs after the discrete spectral lines originating from various ionic species. After the creation of plasma, both the

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signal and background emissions evolved and decayed at their own separate rates. The background emission decayed at a considerably faster rate than the sodium atomic emission. To obtain a good signal-to-background ratio, a proper time-gate was established. Fig. 4 represents the relationship between the cumulative number of laser shots and the smoothness of the signal intensity data. Wavelength [nm] 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 0 5 10 15

]s 20 μ [e 25 im T 30 35 40 45 50

low ← intensity → high Fig. 3. Typical temporal profile of sodium atomic spectra

Single shot

100 shots

1000 shots

Fig. 4. Relationship between laser shot and smoothness of intensity data

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3 Micro-drop Sampling Sub-system Fig. 5 shows the micro-drop sampling system for LIBS measurement. The microdroplet nozzle ejected uniform droplets under stroboscopic illumination by an LED. The piezoelectric dispenser was capable of delivering up to 2,000 droplets/s. The nozzle diameter was 30 μm. The droplet size could be varied in a narrow range by adjusting the voltage and voltage pulse duration. The velocity of the droplets increased with the voltage, and a longer pulse duration led to larger droplets. In order to obtain the best stability and uniformity of droplets, optimum voltage parameters were set for every experiment. The piezoelectric head was a droplet-on-demand device that provided single, isolated droplets with a diameter of 33.89 μm at an initial velocity of 2 m/s. To synchronize the laser pulses with individual droplets, two delay pulse generators were used on the LIBS system.

CCD camera

Microscope tube

Droplet nozzle LED

Fig. 5. Schematic representation of micro-drop sampling sub-system

Fig. 6 shows microphotographs of the ejection of a micro-drop. The photographs show micro-droplet ejection from a nozzle under LED stroboscopic illumination from 150 μs to 300 μs after the laser was triggered, at intervals of 50 µs. Fig. 7 shows the measurement results for the average droplet diameter, which was calculated from the average droplet volume. Each average droplet volume was obtained from the liquid density and average weight of a single droplet, which was calculated from the measured liquid weight consumption divided by the total number of droplets ejected in each 30-min time-span. The results indicate that the micro-drop sampling system was stable during LIBS quantitative measurement and that the droplet sizes were small enough to be confined in the laser beam spot (53.2 μm).

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t = 150μs

t = 250μs

t = 200μs

t = 300μs

Fig. 6. Microphotographs of micro-drop ejection

Droplet diameter 40.00

diameter [μm]

35.00

Average:33.89[μm]

30.00 25.00 20.00 15.00 10.00 5.00 0.00 0

30

60

90

120

150

180

210

240

270

300

330

360

time [min] Fig. 7. Measurement results for droplet diameter

The motion equation of a droplet that was used to obtain an accurate displacement value is shown in the following. Equation (1) represents the motion equation of a droplet.

mw

dv = mw g − κ v , dt

(1)

where mW is the reduced mass of a droplet obtained by subtracting the buoyancy force, v is the droplet velocity, g is the acceleration of gravity, κ is the viscous friction coefficient given by

κ = 6πη r

(2)

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where η is the fluid viscosity (assuming that the surrounding gas is air), and r is the radiation of the droplet. A solution for the above differential equation (1) is given by

v = C1e

−

κ mw

t

+

mw g

(3)

κ

The displacement equation of a droplet is derived from equation (3), and then given by

x=−

mw

κ

−

C1e

κ mw

t

+

mw g

κ

t + C2 ,

(4)

where C1 and C2 are integral constants. C1 and C2 are obtained by substituting the initial values in equations (1) and (2). For the calculation of the above equations, the following parameters were assumed: the initial velocity was 2 [m/s], the droplet liquid density was 1000 [kg/m3], the surrounding gas density was 1.2 [kg/m3], and the fluid viscosity η was 1.82 × 10-5 [Pa·s]. Fig. 8 shows the calculation results for the changes in temporal velocity. The rate history depended on the particle size. In the case of a particle diameter of 30 μm, the droplet velocity almost reached terminal velocity after 10 ms following droplet ejection. Fig. 9 shows a time profile of the droplet displacement. The result shows that when a 30-μm droplet is selected, the intersection of the laser ray with the droplet track should be set within 6 mm of the ejection nozzle. A micro-shutter system was installed just below the ejection nozzle to prevent damage from shockwaves caused by the laser plasma.

Fig. 8. Temporal changes in droplet velocity

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Fig. 9. Time profile of droplet displacement

4 Time-Control System for LIBS Fig. 10 is a photograph of the temporal control device for LIBS, which makes it possible to provide a trigger signal to the LIBS system, an inkjet system, and a micro shutter drive unit. The laser pulse signal and nozzle protection motion that use the micro shutter of this system were temporally controlled by this pulse generator, which was synchronized with the micro-droplet ejection.

Fig. 10. Temporal control device for LIBS

Fig. 11 shows a schematic diagram of the LIBS temporal signal sequence. The temporal setting parameters were determined based on the following considerations. (1) The micro-shutter thickness was 0.7 mm. (2) The path length of a droplet through the shutter was positioned 1.3 mm below the ejection nozzle. Thus, the time required following droplet ejection was 0.75 ms. (3) The lag in response time between the trigger signal and droplet ejection was 105 μs.

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(4) Time sequences (2) and (3) required a 0.85 ms delay time for shutter closing after droplet ejection was triggered. (5) The shutter was operated for 2.0 ms until it was in a fully closed position. (6) The delay time for input signal to the laser was set at 2.85 ms from the triggering of droplet ejection as per the requirements of (4) and (5). (7) The required time for ejection from the nozzle was 2.75 ms (subtracted ejection time lag, 0.1ms), and the mechanical delay time of the laser was 0.2 ms. (8) After 2.95 ms, a droplet was located 4.0 mm beneath the nozzle.

Fig. 11. Schematic diagram of temporal control for LIBS

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5 Gas-Flow Assist System for LIBS Fig. 12 shows the scheme of the micro-droplet subsystem with gas flow assistance. The micro-droplet ejection of this subsystem was temporally controlled by drive electronics that were synchronized with the laser pulse signal. The flow rate of the ambient gas was controlled using the flow-limit valve of the mass flow meter.

Gas inlet

Laser

Sponge material

Micro shutter

Porous ring

Plasma emission

Fig. 12. Schematic of micro-drop subsystem with gas flow assistance

To make the pressure more uniform, the inflow gas was dammed axisymmetrically around the dispenser with sponge material. The gas then passed through the porous metal ring used to straighten the gas flow. Finally, the gas was jetted out from a 5-mm orifice for use as the ambient gas that facilitated the plasma formation of the microdroplet. The gas flow rates were adjusted to be 0.5 L/min. In the experiment, the micro-droplet ejection system with gas flow assistance was operated using two kinds of ambient gas: air and argon gas (Ar).

6 Evaluation of the Effectiveness of Micro-drop System Figs. 13, 14 and 15 show comparisons between the data for the 0.1 M NaCl solution and those for the 0.3 M NaCl solution. The atomic spectral intensity had a tendency to increase with the density of the solution. The intensities using the micro-drop technique were higher than those obtained by the conventional method that uses a bulk liquid technique. From a comparison between the data obtained from micro-droplet technique from and the conventional technique, it could be seen that the new LIBS solution-measuring technique was advantageous.

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Fig. 13. Intensity difference for 0.1 mol/L sodium chloride solution between bulk liquid measurement performed using the conventional technique and 33.89-μm micro-drop measurement obtained from 100 laser pulse shots.

Fig. 14. Intensity difference for 0.2 mol/L sodium chloride solution between bulk liquid measurement performed using the conventional technique and 33.89-μm micro-drop measurement obtained from 100 laser pulse shots.

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Fig. 15. Intensity difference for 0.3 mol/L sodium chloride solution between bulk liquid measurement performed using the conventional technique and 33.89-μm micro-drop measurement obtained from 100 laser pulse shots.

7 Evaluation of the Effectiveness of Ar Ambient Fig. 16 shows a comparison between the experimental results obtained for the use of an air ambient and argon (Ar) ambient. The result shows that even though a higher maximum intensity was obtained in the experiment performed in an Ar ambient than in an air ambient, this does not necessarily mean that an Ar ambient yields a more easily detectable fluorescence signal. One way to ascertain the effect of performing the experiment in an Ar ambient as opposed to an air ambient, is to perform different time integrations of intensities, as shown in Figs. 17 - 21.

Fig. 16. Comparison of intensities for 0.1 mol/L sodium chloride micro-drop between air ambient (below) and argon ambient (above) measurements obtained from 1000 laser pulse shots.

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Fig. 17. Time integration profiles (0–50 μs) for different values of intensities (opposite to the plasma backgrounds) in air and argon ambients

Fig. 18. Time integration profiles (5–50 μs) for different values of intensities (opposite to the plasma backgrounds) in air and argon ambients

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Fig. 19. Time integration profiles (10–50 μs) for different values of intensities (opposite to the plasma backgrounds) in air and argon ambients

Fig. 20. Time integration profiles (15–50 μs) for different values of intensities (opposite to the plasma backgrounds) in air and argon ambients

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Fig. 21. Time integration profiles (25–50 μs) for different values of intensities (opposite to the plasma backgrounds) in air and argon ambients

8 Measurement of Carbonaceous Particles Fig. 22 shows a photograph of a diesel particulate trap filter and scanning electron microscopy (SEM) images.

Fig. 22. Diesel particulate matter deposited on filter after use: trap filter (left); and SEM images (right).

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Particulates of various sizes ranging from a few hundred nanometres to 20 µm were deposited on the filter after use. Particulates with sizes of 833 nm and 2.7 µm that were deposited on the filter are shown in Fig. 22. The trap filter (TX40HI-20WW) was used to test the exhaust emissions from a diesel engine operating in the JE05 mode.

Fig. 23. Results of SEM-EDX quantitative analysis for elemental mol fraction of unused filter using ZAF correction

Fig. 24. Results of SEM-EDX quantitative analysis for elemental mol fraction of diesel particulate using ZAF correction

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These pie charts show that the main component of the diesel particulate was carbon. It was assumed that the other elements, namely boron, oxygen, silicon, and fluorine, were generated from the Teflon-coated borate glass fibre that was used for construction of the filter. The concentration of sulphur in the diesel particulate was very low, which indicates efficient desulphurization of the fuel. The source of aluminium in the particulate was thought to be an effect from the experimental stage used for the SEM-EDX analysis. Figs. 25 and 26 show the atomic signals corresponding to the diesel particulate, as obtained from LIBS measurements. Peaks due to carbon, boron, and silicon were observed in these wavelength ranges.

Fig. 25. Atomic signals (λ = 245–253 nm) and plasma background obtained from LIBS measurement using 100 laser pulses for diesel particulate

Fig. 26. Atomic signals (λ = 277–289 nm) and plasma background obtained from LIBS measurement using 100 laser pulses for diesel particulate

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9 Particle Size Measurement for Carbonaceous Material Using LII Technique The increase in fine particulate emissions resulting from global economic growth has recently become more apparent as a health problem. It is well known that the inhalation of fine particle matter causes a range of health problems [4-7]. Particles that are smaller than 2.5 μm in size (PM2.5) can penetrate the gas-exchange region of the lungs. Thus, the measurement of particle size is important in formulating policies to control particle production and to combat the growing global climate change, which involves aerosol pollution and health problems. In 1977, Eckbreth recognized this concept while working with Raman spectroscopy of flames, and was troubled by the presence of soot [8]. He was able to relate the time dependence of the interference to laser particulate heating. Melton and many other researchers developed Eckbreth's work further [9, 10]. Our simulation model is based upon one of the most detailed studies, which was reported by H.A. Michelsen [11]. For the measurement of LII, a decay simulation model was used based on one of Michelsen’s detailed studies. The carbonaceous particles were assumed as soot in a candle flame. According to Michelsen’s model, the energy balance for the interaction of a particle with a laser is given by

QInt = Qabs − Qrad − Qcond − Qsub + Qann + Qox ,

(5)

where QInt represents the sensible energy stored in a particle, Qabs is the rate of laser energy absorption, Qrad is the rate of radiation by the blackbody emission, Qcond represents the rate of energy dissipation by conduction, Qsub represents the rate of energy loss by the sublimation of carbon clusters and also accounts for the energy consumed during photodesorption of the annealed particle to form small carbon clusters, Qann represents the rate of energy production by particle annealing, and Qox represents the rate of energy generation by oxidation. Each term in this energy flow rate equation is accounted for by a laser beam temporal intensity profile, the time dependence of the particle temperature, and the initial state of the particle. The time-derivative term of the particle temperature yields

dT 6 = (Qabs − Qrad − Qcond − Qsub + Qann + Qox ) , 3 dt π D ρS cS

(6)

where T is the particle temperature, D is the primary particle diameter, ρs is the density of the particle, and cs is the specific heat of solid carbon. From Planck's equation for a blackbody, the LII signal at wavelength λ’ is given by

S = Ωπ D 2 ∫ ε λ λ

2π hc 2 ⎡ ⎛ hc ⎞ ⎤ λ ′ ⎢exp ⎜ ⎟ − 1⎥ ⎝ λ ′k BT ⎠ ⎦ ⎣ 5

Σλ (λ ′) d λ ′ ,

(7)

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where Ω is the normalization constant, ελ is the emissivity at wavelength λ for a Rayleigh particle, h is a Planck constant, c is the speed of light, kB is a Boltzmann constant, and Σλ is accounted for by including it in the integration of the Plank function over wavelength. Solving differential Eq. (6), each term of the energy flow ratio is calculated from the laser beam intensity profile that was obtained from actual measurements. The energy absorption rate was calculated by

Qabs

π 2 D 3 E ( m) = q (t ) , λ

(8)

where D is the primary particle diameter, and λ is the wavelength of the laser. The intensity profile q(t) was given by the experimental result. E(m) is a function of the complex refractive index and is expressed as

E ( m) =

6nm km . ( n − k + 2) 2 + 4nm2 km2 2 m

2 m

(9)

A refractive index of m = nm - kmi was given by the laser wavelength; 1064 nm with nm = 1.63 and km = 0.7. This profile was obtained by substituting the laser profile into Eq. (8). The radiation rate was calculated by

199π 3 D 3 ( k BT ) E (m) = , h(hc)3 5

Qrad

(10)

where kB is a Boltzmann constant, h is a Planck constant, and c is the speed of light. The time profile of the rate of energy loss due to radiation from the particle was obtained by successive iteration from the initial value of the temperature (T0 = 1060 [K]). The thermal conductivity of soot was calculated by

Qcond

2κ aπ D 2 = (T − T0 ) , ( D + GL )

(11)

where κa is the thermal conductivity of the surrounding gas (κa =1.0811 × 10-4 + 5.1519 × 10-7 T 0 was used on this study), L is the mean free pass (L = 2.24 × 10-8 T 0 [cm/K]), G is the heat transfer factor [9, 12] given by

G=

8f αT (γ + 1)

(12)

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where f is the Eucken factor (f = (9γ-5)/4), γ is the heat capacity ratio (γ = 1.3), and αT is the thermal accommodation coefficient of the ambient combustion gases with the surface(αT = 0.3). The sublimation at higher temperatures during laser irradiation producing gasphase carbon atom clusters was calculated by

(

)

Cj Cj ⎡ ⎤ 1 ⎛ dM ⎞ ⎢ ΔH j Psat − Pphot + ΔHλs Pλs + ΔHdiss Pdiss + ΔHλa Pλa ⎥ Qsub = ∑ ⎜ ⎟ C ⎥ Psat j j =1 Wj ⎝ dt ⎠ j ⎢ ⎣ ⎦ 10

(13)

where the summation was assumed by contributing to Qsub from C1 to C10 desorbed species with vaporized molecular weights from W1 to W10. ΔHj is the enthalpy of the formation of carbon vapour species Cj, PsatCj is the saturation partial pressure of Cj, PphotCj is the instantaneous partial pressure of Cj from photodesorption of the particle, ΔHλs is the energy required to remove carbon clusters from the unannealed particle with no thermal photodesorption, Pλs is the effective pressure calculated from the rate of no thermal photodesorption from the unannealed particle, ΔHdiss is the estimated enthalpy of pyrolysis, Pdiss is the effective pressure calculated from the rate of thermal photodesorption from the annealed particle, ΔHλa is the energy required to remove carbon clusters from the annealed particle with no thermal photodesorption, Pλa is the effective pressure calculated from the rate of no thermal photodesorption from the annealed particle. To solve this equation, a more detailed discussion and the setting of parameters were based on Michelsen’s work and that of other previous research. [11], [13 - 18] The annealing energy production rate was calculated by

Qann =

−ΔH imig kimig N d − ΔH vmig kvmig N d Na

,

(14)

where ΔHimig is the estimated enthalpy for interstitial migration (ΔHimig = -1.9 × 104 J/mol) [19], ΔHvmig is the enthalpy for vacancy migration derived from theoretical predictions (ΔHvmig = -1.4 × 105 J/mol) [20, 21], Nd is the number of lattice defects in the particle, and Na is an Avogadro constant (6.02214 × 1023 mol-1). kimig and kvmig were given by

⎛ − Eimig , vmig kimig , vmig = Aimig ,vmig exp ⎜ ⎝ RT

⎞ ⎟, ⎠

(15)

where Aimig and Avmig are pre-exponential factors used to calculate the rate constant for interstitial migration and vacancy migration, (Aimig = 1 × 108 s-1, Avmig = 1.5 × 1017 s-1), Eimig is the activation energy for the annealing rate associated with di-interstitial migration (Eimig = 8.3 × 104 J/mol) [22], and Evmig is the activation energy for the annealing rate from the vacancy migration (Evmig = 6.7 × 105 J/mol). R is the universal gas constant (8.3145 J/molK) [23].

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The oxidation rate was calculated by

Qox = ( −ΔH ox − 2α T CPcoT )

π D 2 kox Na

,

(16)

where ΔHox is the enthalpy of reaction (= -2.215 × 105 J/mol) [24], αT is the thermal accommodation coefficient of the ambient combustion gases with the surface (αT = 0.3), Na is an Avogadro constant, CPCO is the molar heat capacity of CO given in the following Fried and Howard equation [25], −2 −2 2 2 ⎫⎪ ⎛ R ⎞ ⎧⎪ ⎛ θ ⎞ ⎛θ ⎞ ⎡ ⎛θ ⎞ ⎤ ⎛θ ⎞ ⎛θ ⎞ ⎡ ⎛θ ⎞ ⎤ CPco = ⎜ ⎟ ⎨a1 ⎜ 1 ⎟ exp ⎜ 1 ⎟ ⎢exp ⎜ 1 ⎟ −1⎥ + a2 ⎜ 2 ⎟ exp ⎜ 2 ⎟ ⎢exp ⎜ 2 ⎟ −1⎥ + a3T ⎬ ⎝ T ⎠⎣ ⎝ T ⎠ ⎦ ⎝T ⎠ ⎝ T ⎠⎣ ⎝ T ⎠ ⎦ ⎝ a4 ⎠ ⎩⎪ ⎝ T ⎠ ⎭⎪

(17)

where R is the general gas constant, and the parameters are given as a4 = 1, a1 = 3.494, θ1 = 1, a2 = 0.98449, θ2 = 3085.1, and a3 = 2.6164 × 10-5. kox is given by

⎡ kχ ⎤ ⎛ −1.4 ×105 ⎞ 2.8Zox kox = 12 PO2 ⎢ a A + kb (1 − χ A ) ⎥ (1 − X ann ) + exp ⎜ ⎟ X ann T0 ⎝ RT ⎠ ⎣⎢1 + kZ PO2 ⎦⎥

(18)

where PO2 = P0 = 1atm, ka = 5.0 × 1023exp(-1.255 × 105/RT), kb = 5.0×1021exp(-6.352 × 104/RT), χA = 1/(1 + kT/KbPO2), kT = 3.79 × 1027exp(-4.06 × 105/RT), and kZ = 21.3exp(1.713 × 104/RT). Xann is a mass fraction annealed according to equation (19), and Xann was estimated as 1.0 × 10-6 in this calculation, Zox is the collision rate of the ambient O2 with the particle surface given Eq. (20), and Zox was derived as 3.03 × 1022 [1/s cm2].

X ann = 1 −

Nd , Xd Np

(19)

where Nd is the number of lattice defects in the particle, Xd is the initial defect density of soot, and Np is the number of atoms in the particle. Xd and Nd are uncertainties at this point.

Z ox =

P0 k pT0

RmT0 , 2π Wa

(20)

where P0 is the ambient pressure, Wa is the average molecular weight (given by P0 = 0.209 [atm], Wa = 31.99 [g/mol] in this equation), kp is a Boltzmann constant in the effective pressure unit (1.3626 × 10-22 [atm cm3/K]), and Rm is the universal gas constant in effective mass units (8.3145 × 107 [g cm2/mol K s2]). Substituting the initial temperature and initial particle diameter and the above energy flow ratios caused by the laser (Fig. 27) into Eq. (6) yields a time profile of the particle temperature (Fig. 28).

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Fig. 27. Time profile of laser power density (t = 0–1000 ns)

Fig. 28. Time profile of particle temperature (t = 0–1000 ns)

The particle diameter was calculated by 13

⎡ 6M ⎛ X ann X melt 1 − X ann − X melt ⎞ ⎤ + + D=⎢ ⎜ ⎟⎥ , π ρ ρ ρ l s ⎝ a ⎠⎦ ⎣

(21)

where ρa is the mean molecular cross-section of the surrounding gas (4.21 × 104 cm2 for air), ρl is the density of liquid carbon given by ρl = 2.0448 - 7.0809 × 10-5 T [g/cm3], ρs is the density of graphite given by ρs = 2.3031 - 7.3106 × 10-5 T [g/cm3], Xann and Xmelt (a mass fraction melted) were estimated as 1.0 × 10-6 in this calculation, and M represents the particle mass. The particle mass was changed during sublimation and during oxidation. The time profile of the particle mass was calculated by

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dM 10 ⎛ ∂M ⎞ ⎛ ∂M ⎞ = ∑⎜ ⎟ +⎜ ⎟ . dt ⎝ ∂t ⎠ox j =1 ⎝ ∂t ⎠ j

229

(22)

The first term in the above equation is the rate of mass loss during sublimation. This term is given by 10 −π D W U α B ⎛ ∂M ⎞ j j j j = , ∑ ⎜ ⎟ ∑ R pT j =1 ⎝ ∂t ⎠ j j =1 10

2

(23)

where Wj is the molecular weight of a carbon cluster comprised of 1–10 carbon atoms with vapour (Wj = j × 12.011 g/mol), Uj is the mean velocity away from the particle surface given by Uj = (RmT / 2πWj)1/2, Rm is the universal gas constant in effective mass units (8.3145 × 107 [g cm2/mol K s2]), αj is the mass accommodation coefficient of vaporized species (for αj it was assumed that αj = 0.5 for j = 1 and j = 2, αj = 0.1 for j = 3, αj = 1.0 × 10-4 for j = 4 to j = 10), Bj is a parameter that represents the influence of diffusive and convective mass and heat transfer during sublimation (the detailed discussion and the setting of parameters for Bj were based on Michelsen’s work [11]), and Rp is the universal gas constant in effective pressure units (82.058 [atm cm3/mol K]). The second term of equation (22) is given by

2π D W1kox ⎛ ∂M ⎞ , ⎜ ⎟ =− Na ⎝ ∂t ⎠ox 2

(24)

where Na is an Avogadro constant, and the overall rate for oxidation kox was used as the same value of the oxidation. Fig. 29 shows the time profile of the particle diameter. For the graphs presented in this paper, the initial particle diameter was assumed as 35 nm and the ambient temperature was assumed as 1060 K.

Fig. 29. Time profile of particle diameter (t = 0–1000 ns)

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The LII temporal profile was calculated by substituting the particle temperature into Eq. (7). Fig. 30 shows a comparison of the normalized LII signal for an experimental result and for particle size prediction using the above calculations.

Fig. 30. Comparison of a normalized LII calculated profile with the experimental profile obtained for candle soot at a wavelength of 400 nm

The results recorded by the device using the LIBS and LII technique indicate that the particle diameter in the candle flame was about 15 nm - 20 nm.

10 Conclusion In this paper, we proposed an advanced system of particle analysis. The system uses laser-induced breakdown spectroscopy (LIBS) and laser-induced incandescence (LII) temporal analytical techniques. When LIBS was used for measurement, the proposed system provided rapid quantitative information for the components of every kind of particle element. Particle size measurement was accomplished with the help of the LII technique. In conventional particle size measurement, another additional device is required. In the proposed system, one can switch from LIBS to LII by merely controlling the power density of the light source. The proposed system can potentially facilitate the in-situ measurement of aerosols.

References 1. Andreev, A., Ueda, T.: Simulation of laser plasma emission characteristics of small solid particles in different gas atmospheres at various pressures. Trans. IEE of Japan 121-E(11), 593–598 (2001)

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2. Wakamatsu, M., Ikezawa, S., Ueda, T.: Particle element and size simultaneous measurement using LIBS. IEEJ Transactions on Sensors and Micromachines 127(9), 397–402 (2007) 3. Ikezawa, S., Wakamatsu, M., Pawlat, J., Ueda, T.: Sensing System for Multiple Measurements of Trace Elements Using Laser-induced Breakdown Spectroscopy. IEEJ Transactions on Sensors and Micromachines 129(4), 115–119 (2009) 4. Brown, J.H., Cook, K.M., Ney, F.G., Hatch, T.: Influence of Particle Size upon the Retention of Particulate Matter in the Human Lung. American Journal of Public Health Nations Health, 450–458 (1950) 5. Kodavanti, U.P., Schladweiler, M.C., Ledbetter, A.D., Watkinson, W.P., Campen, M.J., Winsett, D.W., Richards, J.R., Crissman, K.M., Hatch, G.E., Costa, D.L.: The Spontaneously Hypertensive Rat as a Model of Human Cardiovascular Disease: Evidence of Exacerbated Cardiopulmonary Injury and Oxidative Stress from Inhaled Emission Particulate Matter. Toxicology and Applied Pharmacology 164(3), 250–263 (2000) 6. Squadrito, G.L., Rafael, C., Dellinger, B., Pryor, W.A.: Quinoid redox cycling as a mechanism for sustained free radical generation by inhaled airborne particulate matter. Free Radical Biology and Medicine 31(9), 1132–1138 (2001) 7. Carll, A.P., Haykal-Coates, N., Winsett, D.W., Rowan III, W.H., Hazari, M.S., Ledbetter, A.D., Nyska, A., Cascio, W.E., Watkinson, W.P., Costa, D.L., Farraj, A.K.: Particulate matter inhalation exacerbates cardiopulmonary injury in a rat model of isoproterenolinduced cardiomyopathy. Inhalation Toxicology, 1–14 (2010) 8. Eckbreth, A.C.: Effects of Laser-Modulated Particulate Incandescence on Raman Scattering Diagnostics. Journal of Applied Physics 48, 4473–4479 (1977) 9. Melton, L.A.: Soot Diagnostics Based on Laser Heating. Applied Optics 23, 2201–2208 (1984) 10. Jenkins, T.P., Bartholomew, J.L., DeBarber, P.A., Yang, P., Seitzman, J.M., Howard, R.P.: Laser Induced Incandescence for Soot Concentration Measurements in Turbine Engine Exhausts. In: AIAA paper 2002-0828 (2002) 11. Michelsen, H.A.: Understanding and Predicting the Temporal Response of Laser-induced Incandescence from Carbonaceous Particles. Journal of Chemical Physics 118, 7012–7045 (2003) 12. McCoy, B.J., Cha, C.Y.: Transport phenomena in the rarefied gas transition regime. Chemical Engineering Science 29(2), 381–388 (1974) 13. Wu, C.H., Mszanowski, U., Martin, J.M.L.: The impact of larger clusters formation C5, C6, C7, C8, C9, and C10 on the rates of carbon sublimation at elevated temperatures. Journal of Nuclear Materials 258-263(Part 1), 782–786 (1998) 14. Arepalli, S., Scott, C.D., Nikolaev, P., Smalley, R.E.: Electronically excited C2 from laser photodissociated C60. Chemical Physics Letters 320(1-2), 26–34 (2000) 15. Vietzke, E., Refke, A., Philipps, V., Hennes, M.: Energy distributions and yields of sputtered C2 and C3 clusters. Journal of Nuclear Materials 241-243, 810–815 (1997) 16. Philipps, V., Vietzke, E., Flaskamp, K.: Sticking probabilities of evaporated C1, C2 and C3 on pyrolytic graphite. Surface Science 178(1-3), 806–812 (1986) 17. Brewer, L., Kane, J.S.: The Importance of Complex Gaseous Molecules in High Temperature Systems. The Journal of Physical Chemistry 59(2), 105–109 (1955) 18. Bukatyi, V.I., Zhdanov, E.P., Shaiduk, A.M.: Combustion of aerosol particles in an electromagnetic field. Combustion, Explosion, and Shock Waves 18(3), 309–312 (1982) 19. Konno, T.J., Sinclair, R.: Crystallization of amorphous carbon in carbon—cobalt layered thin films. Acta Metallurgica et Materialia 43(2), 471–484 (1995)

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20. Marcos, P.A., López, M.J., Rubio, A., Alonso, J.A.: Thermal road for fullerene annealing. Chemical Physics Letters 273(5-6), 367–370 (1997) 21. Samsonidze, G.G., Samsonidze, G.G., Yakobson, B.I.: Kinetic Theory of SymmetryDependent Strength in Carbon Nanotubes. Physical Review Letters 88(6), (065501-1) – (065501-4) (2002) 22. Banhart, F., Füller, T., Redlich, P., Ajayan, P.M.: The formation, annealing and selfcompression of carbon onions under electron irradiation. Chemical Physics Letters 269(34), 349–355 (1997) 23. Wang, B.-C., Wang, H.-W., Chang, J.-C., Tso, H.-C., Chou, Y.-M.: More spherical large fullerenes and multi-layer fullerene cages. Journal of Molecular Structure: THEOCHEM 540(1-3), 171–176 (2001) 24. Atamny, F., Bloecker, J., Henschke, B., Schloegl, R., Schedel-Niedrig, T., Keil, M., Bradshaw, A.M.: Reaction of oxygen with graphite: x-ray absorption spectroscopy of carbonaceous materials. The Journal of Physical Chemistry 96, 4522–4526 (1992) 25. Fried, L.E., Howard, W.M.: Explicit Gibbs free energy equation of state applied to the carbon phase diagram. Physical Review B 61(13), 8734–8743 (2000)

Electromechanical Sensors Based on Carbon Nanotube Networks and Their Polymer Composites P. Slobodian1, P. Riha2, and R. Olejnik1 1

Polymer Centre, Faculty of Technology, Tomas Bata University in Zlin, Czech Republic 2 Institute of Hydrodynamics, Academy of Sciences, Prague, Czech Republic

Abstract. A network of entangled multiwall carbon nanotubes and the composite consisting of filter-supported multiwall carbon nanotube network are conductors whose conductivity is sensitive to compressive stress both in the course of monotonic stress growth and when loading/unloading cycles are imposed. The testing has shown as much as 100% network conductivity increase at the maximum applied stress. The entangled carbon nanotube networks are prepared by vacuum filtration method and peeled off from the filter. The carbon nanotubes are used in pristine condition or chemically functionalized. The filtersupported entangled networks are prepared by the nanotube dispersion filtration through a non-woven flexible polystyrene filter. The nanotubes infiltrate partly into the filter surface pores and link the accumulated filtrate layer with the filtering mat. The filter-support increases nanotube network mechanical integrity, the composite tensile ultimate strength and affects favorably the composite electrical resistance. Other obvious effect of the supporting polymer is reduction of the resistance temperature dependence. Moreover, the conductivity of carbon nanotube networks manifests also organic vapor dependence. The dependence is reversible, reproducible, selective as well as influenced by nanotube oxidation. Keywords: Carbon nanotube network, Compression, Electrical conductivity, Stress sensor, Gas sensor.

1 Introduction Recent technological progress heavily relies on the use of materials that can offer advanced structural and functional capabilities. In this respect, entangled carbon nanotube (CNT) network structures show a great potential for developing high-performance polymer composites and enhanced sensors. CNT networks can proportionally transfer their unique properties into reinforced composite materials and films for sensors and bring substantial improvements in structural strength, electrical and thermal conductivity, electromagnetic interference shielding and other properties [1,2]. The first carbon nanotube network was fabricated by Walters et al., who dispersed nanotubes into a liquid suspension and then filtered through fine filtration mesh [3]. Consequently, pure nanotubes stuck to one another and formed a thin freestanding entangled structure, later dubbed buckypaper. S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 233–251. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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The fabrication of CNT network based polymer composite described above is rather laborious. A novel idea is to circumvent the laborious technology and to suggest a way leading to continuous and technologically easier manufacturing of CNT network based polymer composites. The novel process consists of using the nonwoven polystyrene (PS) filter on which CNT collect and form a network during CNT suspension filtration, as integrating and supporting element. The CNT slightly infiltrate into the filter and adhere to it, finally forming CNT layer. The obtained CNT/PS composite is compression moulded above PS melting temperature when PS filter transforms into flexible PS polymer film. The repeated layering of CNT/PS units yields bulky forms. A recent study [4] investigates the mechanical behavior of entangled mats of carbon nanotubes and several other fibers during compression and cyclic tests. The obtained hysteresis loop between loading and unloading was linked with mat morphology and motion, friction and rearrangement of fibers during compaction. However, the electric resistance of CNT networks at compression has only been measured in [5,6]. The obtained data were analyzed to get an estimate of the resistance of CNT tangles and the contact resistance between nanotubes [5]. The purpose to reveal the effect of surface-doping of multiwall carbon nanotubes (MWCNT) networks on their pressure sensitivity is pursued in [6]. The homogeneity of MWCNT dispersion, the porosity of final entangled network and tube-tube interactions can be purposely influenced by the proper surface functionalization. Kastanis et. al [7] tested three different oxidizing agent, that is, ammonium hydroxide/hydrogen peroxide, sulfuric acid/hydrogen peroxide and hot nitric acid. They found that the increasing oxygen content on the surface of CNT leads to CNT networks with more uniform pore structure and dense morphology with lower porosity. The modified structure enhances network mechanical properties owing to stress undertaking by more inter-tube contacts. The aim of this survey is to introduce carbon nanotube networks and their composites as electromechanical sensing elements. The electrical conductivity of MWCNT networks is studied both in the course of monotonic stress growth and when loading/unloading cycles are imposed. In addition to it, the stress dependent deformation, the effect of temperature and chemical vapors as well as the effect of chemical functionalization by KMnO4/H2SO4 oxidizing system which significantly modifies properties of MWCNT networks, is introduced.

2 Multiwall Carbon Nanotube Networks and Composites The purified MWCNT of acetylene type (Sun Nanotech Co. Ltd., China) are used for the preparation of aqueous paste: 1.6 g of MWCNT and ∼ 50 ml of deionized water are mixed with the help of a mortar and pestle. The paste is diluted in deionized water with sodium dodecyl sulfate (SDS) and 1-pentanol. Then NaOH solved in water is added to adjust pH to the value of 10 [8]. The final nanotube concentration in the dispersion is 0.3 wt.%, concentration of SDS and 1-pentanol 0.1M and 0.14M, respectively [9]. The dispersion is sonicated in Dr. Hielscher GmbH apparatus (ultrasonic horn S7, amplitude 88 μm, power density 300 W/cm2, frequency 24 kHz) for 2 hours and the temperature of ca 50°C.

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Pristine MWCNT are oxidized by KMnO4 in a glass reactor with a reflux condenser filled with 250 cm3 of 0.5M H2SO4, 5g of KMnO4 and 2g of MWCNT. The dispersion is sonicated using thermostatic ultrasonic bath (Bandelin electronic DT 103H) at 85°C for 15 hours. The product is filtered and washed with concentrated HCl to remove the MnO2 and then washed by deionized water and dried. The polyurethane (PU) non-woven membranes for MWCNT dispersion filtration are prepared by technology of electrospinning from PU dimethyl formamide solution using NanoSpider (Elmarco, s.r.o.). For more details of PU chemical composition and particular process characteristics see reference [10]. To make entangled MWCNT network on PU porous filtration membrane, the vacuum-filtration method is used. The formed network of disk shape is washed several times by deionized water (till neutral pH) and methanol in situ, then gently peeled off the membrane and dried between filter papers. The thickness of the disks is typically 0.15-0.6 mm. The PS non-woven filters for MWCNT dispersion filtration and composite formation are prepared by electrospinning from polymer solution. PS (commercial polystyrene Krasten 137, Kaucuk-Unipetrol Group, Mn = 102 530, Mw/Mn = 2.75 [11]) is solved in a mixture of MIBK/DMF with the volume ratio 3:1 and PS weight concentration 15 wt%. The solution electrical conductivity is adjusted to value 75 μS/cm using tetraethylamonium bromide. PS nanofiber layer is using NanoSpider (Elmarco, s.r.o.) The experimental conditions of the electrospinning process are the following: The electric voltage 75 kV (Matsusada DC power supply), the temperature 20-25°C, the relative humidity 25-35 %, the electrode rotation speed 8 r/min and the motion rate of antistatic polypropylene non-woven fabric which collects nanofibers is 0.16 m/min. To prepare final PS non-woven filters, the prepared nanofiber porous layer (thickness of about 1 mm) is subjected to hot pressing at pressure 0.6 MPa and temperature 80°C. To make entangled MWCNT network on PS filter, the vacuum-filtration method is used. The MWCNT slightly infiltrate into the filter and adhere to it, finally forming MWCNT layer. The formed network is washed several times by deionized water (till neutral pH) and methanol in situ. Subsequently, PS filter-supported MWCNT network is placed between two filter papers moistened by acetone and dried between two iron plates at the room temperature for one day. The final drying continues without iron plates at 40 °C throughout another day. The thickness of the non-woven PS filter is typically 0.5-0.8 mm and the height of MWCNT entangled network according of used amount of dispersion from 0.02 to 0.4 mm. The formed PS filter-supported MWCNT network is then compression moulded above PS melting temperature (190°C) when PS filter transforms in flexible PS film.

3 Experimental Techniques The structure of both MWCNT networks prepared from pristine tubes and their oxidized form are investigated with a scanning electron microscope (SEM) made by Vega LMU Probe (Tescan s.r.o., Czech Republic). The sample is deposited onto the carbon targets and covered with a thin Au/Pd layer. The observation is carried out in the regime of secondary electrons.

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Pure MWCNT are also analyzed via transmission electron microscopy (TEM) using microscope JEOL JEM 2010 at the accelerating voltage of 160 kV. The sample for TEM is fabricated on 300 mesh copper grid with a carbon film (SPI, USA) from MWCNT dispersion in acetone prepared by ultrasonication, which is deposited on the grid and dried. The MWCNT networks and composites are tested for deformation using a simple set-up. The samples (length 10 mm and width 8 mm) cut out from the manufactured disks of entangled nanotubes is first stepwise compressed between two glasses to the maximum value with 20 s delay of strain reading in each step. Then the down-stress curve is measured in the same manner. The conductivity characteristic of network stripes with dimensions given above is measured in a similar way as deformation. The loading area between glass plates is 8x8 mm. Two electrical contacts are fixed to the stripe by silver colloid electroconductive paint Dotite D-550 (SPI Supplies) and the electrical conductivity is measured lengthwise by the two-point technique using multimeter Sefram 7338. The temperature dependence of the electrical resistivity of MWCNT networks and MWCNT/PS composites are measured by the four point method according to van der Pauw method [12]. The apparatuses used in a set up are the scanner Keithley K7002, the switching card Keithley K7011-S, the programmable source of current Keithley K2410, the electrometer Keithley K6517 and PC with transducer cards GPIB cec488 and AD25PCI SE with connector SVOR25TER [11]. The resistivity is measured at constant current 0.01 A in the course of heating from -40°C to 150 using thermostatic box Haake SPEC SU241. The temperature increments are 10°C. The chemical vapors dependence of networks electric resistance is measured along stripe length (length 15 mm, width 5 mm, thickness ~ 0.3 mm) by the two-point technique using the multimeter Sefram 7378. The stripe is fixed on a planar holder with Cu electrodes and put into the saturated vapors of an organic solvent. The resistance variation is measured during the cycles of sorption (6 min) and desorption (6 min) at the temperature 25oC, the ambient pressure and relative humidity 40 %.

4 Results 4.1 Free-Standing Entangled MWCNT Networks To examine the length, thickness, waviness, multi-wall arrangement and possible structural defects of MWCNT, TEM analysis is used. The diameter of individual nanotubes is determined to be between 10 and 60 nm, their length from tenth of micron up to 3 μm. The number of coaxially rolled layers of grapheme is typically from 10 till 35 with interlayer distance about 0.35 nm. The used KMnO4 oxidation procedure of MWCNT pristine material leads to significant tubes degradation. The effect of tubes shortening together with creating of smaller tubes bundles is determined using TEM analyses. The both types of MWCNT aqueous dispersions (using MWCNT pristine tubes and their oxidized forms) are filtered thought PU non-woven membrane to form intertwined networks. As follows from Fig. 1 (left), PU fibers of the membrane are straight with average diameter 0.14±0.09 μm ranging between 0.05-0.39 μm. The fibers surface is smooth and the main pore size is around 0.2 μm. Prepared MWCNT layer is

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then peeled of the filter achieving self-standing MWCNT entangled network. The typical thickness of prepared (Fig. 2) is about 0.15-0.6 mm. The SEM micrograph of PS filter prepared by technology of electrospinning is shown in Fig. 1 (right). PS fibres are straight with smooth surface and submicron sizes with average diameter 0.6±0.3 μm. The average pore size is about 0.5 μm. The apparent density of PS filter is ∼ 0.1 g/cm3 giving porosity of about 0.9 for the measured density of PS 1.04 g/cm3 at 25°C. The prepared MWCNT layer is not peeled of the filter and remains as a part of PS/MWCNT composite.

Fig. 1. SEM micrograph of PU (left) and PS non-woven filtering membrane at the same magnification (displayed scaler 10 μm).

Fig. 2. Free-standing randomly entangled MWCNT network (disk diameter 75 mm, thickness 0.15 mm).

The upper surfaces of both principal MWCNT networks are studied, and their SEM micrographs can be seen in Fig. 3. The pictures show some differences between both structures. The surface of the network made of oxidized tubes seems to be smoother, with more densely packed tubes and a smaller diameter of inter-tube pores. Oxidation causes shortening of MWCNT (which is proved by TEM analyses), creation of defect sites and opened ends of the nanotubes. KMnO4 produces mainly carboxylic acid groups (-COOH) with some amounts of other oxygenated functional groups, such as hydroxyl (-OH) or carbonyl (=O) groups, on the nanotube surface [13,14]. Functionalized nanotubes are better individualized from the bundles and aggregates since they are shorter and the functional groups tend to push away individual nanotubes from each other [15]. Commonly used high-energy ultrasound can also cause degradation of CNT when the aspect ratio of the tubes decreases and their ends open [16,17]. The differences in both CNT network structures appear also after

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network drying. Drying causes different shrinkage of the network, that is, about 7 % in the case of pure nanotubes but significantly higher in oxidized nanotubes reaching up to 20 %. The porosity φ of MWCNT network is calculated to be 0.67 and 0.56 for the pure and oxidized form, respectively. The results are obtained from relation φ = 1− ρnet/ρMWCNT, where ρnet = 0.56±0.03 g/cm3 and ρnet = 0.75±0.03 g/cm3 denote measured apparent densities of the network made of pure and oxidized nanotubes, respectively. ρMWCNT = 1.7 g/cm3 is the average density of MWCNT used in the research. This result is consistent with previously published findings that increasing oxygen content on the surface of CNT [7] or shortening of tubes [15] leads to the network with a more uniform pore structure and denser morphology, i.e., the lower porosity. It indicates better nanotube dispersion in the aqueous suspension during filtration, when tubes are deposited as more individualized units or as smaller agglomerates. The resistivity of the networks is measured to be 0.084±0.003 Ω.cm for pristine and 0.156±0.003 Ω.cm for oxidized nanotubes, respectively.

Fig. 3. SEM image of the surface of entangled MWCNT network of buckypaper made of a) pure, and b) oxidized MWCNT.

Fig. 4. Cross-section of composite consisting of MWCNT network (above) and PS filter before compression molding, part a) (scaler 100 μm). The arrow indicates nanotube infiltration illustrated in details in the enlarged image, part b) (scaler 2 μm).

4.2 PS Filter-Supported Multi-wall Carbon Nanotube Networks The composite consisting of filter-supported entangled multiwall carbon nanotube networks are prepared by the nanotube dispersion filtration through a non-woven flexible polystyrene filter. The PS filter pores allows partial MWCNT infiltrating into the filter at the beginning of filtration as far as the pores are lined by nanotubes and pure nanotube network is formed above the filter surface, Fig. 4.

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Fig. 5 shows composite arrangement after compression molding. The thickness of MWCNT network is proportional to volume of filtered MWCNT dispersion. The typical used dispersion volume is 0.26 to 4.07 cm3/cm2 (filter area) and the corresponding network thickness is from 26 μm to 260 μm. The arrangement of nanotube networks and PS layers is arbitrary. MWCNT-PS-MWCNT composite layer arrangement is introduced in Fig. 4c. This composite is prepared by double-sided filtration. Other layer arrangements can be prepared by overlaying several MWCNT/PS composite units prior to compression molding. The comparison of tensile tests for pure MWCNT network and composite are shown in Fig. 6. The measured stress/strain dependence for pure MWCNT network indicates the tensile modulus about 600 MPa and the ultimate tensile strength ~1 MPa. The PS filtersupport has a positive effect on the tensile strength of MWCNT/PS composite as shown in Fig. 6. The measured tensile modulus is about 1300 MPa and the ultimate tensile strength is 10.3 MPa (thickness of PS film is 70 μm and MWCNT network 116 μm). The corresponding tensile values for pure PS film are 2100 MPa and 15.4 MPa (212 μm).

Fig. 5. SEM micrographs of MWCNT/PS composite after the compression molding. a) PS film (thicknesses about 80 μm) with attached nanotube network (260 μm), b) the interface between nanotube network and PS film, c) MWCNT-PS-MWCNT composite layer arrangement to decrease composite resistance. 16 PS MWNT PS/MWNT

Tensile Stress [MPa]

14 12 10 8 6 4 2 0

0.002

0.004

0.006

0.008

0.01

Tensile Strain

Fig. 6. Comparison of tensile properties of PS filter-supported MWCNT network (circles), MWCNT network (squares) and pure PS (triangles) in tensile test. The lines represent the power law fitting.

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4.3 Effect of Compressive Strain/Stress on Electric Resistance of Multi-wall Carbon Nanotube Networks The resistance/compressive strain dependence is shown in Fig. 7 (compressive strain as well as compression is defined as positive deformation and loading, respectively). The measurement show that compression causes a decrease in MWCNT network resistance, as clearly visible in the figure. The plotted resistance values, R, are normalized with respect to the initial resistance, Ri, recorded at the start of the test at no load. For each network thickness, i.e. 0.23 and 0.38 mm, four samples are investigated. Their resistance is measured after each compression step to the preset deformation and for the subsequent unloaded state. The resistance in the unloaded states is reduced similarly to the resistance of compressed samples. The observed effect of repeated compression on the network resistance is presented in Fig. 8. As can be seen, with increasing number of deformation cycles the resistance of MWCNT network first declines more steeply but after several cycles the decrease 0

Strain before load removing 0.2 0.4 0.6

0.8

Normalized resistance R/Ri

1.0

0.9

0.8

0.7

0

0.2

0.4

0.6

0.8

Compressive strain

Fig. 7. Normalized resistance vs. strain dependence of entangled carbon nanotube network. The network thickness is 0.23 mm (squares) and 0.38 mm (circles). The full and open symbols denote the network with and without load, respectively. Data presented as a mean ± standard deviation, n = 4. The solid and dotted line represents the prediction given by equation (2) and the contact network model [5], respectively.

Normalized resistance R/Ri

1.0

0.9

0.8

0.7 0

4

8

12

16

20

Number of Compressive Cycles

Fig. 8. Normalized resistance of the entangled carbon nanotube network vs. the number of compression/relaxation cycles; network thickness 0.38 mm. The applied compressive strain: 0.21 (circles), 0.47 (squares) and 0.74 (triangles). Full and open symbols denote the network with and without load, respectively.

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is very slight, and eventually no resistance change is observed. It indicates that the network rearrangement becomes steady what is favorable for MWCNT network use as the sensing element of compressive stress sensor when the network is suitably deformed in advance. Electrical properties of manufactured structures are followed also in the course of twelve compression and relaxation cycles with cyclic accumulation of residual strain (compression as well as compressive strain is defined again as positive loading and deformation, respectively). The measured data are shown in Fig. 9 as a plot of conductivity values σ vs. applied compressive stress τ. Compression causes a conductivity change during both the up-stress and down-stress periods due to specific deformation of porous structure. According to [5], the local contact forces increase during compression, allowing a better contact of nanotubes, which in turn leads to the decrease of contact resistance between crossing nanotubes; in release the dependence is just the opposite. At the same time, the possible effect of the distance between contacts on CNT tangle resistance is considered in [5]. The distance between contacts may decrease during compression owing to evoked relative motion of nanotubes, which corresponds to a lower intrinsic resistance of nanotube segments between contacts. Last but not least, compression may also bend the nanotubes sideways, which results in more contacts between nanotubes [19]. Since the contact points may act as parallel resistors, their increasing number causes an enhancement of MWCNT network conductivity. The conductance mechanisms are apparently not reversible in the initial cycle since the down-stress curve indicates residual conductivity increase in the off-load state. Nevertheless, the ongoing compression cycles have a stabilizing effect on the conductivity-compression loops similarly to their effect on mechanical properties. The conductivity enhancement σr, defined as the residual minimum conductivity during each cycle, increases with the increasing number of cycles, and after about 7 cycles, σr tends to reach to an asymptotic value, Fig. 10.

Electrical conductivity σ, S/cm

28 26 24 22 20 18 16 14 12 10 0

2

4

6

8

10

Stress τ, MPa

Fig. 9. Electrical conductivity-compressive stress loops for MWCNT network subjected to 12 successive compression/expansion cycles (network thickness 0.42 mm). The solid lines (first loading and unloading cycle) and dotted lines (twelfth cycle) represent the prediction given by Equation (2).

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17 16

σr , S/cm

15 14 13 12 11 0

2

4

6

8

10

12

14

Number of cycles

Fig. 10. Conductivity enhancement in each cycle vs. number of compression/expansion cycles.

Mechanical properties of manufactured structures are also followed in the course of twelve compression and relaxation cycles with cyclic accumulation of residual strain (compression as well as compressive strain is defined as positive loading and deformation, respectively). The results in the form of compressive stress vs. strain dependence are presented in Fig. 11.

Stress τ, MPa

10 8 6 4 2 0 0.1

0.2

0.3

0.4

0.5

0.6

Strain ε

Fig. 11. Stress-strain loops in cyclic compression test for MWCNT network subjected to 12 compression/expansion cycles (the network thickness 0.42 mm). The dotted lines (first loading and unloading cycle) and solid lines (twelfth cycle) represent the power law fitting.

The accumulation of residual strain is often called ratcheting and the minimum strain in each cycle is defined as ratcheting strain, εr. The ratcheting strain appears in MWCNT network after the first compression cycle, probably due to the initial deformation of porous structure and blocked reverse motion of nanotubes inside the compact network, as hypothesized in [5]. Thus the ability of the network to be repeatedly

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Residual strain εr

highly compressed is reduced. Moreover, during successive cycles of loading and unloading the change of ratcheting strain per cycle decreases and an asymptotic value of εr is obtained, as demonstrated in Fig. 12. Then stress-strain hysteresis loops reach a steady-state cyclic regime. The oxidation of MWCNT modifies electrical conductivity of MWCNT networks under compression. It follows from Fig. 13 where the stress dependent conductivity of pristine and oxidized MWCNT networks is plotted for 4 compression and relaxation cycles. Compression causes a conductivity change during both the up-stress and down-stress periods due to specific deformation of porous structure. Nevertheless, the conductivity of nanotube network prepared from chemically functionalized MWCNT in KMnO4/H2SO4 oxidizing system is lower and less deformation affected than pristine MWCNT network. It shows a stabilizing character of oxidation process. 0.4

0.3

0.2

0.1

0

2

4

6

8

10

12

14

Number of cycles

Electrical conductivity, σ [S/cm]

Fig. 12. Residual strain versus number of compression/expansion cycles.

24

MWCNT

20 number of cycle

16

1st 2nd 3rd 4rd

12

MWCNT (KMnO4)

8 0

2

4

6

8

Stress, τ [MPa]

Fig. 13. Electrical conductivity-compressive stress loops for MWCNT and MWCNT (KMnO4) network subjected to four successive compression/expansion cycles (the network thickness is 0.41 mm and 0.40 mm, respectively.).

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4.4 MWCNT Network and Contact Resistance The resistance of the CNT network is affected by many factors ranging from nanotube conduction mechanisms through their size to contact resistance between nanotubes and the network architecture [5,18,19]. However, according to the research carried out for short single wall carbon nanotube (SWCNT) network, the network resistance is dominated by the contact resistance between nanotubes while the intrinsic resistance of nanotubes is negligible [18]. Moreover, a uniform distribution of the intercontact resistances for unloaded SWNT network is supposed. In accordance with the evidence given in [5], the local force between nanotubes increases during the compression. This allows better contacts between them, which consequently leads to the decrease of contact resistance. Besides electron transfer facilitated this way, the residual resistance decrease shown in Fig. 7 suggests an additional mechanism of resistance/strain relation. As indicated in [19], compression may also buckle the nanotubes, which results in more contacts between them. Since the contact points act as parallel resistors, their increasing number cause reduction of overall network resistance. This structure reorganization, i.e. more contact points, probably partly remains when the compressive strength is released, which may be the reason for off-load resistance decrease. If this more complicated picture of network electrical resistance change is assumed rather than the effect of contact resistance between nanotubes only [5,18], the possible distribution of individual intercontact resistances which is considered to govern the total resistance of the network [5] may change. In this regard, we assume that the distribution of intercontact resistances in compressed MWCNT network is such that the joint probability for this total network resistance change under strain ε is described by the cumulative distribution function F(ε) for the two-parameter Weibull distribution,

[

Pr (ε ) = F (ε ) = 1 − exp − (ε ε 0 )

m

]

(1)

where F(ε) is the cumulative distribution function of Weibull distribution. F(ε) is an increasing function, 0 ≤ F(ε) ≤ 1 and represents the probability of network resistance change Pr(ε) under strain no greater than ε. The two parameters of Weibull distribution are the shape parameter m and the normalizing factor ε0. The shape parameter describes the spread in strain to change the resistance. The assumption that the probability of the whole network resistance follows the Weibull distribution is substantiated in Fig. 14. The figure shows that all the experimental points very closely follow a straight line when plotted in Weibull coordinates ln(ln(1/(1-Pi))) vs. ln ε, where Pi = (i-0.5)/n and i ranges from 1 to n, which is the number of tests. The goodness-of-fit to the straight line is reflected in the value of the correlation coefficient r = 0.99. The tendency of the measured reduction of the macroscopic, i.e. network resistance with compressive strain is bound to the probability of network resistance change Pr(ε) under strain no greater than ε. Consequently, the following relation of the normalized network resistance R/Ri to function F(ε) links appropriately the model prediction with the observed strain-dependent network resistance decrease

(

[

R Ri = α + βF (ε ) = α + β 1 − exp − (ε ε 0 )

m

])

(2)

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where α and β are location parameters and Ri the initial network resistance at the start of experiments at no load. The reasonably good description of the measured data by the predictive relation (2) is shown in Fig. 7 (parameters α = 1, β = -0.26, m = 0.91, ε0 = 0.33). We exploit for data description in Fig. 7 also the contact network model despite of its original use to represent the electronic transport properties of carbon nanofiber/epoxy resin composites and CNT tangles of moderate CNT volume fractions (up to 0.2) [5,20]. Moreover, the contact resistance power law model RCONTACT = Kφ n (see [5]) is used here also to evaluate the contact resistance between crossing MWCNT in our network (Fig. 14) though besides the contact resistance also the increase number of contacts in the course of compression owing to buckling of nanotubes may be expected. As follows from Fig. 7, the contact network model describes the measured resistance data quite well similarly as Eq. 2 what may suggest also dominant effect of local contact resistance between MWCNT on the decrease of total resistance of entangled MWCNT network structures of buckypaper during the compression. The dependence of contact resistance RCONTACT on the network deformation expressed in terms of MWCNT volume fraction to compare it with similar data given in [5] is presented in Fig. 14 The data show that the contact resistance between the crossing MWCNT of entangled network structures of buckypaper (the considered average MWCNT diameter is 20 nm) is lower by up to two orders of magnitude in comparison to the contact resistance of powdery CNT layer [5]. The probable reason is higher MWCNT volume fraction and compactness of filtered MWCNT network with respect to powdery CNT layer investigated in [5]. 7500 Contact resistance, Ω

1

ln(ln(1/(1-Pi)))

0 -1 -2 -3

7000 6500 6000 5500

-4 -3

-2

-1 ln ε

0

5000 0.3

0.4

0.5

0.6 0.7 0.8 Volume fraction

Fig. 14. Weibull plot for compressive stress deforming conductive MWCNT network (left); Contact resistance between crossing MWCNT evaluated from data in Fig. 7 as a function of MWCNT volume fraction. The filled circles represent calculated values, the solid line a power law fit.

The best description of the data in Fig.15 is obtained by the series heterogeneous model when the resistance is described as the sum of metallic (MWCNT are regarded as metallic conductors) and barrier portions of conduction path [21-23],

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(3)

where a the temperature coefficient arising from the metallic resistance and the second term (hopping/tunneling term) represents fluctuation-induced tunneling through barriers between metallic regions. T denotes temperature, whereas b, c, d the fitted parameters. 4.5 Effect of Temperature on MWCNT and PS/MWCNT Network Resistance The effect of temperature on the electric resistance is presented in Fig. 15. The composite as well as the pure MWCNT network exhibit non-metallic behavior (d(R/Ri)/dT < 0 over the investigated temperature range from 230 to 420 K. The negative d(R/Ri)/dT indicates the presence of tunneling barriers, which dominate both the pure MWCNT network and the filter-supported network resistance. The obvious effect of the supporting polymer is reduction of the resistance temperature dependence. The resistance ratio R230/R420 for resistances at the corresponding temperature is reduced form 1.35 in MWCNT network case to 1.12 in the composite case.

Normalized resistance R/Ri

1.1

1.0

0.9

0.8

0.7 200

250

300

350 400 450 Temperature, K

Fig. 15. Temperature-dependent normalized resistance of MWCNT/PS composite (circles) and MWCNT network (squares). The solid lines represent description by Eq. (2).

4.6 Effect of Organic Vapors on MWCNT Pure and Oxidized Networks Single-wall carbon nanotubes and MWCNT show remarkable sensitivity to the change of chemical composition of the surrounding environment. This property is favorable for their use in the form of membranes [24], adsorbents [25] or gas sensors [26,27]. Gas and vapor adsorption as well as desorption usually proceeds at high rates and amounts [28]. The molecules are adsorbed on the carbon nanotube (CNT) surface by van der Waals attracting forces, which leads to remarkable changes in CNT electrical resistance. A smart application of this principle can eventually lead to development of

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CNT-based electrochemical biosensors and gas sensors with a useful ability to detect various gases and organic vapors. Conductivity measurement is then a simple and convenient method to register CNT response to vapor adsorption/desorption. Previous research [29,30] found that physisorbed molecules influence the electrical properties of isolated CNT and also inter-tube contacts. The resistance of macroscopic CNT objects like aggregates or network structures used in gas sensors is predominantly determined by contact resistance of crossing tubes rather than by resistance of CNT segments. Here, the tubes are much shorter than sensor dimensions and intertube contacts act as parallel resistors between highly conductive CNT segments. Such CNT macroscopic objects contain four different adsorption sites: internal, interstitial channels, external grooves and external surfaces [25]. The dominating process influencing macroscopic resistance is probably gas or vapor adsorption in the space between nanotubes, which forms non-conductive layers between the tubes. This process decreases both the quantity and quality of contacts between nanotubes and consequently increases macroscopic resistance [26]. The strips made of both types of CNT networks are exposed to the saturated vapors of organic solvent - acetone (adsorbate), when the adsorption/desorption response cycles 6-minute intervals are measured, see Fig 16. The adsorption of acetone molecules causes increase in resistance with concentration and time, which is presented in the figure as sensitivity or gas response, S, defined as Rg − Ra

S=

Ra

=

ΔR Ra

(4)

S [%]

Adsorption

where Ra represents specimen resistance in air and Rg resistance of the specimen exposed to gas/vapor, ΔR stands for the resistance change. As can be seen from the figure the responses are sensitive and reversible. The measurement performed for several specimens also proves its reproducibility. Finally, the response is significantly sensitive for oxidized nanotubes. Vapors of acetone; 5 adsorption/desorption cycles KMnO4 oxidised MWNT network

45

pure MWNT network

Desorption

30

15

0 0

1000

2000

3000

t [s]

Fig. 16. Five adsorption/desorption cycles for MWCNT (empty symbols) and MWCNTN(KMnO4) (full symbols) exposed to vapors of acetone.

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methanol

KMnO4 oxidised MWNT network

40

S [%]

pure MWNT network

acetone

Diethyl ether

30

iso-pentane

20 10 0 0

20

40

60

80

100

Volume fraction of solvents [%]

Fig. 17. Dependence of sensitivities of MWCNT-N and MWCNT-N(KMnO4) networks on volume fraction of four different organic solvents during adsorption/desorption cycles Table 1. Sensitivity of pristine and oxidized MWCNT networks exposed to saturated vapors of different organic solvents. Organic solvent iso-pentane diethyl ether acetone methanol

S1 [%] MWCNT-N 20.6±1.4 20.1±0.8 15.6±0.5 12.9±0.7

S2 [%] MWCNT-N(KMnO4) 12.0±0.3 27.2±0.6 34.1±0.6 46.6±1.8

S2/S1 0.6 1.4 2.2 3.6

The effect of different adsorbates (iso-pentane, diethyl ether, acetone and methanol) is tested. The chosen solvents cover a broad range of Hansen solubility parameters overall defining the total Hildebrand solubility parameters, δt. The polarity of solvents increases in order iso-pentane, diethyl ether, acetone and methanol with values of δt, δt = 13.7 MPa1/2, δt = 15.6 MPa1/2, δt = 20.0 MPa1/2 and δt = 29.6 MPa1/2, respectively. The saturated vapor pressures of the solvent defining volume fractions of saturated vapors at defined conditions decreases in the same order from values 90.2 vol. % for iso-pentane, 70.9 vol. % for diethyl ether, 30.1 vol. % for acetone and 16.5 vol. % for methanol. Adsorption/desorption cycles for other solvents are measured under the same conditions as for acetone. The sensitivities S1 and S2 calculated for pure MWCNT-N (S1) and its oxidized form MWCNT-N(KMnO4) (S2) are presented in Fig. 17 and Table 1. Finally, to follow the selectivity of networks made of functionalized (oxidized) tubes, the ratio of S2/S1 is calculated for each solvent vapor. The only case when this value is lower than one, i.e., the oxidized nanotubes have lower sensitivity than their pure form, is for the non-polar solvent iso-pentane (S2/S1 = 0.6). On the other hand, the sensitivities to other solvents tested (those containing oxygen functional groups) are found higher for the oxidized form of MWCNT, the ratio of sensitivities ranging from 1.4 (diethyl ether) to 3.6 (methanol). Thus S2/S1 may be correlated with δt, which means that the ratio is typical of each individual solvent.

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5 Concluding Remarks and Discussion The conductivity-compression characteristics for entangled CNT network structures of buckypaper produced by filtering a nanotube suspension have not been studied in details so far. Thus the primary aim of our research was to find out the effect of compression on network electrical conductivity when a simple and repeated loading is exerted. The measurements have shown over 100% network conductivity increase at the maximum applied compressive stress. It indicates a good potentiality of MWCNT entangled network to be applied as a compression sensing element. The conversion of compressive stress into improved conductivity is achieved by deformation of porous structure. The structure recovering mechanism projects into the ratcheting strain, the change of which decreases with the increasing number of applied cycles, and finally an asymptotic value of this residual strain is reached. During successive cycles of loading and unloading the nanotubes rearrangement becomes steady and MWCNT network reaches a stable stress-strain hysteresis loop shape. This mechanical stabilization is reflected also in conductivity data. The conductivity-stress loop is stable during the same number of cycles as in the mechanical cyclic loading. It shows that the entangled carbon nanotube network structure of buckypaper can be used as a sensing element of compressive stress, especially when the network is suitably deformed in advance. A new type of composite consisting of PS filter-supported entangled multiwall carbon nanotube network is introduced as a conductive and/or sensoric polymeric material. PS filter-support increases nanotube network mechanical integrity and eliminates the laborious process of peeling off the nanotube network from the usual micro-porous (polycarbonate, nylon) filter followed by the network impregnation to increase its compactness. The prepared composite is flexible allowing its bending (radius up to 5-10 mm) without damage of MWCNT layer. The combined mechanical and electrical properties as well as vapor sensing open new opportunities for the composite use as polymer composite conductors, pressure and vapor sensing elements as well as an electromagnetic interference shielding and lightning strike protection. A hot press molding process can produce solid bulk composites consisting of multiple-layers of PS filtersupported MWCNT network.

Acknowledgement The authors (P.S. and R.O.) gratefully acknowledge support by the internal grant No. IGA/12/FT/10/D for the specific university research from TBU in Zlin. P.R. acknowledges support from the Grant Agency of the Academy of Sciences (GAAV IAA200600803) and the Institute of Hydrodynamics Fund AV0Z20600510.

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23. Kulesza, S., Szroeder, P., Patyk, J.K., Szatkowski, J., Kozanecki, M.: High-temperature electrical transport of buckypapers composed of doped single-walled carbon nanotubes. Carbon 44, 2178–2183 (2006) 24. Smajda, R., Kukovecz, A., Konya, Z., Kiricsi, I.: Structure and gas permeability of multiwall carbon nanotube buckypapers. Carbon 45, 1176–1184 (2007) 25. Agnihotri, S., Mota, J.P.B., Rostam-Abadi, M., Rood, M.J.: Theoretical and experimental investigation of morphology and temperature effects on adsorption of organic vapors in single-walled carbon nanotubes. J. Phys. Chem. B 110, 7640–7647 (2006) 26. Romanenko, A., et al.: Influence of helium, hydrogen, oxygen, air and methane on conductivity of multiwalled carbon nanotubes. Sensor Actuat. A-Phys. 138, 350–354 (2007) 27. Qureshi, A., Kang, W.P., Davidson, J.L., Gurbuz, Y.: Review on carbon-derived, solidstate, micro and nano sensors for electrochemical sensing application. Diam. Relat. Mater. 18, 1401–1420 (2009) 28. Hussain, C.M., Saridara, C., Mitra, S.: Microtrapping characteristics of single and multiwalled carbon nanotubes. J. Chromatogr. A 1185, 161–166 (2008) 29. Tournus, F., Latil, S., Heggie, M.I., Charlier, J.C.: Pi-stacking interaction between carbon nanotubes and organic molecules. Phys. Rev. B 72, Article No. 075431 (2005) 30. Mowbray, D.J., Morgan, C., Thygesen, K.S.: Influence of O-2 and N-2 on the conductivity of carbon nanotube networks. Phys. Rev. B 79, article No. 195431 (2009)

Novel Planar Interdigital Sensors for Detection of Domoic Acid in Seafood A.R. Mohd Syaifudin1,3, K.P. Jayasundera2, and S.C. Mukhopadhyay1 1 School of Engineering and Advanced Technology, Massey University, Palmerston North 4412, New Zealand 2 Institute of Fundamental Sciences, Massey University, Palmerston North 4412, New Zealand 3 Malaysian Agricultural Research and Development Institute, Serdang 43300, Selangor, Malaysia

Abstract. A novel planar interdigital sensor based sensing system has been developed for detection of dangerous marine biotoxins in seafood. Our main objective is to sense the presence of dangerous contaminated acid in mussels and other seafoods. Initial studies were conducted with three peptide derivatives namely Sarcosine, Proline and Hydroxylproline. These three chemicals are structurally closely related to our target molecule. The proline molecule is arguably the most important amino acid in peptide conformation, contains the basic structural similarity to the domoic acid. Three novel interdigital sensors have been designed and fabricated. All sensors have the same effective area but having different sensor configurations. The initial results show that sensors respond very well to the chemicals and it is possible to discriminate the different chemicals from the output of the sensor. The sensors were also being tested with three seafood products. Results from the analysis have shown that sensor with configuration #1 (Sensor_1) has better sensitivity compared to other sensors. Sensor_1 was chosen for experiment using proline and mussels. The changes in sensor sensitivity were analysed with mussels before and after adding the proline. The presence of proline on the mussel surface and also injected proline to the mussels was very clearly detected by the sensor. Further experiment was conducted with small amount of domoic acid (0.5 µg to 5.0 µg) injected to a mussel and it was found that Sensor_1 was able to detect small amount of domoic acid (1.0 µg) injected into the mussel sample. The result shows that Sensor_1 was able to detect approximately 12.6 µg/g of domoic acid in mussel meat. Three threshold levels of particular sample thickness have been established for detection of domoic acid. The first prototype of a low cost sensing system known as SIT (Seafood Inspection Tool) has been developed. The outcomes from the experiments provide chances of opportunity for further research in developing a low cost miniature type of sensors for reliable sensing system for commercial use.

1 Introduction Illness from seafood poisoning were caused by dangerous contaminated chemicals or marine biotoxins [1-3]. Seafood contaminated by marine biotoxins apparently look, S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 253–278. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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smells or tastes normal but after human or animals eat the seafood, they may suffer a variety of gastrointestinal and neurological illnesses [2]. Shellfish toxins and ciguatoxins are the most dangerous marine biotoxins [1]. Shellfish toxins can cause paralytic shellfish poisoning (PSP), diarrhoeic shellfish poisoning (DSP), amnesic shellfish poisoning (ASP), neurotoxic shellfish poisoning (NSP) and azaspiracid shellfish poisoning (AZP), while ciguatoxins can cause ciguatera fish poisoning (CFP) [1, 4]. In late 19th and early 20th century a large number of illnesses were linked with the consumption of raw oysters, claws and mussels. It was found that these illnesses were related to the ingestion of domoic acid-contaminated mussels which led to ASP [1-4]. ASP is characterized according to both gastrointestinal and neurological symptoms, including severe headache, confusion, and either temporary or permanent memory loss. Domoic acid (DA) is a naturally occurring toxin produced by microscopic algae, specifically the diatom species Pseudo-nitzschia [5-7]. DA is a chemical that is produced by algae or plankton when it blooms. Shellfish ingest these algae, where the toxin concentrates and can accumulate this toxin without apparent ill effects [1, 4]. However, for marine mammals and humans, DA is ticarboxylic acid that acts as a neurotoxin. The toxin is not destroyed by cooking or freezing. Figure 1 shows the chemical structure of domoic acid. The presence of DA in shellfish has been reported in various regions of the world [1, 8]. There have been numerous reports of toxicity in a variety of wildlife species indicating that DA moves up the food chain in marine ecosystems.

CO2H

CO2H N H

Domoic acid

CO2H

Fig. 1. Chemical structure of Domoic Acid

Studies have proven that certain amount of DA can cause health problems to animals and humans [9-14]. In 1987, DA was identified as the toxin responsible for an outbreak of illness in Prince Edward Island, Canada [15, 16]. It was caused by eating blue mussels. Effects on both the gastrointestinal tract and the nervous system were observed. It was reported that 107 patients (all adult) met the case definition [17]. Dose-related symptoms included nausea, vomiting, abdominal cramps, diarrhoea, headache, memory loss and convulsions and several deaths were attributed

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to the toxin. As a result of the episode of human illness in Canada, most countries have set a regulatory guideline of 20 µg/g of domoic acid in shellfish meat [2]. In order to protect consumers from shellfish poisoning, most countries have a set of regulatory guidelines. All the products need to be tested for the toxin, which places a heavy workload on the laboratories and is extremely expensive to the industry. This has motivated us towards the development of a low cost sensing system which can detect the presence of DA and their related analogs without much difficulty.

2 Motivation The existing method of DA detection is based on using chromatography technique, surface plasmon resonance (SPR), and immunoassay technique using enzyme-linked Immunosorbent assay (ELISA). Chromatographic techniques have been widely used for the detection of marine toxins. Overview of different chromatographic techniques for marine toxins detection has been reported by Quilliam [18]. A new sensitive determination method of DA using high-performance liquid chromatography (HPLC) has been reported in [5, 10, 19, 20]. Although chromatographic technique is one of the best methods for the detection of DA but they require expensive equipments, trained personnel, sophisticated method of sampling preparation and also it is a time consuming method. Surface plasmon resonance (SPR) has been widely used as detection technique in biosensing system [3]. A rapid and sensitive immuno-based screening method was reported to detect DA present in extracts of shellfish species using a surface plasmon resonance-based optical biosensor [21]. An immunosensor based on surface plasmon resonance (SPR) was used for the detection of DA [22]. The detection method based on SPR is suitable for laboratory analysis and not suitable for in-situ monitoring since the samples need to be prepared accordingly, analysis may take longer time and the equipment (SPR) is expensive. Immunoassay techniques are based on the affinity recognition between antibodies and antigens, and the most commonly found format is the enzyme-linked immunosorbent assay (ELISA) [3]. Research of using ELISA to determine DA has been reported in [7, 23-26]. ELISA method normally can be used to detect only one particular toxin. Only one research work reported by Garthwaite et.al [25], which integrates ELISA for screening of DSP, PSP, ASP and NSP toxins. To develop the ELISA strip and to prepare the samples will need to follow some laboratory procedures and tedious work. Also there is no guarantee that the ELISA strip will respond very well to all samples. Looking at the complexity of the existing methods, where samples have to be prepared accordingly to certain laboratory procedures, we have designed and fabricated novel planar interdigital sensor based sensing system with the purpose of an easy detection of molecules for DA. The developed sensing system is easy to be used for the purpose of sampling inspection and can provide fast analysis of DA within shellfish meat for in-situ monitoring. The pre-screening process or sampling inspection can be conducted at the site and if the results are suspicious, therefore

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further analysis of contaminated chemicals (DA) in the seafood can be done in the laboratory using expensive techniques. The developed sensing system should be reliable and cost effective. A smart low cost sensing system will be designed and developed. The prototype will consist of dual power supply, novel planar interdigital sensor, a new design of microcontroller circuit board, a signal processing circuit and a display. A user friendly software will be developed to make it ease of use.

3 Development of Novel Planar Interdigital Sensors 3.1 Sensor Design and Fabrication The operating principle of a planar interdigital sensor basically follows the rule of a parallel plate capacitor. The electric field lines generated by the sensor penetrate into the material under test (MUT) and interact with it [27]. The sensor behaves as a capacitor in which the capacitive reactance becomes a function of system properties. Therefore by measuring the capacitive reactance, the system properties can be evaluated [28-30]. The electric field distribution of a conventional planar interdigital sensor is shown in Figure 2. The distance between the positive and negative electrode will decide the penetration depth of electric field.

Fig. 2. Electric field distribution of planar interdigital sensor

Three interdigital sensors of different configurations have been designed, analysed and fabricated. Each sensor has the same effective area of 4750 µm by 5000 µm and having pitches (the distance between two adjacent electrodes) of 250 µm. The positive and negative electrodes have the same length and width of 4750 µm and 125 µm respectively. All sensors were designed to have equal numbers of electrodes. The only parameter which has been changed in the design is d, the spacing between the two adjacent positive and negative electrodes. The Sensor_1 was designed to have

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two positive electrodes at each end separated by eleven negative electrodes. The Sensor_2 and Sensor_3 were designed with the same dimensions but with different configurations. The Sensor_2 has five negative electrodes between two positive electrodes and has the same pitch like Sensor_1. The Sensor_3 has three negative electrodes between two positive electrodes. Table 1 shows the parameters used to design the sensors with different configurations. Figure 2 shows the representation of interdigital sensor (Sensor_1) and Figure 3 shows Sensor_2 and Sensor_3 having different configurations. All sensors were designed using Altium Designer 6 software. The fabrication process of the sensors was done at Massey University. The final design of each sensor from the Altium Designer was printed on a special film. The conducting layers of the board are typically made out of thin copper foil. The insulating layers (dielectric) are typically laminated together with epoxy resin pre-impregnated. The film together with the board was exposed to UV light. This process will impress and burn the desired sensor design onto the board. The printed circuit was developed. The printed circuit board was immersed into a special chemical for etching process to remove the unwanted copper, leaving only the desired copper trace. The sensor was cut to a desired design to make it suitable for testing. Figure 4 shows the picture of the fabricated interdigital sensors. The capacitance between a positive and negative electrode is given by;

C=

ε 0ε r A

(1)

d

where C = capacitance in farads, F İ 0 WKHSHUPLWWLYLW\RIIUHHVSDFHİ 0  .854x10-12 F/m ) İ r WKHUHODWLYHVWDWLFSHUPLWWLYLW\RUGielectric constant (vacuum = 1) A = effective sensing area, square meters d = effective spacing between positive and negative electrode, meters

Table 1. Novel interdigital sensor parameters of different configurations Sensor

Sensing

Pitch

Area,

Length,

(mm²)

(µm)

Sensor_1

23.75

Sensor_2 Sensor_3

Number of Electrodes Positive

Negative

250

2

11

23.75

250

3

10

23.75

250

4

9

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Positive Terminal

Negative Terminal

Fig. 3. Representation of interdigital sensor (Sensor_1)

Positive Terminal

Negative Terminal Fig. 4. Sensor_2 and Sensor_3 of different configurations

Fig. 5. The fabricated interdigital sensors

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3.2 Sensor Impedance Characteristics The experiment was carried out to get the measurement data of impedance and capacitance value for each sensor. A resistor of known value is connected in series with the sensor for the measurement of sensing signals. The excitation voltage of 10 Vpp (peak to peak voltage) with operating frequency range of 200 Hz to 50 kHz has been applied to each sensor. Agilent Function Waveform Generator and Agilent Signal Oscilloscope were used in the experiment as is shown in Figure 6.

Fig. 6. Experiment setup for sensor analysis

The impedance of the sensor is calculated by; Z=

Ve Ve = I Vsen / Rs

Z=

Ve ∗ Rs Vsen

(2)

Ve : Voltage across the sensor Vsen : Voltage across the series resistor, R s (120 k Ω) I : The current flowing through the sensor

Both the magnitude and the phase of the sensor impedance were calculated. The real part of the sensor (R) and the imaginary part (Xc) is given by; (3) R = Z cosθ − Rs X c = Z sin θ

(4)

Since the phase angle, θ measured was close to 90°, the real part of the sensor impedance, R was very small compared to the imaginary part. Figure 7 shows the impedance characteristic and Figure 8 shows the phase angle of each sensor. Results in Figure 7 shows variation of the real and the imaginary part of the sensors as a

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Re Par t Sensor_1

Im Par t Sensor_1

Re Par t Sensor_2

Im Par t Sensor_2

Re Par t Sensor_3

Im Par t Sensor_3

6.0E+06 5.5E+06

ΩΩ

5.0E+06 4.5E+06 4.0E+06 3.5E+06 3.0E+06 2.5E+06 2.0E+06 1.5E+06 1.0E+06 5.0E+05 0.0E+00 0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

F r eq uency, Hz

Fig. 7. Relationship between frequency, real and imaginary part of the sensors

-40 0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

-45 -50 -55 -60 -65 -70 -75 -80

Phase Angle Sensor_1

-85

Phase Angle Sensor_2

-90

Phase Angle Sensor_3

-95 - 100

F r eq uency ( Hz )

Fig. 8. Phase angle of sensors of different configurations

function of frequency. It is seen that all sensors have very small change of real part compared to the change of imaginary part. Therefore, the imaginary part of the sensor becomes the only parameter to evaluate the dielectric property of material under test. It was observed at low frequency, there is no change of phase angle between all sensors. The sensing signal is adequate to measure at the frequency of 10 kHz and it

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allows for the development of a microcontroller based low cost smart sensing system. The effective capacitance can be calculated by; C=

1 2π fX c

(5)

Table 2 shows all parameters calculated from the experimental analysis. It was observed that Sensor_1 has the highest capacitive reactance value to evaluate the dielectric property of material under test. Table 2. Relationship between excitation voltage, sensing voltage, current, impedance and capacitance for novel interdigital sensors Sensor Type

Sensor_1 Sensor_2 Sensor_3

Excitation Parameters Freq Ve (V) (kHz) 10.0 10.0 10.0 10.0 10.0 10.0

Capacitance (pF)

Sensing Parameters Vsen (mV) 156.3 187.5 192.1

Phase Angle 83.0 83.0 83.0

I (mA) 1.303 1.563 1.601

2.072 2.486 2.547

4 Experimental Results and Discussion 4.1 Initial Studies with Chemical Samples The fabricated sensors were then used in the experiment to see the response with three different chemicals. The initial goal is to evaluate which of these three sensors give a good response and could discriminate well between the three different chemical samples. Three peptide derivatives namely sarcosine, proline and hydroxylproline were used for the initial studies, which are structurally and closely related to the target molecule. N-methyl glycine represents the simplest structure. The proline molecule is arguably the most important amino acid in peptide conformation, containing the basic structural similarity to the domoic acid. The hydroxyproline containing hydroxyl group at 4-position represents the susbtituent at C4, which is particularly crucial for the binding. Figure 9 shows the chemical structure of the three samples used for the initial studies. These three samples were chosen because their prices are cheaper compared to the domoic acid. Table 3 shows the prices of each chemical in the market.

HO N CO2H H N-methylglycine (Sarcosine)

N CO2H H proline (Pro)

N CO2H H trans-4-hydroxyproline (Hyp)

Fig. 9. Sarcosine, Proline and Hydroxyproline molecules used for initial studies

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A.R. Mohd Syaifudin, K.P. Jayasundera, and S.C. Mukhopadhyay Table 3. Prices of different chemicals in the market No. 1 2 3 4 5

Chemicals

Quantity

Price (USD)

Sarcosine L-Proline Hyroxy Proline Domoic Acid Kaimic Acid

100 g 100 g 25 g 1 mg 10 mg

$ 53.30 $ 100.00 $ 102.80 $ 508.00 $ 54.80

A small amount of samples of sarcosine, proline and hydroxyproline (1.4 mg each) were used for the experiment. Each chemical were placed on the effective area of the sensor as shown in Figure 10. The electrodes were separated from the sample with the help of glad-wrap. The experimental setup is shown in Figure 11.

Sample on sensor

Fig. 10. The sample on the sensor of measurement

Signal Generator

Oscilloscope DC Power Supply

Microcontroller

Sensor & Sample

Fig. 11. The experimental set-up

Computer

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Measurement data of air (VAir) for calibration was first collected before measuring the chemical samples. The output voltage, VOut from the rectification circuit and digital value from microcontroller were collected. The sensitivity can be calculated by; Sensitivity =

(VOut − VAir ) ∗ 100 VAir

(6)

Figure 12 and Figure 13 show the comparative values of the sensor output for three different sensors for the three samples. It can be said that all the sensors respond very well to the chemicals and it is possible to discriminate the different chemicals from the output of the sensor. Sensor_1 shows a good response and better sensitivity compared to Sensor_2 and Sensor_3. This provides an opportunity to develop a sensing system to detect the presence of domoic acid in raw oysters, claws and mussels.

12.00

SARCOSINE PROLINE 10.00

HYDROXY

8.00

6.00

4.00

2.00

0.00 1

2

3

Senso r co nf ig ur at i o n

Fig. 12. Comparison of sensors’ sensitivity with different configurations

12. 00

Sensor_1 Sensor_2

10. 00

Sensor_3 8. 00

6. 00

4. 00

2. 00

0. 00 1

2

3

D if f er ent samp l es

Fig. 13. Comparison of sensors’ sensitivity with different samples

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4.2 Experiments with Sea food Products Experiments were repeated to analyse the responds of the fabricated sensors with different seafood products. 10 samples of fishes, squids and mussels were prepared for the experiment. The same experiment setup shown in Figure 11 was used. Data was then analysed to choose the best sensor to be used for further analysis with chemicals. As shown in Figures 14, 15 and 16, all three sensors respond very well with the seafood products. Results from the experiments with chemical samples and seafood products have shown that Sensor_1 shows a good response and give better sensitivity compare to other sensors. It can clearly discriminate the samples compared to Sensor_2 and Sensor_3. Therefore Sensor_1 was chosen for further analysis. 18 16 14 12 10 8 6 4

Sensor_1 Sensor_2

2

Sensor_3 0 1

2

3

4

5

6

7

8

9

10

Samp l e numb er

Fig. 14. Comparison of sensors’ sensitivity with fish samples

16 14 12 10 8 6 4

Sensor_1 Sensor_2

2

Sensor_3

0 1

2

3

4

5 6 Samp l e numb er

7

8

9

Fig. 15. Comparison of sensors’ sensitivity with mussel samples

10

Novel Planar Interdigital Sensors for Detection of Domoic Acid in Seafood

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20 18 16 14 12 10 8 6 Senso r_1

4

Senso r_2

2

Senso r_3

0 1

2

3

4

5

6

7

8

9

10

Samp l e numb er

Fig. 16. Comparison of sensors’ sensitivity with squid samples

4.3 Experiment with Mussels and Proline Good, clean mussels have been used for the experiments. A set of 8 mussels were randomly selected and were cut from 5 different locations. Figure 17 shows that how the samples were cut from each mussel. All samples were placed onto the non-stick paper. Samples were wiped with tissue paper to remove the water and then left them to be dried for 1-2 hours at a controlled laboratory temperature of 23 ºC with humidity of 40%. In practical situation, it may not be a mandatory requirement to dry the samples. But it is important to wipe out water present in and out of the samples. Each sample thickness and surface temperature was measured using digital calliper and temperature tester respectively. The samples thickness measured were between 1.4 mm to 3.2 mm having the temperature of range between 22.0 ºC to 22.5 ºC which was closed to laboratory temperature. Each sample was placed on the sensor as shown in Figure 18.

1 3

5 2

4

Fig. 17. The samples were cut from each mussel

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Fig. 18. The sample (mussel) on the sensor of measurement

The output voltage from the signal rectification circuit and the digital values from microcontroller were taken. Data of each sample was taken for analysis before and after a very small amount of proline (0.7 mg) was put on the surface of the samples. Proline was chosen in the experiment because it has a close chemical structure to domoic acid and is affordable to start with. Result in Figure 19 shows that there was a significant difference of Sensor_1 sensitivity before and after adding the proline. It is shown that Sensor_1 was able to detect the presence of proline on the mussels surface. Graph on Figure 20 shows the average sensor sensitivity is higher at location 5 and lower at location 1. This is because the average thickness at location 5 is 2.7 mm, which is thicker compared to other locations. The sensitivity is lowest at location 1 of having average thickness of 2.3 mm. The result in Figure 21 shows a good correlation with R² = 0.717, between sensor’s sensitivity with sample thickness. This is true for certain level of thickness because as the thickness increase the electric field distribution becomes weak and the sensitivity is expected to be indifferent.

Bef ore

+Proline

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7 1

3

5

7

9

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21 23

25 27

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33 35

37

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Fig. 19. Sensor sensitivity with sample before and after adding Proline

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20 18

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16

M ussel2

14

M ussel3 12

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10 8

M ussel5

6

M ussel6

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M ussel8 0

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Fig. 20. Results of mussel samples from different locations (before + proline)

20 18 16 14 12 10

y = 5.9979x - 0.4784 R2 = 0.7173

8 6 4 1.0

1.5

2.0

2.5

3.0

3.5

Samp l e t hi ckness, mm

Fig. 21. The relationship between sensor sensitivity with sample thickness (before +proline).

Experiment was repeated with another 20 samples of mussels at the same location 5. Location 5 was chosen because it is thicker compared to other locations and the sensor sensitivity is higher at location 5 compared to other location which is shown in Figure 20. Although samples were taken from the same location, but the thickness of each sample is not homogeneous. This is because of the different size of mussels. In the experiment, a small amount of proline was injected to the sample, instead of putting it on the sample surface. Each sample was injected with 0.7 mg of proline. Data was collected before and after the proline was injected.

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Figure 22, shows that the sensor is able to detect the present of proline injected to the samples. This result has shown that mussels with different concentration of chemical can be evaluated. 25

22

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+Proline 10 1

2

3

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11 12

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17 18

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Samp l e numb er

Fig. 22. Sensor sensitivity with sample before and after injected by Proline

In order to get more concrete results, 10 samples (Sample 11-20) have been used for further analysis with different amount of proline. The amount of proline injected to the samples was 1.4 mg and 2.1 mg and data of each sample was measured and analysed. Figure 23 shows that there was an increase in sensor sensitivity for different amount of proline injected. The result has shown a good linearity between injected proline with sensor sensitivity as shown in Figure 24.

Bef or e +Pr ol ine 0.7

28

+Pr ol ine 1.4 +Pr ol ine 2.1

25 22

19 16 13 10 1

2

3

4

5

6

7

8

9

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Samp l e numb er

Fig. 23. Sensor sensitivity with different amount of injected proline

Novel Planar Interdigital Sensors for Detection of Domoic Acid in Seafood 30

25

M_1

M_2

M_3

M_4

M_5

M_6

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269

20

y = 2 . 3 3 9 6 x + 10 . 4 9 1

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10 0

0.7 1.4 A mo unt o f Pr o l ine Inject ed , mg

2.1

Fig. 24. Linearity of sensor sensitivity with injected proline

Taking sample M_10, the thinnest (2.3 mm) among the samples, the equivalent linear equation is;

y = 2 . 3396 x + 10 . 491

(7)

It was observed from the experimental results that we can estimate small amount of proline present in the mussel samples. Therefore, Sensor_1 was used for further analysis to detect the presence of DA in mussels. 4.4 Experiment with Mussel and Domoic Acid Experiment with DA was conducted in the laboratory. Small amount of DA of 0.1 mg was diluted in 2.0 ml (2.0 g) of water to give 50 ppm of DA. Table 4 shows the particulars of the prepared mussel sample. The experiment was conducted at the laboratory temperature of 22.5°C with humidity of 48%. Mussel sample was injected with DA from 0.5 µg until 5.0 µg. Measurement data of air, voltage output and digital value were collected. Result in Figure 25 shows that Sensor_1 respond to the presence of DA after 1.0 µg of DA injected into the mussel with minimum weight of 0.0795 g. The results show with 95% confident intervals. Sensor_1 was able to detect approximately 12.6 µg/g of DA in mussel meat. The regulatory guideline for DA is 20 µg/g. The sensor has the potential to detect an amount even lower than 10 µg/g of DA in mussel meat. The experiment was repeated by injecting 0.01 ml of water and 0.01 ml of DA of different concentrations to the samples having the same thickness of 2.4 mm. The amount of water and DA injected were increased until 0.05 ml. The purpose of this experiment is to observe the effect of sensor sensitivity with water and also DA of different concentrations. Results from the experiment are shown in Figure 26, where

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the sensor was able to clearly discriminate the water and also the DA at different concentrations. Experiment was repeated by injecting 1.0 µg of DA into several mussel samples of different thickness. The experiment was conducted to find the threshold level of sensor sensitivity with respect to sample thickness. The sensitivity will change depends on the corresponding sample thickness, therefore it is very important to determine the sensitivity threshold level of samples at different thickness. Data was collected for normal samples and also samples injected with DA of respective samples thickness. Figure 27 shows that the threshold sensitivity level can be determined with respect to different thickness of mussel samples. There are three level of sensitivity threshold that were observed from the experiment. The result is simplified into a table shown in Table 5. This result will be used as a guideline to a user (fisherman) who will conduct the pre-screening or sampling process at their site. Results from experiments with DA indicate that novel interdigital sensing system has a good potential to detect the contaminated DA in mussels.

Table 4. Sample prepared for the experiment with DA Sample Mussel

Weight (g) 0.0795

Thickness (mm) 2.2

Surface Temperature (°C) 22

26

24

22

20

18

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14

12 0.0

0.5

1.0

1.5

2.0

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4.5

A mo ut o f D A , μ g

Fig. 25. Sensor sensitivity with injected DA to a sample

5.0

Novel Planar Interdigital Sensors for Detection of Domoic Acid in Seafood

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31 29 27 25 23 21

Wat er 10.1 µg DA

19

21.2 µg DA 17

42.5 µg DA 85.0 µg DA

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170 µg DA 13 0.00

0.01

0.02

0.03

0.04

0.05

0.06

A mo ut o f chemi cal s i nj ect ed t o samp le, ml

Fig. 26. Sensor sensitivity with water and different concentration of DA injected to the mussels 35

30

25

20

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Normal 5

+DA

0 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5

Samp l e t hi ckness, mm

Fig. 27. Sensitivity threshold value for normal samples and samples injected with DA of different thickness Table 5. Sensitivity threshold level with respect to sample thickness Sample Thickness 1.3 mm – 1.9 mm 2.0 mm – 2.8 mm 2.9 mm – 3.4 mm

No. of Samples 23 17 13

Threshold Sensitivity 13.0 19.0 25.0

Sensor Detection Yes Yes Yes

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5 A Microcontroller Based Sensing System A SiLab C8051F020 microcontroller as shown in Figure 28 was used in the project. The main purpose of using a microcontroller based sensing system is to develop a low-cost sensing system. The experiment setup for a low cost sensing system is shown in Figure 29. The system was programmed to generate an excitation voltage of the sensor. A sinusoidal wave of peak to peak voltage 7.50 Vpp at 10 kHz was generated. The pulsed sinusoidal waveform was first generated by the microcontroller (2.4 Vpp) and the smooth sine wave was obtained from the smoothing circuit output (7.5 Vpp), which was fabricated on a single fabricated board of signal processing circuit. The sine-wave generated by the microcontroller is shown in Figure 30. The circuit diagram of the sine-wave smoothing circuit is shown in Figure 31.

Fig. 28. A SiLab C8051F020 microcontroller board

Signal Processing Circuit Power Supply Micro-controller

Sensor and sample

Fig. 29. Experimental setup of low cost sensing system

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Before

After

Fig. 30. Sine-wave generated by the microcontroller before and after the smoothing circuit.

Fig. 31. Sine-wave smoothing circuit diagram

The second part of the signal processing circuit is the signal conditioning circuit. The signal conditioning circuit is required to interface the sensor signals to the microcontroller and is shown in Figure 32. It consists of a full wave rectification circuit and an amplification circuit. The signal output from the sensor is small and alternating in nature. Two Low Power Quad Operational Amplifiers, LM324 were used in the circuit. The amplified signal will pass through a low pass filter with cut off frequency of 13 Hz. The output dc signal from the circuit is fed to the analog input of microcontroller to obtain digital value. The fabricated signal processing board is shown in Figure 33.

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Fig. 32. Circuit diagram of full wave signal rectification

Fig. 33. The fabricated signal processing board

A dc voltage of ± 9 V is needed to power the developed sensing system. A battery of 9V supply was introduced to the sensing system. A negative 9V power supply was generated using the NE 555. The circuit diagram in Figure 34 shows how negative 9V is generated from a 9V battery. The fabricated power supply board is shown in Figure 35. The generated negative voltage together with the positive supply was used to power the op-amps and other circuits requiring a dual supply. A square wave is obtained at the Output pin 3 of the IC. The 22uF capacitor charges through the diode D1 when the output is positive. When the output at pin 3 is ground, the 22uF discharges through the diode D2 and charges the 100uF capacitor is charged. The output is taken across the 100uF capacitor.

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D2

D1

Fig. 34. A negative voltage from a single positive supply

Fig. 35. The power supply board

The first prototype of seafood inspection tool (SIT) was developed to detect the domoic acid (DA) in mussels. SIT consist of ± 9 V power supply, novel planar interdigital sensor, a SiLab C8051F020 microcontroller, a signal processing circuit and an expansion board (for display). A user friendly software was developed to make it ease of use. It can be used by anyone especially by fisherman for pre-screening process at the ranch site. The first prototype of SIT is shown in Figure 36.

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Fig. 36. The first prototype seafood inspection tool

6 Conclusion Novel interdigital sensors have been designed and fabricated. Sensors with different configurations were analysed for the best sensing performance. Initial studies on three different chemicals and different seafood products have shown that the fabricated interdigital sensors can respond very well to each sample and can clearly discriminate between them. Sensor_1 was selected for further analysis with real mussels due to its better sensitivity. Analysis of results with real mussels shown that Sensor_1 can detect the presence of proline on the surface as well as injected proline to each sample. It can be said that Sensor_1 was able to detect the presence of different amount of proline injected into the mussel samples. Experiment with DA has shown that novel interdigital sensor can be used to detect the small amount of domoic acid in mussels. Results from the experiments have shown that novel interdigital sensing system has the potential to be one of the options to assess the quality of seafood products for in-situ monitoring. The sensing system can detect the presence of DA in shellfish meats. The outcomes from the experiments provide chances of opportunity for further research in developing a low cost miniature type of sensors with reliable sensing system for commercial use. Further experiments are also conducted to study the effect of other parameters on the sensor sensitivity. Further research works are currently conducted to detect dangerous pathogen in meat.

References [1] FAO, Marine Biotoxins: Food and Nutrition Paper 80, Food and Agriculture Organization (FAO) Rome, Report (2004) [2] Osek, J., Wieczorek, K., Tatarczak, M.: Seafood as potential source of poisoning by marine biotoxins. Medycyna Weterynaryjna 62, 370–373 (2006)

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[3] Campas, M., Prieto-Simon, B., Marty, J.L.: Biosensors to detect marine toxins: Assessing seafood safety. Talanta 72, 884–895 (2007) [4] Garthwaite, I.: Keeping shellfish safe to eat: a brief review of shellfish toxins, and methods for their detection. Trends in Food Science & Technology 11, 235–244 (2000) [5] Costa, P.R., Rosa, R., Pereira, J., Sampayo, M.A.M.: Detection of domoic acid, the amnesic shellfish toxin, in the digestive gland of Eledone cirrhosa and E-moschata (Cephalopoda, Octopoda) from the Portuguese coast. Aquatic Living Resources 18, 395– 400 (2005) [6] Cusack, C.K., Bates, S.S., Quilliam, M.A., Patching, J.W., Raine, R.: Confirmation of domoic acid production by Pseudo-nitzschia australis (Bacillariophyceae) isolated from Irish waters. Journal of Phycology 38, 1106–1112 (2002) [7] Maucher, J.A., Ramsdell, J.S.: Ultrasensitive detection of domoic acid in mouse blood by competitive ELISA using blood collection cards. Toxicon 45, 607–613 (2005) [8] Dolah, F.M.V.: Marine Algal Toxins: Origins, Health Effect, and Their Increased Ocurrence. Environmental Health Perspectives 108, 133–141 (2000) [9] Doucette, G.J., King, K.L., Thessen, A.E., Dortch, Q.: The effect of salinity on domoic acid production by the diatom Pseudo-nitzschia multiseries. Nova Hedwigia, 31–46 (2008) [10] McCarron, P., Burrell, S., Hess, P.: Effect of addition of antibiotics and an antioxidant on the stability of tissue reference materials for domoic acid, the amnesic shellfish poison. Analytical and Bioanalytical Chemistry 387, 2495–2502 (2007) [11] Qiu, S.F., Pak, C.W., Curras-Collazo, M.C.: Sequential involvement of distinct glutamate receptors in domoic acid-induced neurotoxicity in rat mixed cortical cultures: Effect of multiple dose/duration paradigms, chronological age, and repeated exposure. Toxicological Sciences 89, 243–256 (2006) [12] Duran, R., Arufe, M.C., Arias, B., Alfonso, M.: Effect of Domoic Acid on Brain AminoAcid Levels. Revista Espanola De Fisiologia 51, 23–27 (1995) [13] Arias, B., Arufe, M., Alfonso, M., Duran, R.: Effect of Domoic Acid on Metabolism of 5Hydroxytryptamine in Rat-Brain. Neurochemical Research 20, 401–404 (1995) [14] Debonnel, G., Beauchesne, L., Demontigny, C.: Domoic Acid, the Alleged Mussel Toxin, Might Produce Its Neurotoxic Effect through Kainate Receptor Activation - an Electrophysiological Study in the Rat Dorsal Hippocampus. Canadian Journal of Physiology and Pharmacology 67, 29–33 (1989) [15] Wright, J.L.C., Boyd, R.K., Defreitas, A.S.W., Falk, M., Foxall, R.A., Jamieson, W.D., Laycock, M.V., Mcculloch, A.W., Mcinnes, A.G., Odense, P., Pathak, V.P., Quilliam, M.A., Ragan, M.A., Sim, P.G., Thibault, P., Walter, J.A., Gilgan, M., Richard, D.J.A., Dewar, D.: Identification of Domoic Acid, a Neuroexcitatory Amino-Acid, in Toxic Mussels from Eastern Prince-Edward-Island. Canadian Journal of Chemistry-Revue Canadienne De Chimie 67, 481–490 (1989) [16] Perl, T.M., Bedard, L., Kosatsky, T., Hockin, J.C., Todd, E.C.D., Remis, R.S.: An Outbreak of Toxic Encephalopathy Caused by Eating Mussels Contaminated with Domoic Acid. New England Journal of Medicine 322, 1775–1780 (1990) [17] FAO/IOC/WHO, Report of the Joint FAO/IOC/WHO ad hoc Expert Consultation on Biotoxins in Bivalve Molluscs. FAO, IOC, WHO, Oslo, Norway, Report (September 2630, 2004) [18] Quilliam, M.A.: The role of chromatography in the hunt for red tide toxins. Journal of Chromatography A 1000, 527–548 (2003)

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[19] Kodamatani, H., Saito, K., Niina, N., Yamazaki, S., Muromatsu, A., Sakurada, I.: “Sensitive determination of domoic acid using high-performance liquid chromatography with electrogenerated tris(2,2 ’-bipyridine)ruthenium(III) chemiluminescence detection. Analytical Sciences 20, 1065–1068 (2004) [20] Hummert, C., Reichelt, M., Luckas, B.: Automatic HPLC-UV determination of domoic acid in mussels and algae. Chromatographia 45, 284–288 (1997) [21] Traynor, I.M., Plumpton, L., Fodey, T.L., Higgins, C., Elliott, C.T.: Immunobiosensor detection of domoic acid as a screening test in bivalve molluscs: Comparison with liquid chromatography-based analysis. Journal of Aoac International 89, 868–872 (2006) [22] Yu, Q.M., Chen, S.F., Taylor, A.D., Homola, J., Hock, B., Jiang, S.Y.: Detection of lowmolecular-weight domoic acid using surface plasmon resonance sensor. Sensors and Actuators B-Chemical 107, 193–201 (2005) [23] Hesp, B.R., Harrison, J.C., Selwood, A.I., Holland, P.T., Kerr, D.S.: Detection of domoic acid in rat serum and brain by direct competitive enzyme-linked immunosorbent assay (cELISA). Analytical and Bioanalytical Chemistry 383, 783–786 (2005) [24] Kania, M., Hock, B.: Development of monoclonal antibodies to domoic acid for the detection of domoic acid in blue mussel (mytilus edulis) tissue by ELISA. Analytical Letters 35, 855–868 (2002) [25] Garthwaite, I., Ross, K.M., Miles, C.O., Briggs, L.R., Towers, N.R., Borrell, T., Busby, P.: Integrated enzyme-linked immunosorbent assay screening system for amnesic, neurotoxic, diarrhetic, and paralytic shellfish poisoning toxins found in New Zealand. Journal of Aoac International 84, 1643–1648 (2001) [26] Tsao, Z., Liao, Y., Liu, B., Su, C., Yu, F.: Development of a Monoclonal Antibody against Domoic Acid and Its Application in Enzyme-Linked Immunosorbent Assay and Colloidal Gold Immunostrip. Journal of Agriculture and Food Chemistry 55, 4921–4927 (2007) [27] Mamishev, A.V., Sundara-Rajan, K., Yang, F., Du, Y.Q., Zahn, M.: Interdigital sensors and transducers. Proceedings of the IEEE 92, 808–845 (2004) [28] Mukhopadhyay, S.C.: Novel high performance planar electromagnetic sensors. The eJournal on Nondestructive Testing 10 (August 2005) [29] Mukhopadhyay, S.C., Goonerate, C., Gupta, G.S., Demidenko, S.: A Low Cost Sensing System for Quality of Dairy Products. IEEE Transactions on Instrumentation and Measurements 55, 1331–1338 (2006) [30] Mukhopadhyay, S.C., Gooneratne, C.P.: A Novel Planar-Type Biosensor for Noninvasive Meat Inspection. IEEE Sensors Journal 7, 1340–1346 (2007)

Nano-Biosensor Development for Biomedical and Environmental Measurements D.M.G. Preethichandra and E.M.I. Mala Ekanayake School of Engineering and Built Environment, Central Queensland University, Rockhampton, Australia

Abstract. Nano biosensor development for biomedical and environmental measurements has been tried by research groups all over the world. Identifying and fabricating suitable nano-materials as enzyme immobilizing matrices to enhance characteristics of the sensor is the most important and difficult task of it. In this chapter we discuss about polypyrrole(PPy) nano-tube array grown on Alumina AnodiscTM to serve this purpose successfully. When this novel PPy nano-tube array sensor was used as a glucose biosensor, Glucose Oxidase(GOx) entrapment has been done by three different methods (for single sided PPy arrays), ie. Co-entrapment, physical adsorption and cross-linking aided physical adsorption. Best results were shown with the latter method with a sensitivity of 62.53 mA/cm2/M with a 4s response time. When the electrode was fabricated as a double-layered PPy nano-tube array, the sensitivity was increased up to 90 mA/cm2/M compared to the 7.4 mA/cm2/M for physical adsorption. A similar type of single sided sensor has been used to monitor Hydrogen Peroxide(H2O2) by using Horseradish Peroxidase (HRP) as the catalyst. This sensor worked both in anodic and cathodic modes adding the bi-functional feature to the enhanced characteristics. Anodic mode gave a high sensitivity of 3.8A/cm2/M and cathodic mode gave a linear range of 5nM to 25µM while showing 5s response time in both features.

1 Introduction A biosensor is a device that detects and transmits information regarding a physiological change or the presence of various chemical or biological materials in different environments by means of a biological product (e.g., an enzyme or antibody) converted to a measurable signal. Biosensor consists of two elements basically, biological element and the transducer. If an enzyme is employed as the bio-recognition element, the presence of a reactive substrate can be identified with substrate changing into a product because of its catalytic support towards specific molecules. Transducer which is attached to the enzyme immobilized matrix can convert the change of states of substrate to an easily understandable output completing the task of the biosensor[1,2,3,4]. In the case of conducting material occupies the space of immobilizing matrix it serves the two purposes of immobilizing and detecting the electrons from the reaction in parallel for amperometric, potentiometric or conductometric electrochemical sensors. In such a sensor a separate electrical wiring of the bio-element to the transducer is not needed and enhancement of characteristics such as sensitivity and response time are inherent[5,6,7,8]. S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 279–292. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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The function of an enzyme within the biosensor is crucial in all aspects of its performances. Enzymes are folded polypeptides which catalyze chemical reactions without being used up while converting substrates to products (equation 1). During this process enzyme temporarily binds up with the substrate molecules and makes an intermediate compound thereby lowering the activation energy required for the total reaction as shown in Fig. 1. Without Enzyme

Energy

Activation energy without Enzyme

Activation energy with enzyme

Overall energy released

With Enzyme

Reaction coordinate Fig. 1. Reduction of activation energy by introducing an enzyme.

E+S

K +1

←→

ES

K+ 2 ⎯⎯→ ⎯

E+P

(1)

K −1

where, E is enzyme, S is substrate and P is product. At equilibrium, K +1 [E ][S ] = K −1 [ES ] Then dissociation constant, K S = K −1 K +1

∴ K S = [E ][S ] [ES ]

if K m = K + 2 + K −1 K +1 Then the Michaelis–Menten equation (2) describes how the reaction rate V depends on the position of the substrate-binding equilibrium and the rate constant k2. Michaelis and Menten showed when k2 is much less than k-1 (called the equilibrium approximation) they could derive the following equation:

V0 =

Vm [S ] K m + [S ]

(2)

where, V0 = initial rate, Vm = maximum rate, [S] = concentration and Km = Michaelis-Menten constant.

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V0 Vmax

Vmax/2

Km

[S]

Fig. 2. Michaelis-Menten kinetic model of a single-substrate reaction.

When an enzyme is immobilized onto the substrate, the enzyme molecules get attached to the substrate and we call them active sites. If number of active sites occupied by the substrate molecules are low, the first order kinetics prevail at low concentrations of the enzyme. Hence at low concentrations, substrate concentration is directly proportional to the reaction rate. At high substrate concentrations, all of the active sites of enzyme molecules are filled and reaction rate is independent of substrate concentration. Further increase of substrate concentration would lead to a saturation in reaction rate which is called as maximum rate or the Vmax. Michaelis Menten constant or Km is responsible for the concentration at half of Vmax according to equation (2). When an enzyme is immobilized, the apparent values of Vmax and Km can predict the behaviour of the enzyme-substrate combination. Usually temperature, pH, time and solvents cause denaturisation of enzymes ultimately leading to unfolded and low active enzyme molecules. Therefore securing the enzyme molecules from denaturising to acquire the maximum catalytic function is vital in enzymatic biosensors. Further to immobilization of the enzyme without limiting the activity, resistance to leach out enzyme molecules from the immobilization matrix, good interaction between enzyme and the sensor electrode, provision of smooth flow of substrates and products in and out from the enzyme layer and long term stability of the enzyme after immobilization are considered as preferred parameters of a quality biosensor. Aforementioned facts comprehend the significance of designing suitable enzyme immobilization matrices for biosensor applications. Nobel metals are commonly used as substrates, but their cost contributes to the high production cost of biosensors. In order to find alternative substrates, conducting polymers such as polypyrrole(PPy), polyaniline and poly(3-hexylthiopene) have been tested in the past by many research groups[9,10,11,12]. Conducting polymers serve as efficient immobilizing matrices mostly because of their good conductivity and biocompatibility. PPy among other conducting polymers takes the lead in biosensor

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applications with its easy and fast preparation, ability to control the thickness and workability at atmospheric conditions. Modern researchers largely rely on PPy to immobilize enzymes and try to discover novel methods to increase the effectiveness of enzyme entrapment by means of various techniques. The majority effort is towards chemical synthesizing methods to gain nano-structured PPy and its derivatives for high enzyme loading. In this chapter, a novel method of fabricating artificial nanoporous PPy is introduced and experimented to enhance the glucose and hydrogen peroxide sensing. H 2 O2 → O2 + 2 H + + 2e −

(3)

H 2 O2 + 2 H + + 2e − → 2 H 2 O

(4)

In order to increase the number of active sites we need to increase surface area of the substrate[13,14,15]. However, increased planar surface area causes increased sensor size. Therefore we tried to increase the effective surface area by introducing artificial nano-pores to the PPy surface by nano-templating. In Glucoseoxidase(GOx) based biosensors glucose reacts with GOx and produces H2O2. Then the electrochemical system in the sensor participates in the following reactions and release electrons which is collected by the sensing electrode as response current. If there are sufficient GOx entrapped in the sensor, the response current is proportional to the glucose concentration.

2 Sensor Fabrication Alumina AnodiscTM membranes having tubular nano-pores were selected as the base material or template to produce artificial nanoporous PPy. Anodisc™s are hydrophilic thin membrane filters with non interconnected parallel nano-pores running from one surface to the other. Alumina and animal charcoal had been tried as substrates for enzyme entrapment in the past and found that though they have entrapment property, their depletion rate is very high. By introducing a thin biocompatible PPy layer on top of pre-deposited Platinum(Pt), several achievements can be gained. This newly introduced Pt/PPy acts as a very good catalytic surface for H2O2 oxidation when employed this for glucose biosensing purposes while PPy provides a biocompatible conducting surface for enzyme immobilization. This prevents the denaturing problem arises with employing Alumina as enzyme immobilizing matrix. Alumina templates are chemically inert to solutions used in electrochemical deposition techniques and can withstand the relatively high temperatures required by other fabrication techniques. Again the template serves as a fine structural support in this biosensor application. Here, careful selection of Pt and PPy deposition procedures has allowed obtaining a novel nano-tube array which can be used as an enzyme immobilizing matrix in biosensors. Before using Alumina Anodiscs as electrodes, a thin layer of Pt was deposited to make them conducting. Here Pt was plasma sputtered on masked Anodiscs to obtain nano-porous Alumina/Pt electrodes. The thickness of deposited Pt was optimized in order to retain the nano-porosity of the Alumina membrane for further use. Deposition of thick layer of Pt would block most of the nano-pores by deposited Pt particles and

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a very thin layer of Pt would not be consistent enough to make Alumina disc conducting. By analyzing different thicknesses of Pt deposition, 50nm was selected as the best thickness to serve our purpose. During deposition of a thin layer of Pt, it is expected to get deposited as shown in Fig. 3. This will provide a nano-porous Pt electrode to polymerize PPy. If a thin film of PPy can be deposited on this nano-porous Pt electrode in such a way that pores are not getting blocked by overgrown PPy, a nanoporous PPy electrode can be achieved.

Fig. 3. Fabrication process of nano-porus polypyrrole

The experimental setup during electrochemical deposition of PPy consist of a three electrode cell consisting of Alumina/Pt working electrode, Pt counter electrode, a Ag/AgCl reference electrode, and an aqueous monomer solution of 0.05M Pyrrole with 0.1M NaPF6 as the dopant. A constant current density of 0.3mAcm-2 was used during polymerization and a potential variation with time was observed. The smooth variation of potential in a constant manner provides evidence to a uniformly deposited Pt layer during plasma sputtering as well as an even deposition of PPy film on the nano-porous electrode[16]. Fig. 4. shows the polypyrrole nano-tubes developed through this process. This was obtained by dissolving the alumina in 0.1M NaOH. These PPy nano-tubes enhance the effective surface are by a significant factor explained in the next section.

Fig. 4. PPy Nanotubes seen through the Scanning Electron Microscope after dissolving alumina.

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Enzyme immobilizing on fabricated nano-porous PPy membrane was done by drop casting. During this process a predetermined concentration of enzyme solution was dropped onto the fabricated PPy surface and allowed to dry for 30 seconds. Then it is washed in a NaPH buffer solution with controlled PH value and allowed to dry in 4°C.

3 Glucose Nano-Biosensors In the case of glucose biosensors, Glucoseoxidase(GOx) was used as the enzyme. Amperometric response measurements for addition of 0.05M concentrations of glucose was carried out with two different types of sensors with pore diameters of 100nm(A1) and 200nm(A2) as shown in Fig. 5 and Fig. 6 respectively. A high stability was clearly seen during the extended dynamic range of the responses and high sensitivity values of 7.4mAM-1cm-2 and 3mAM-1cm-2 were reported for sensors A1 and A2 respectively. The reason for A1 sensor to get a sensitivity of more than double the value of A2 can be easily explained by the surface area increment calculations described in this section. According to the approximated calculations, the effective surface area is getting boosted by a factor depending on the radii and depth of nano pores when porous electrode is used instead of a flat electrode. Radius of pore is inversely proportional to the area increment and thereby to the sensor characteristics. This is inline with the observed sensitivity results. Further, sensitivity of the glucose biosensor is a dependent of the activity of the GOx entrapped in the artificial nanopores of PPy matrix. It implies the affinity between the substrate and the enzyme, too. The ideal platform provided by nano-porous electrodes for enzyme immobilization with size matching of enzyme molecules to enter and to get entrapped without leaching-out has contributed to most part of these characteristic enhancements. By introducing nano-pores on a flat surface, the effective surface area is getting increased by a certain factor depending on the density, depth and size of the pores. When all the pores are assumed to be circular in shape having a radius of r and a depth of h, the surface area earlier occupied by πr2 will be changed to a new value of 2πrh with the introduction of pores. By introducing pores, π r2 → Factor increasing the area →

2πrh 2h/r

When two different pore densities of 50% and 25% were taken into consideration, At a pore density of 50% Sn/S0 = (0.5 + 0.5 * 2h/r)

At apore density of 25% Sn/S0 = (0.75 +0.25 * 2h/r)

Where, S0 = surface area of a flat surface Sn = new surface area after introducing pores From SEM images, h ≈ 10µm and rminimum ≈ 10nm, rmaximum ≈ 100nm. Therefore, the effective surface area of the new nanoporous electrode is 50 ~ 1000 times higher compared to a flat electrode.

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Fig. 5. Amperometric response measurements for addition of different concentrations of glucose to sensor A1 (with 100nm pores)

Fig. 5 and 6 show the Ampherometric responses of developed sensors against addition of GOx. This is the fundamental response obtained from the sensors and the rest is derived from these graphs.

Fig. 6. Amperometric response measurements for addition of different concentrations of glucose to sensor A2 (with 200nm pores)

The calibration curve in Fig.7 indicates the Michaelis Menten behaviour of the proposed biosensor. Mean current and standard deviations for four identical sensors are shown there. At a specific value of concentration (i.e. at 5mM) mean current and relative standard deviation (RSD) were found to be 41.2 µAcm-2 and 5.09% respectively. Correlation coefficient of the linear region of calibration curve is 0.9962 for 26 numbers of readings. When checked for the repeatability the sensor performance dropped down to less than 50% in 14 days for both A1 and A2.

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The lowest possible measurement was calculated to be 30μM (at SNR=3) and the apparent Michaelis menten constant (Km) calculations were done using LineweaverBurk method and it was calculated to be 7.01mM. Lineweaver- Burk plot derived from calibration curve for A2 is shown in Fig. 8. This apparent value of Km is smaller than the reported value for free enzyme from Aspergillus Niger[32] providing proof for the non denaturing characteristic and the good affinity between substrate and the enzyme of the newly devised PPy nano-tube glucose biosensor. Limiting values of the response current Imax are 108µA and120µA for A1 and A2 respectively. It is important to appreciate that whilst Km is a characteristic of an enzyme and for its substrate and is independent of the amount of enzyme used for its experimental determination, this is not true for Imax. It has no absolute value but varies with the amount of enzyme used. Therefore a high value of Imax gives the proof for the high adsorption of GOx into the porous electrode and the difference in two values implies the difference of enzyme adsorption in two types of Anodisc™s (A1 and A2). Extended linear range provides the feasibility of using this sensor for detecting diabetes as the glucose concentration for diabetic patient is above 7mM. Though the physical adsorption method provides a good sensitivity of 3mA/cm2/M, we tried to improve the sensitivity of this sensor type. In order to increase the sensitivity the amount of active sites has to be increased. First the effective surface area was increased by introducing nano-porous structure and then we tried to improve the number of active sites by increasing the enzyme entrapment by different techniques. First one is to add the enzyme to the pyrrole monomer solution during the electro polymerization of the sensor electrode, which we called co-entrapment. This provided an improved result of 3.753mA/cm2/M. Then we tried physically adsorbing GOx to a nano-sensor fabricated using co-entrapment method. The two step method has further increased the sensitivity reaching 4.453mA/cm2/M. However, the response time has also changed from 3s for the physical adsorption method to 9s for the coentrapment method and 8s for two step method. The main reason is it takes time for the glucose to reach GOx entrapped inside the PPy structure.

Fig. 7. Calibration curve for sensor A2.

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Fig. 8. Lineweaver- Burk plot derived from calibration curve for A2 shown in Fig. 7

Then we tried using a cross-linker to entrap the enzyme into the PPy matrix. We have tried with many different cross-linking agents and had a very good result with Glutaraldehyde. Under this scheme, GOx was added just like it was done in the case of physical adsorption and before it getting dried, a 5µl droplet of 0.1 wt% Glutaraldehyde was dropped on the fabricated PPy nanobiosensor and allowed it to dry in cool air for 30minuits. The sensitivity obtained was 62.53mA/cm2/M with a 4s response time. This has not only increased the sensitivity, but also increased the shelf lifetime of the sensor. Under the physical adsorption method lifetime was shorter than a couple of weeks which is not acceptable in commercial use. The cross-linked sensor was tested for seven months and still got a fair response but with a reduced sensitivity.

4 Double Layered Nano-Biosensors Up to this point we have discussed PPy based nano-biosensors of single layer type. All these sensors were fabricated using single side Pt coated nano-porous Anodiscs. In order to increase the effective surface are this time both the sides of Anodisc were coated with Pt and PPy was electrodeposited on both sides simultaneously. Differenmt polymerization currents and durations were tested and Fig. 9 shows the potential response of PPY deposition on Pt plated Anodiscs under constant currents for optimal results. The equipment used was HSV100 from Hakuto-Denko, Japan. Fairly flat response potential illustrates a homogeneous layer of PPy deposition on the Pt layer. Fig.10 shows the Ampherometric response of the double layered nano-biosensor. Table 1 compares the performances of this sensor with similar sensors developed by other research groups.

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Fig. 9. Comparison of voltage responses during polymerization of PPy on the Pt plated nanoporous Alumina electrode. (a) 90s polymerization at 0.3mA/cm2 for single sided sensor (b) 60s polymerization at 0.5mA/cm2 for double sided sensor.

Fig. 10. Current response with an addition of 0.5mM of glucose in to 0.05M Phosphate Buffer solution using double sided Pt/PPy sensor. Table 1. Analytical performance comparison of recently published Glucose bio sensors Biosensor configuration

Sensitivity (mAcm-2 M-1) 90

Response time (s)

Life time

Reference

4

6 months

This work

7.4

4

<2 weeks

Au/ZnO/GOx

21.7

3

~2 months

C/NiO/GOx

3.43

8

~20 days

Chitosan/MWNTs/GOx

0.45

8

~1 month

(Ekanayake et. al.,2007)[17] (Kong T., et. al., 2009)[18] (Li C., et. al., 2008)[5] (Wu B., et. al., 2009)[19]

Al2O3/Pt/PPy/GOx (DS) Al2O3/Pt/PPy/GOx (SS)

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289

5 Environmental Sensors Environmental monitoring is becoming a very critical issue as the consumerism has led the global community to add more and more pollutants to the environment directly or indirectly. This includes monitoring of air quality, monitoring of waterways, reservoirs and swamps, and soil health monitoring in different environments. Previously described type of nano-biosensors are really appealing for fast monitoring of liquid quality and we have developed one nano-biosensor based on their principle of operation for environmental monitoring. Hydrogen peroxide(H2O2) is an ubiquitous molecule in the environment as a result of both natural and industrial processes over a long period. It is well established as a deodorizing and a bleaching agent. Other usages include organic and inorganic chemical processing, textile and pulp bleaching, metal treating, cosmetic applications, catalysis of polymerization reactions, odour control, industrial waste treatment, and control of sludge bulking in waste waters[20, 21]. These usages are continuously expanding, making it a necessity not only to understand the mode of H2O2 application but also to sense the amount of this chemical accurately[22]. Several conventional methods such as spectrophotometry, chemiluminescent flow sensors, and oxidimetry have been used to detect H2O2 during the past[23,24]. There is a disadvantage of using most of these techniques for accurate measurements in biological samples as these sensors reveal only micro molar level concentrations, where the existence may be in nanomolar levels. Therefore the need for highly sensitive H2O2 sensors is vital in this field[25]. The developed senor uses horseradish peroxidise(HRP) (EC 1.11.1.7, 225U/mg) as enzyme for H2O2 measurements. This sensor can work either on anodic mode or on cathodic mode and it is a very special and useful feature. Fig. 11 illustrates the bifunctional characteristics of this sensor. It clearly shows a good response in either mode. Fig. 12 (a) and (b) shows the anodic and cathodic responses of the developed

Fig. 11. Cyclic-Voltammograms of the fabricated sensor electrode with and without H2O2.

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(a)

(b)

Fig. 12. Calibration curves for the H2O2 sensor at (a) anodic mode at 0.2V and (b) cathodic mode at -0.1 V

sensor for H2O2. However, anodic mode has got a high sensitivity of 3.8A/cm2/M and a low linear range of 10nM to 15µM compared to the relatively low sensitivity of 1A/cm2/M and high linear range of 5nM to 25µM that of the cathodic mode. Both features showed a very fast response of 5s.

6 Conclusions Conducting polymer based nano-biosensors have proved their suitability in biomedical and environmental parameter measuring with enhanced performance through nano-templated polymer matrices. The method used to develop nano-porous polypyrrole sensor electrode is a cost effective alternative for the high-cost noble metal based sensor electrodes which causes the high-cost of sensors. The proposed PPy based sensor electrodes provide very high sensitivity with a reasonable shelf lifetime and will definitely be an attractive alternative in future biomedical and environmental sensing.

Acknowledgement The authors would like to thank Professor Keiichi Kaneto of Kyushu Institute of Technology, Japan for his advice and kind support for this research to be carried out in his laboratory.

References [1] Bai, Y., Yang, W., Sun, Y., Sun, C.: Enzyme-free glucose sensor based on a three dimensional gold film electrode. Sens. Actuators B: Chem. 134(2), 471–476 (2008) [2] Lei, C.-X., Wang, H., Shen, G.-L., Yu, R.-Q.: Immobilization of enzymes on the nano-Au film modified glassy carbon electrode for the determination of hydrogen peroxide and glucose. Electoanalysis 16, 736–740 (2004)

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[3] Wilson, R., Turner, A.P.F.: Glucose oxidase: an ideal enzyme. Biosens. Bioelectron. 7, 165–185 (1992) [4] Li, L., Sheng, Q., Zheng, J., Zhang, H.: Facile and controllable preparation of glucose biosensor based on Prussian blue nanoparticles hybrid composites. Bioelec. Chem. 74(1), 170–175 (2008) [5] Li, C., Liu, Y., Li, L., Du, Z., Xu, S., Zhang, M., Yin, X., Wang, T.: A novel amperometric biosensor based on NiO hollow nanospheres for biosensing glucose. Talanta 77(1), 455–459 (2008) [6] Liao, C.-W., Chou, J.-C., Sun, T.-P., Hsiung, S.-K., Hsieh, J.-H.: Preliminary investigations on a glucose biosensor based on the potentiometric principle. Sens. Actuators B: Chem. 123(2), 720–726 (2007) [7] Chen, X., Chen, J., Deng, C., Xiao, C., Yang, Y., Nie, Z., Yao, S.: Amperometric glucose biosensor based on boron-doped carbon nanotubes modified electrode. Talanta 76(4), 763–767 (2008) [8] Cabaj, J., Idzik, K., Sołoducho, J., Chyla, A., Bryjak, J., Doskocz, J.: Well-ordered thin films as practical components of biosensors. Thin Solid Films 516(6), 1171–1174 (2008) [9] Kang, X., Mai, Z., Zou, X., Cai, P., Mo, J.: A novel glucose biosensor based on immobilization of glucose oxidase in chitosan on a glassy carbon electrode modified with gold– platinum alloy nanoparticles/multiwall carbon nanotubes. Anal. Biochem. 369(1), 71–79 (2007) [10] Kurt, R., Simon, L., Penterman, R., Peeters, E., de Koning, H., Broer, D.J.: Control over the morphology of porous polymeric membranes for flow through biosensors. J. Mem. Sci. 32(1), 51–60 (2008) [11] Choi, H.N., Han, J.H., Park, J.A., Lee, J.M., Lee, W.-Y.: Amperometric Glucose Biosensor Based on Glucose Oxidase Encapsulated in Carbon Nanotube-Titania-Nafion Composite Film on Platinized Glassy Carbon Electrode. Electroanalysis, 19-17, pp. 1757– 1763 (2007) [12] Shan, D., He, Y., Wang, S., Xue, H., Zheng, H.: A porous poly(acrylonitrile-co-acrylic acid) filmbased glucose biosensor constructed by electrochemical entrapment. Anal. Biochem. 356, 215–221 (2006) [13] Ekanayake, E.M.I., Preethichandra, D.M.G., Kaneto, K.: An Amperometric Glucose Biosensor With Enhanced Measurement Stability and Sensitivity Using an Artificially Porous Conducting Polymer. IEEE Trans. Instr. Meas. 57(8), 1621–1627 (2008) [14] Cai, D., Yu, Y., Lan, Y., Dufort, F.J., Xiong, G., Paudel, T., Ren, Z., Wagner, D.J., Chiles, T.C.: Glucose sensors made of novel carbon nanotube-gold nanoparticle composites. BioFactors 30(4), 271–277 (2007) [15] Xu, T., Zhang, N., Nichols, H.L., Shi, D., Wen, X.: Modification of nanostructured materials for biomedical applications. Mat. Sci. Eng.: C 27(3), 579–594 (2007) [16] Gade, V.K., et al.: Immobilization of GOD on electrochemically synthesized Ppy-PVS composite film by cross-linking via glutaraldehyde for determination of glucose. Reactive and Functional Polymers 66, 1420–1426 (2006) [17] Mala Ekanayake, E.M.I., Preethichandra, D.M.G., Kaneto, K.: Polypyrrole nanotube array sensor for enhanced adsorption of glucose oxidase in glucose biosensors. Biosens. Bioelectron. 23(1), 107–113 (2007) [18] Kong, T., Chen, Y., Ye, Y., Zhang, K., Wang, Z., Wang, X.: An amperometric glucose biosensor based on the immobilization of glucose oxidase on the ZnO nanotubes. Sens. Actuators B (2008), doi:10.1016/j.snb.2009.01.002

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[19] Wu, B.-Y., Hou, S.-H., Yu, M., Qin, X., Li, S., Chen, Q.: Layer-by-layer assemblies of chitosan/multi-wall carbon nanotubes and glucose oxidase for amperometric glucose biosensor applications. Mat. Sci. Eng. C 29(1), 346–349 (2009) [20] Halliwella, B., Veronique, M., Longvsdfa, L.H.: Hydrogen peroxide in the human body. FEBS letters 486, 10–13 (2000) [21] Westbroek, P., et al.: Voltammetric detection of hydrogen peroxide in teeth whitening gels. Sensors and Actuators B: Chemical 124, 317–322 (2007) [22] Song, Y., et al.: A novel hydrogen peroxide sensor based on horseradish peroxidase immobilized in DNA films on a gold electrode. Sensors and Actuators B: Chemical 114, 1001–1006 (2006) [23] Villar, I.D., et al.: Strategies for fabrication of hydrogen peroxide sensors based on electrostatic self-assembly (ESA) method. Sensors and Actuators B: Chemical 108, 751–757 (2005) [24] Qi, H., Zhang, C., Xiaorong, L.: Amperometric third-generation hydrogen peroxide biosensor incorporating multiwall carbon nanotubes and hemoglobin. Sensors and Actuators B: Chemical 114, 364–370 (2006) [25] Kulys, J., Tetianec, L.: Highly sensitive biosensor for the hydrogen peroxide determination by enzymatic triggering and amplification. Sensors and Actuators B: Chemical 113, 755–759 (2006)

Nondestructive Evaluations of Iron-Based Materials by Using AC and DC Electromagnetic Sensors Koji Yamada1, Jiaoliang Luo1, and Masato Enokizono2 2

1 Centre for Innovation, Saitama University, 338-8570 Saitama, Japan Dept. Electric and Electronic Eng. Oita University, 870-1192 Oita, Japan

Abstract. Sensitive and precision nondestructive evaluations of iron-based material have been investigated by observing the magnetoresistance effects, magnetic Barkhausen noises, conductivity and magnetic susceptibility. The magneto-resistance was found very small as 0.04% or less for the samples with residual strain of 10% in the stainless steel of SUS304, caused by the martensitic transformation up to 60emu/g. Barkhausen noise analyses for Fe-based material in general with a resolution of 1mm was also performed. All these results developed here were cross-checked with the real residual strain by X-ray diffraction spectroscopy, electron-microscope observation and compared with the leakage flux distribution observed by a flux-gate sensor. We developed the sensing techniques independent of sample shape which are really available in factories. Keywords: Nondestructive evaluation, residual stress, SUS304, magnetoresistance, precision of 0.005%, martensitic transformation.

1 Introduction Non-destructive evaluations (NDE) of structural materials are very important nowadays for the safety of civilized societies So many electrical and magnetic tools of NDE methods have been developed by researchers in these several decades of years [1-3]. We have been developing many diagnostic tools as resistance observations, internal residual stresses by using electro-microscope, Vickers test, optical surface observation by using a laser interference method, magnetization observation [4-11], magnetic Barkhausen noises [12-15] by using a small transducer, and the magnetoresistance measurements. In this paper, we present the last two tools listed above which will be really available in factories. Among the criteria of the availability of NDE, the most important factor is to obtain the essential property change of material for safety and to exclude the spurious effects caused just by geometrical shape in the built-in structure. For an example, the absolute resistance change along with the positions at some structures must be eliminated by subtracting the values observed in that as made state or eliminated by dividing the two values of those with and without mechanical stresses, electrical or magnetic field application. In this paper, we show several sensing techniques on residual strains in SUS304 or in iron based material, composed of a magnetic Barkhausen noise detector to derive a factor of degradation, magnetoresistance effect (MR) and the leakage flux observation S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 293–303. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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to find out the local inhomogeneity of residual strain at each position and magnetoresistance detection with 4-electrode methods. To clarify the relationship between the parameters obtained by these tools and the observed results by the electromicroscope, X-ray spectroscopy, Vickers test etc were performed.

2 Experiments The samples of SUS304 (30x120x2t mm or 60x165x5t mm) were prepared by heating at 1050-1150 C˚ for 1h and rapid quenching into water. After these processes, tensile stresses were applied up to 630MPa. At each stage of degradation, we performed the following diagnoses of MR effect, Barkhausen noises and leakage flux observations.

Fig. 1. Martensitic transformation as a function of applied tensile tresses up to 630MPa

As shown in Fig.1 the samples with residual stresses shows martensitic transformations represented by the saturation magnetization (Ms) and the remanence (MR ) which is recognized by the magnetization. Here, the martensitic transformation stands for the crystal transformation between f.c.c. lattice and b.c.c. lattice, respectively[4]. 2.1 Magnetoresistance Effect As it is explained in the former subsection, the resistance change in the magnetic field will be enhanced due to the martensitic transformation especially in samples with heavy residual stresses. Therefore observation of magnetoresistance (MR) effects in the samples were performed by using 24 bit ADC and by the conventional 4-electrode method with the voltage electrodes of 10mm interval and with round shaped top at the electrodes onto the contacting surface, which should not be injured. The .trace as shown in Fig.2 depicted the basic experimental resolution in this experiment. The upwards trace is caused by the resistance increases during the observation where the sample of SUS304 was heated up about by 0.05 at RT by the current (8A) through the sample of about 100μOhm. The resolution could be estimated here about 0.005% in the 10μOhm of resistance between the voltage electrodes. Fig. 3 shows the MR in the magnetic field configurations of the perpendicular (I B) and of

℃

⊥

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295

Fig. 2. Resistance change of a SUS304 sample as a function of time elapses within 15s at RT

the longitudinal (I//B) configurations, respectively. It is worthwhile to note here that the minimum significant resistance reached as small as 50pOhm in 10μOhm. The MR effects were estimated by the following way as shown in Fig. 3. The magnetic field was set at B=0 for 15s, B=3kA/m for 40 s and again B=0 for 15 s, respectively. The resistance change was estimated by the subtractions of the background change as expressed by MR =

< R ( B , t ) > t − < R (0, t ) > t < R (0, t ) > t

(1)

Fig. 3. The reliable method to obtain the MR effects by subtracting R(B) from R(0) in the perpendicular and parallel configurations, respectively

Where <..>t stands for the average in the time interval during with and without magnetic field application about for 40s. The temperature dependence of the resistance during the measurement was also considered by subtracting the back-ground trace change in this method. By using the precision method under all the fluctuations, we measured MR effects for differently strained samples up to 10% as shown in Fig.4. It is important to note that the MR effect is independent of sample current I and shape of the sample due to the normalization of the sample resistance in B=0. The MR ratio increased with increasing residual stresses as shown in this figure. MR in the longitudinal configuration shows always positive and monotonically increases with increasing magnetic field which were reflected by the amount of martensitic transformation.

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Fig. 4. Magnetoresistance effects for differently strained samples in the longitudinal and perpendicular configurations of I, P and B, respectively.

2.2 Magnetic Barkhausen Noise We performed the observations of magnetic Barkhausen noises (MBN) during magnetizations for the samples with lattice defects of the martensitic transformations. MBN, therefore, reflects the lattice defect density in the sample. Fig. 5 shows the tool for the MBN measurements with a small coil with a diameter of 1mm. in the configuration of I//B//P and I//P ⊥ B, respectively.

Fig. 5. The probe of the MBN with the magnetic field slope depicted at the right top corner

The application of magnetic field B is illustrated in the insertion in Fig.5, where the fields were linearly and slowly increased with time elapses. To observe the MBN signals exactly, the slow movements of the magnetic domains must be considered in this way. The sequential MBN’s were obtained as voltages VNk (k=0-N) with discrete k during the field increase up to the saturation magnetization. The expectations of VN k as denoted by were derived as a function of the positions (r) and the relative angles(θ) against the applied P by the following equations as

Nondestructive Evaluations of Iron-based Materials by Using AC and DC

t max

< H

D

∫ H int

( r ,θ ) > ≡

297

( t )V ( x , y , h , θ , t ) dt

0

t max

∫ V ( x , y , h , θ , t ) dt

(2)

0

N

=

∑

H int k V k ( x , y , θ )

k =0 N

∑V

R

k

( x , y ,θ )

(3)

k =0

Here, Hint stand for the internal fields to give rises of the jumps. The physical mechanism was interpreted in the ref. [3]. For an example , the analytical results of the strain anisotropy is demonstrated in an iron-based sample with the tensile stresses of 500MPa in Fig. 6. Here, Fig.6(a) shows the at a heavily strained position, Fig.6(b), a slightly strained position, respectively. Apparently, the heavily strained position shows the larger anisotropy than that at the slightly. These results are reflected by the strength of the average pinning forces at each position.

Fig. 6. The expectations () of magnetic Barkhausen noises observed at differently strained positions in a sample

2.3 Leakage Flux The observations of the leakage flux distribution was performed at the sample surfaces by using a fluxgate sensor which does not emit flux during the flux-gate switching actions [16], and with the spatial resolution of 50μm with a condition of the lift-off distance of 50μm from the sample surface. The leakage flux distribution of the normal components ΦL(x,y) is displayed in Fig. 7 at positions (x, y) and its gradient dΦL(x,y)/dx along with x direction, in which the magnetization was performed before measurements. The leakage flux generates around at positions where the permeability become smaller than those at surrounding positions. This mechanism is quite natural to consider the flux accommodation along the sample. Namely, the gradient of flux (-dΦ L (x,y)/dx )x=x0 becomes larger at x=x0 due to the total flux μ(x,y)S(y) become smaller at the position with the cross section S(y). Therefore, the gradient of leakage flux along the applied strain at the sample surface depicts the internal flux fluctuations or in another word, the permeability distribution along the strained direction. Note here, that even a sample without inhomogeneity of

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material constants and with the same cross-section along x direction shows leakage fluxes. However, the leakage flux shows an only monotonic increase or decrease along the sample direction x.

Fig. 7. Sample deformation, the leakage flux distribution and the derivatives in an A533B sample.

Fig. 7 shows a leakage flux distribution and the sample deformations in a sample with the maximum thickness deformation about of -0.8%. The leakage flux occurs not only by the thickness deformation but also by the permeability fluctuation as shown in the top figure. The second top figure shows 2-dimentional distribution of the leakage flux and ΦL(x,0). The bottom figure shows the distribution of -dΦL(x,y)/dx and -dΦL(x,0)/dx along x direction in B&W. These distributions show most plausible to understand the physical mechanism as explained just before. However, we must be careful to examine whether the geometry effects are included or to examine whether the lift-off distance of the Hall element is constant during the measurement over the scanning. 2.4 AC Conductivity and Magnetic Susceptibility We developed a sensor using small transformers with open poles, attached at materials under evaluation [17]. Fig. 8 illustrates the schematic structure of the probes. The

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Fig. 8. The experimental set-up of magnetic ac parameters using two transformer probes (Tr1,Tr2).

output voltages (V0j=1,2) of the two probes are connected in reverse to obtain the difference signal. Here, we performed the basic experiment to characterize a probe output (V0) as a function of frequency f. For this purpose, we define the ac magnetic circuit like that in the ac circuit theory as Φ (ω ) =

NI ( ω ) ∑ R j (ω ) j

,

(4)

where, N stands for the primary coil turns, I( ω), the current and ω(=2πf), angular frequency, respectively. The magnetic resistance Rj(ω) is defined as frequency dependent and complex variable, and the suffix (j) runs over several circuit elements. The current I( ω) is also dependent on frequency as I0 exp(-iωt),( i ≡ − 1 ) . Therefore, we obtain the output voltage V0 as V o (ω ) = i

nN ω I ( ω ) ∑ R j (ω ) j

.

(5)

Here, n stands for the winding turns in the secondary coil. Now, the transfer function C( ω) is defined by C (ω ) ≡ K =

V 0 (ω ) ω I (ω )

1 ∑ R j (ω ) j

.

(6)

Here, K stands for a constant (=-i/Nn) It is easily understood that C(2πf) reflects the flux conductive properties as that in the dc magnetic circuit. Fig. 9 shows the drastic change of the magnetic transfer function C(f), between the probe without air-gap immersed with the original transformer core, and with the airgap. Namely the magnetic transfer function without air-gap increases by 50 times larger than that with air-gap of 0.2 mm. By using the probe in this study, we can obtain the stable value of magnetic susceptibility by some calibration in the frequency range less than 1 kHz as shown in Fig.10. In the lower figure of Fig.10, the eddy

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Fig. 9. The experimental characteristics of a transformer type probe with completely closed magnetic circuit, immersed with the original transformer core (open circle), and a probe with open magnetic poles attaching at the same core surface (closed circle).

current losses in Cupper plates are enhanced in a range larger than 1kHz. Note here that C(f) is normalized by that with air-gap and attached no sample. Hence, the value of C(f) larger than unity means that the magnetic field inside the probe increased than that in air. In other words, we can determine the magnetic susceptibity and conductivity precisely in comparison with the difference material with calibrated permeability. In this way, we attained the resolution of 0.2% for conductance changes in conductive materials and the susceptibility changes of 0.1 % for magnetic materials, respectively.

Fig. 10. The permeance of several magnetic materials (upper figure) as a dust core (5mmt), a sheet for shield(10μmt) and a soft iron plate(1mmt) and cupper plates with different thicknesses (lower figure).

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In addition to these results, the phase shifts were also observed by excitation current I and the difference output VD in Fig.8 reflects the preceding or retarding phase caused by the imaginary part of the magnetic resistances.

3 Discussions We presented many NDE tools in this study together with the additional experiments on the ac magnetic diagnosis tools. In this discussion, we examine the validity of ac magnetic circuit and the experimental results. Namely, here, we suppose the complex variables of the magnetic resistance R(ω) as Z( ω) in the conventional circuit theory. Further, 3 components are proposed in each probe of Tr1 and Tr2 as shown in Fig. 11.

Fig. 11. Equivalent magnetic components composed of RLC (the leakage flux), RLG (air gap at the magnetic poles) and RLG (the material).

Namely as shown in Fig. 11, the component RLC originated by the leakage flux around magnetic core positions expanded from the winding coil and RLG , by the airgap between the magnetic pole and sample surface, respectively. The dependence of gap resistance on the frequency could be neglected as pure resistance (real number). Now, in general, the difference signal of Tr2 and Tr1 (=ΔC21(f)) is exactly expressed by the difference outputs of Tr2 and Tr1 (=ΔC21(f)) as Δ C 21 ( f ) ≈

1 RM 2 [ RL G RL G + R M

− 2

RM 1 ] . RL G + R M 1

(7)

Note here that RLG1=RLG2=RLG due to the exactly the same current flows in the primary coils of the two probes with the same structure. Now, ΔC21(f) behaves always positive due to the increasing function of RM in the case of real numbers and RM2>RM1. In this case of RM , the inequality holds in general as RL C >> RL G >> R M .

(8)

The difference permeance ΔC21(f) (=1/RM1 - 1/RM2) becomes simple as Δ C 21 ( f ) ≈

1 [RM 2 RL G

2

− R M 1 ].

(9)

Here RLC is common for the two probes because the excitation current flows in common with the two probes, and so does RLG. In general case of complex variable of RM , ΔC21(f) is exactly expressed by Δ C 21 ( f ) ≈

1 RM 2 [ RL 2 RL 2 + R M

− 2

RM 1 ] . RL 2 + R M 1

(10)

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Here, ΔC(f) is always positive in the case of real numbers and RM2>RM1 because Eq. (10) is increasing function with increasing RMj. Now, the frequency dispersions in the material is variously defined by the relaxation approximations of eddy current or of the magnetic wall movements etc as R M ( f ) ≈ 2π f

E

R M 0 exp( − 2 π f τ M ) .

(11)

Here RM0 stands for a complex number depending on the magnetic susceptibility and the conductance of the material, E, the power number (E=1-2: for eddy current loss). The relaxation time constant might be determined well by the skin depth (d) and the sample thickness (D). Further, in the frequency range of relaxations of any kinds, τM could be complex number to give rise the phase shifts between I(ω) and VD(ω). Fig.12 shows the experimental results of the magnetic material as a sample, which shows the phase shift between I( ω) and VD(ω) as expected before in the case without rectifying diodes in Fig.8.

Fig. 12. The observed phase shifts observed by the device.

Here, we found the almost 170 degree phase shift between that of iron and that of cupper in the difference signal (VD). The phase shift will be correctly determined by using a Lock-in amplifier.

4 Conclusions We would like to emphasize here that the analytical results of MR are almost independent of the sample thicknesses in MR ratio due to the division by the resistance in zero-field. This is an out-standing feature in NDE, with the demerit of the necessary high precision. In MBN observations at the surface, the amplitudes of VN reflect the true inhomogeneity inside of the material by using the probe in this experiment. Therefore, the tools developed here are available in the factory except noise problems. The most important feature in ac magnetic probe is the non-contacting method to the sample surface. Because of the atomic power station, all the material should not be injured by the diagnosis. The method of MBN observation, the reference is always

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necessary to compare because the value of expectations is relatively determined. Therefore as a conclusion, NDE might be performed by cross-checks and compare with the material without degradation.

References [1] [2] [3] [4]

[5] [6]

[7]

[8]

[9]

[10]

[11]

[12] [13] [14]

[15] [16] [17]

Jiles, D.C.: Review of magnetic methods for NDE. NDT Int. 21(5), 311–319 (1988) Kronmuler, H.: Canadian J. Phys. 45 (1967) Shoji, S.: Doctor Thesis, Saitama Univ. (March 1999) (in Japanese) Yamada, K., Shoji, S., Yamaguchi, K., Tanaka, Y.: Fractal Dimension of Magnetic Noises: A Diagnosis Tool for Nondestructive Testing. In: Kose, Sievert, J. (eds.). Studies in Applied Electromagnetic and Mechanics, vol. (13), pp. 153–156. IOS Press, Tokyo (1998) Yamada, K., Saitoh, T.: Observation of Barkhausen effect in ferromagnetic amorphous ribbon by sensitive pulsed magnetometer. J. Mag. Magn. Mater. 104, 341–343 (1991) Yamada, K., Shoji, S., Tanaka, Y., Uno, Y., Takeda, H., Uesaka, M., Miya, K.: Nondestructive Evaluation of Iron-based Material by Magnetic Sensor. In: Proc. of the 2nd Int. Workshop on Advanced Mechatronics (IWAM 1997), Nagasaki, pp. 114–119 (1997) Yamada, K., Shoji, S., Tanaka, Y., Uno, Y., Takeda, H., Toyooka, S., Spurapedi, Isobe, Y., Ara, K., Uesaka, M., Miya, K.: Nondestructive Cross Evaluation of Iron-based Material by Magnetic Sensors and by Laser Speckle Interferometry. J. Magn. Soc. Jpn. 23, 718–720 (1999) Hagiwara, N., Fukuda, N., Masuda, T., Yamada, K.: Nondestructive Evaluation of Plastic Strain in Pipeline Using Barkhausen Noises. In: Proc. Workshop on Magnetism and Lattice Imperfections, Hanamaki, Iwate, pp. 97–100 (April 2000) Uesaka, M., Sukegawa, T., Miya, K., Takahashi, S., Echigoya, J., Yamada, K., Kasai, N., Morishita, K., Ara, K., Ebine, N., Isobe, Y.: Round-robin test work for magnetic nondestructive evaluation of structural materials in nuclear power plants. In: Proc. Workshop on Magnetism and Lattice Imperfections, Hanamaki, Iwate, pp. 59–65 (April 2000) Yamada, K., Yamaguchi, K., Toyooka, S., Isobe, Y.: Magnetic and Optical Nondestructive Evaluation for Iron-based Materials. In: Green, R.E., et al. (eds.) Nondestructive Characterization of Materials X, pp. 333–340. Elsevier Publ., Karuizawa (2000) Yamada, K., Yamaguchi, K., Takeda, H., Tonooka, S., Masuda, T., Hagiwara, N.: Nondestructive cross evaluations iron-based material by optical and magnetic diagnosis tools. Invited paper for 3rd Int. Workshop on Advanced Mechatronic (IWAM 1999), Chunchon, Korea, pp. XXV–XXX (December 1999) Kasuya, T.: Prog. Theo. Phys. 16, 45–57 (1956) Yoshida, K.: Phys. Rev. 106, 893–898 (1957) Yamaguchi, K., Yamada, K., Isobe, Y.: Monte-Carlo Simulation of Magnetization Processes including Lattice Imperfections. In: Proc. Int. Meeting on the relationship between Lattice Imperfection and Magnetism, Hanamaki, Iwate, pp. 69–71 (April 2000) Yamaguchi, K., Yamada, K.: Simulation of Spin system for Nondestructive Evaluations of Iron-based Materials to be published by Proc. EMMA 2000, Kiev (May 2000) Liu, B.: Doctor Thesis, Saitama University (March 2006) Yamada, K., et al.: To be presented in Magda Conf. in Hokkaido, Hokkaido (November 22-23, 2010)

STACK: Sparse Timing of Algorithms Using Computational Knowledge Vasanth Iyer1 , S. Sitharama Iyengar2, Garmiela Rama Murthy1 , Kannan Srinathan1 , Mandalika B. Srinivas3 , and Regeti Govindarajulu1 1

International Institute of Information Technology, Hyderabad, India-500 032 [email protected], [email protected], [email protected], [email protected] 2 Louisiana State University, Baton Rouge, LA 70803, USA [email protected] 3 Birla Institute of Technology & Science, Hyderabad Campus, Hyderabad-500078, India [email protected]

Abstract. Research in computational aspects and algorithm optimizations help design tools to acceleration the execution of algorithms. Cost and availability of FPGA design boards have driven number of computations per second close to the general-purpose model of CPUs. In this chapter, we study the effects of algorithms with the knowledge of the underlying computing model for getting consistent and coherent view of the sensed data. The computing model uses uniprocessor, multiprocessor and acceleration using pipeline and data-path forwarding with Byzantine fault-tolerance. The pre-processing approach of the modified algorithm for sparse sensing gives better consistency and the application based calibration allowing coherent view of the data and at the same time reduces the total power consumption. This is analogous to the needle in a hay stack. The STACK implementation runs 4 times faster than the normal program based optimizations for static and dynamic scheduling. Keywords: Sensor-centric data fusion; Algorithm design; Compressed Sensing (DCS); Sensor Fusion; Pre- and Post processing of Sensors; Cross-layer Protocols; Processor Computational Models.

1

Introduction

We study the optimization of data-centric algorithms, which need to process data locally and aggregate and optimize in a distributed way. Most of the time these families of algorithms are compared measure based on computational complexities in time and space, which allow studying its scalability. The lifetime of sensor network [1,8,13] is also an important measure to benchmark the performance of these algorithms. When using this measure as they are prone to error than a general deployment error correction needs to be included in terms of a Byzantine agreement algorithm. S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 305–320. springerlink.com Springer-Verlag Berlin Heidelberg 2011

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The computational model for the programs shown in Figure 1, Algorithm 1 and 2 assume sequential execution. The values of Flag and turn in algorithm decide which process enters the critical section allowing shared data modeling [3,4] for consistency [3] and atomic operations. Message passing model is used in a distributed power-aware topology; this is shown in Algorithm 3. The data coherency [3] cannot be achieved due to – unknown number of processors – independent inputs at different locations – several programs executing at once, staring at different times, and operating at different speeds – processor nondeterminism – uncertain message delivery times – unknown message ordering – processor and communication failures The value of UID is pre assigned and the value of the leader is based on having a reliable message passing FIFO available at each processing node element (PE). Computational Model for Consistency: – – – – –

Pre-processing - reduce computation per application Dynamic Range - register bit usage Pipe-line (1-CPI) fine grain instruction level parallelization Double buffer for datapath support Dynamic power dissipation

The computational model is affected by software compiler optimizations, having sequential consistency [3], this is called program order. For embedded systems, most of the optimization is targeted for space as the target has resource limitations. In- order sequential consistency is performed by the compiler by using a window and appropriate scheduling of instructions to enhance performance. The pipe-line optimizations are done dynamically (out-of-order) [3] execution, which furthers performance which is not possible in the earlier case such as memory access and register allocations. Due to this data hazards are possible due to data coherency needs. Processor and hardware support needs to support the high-level construct of the language in context with sequential consistency [1]. The non-blocking memory operations can be implemented using specific address ranges and explicit addresses can be assigned for shared memory operations. The processor can distinguish it by physical and virtual address space during dynamic execution. Unused op-code fields can be used to implement the latter case. Even in applications where reasonable amounts of parallelism are available, significant serial, or nearly serial, code segments may occur. A problem where 90% of the program can obtain a speed-up of 10, and the remaining 10% has no parallelism, will actually run only about 5 times faster. In the hardware implementation, the same clock runs the memory that allows access to memory complete in one clock cycle. We show that the STACK model of execution gives superior performance than the sequential consistency model for data-path based algorithms, which effect sampling rate and power.

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Computational Models

The modern processors use efficient Instruction Set Architecture (ISA) such as RISC processors. The performance measures defined as below P erf = 1.2

clock speed instruction count ∗ cycles per instruction

(1)

Van Numen

RISC [12] machines attempt to maximize performance by producing improvements in clock speed (factors of 2-5, typically) and major improvements in the cycles per instruction (factors of 5 to 10). They allow a slight increase in the instruction count (less than a factor of 2). 1.3

Distributed Architecture

A synchronous network system consists of a collection of computing elements located at the nodes of a directed network graph. We refer to these computing elements as processing elements (PES), which suggests that they are pieces of sensor hardware. In order to define synchronous network system start with a directed graph G = (V, E). We use the letter n to denote | V |, the number of nodes in the network digraph. 1.4

FPGA

The hardware implementation uses a data driven pipelining, the clock rate can be calculate as CLK = Active event Q + Longest delay + Setup time + ClkSkew

(2)

1 Capacitive Load × V oltage2 × F req Switch (3) 2 The clock cycles shared between execution units and the memory unit, which allows transferring data from memory in one clock cycle. The actual implementation, which needs to calculate the longest delay while completing a calculation determines the clock period, it tends to be longer to accommodate all the variations. P ower =

1.5

Computational IP Cores

FPGA design framework [7] allows using many available algorithms in a form of a library protected by intellectual rights. Pre-processing of data needs many steps which can use these algorithm libraries, such as Scaling [7], Interpolation [7] and Mixing [7]for video oriented data streams.

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Algorithm 1. Process P1 1: statesi : 2: flag[0] = 0; 3: flag[1] = 0; 4: turn; 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18:

Algorithm 2. Process P2 1: statesi : 2: flag[0] = 0; 3: flag[1] = 0; 4: turn;

msgsi : P0: flag[0] = 1; P1: flag[1] = 1; turn = 1;

msgsi : P1: flag[0] = 1; P1: flag[1] = 1; turn = 0;

== 1) do

while (flag[0] == 1 && turn == 1) do

5: 6: 7: 8: while (flag[1] == 1 && turn 9:

// busy wait

end while // critical section ... // end of critical section flag[0] = 0;

10: 11: 12: 13: 14: 15: 16: 17: 18:

// busy wait

end while // critical section ... // end of critical section flag[1] = 0;

Algorithm 3. Process P1,P2,...Pn 1: Each process begins by sending its UID to its clockwise neighbor. 2: Each process checks its UID (u) against the one it just received (v), 3: if v > u then 4: the process sends v on to the next process 5: end if 6: if v = u then 7: the process is chosen and sends out a leader message 8: end if Fig. 1. Program for atomicity

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Overlaping sensor intervals

(a) Sparse sensor readings

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Overlaping sensor intervals

(b) Averaging cost function

Fig. 2. Sensor-centric Data aggregation using atomic averaging

1.5.1 Scaling The raw data need to be scaled to a metric before an application can further process, this family of algorithms use register size, integer with fixed bits or floating point scaling to ensure metric. 1.5.2 Interpolation Sampling rate helps to define how to represent the input signal and interpolate it, when calculating its dynamic range. 1.5.3 Mixing Data stream which represents images often needs an algorithm to fuse data, it uses a pre-processing step on every pixel before the fusion can be performed.

2 2.1

Data Pre-processing – Non Blocking Double Buffering

The synchronization methods used by IPC [5] are generally fall into the category of waited and non-waited. If the algorithm is designed with data-path approach using buffering then less computationally complex algorithm can be implemented which is wait free. The porting issues, which is processor dependent, is the availability of atomic operations to access and operate on memory variables atomically. Algorithm 4, DoubleBuffer() shows how the value of latest is shared between many Readers and continuously updated by one writer. Line 4 used the flag pointing to the buffer pairs which contain the latest update, and the non-blocking write uses number of readers (N + 1) if all are in use, its new value as shown in line 12. The design of double buffering allows to simultaneously have multiple readers access the same buffer. Writer can only interfere with the reader when they both choose to use the same row. This can occur in two cases. The first case can occur when a reader is interrupted after it has chosen a row (after line 3 in Algorithm 4),

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but before it updates the use count. The writer then executes, and can potentially choose the same row as the reader. The second case occurs when the writer is interrupted after it has chosen a row (line 9 in Algorithm 4). If this row happens to be Latest, then the reader can also choose to read from the same row. So, it is possible for the readers and the writer to select the same row i. However, the reader will read from the buffer indicated by C1[i], while the writer will use the opposite one. As the writer updates C1[i] only after the complete message is written, and the reader always increments the use count before reading Cl[i], we can guarantee that the writer and readers cannot interfere with each other in this algorithm, even if they happen to use the same row. Algorithm 4. DoubleBuffer 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15:

3

Reader() ridx = Latest inc ReaderCnt[rindx] cl = Cl[ridx] read Buff[ridx][cl] dec ReaderCnt[ridx] Writer() for (ii=Latest;;ii++) if ReaderCnt(ii mod NRows]==0 then break; end if cl= not Cl[ii] write Buff[ii][cl]; Cl[ii]=c; Latest=ii;

Algorithms for Data Coherency

As different sensors are connected to each node, the nodes have to periodically measure the values for the given parameters which are correlated. The inexpensive sensors may not be calibrated, and need processing of correlated data, according to intra and inter sensor variations. The pre-processing algorithms allow to accomplish two functions, one to use minimal number of measurement at each sensor, and the other to represent the signal in its loss-less sparse representation, which allows application level views. Figure 2(a) and 2(b) shows the cluster tree of inter-sensor intervals for a batch of sensors from Table 1. 3.1

Compressed Sensing (CS)

The pre-processing steps used by CS allows to sample at rates lower than the Nyquist rate, it recovers the original signal by rescaling and interpolation, which are save as compressed co-efficient during pre-processing.

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– Sampling rate of i.i.ds [8,13] – Aggregation window of the ensemble – The delta ranges possibly measured for the complete ensemble - calibration The signal measured if it can be represented at a sparse representation, then this technique is called the sparse basis as shown in equation (4), of the measured signal. The technique of finding a representation with a small number of significant coefficients is often referred to as Sparse Coding. When sensing locally many techniques have been implemented such as the Nyquist rate [1], which define the minimum number of measurements needed to faithfully reproduce the original signal. Using CS it is further possible to reduce the number of measurement for a set of sensors with correlated measurements [8,9].   x= ϑ(n)Ψn = ϑ(nk )Ψnk , (4) Consider a real-valued signal x ∈ RN indexed as x(n), n ∈ 1, 2, ..., N. Suppose that the basis Ψ = [Ψ1 , ..., ΨN ] provides a K-sparse representation of x; that is, where x is a linear combination of K vectors chosen from, Ψ, nk are the indices of those vectors, and ϑ(n) are the coefficients; the concept is extendable to tight frames. Alternatively, we can write in matrix notation x = Ψ ϑ, where x is an N × 1 column vector, the sparse basis matrix is N × N with the basis vectors Ψn as columns, and ϑ(n) is an N × 1 column vector with K nonzero elements. Using  . p to denote the p norm, we can write that  ϑ p = K; we can also write the set of nonzero indices Ω1, ..., N , with |Ω| = K. Various expansions, including wavelets [6], Gabor bases [6], curvelets [6], are widely used for representation and compression of natural signals, images, and other data. Algorithms 5, 6 and 7 allow applications to select how it views the state of the nature. FloodMinVal() algorithm uses a k-agreement [5], which allow to represent real-time floating values with lower-bound intervals. Figure 2, uses an averaging algorithm from R-Systems, general tree structure to calculate the dendrogram for the data-set in Table 1. The algorithm gives a good lower bound which has the values between (1.6, 2.25). The power-aware algorithms use clustering which allows to read correlated value, due to lack of calibration in small sensors, the algorithm should be able to update only higher confidence values seen. The algorithm FloodMinRange() allows maximizing the coherency of the previous algorithm FloodMinVal() by calibrating between overlapping ranges, which are active during the cluster formation. FloodMinRange(), Line 12 allows to find the minimum of the overlapping ranges, which is a better representation of the signals value in terms of the sensors current sampling and density of coverage. To check the validity and the effectiveness, we use regression analysis using off-line statistical methods in section 5. 3.2

Coherency Cost Function of Sparse Representation

A single measured signal of finite length, which can be represented in its sparse representation, by transforming into all its possible basis representations. The number of basis for the for each level j can be calculated from the equation as

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Aj+1 = A2j + 1

(5) 2

2

So staring at j = 0, A0 = 1 and similarly, A1 = 1 + 1 = 2, A2 = 2 + 1 = 5 and A3 = 52 + 1 = 26 different basis representations. Let us define a framework to quantify the sparsity of ensembles of correlated signals x1 , x2, ..., xj and to quantify the measurement requirements. These correlated signals can be represented by its basis from equation (5). The collection of all possible basis representation is called the sparsity model. x = Pθ

(6)

Where P is the sparsity model of K vectors (K << N ) and θ is the non zero coefficients of the sparse representation of the signal. The sparsity of a signal is defined by this model P , as there are many factored possibilities of x = P θ. Among the factorization the unique representation of the smallest dimensionality of θ is the sparsity level of the signal x under this model. 3.3

Proposed Algorithm Definition

The basis of the sensing algorithm design is presented, which is further adapted for data-centric with sensor based optimizations for large deployments. Theorem 1. Each Process Element (PES) maintains a variable min-val, originally set to its own initial value. For each of fk + 1 rounds, the PES all broadcast their minvals, then each process resets its min-val to the minimum of its old min-val and all the values in its incoming messages. At then end, the decision value is min-val. Algorithm 5. FloodMinVal 1: 2: 3: 4:

statesi : rounds ∈ ℵ, initially 0 decision ∈ V ∪ unknown, initially unknown min-val ∈ V, initially i’s value

5: msgsi : f 6: if rounds ≤ k then 7: send min-val to all other processes 8: end if 9: 10: 11: 12: 13: 14: 15:

transi : rounds := rounds+ 1 let mj be the message from j, for each j from which a message arrives min-val := min(min − val ∪ mj : j = i) if rounds= fk + 1 then decision := min-val end if

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Algorithm 6. FloodMinRange 1: 2: 3: 4:

statesi : rounds ∈ ℵ, initially 0 decision ∈ V ∩ unknown, initially unknown min-range ∈ V, initially i’s value

5: msgsi : 6: if rounds N − τ then 7: send min-range to all other processes 8: end if 9: 10: 11: 12: 13: 14: 15:

transi : rounds := rounds + 1 let mj be the message from j, for each j from which a message arrives min-range := min(min − range ∩ mj : j = i) if rounds= N − τ then decision := min-range end if

Algorithm 7. BestBasis 1: 2: 3: 4:

statesi : Mark all elements on the bottom level J Let j = J Let k = 0

5: msgsi : 6: Compare the cost value v1 of elements k on level j − 1 (couting from the left

7: 8: 9: 10: 11: 12: 13: 14: 15: 16:

on the level) to the sum v2 of the cost values of the elements 2k and 2k + 1 on the level. if v1 ≤ v2 then all marks below element k on the level j − 1 are deleted, and element k is marked. end if if v1 ≥ v2 then the cost value v1 of element k is replaced with v2 . end if transi : k = k + 1. If there are more elements on level j if k < 22j−1 − 1, go to step 6. j = j − 1. If j > 1, goto step 4. The marked basis has the lowest possible cost value, which is the value currently assigned to the top element.

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Sensor Centric Algorithm

DCS allows to enable distributed coding algorithms to exploit both intra-and inter-signal correlation structures. In a sensor network deployment, a number of sensors measure signals that are each individually sparse in the some basis and also correlated [6,9] from sensor to sensor. If the separate sparse basis are projected onto the scaling and wavelet [8] functions of the correlated sensors(common coefficients), then all the information is already stored to individually recover each of the signal at the joint decoder. This does not require any pre-initialization between sensors. The expanded wavelet optimization and its cost-functions are shown in Figure 3(a) and 3(b). 3.4.1 Joint Sparsity Representation For a given ensemble X, we let PF (X) ⊆ P denote the set of feasible location matrices P ∈ P for which a factorization X = P Θ exits. We define the joint sparsity levels of the signal ensemble as follows. The joint sparsity level D of the signal ensemble X is the number of columns of the smallest matrix P ∈ P. In these models each signal xj is generated as a combination of two components: (i) a common component zC , which is present in all signals, and (ii) an innovation component zj , which is unique to each signal. These combine additively, giving xj = zC + zj , j ∈ ∀ (7) X = PΘ

(8)

We now introduce a bipartite graph G = (VV , VM , E), as shown in Figure 4, that represent the relationships between the entries of the value vector and its measurements. The common and innovation components KC and Kj , (1 < j < J), as well as the joint sparsity D = KC + KJ .

Signal 4.7 6.7 2.7 1.6 3.2

0

3.0 4.5 1.5 1.8 2.8 0.8 0

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0 0.03 0 -0.1 0.2 -0.4 0.6 0.28 0 +

(a) Sparse sensor readings

+

-0.2

0

+

(b) Basis representation with maximal overlap

Fig. 3. Sensor-centric Data Fusion during the aggregation step using sparse STACK model.

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Measurement (1,1)

1

(1,2) 2 (2,1) 3

(2,2)

4

(j,Mj)

D Vv

Vm

Fig. 4. Bipartite graphs representing fused aggregation for data coherency

The set of edges E is defined as follows: – The edge E is connected for all Kc if the coefficients are not in common with Kj . – The edge E is connected for all Kj if the coefficients are in common with Kj . A further optimization can be performed to reduce the number of measurement made by each sensor, the number of measurement is now proportional to the maximal overlap of the inter sensor ranges and not a constant as shown in equation (4). This is calculated by the common coefficients Kc and Kj , if there are common coefficients in Kj then one of the Kc coefficient is removed and the common Zc is added, these change does not effecting the reconstruction of the original measurement signal x. 3.5

Distributed Fused Parameter Dictionary

The sample sensor measurements of Table 1 and its transformed basis are shown in Figure 3 (a) and Figure 3 (b), illustrate all its possible basis representations. The cast-function [7] searches to find an optimal (grey rectangles) best basis matching the least number of coefficients to represent the signal without overlaps. The lowest range is calculated by selecting consecutive significant coefficients (1.3, 1.7), which determine the maximal overlap for the sensor intervals. This best basis dictionary is stored in the hashed location of the application’s search tree.

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STACK Model Validation Lower Bound Validation Using Covariance

The Figure 3(b) shows lower bound of the overlapped sensor i.i.d. of S1 − S8 , as shown it is seen that the lower bound is unique to the temporal variations of S2 . In our analysis we will use a general model which allows to detect sensor faults. The binary model can result from placing a threshold on the real-valued readings of sensors. Let mn be the mean normal reading and mf the mean event reading for a sensor. A reasonable threshold for distinguishing between the two m +m possibilities would be 0.5( n 2 f ). If the errors due to sensor faults and the fluctuations in the environment can be modeled by Gaussian distributions with mean 0 and a standard deviation σ, the fault probability p would indeed be symmetric. It can be evaluated using the tail probability of a Gaussian [9], the Q-function [9], as follows:   m +m   (0.5( n 2 f ) − mn ) mf − mn p=Q =Q (9) σ 2σ From the measured i.i.d. value sets we need to determine if they have any faulty sensors. This can be shown from equation (9) that if the correlated sets can be distinguished from the mean values then it has a low probability of error due to sensor faults, as sensor faults are not correlated. Using the statistical analysis package R, we determine the correlated matrix of the sparse sensor outputs as shown This can be written in a compact matrix form if we observe that for this case the co-variance matrix is diagonal, this is, ⎛ ⎞ ρ1 0 .. 0 ⎜ ⎟ ⎜ 0 ρ2 .. 0 ⎟ ⎟ Σ=⎜ (10) ⎜ : : : ⎟ ⎝ ⎠ 0 0 .. ρd The correlated co-efficient are shown matrix (11) the corresponding diagonal elements are highlighted. Due to overlapping reading we see the resulting matrix shows that S1 and S2 have higher index. The result sets is within the desired bounds of the previous analysis using DWT. Here we not only prove that the sensor are not faulty but also report a lower bound of the optimal correlated result sets, that is we use S2 as it is the lower bound of the overlapping ranges. Table 1. Sparse representation of sensor values

Sensors S1 i.i.d.1 2.7 i.i.d.2 4.7 i.i.d.3 6.7

S2 0 1.6 3.2

S3 1.5 3 4.5

S4 0.8 1.8 2.8

S5 3.7 4.7 5.7

S6 0.8 1.6 2.4

S7 2.25 3 3.75

S8 1.3 1.8 2.3

STACK: Sparse Timing of Algorithms Using Computational Knowledge

⎛− ⎞ → 4.0 3.20 3.00 2.00 2.00 1.60 1.5 1.0 ⎜ ⎟ −→ ⎜ 3.2 − 2.56 2.40 1.60 1.60 1.28 1.20 0.80 ⎟ ⎜ ⎟ −−−→ ⎜ 3.0 2.40 2.250 1.50 1.50 1.20 1.125 0.75 ⎟ ⎜ ⎟ ⎜ ⎟ −→ ⎜ 2.0 1.60 1.50 − 1.00 1.00 0.80 0.75 0.5 ⎟ ⎜ ⎟ Σ=⎜ ⎟ −−→ ⎜ 2.0 1.60 1.50 1.00 1.00 0.80 0.75 0.5 ⎟ ⎜ ⎟ −→ ⎜ 1.6 1.28 1.20 0.80 0.80 − 0.64 0.60 0.4 ⎟ ⎜ ⎟ ⎜ ⎟ −−−−→ ⎜ 1.5 1.20 1.125 0.75 0.75 0.60 0.5625 0.375 ⎟ ⎝ ⎠ −−−→ 1.0 0.80 0.750 0.50 0.50 0.40 0.375 0.250

317

(11)

The sensor data are correlated due to variations in deployment they are difficult to calibrate. The pre-processing of data will needs to correct the coefficients before applying the fusion function. As this constraint the design of the algorithm, we need custom sensor hardware. FPGA allows to design data-paths(see Table 2), which keep the algorithm design independent of any pre-processing step. Once such example is double buffering which allows slow data stream from flash memory to settle before the next data-set is applied.

5

Algorithm Acceleration

Table 2 shows all the optimizations available for algorithm performance tuning. The efficiency of the algorithm[4] depends on the Instruction count and CPI [12]. These parameters are determined by the processor design, once the type of tool sets is chosen then further optimization of the program size and speed are targeted. The notion of throughput of algorithm execution and its design dependency on the data-path demands is studied for all the computation models. 5.1

Static Program Order Model

The compiler uses basic block optimization techniques, which can use Instruction Level Parallelism (ILP). Optimization of this type uses infinite resources such as window size and base register counts. The expected throughput may be reduced, as it is dependent on the target. Program consistency and shared data coherency is available in the higher level program construct and can be addressed with simplicity. Some data-path optimizations are possible, memory dependencies, which are architecture dependent, are not addressed at this level, this step allow for fine grain instruction level parallelism. 5.2

Dynamic Out-of-Order Model

The performance measures computed with processor-based architecture and optimization from Table 2, Figure 5 illustrates that there is increase in clock rate

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if the pipeline [1,12] is used. In our case, the pipeline depth is varied during re-configuration. The simulation results show that total increase is around 10% using dynamic optimizations. As long theyre available registers, the ILP can execute in parallel by increasing the issue rate gradually. RISC uses Tomusulu’s algorithm [12] for register reservation to hide any load and read latencies. Processor based IPC mechanism and data cache allows to accelerate the data-path depending on the architecture. General purpose data forwarding, pipeline optimizations including branch predictions and speculation using outof-order execution are supported in the hardware. The pipeline utilizes temporal parallelism. 5.3

STACK

Temporal and spacial data coherency is achieved by using the pre-processing and compressed sensing techniques. The algorithm allows to compressed signal without any information loss and further enhances the working data range of the algorithm. As there are no delays in load and store instruction in the hardware register language [7], it can further accelerate 40% of the instructions as shown in Figure 5(a), which uses dynamic range for bit growth and register allocation as shown in Figure 5(b) and equation (12). fine grain parallelism. At the same time, it can take advantage of instruction level parallelism, which is pipeline optimization technique used in the previous models. k
(12)

k=0

Sparse Data Model 100000

Out-of-order

Program Order

Gate

Bit growth for the entire Dynamic range

CPI - Throughput

Where k is the number of co-efficient used by the algorithm, |f [k]|, gives the total possible range.

90000 80000 70000 60000 50000

Clock -

40000 30000 20000 10000 0 Fixed

4

16

32

Reconfigurable Issue:width = 2

n

(a) Algorithm Throughput

Sparse Data Model

Processor

14 12 10 8 6 4 2 0 Register storage for algorithms

(b) Dynamic Range and Bitwidth

Fig. 5. STACK model algorithm performance versus static program order and dynamic out-of-order pipe-line optimizations

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Table 2. Execution Optimization Parameters Tools

Instruction Count Clocks cycles per instruction (CPI) Clock Rate

Program Compiler Instruction-set

Program

Program and Data

Consistency

Chorency

√

√

√ √

√

√

√

Computational Model Technology

√ √ √

Software/Hardware

6

Summary

Due to IP core requirements and dedicated algorithm implementations, STACK model gives the most flexibility compared to high-level software and hardware based IPC synchronization. The requirements for error correction by data coherency program are needed when sequential and atomic operations cannot be guaranteed in concurrent executions. Due to implementation in the hardware for the same algorithm, the program footprint is smaller keeping overheads low and avoiding the need for synchronization libraries to be included. The computational model choice of hardware implementation for a given gate count gives a higher bound on performance for the same clock rate compared to the Van Numen model. Higher performance for the same clock rate equates to lower power thus enhancing the lifetime of the sensor network.

Acknowledgments One of the authors would like to thank the members of the Intel Parallel Processing lab at IIIT-Hyderbad and specially Nayan Mujadiya for extending his time and effort during CS4200 with Prof. Govindarajulu.

References 1. Iyer, V., Iyengar, S.S., Rama Murthy, G., Srinathan, K., Rakee, Srinivas, M.B.: Intelligent Networks Sensor Processing of Information using Key Management. In: Proc. 4rd International Conference on Sensing Technology - ICST, Leece, Italy (2010) 2. Baron, D., Duarte, M.F., Wakin, M.B., Sarvotham, S., Baraniuk, R.G.: Distributed Compressive Sensing. In: Proc: Preprint, Rice University, Texas, USA (2005) 3. Adve, S.V., Gharachorloo, K.: Shared Memory Consistency Models: A Tutorial 4. Brook, R.R., Sitharama Iyengar, S.S.: Robust Distributed Computing and Sensing Algorithm. ACM, New York (1996) 5. Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann, San Francisco (1996) 6. Jensen, A., la Cour-Harbo, A.: Ripples in Mathematics, p. 246. Springer, Heidelberg (2001); Softcover ISBN 3-540-41662-5

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7. Digital Signal Processing with Field Programmable Gate Array, UWe Meyer-Baese. Springer, Heidelberg (May 2001) 8. INSPIRE-DB: Intelligent Networks Sensor Processing of Information using Resilient Encoded-Hash DataBase. In: 2010 Fourth International Conference on Sensor Technologies and Applications (2010) 9. Krishnamachari, B., Member, IEEE, Sitharama Iyengar, S., Fellow, IEEE: Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks. IEEE Transactions on Computers 53(3) (March 2004) 10. Iyer, V., Sitharama Iyengar, S., Rammurthy, G., Srinivas, M.B.: SenseSIM: Sensor Network Simulator. In: ISSNIP, Melbourne, Austrlia (2009) 11. Slepian, D., Wolf, J.: Noiseless coding of correlated information sources (1973) 12. Hennessy, J.L., Horowitz, M.A.: An Overview of the MIPS-X-MP Project, Stanford University, Technical Report No. 86-300 (1986) 13. Iyer, V., Sitharama Iyengar, S., Murthy, G.R., Parameswaran, N., Singh, D., Srinivas, M.B.: Effects of channel SNR in Mobile Cognitive Radios and Coexisting Deployment of Cognitive Wireless Sensor Networks. In: 29th IEEE International Performance Computing and Communications Conference, IPCCC 2010, Albuquerque, New Mexico, USA (December 9-11, 2010)

A New Approach to Estimation of Protein Networks for Cell Cycle Based on Least-Squares Method Takehito Azuma1, Masachika Kurata1, Noriko Takahashi2, and Shuichi Adachi2 1

Department of Electrical and Electronics Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya 321-8585, Japan 2 Department of Applied Physics and Physico-Informatics, Keio University, 3-14-1 Hiyoshi, Yokohama, Japan [email protected]

Abstract. This article considers a method to estimate protein networks for cell cycle based on system identification in control engineering. The approach provides a new approach to estimation problems of protein networks for cell cycle in systems biology. First the considered estimation problems of protein networks are defined. Second our approach based on least-squares method is shown. Finally by applying the proposed approach in a numerical example, a protein network of 6-dimensional cell cycle systems is estimated and this network consists of some known protein network for 6-dimensional cell cycle systems. Keywords: Systems biology, estimation, protein networks, cell cycle, least squares method.

1 Introduction Molecular biology contributes to find out basic components of life such as genes, mRNAs and proteins[1]. By developments in molecular biology, various researches have been reported about some genes that cause diseases. These researches help to establish many efficient cure methods and to develop many useful medicines. However it seems difficult to understand dynamic behaviors of life phenomena only from molecular biology. Systems biology has been proposed [2][3]. Researches about systems biology expect to give us much interest in our lives, improvement of medical care and so on [4][5][6]. In the paper [5], cancer is dealt as a robust system and an anti cancer medical care is discussed. When cancer grows up, the number of malignant cells increases. Increment of malignant cells is governed by cell cycle. Thus cell cycle has been focused on by many researchers and some protein networks for cell cycle are reported [7-11]. However all protein networks for cell cycle have not been discovered and there can be still unknown proteins and networks. Thus it is important to estimate existence of unknown proteins or protein networks. The one way is based on experimental methods in molecular biology. By using this method, however, it seems difficult to S.C. Mukhopadhyay et al. (Eds.): New Developments and Appl. in Sen. Tech., LNEE 83, pp. 321–331. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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estimate complicated protein networks because the experimental methods needs to have many experimental results and data. To estimate complicated protein networks, new approaches and systematic approaches to estimate unknown proteins or protein networks are needed. In this article, an approach to estimating protein networks for cell cycle is proposed. The proposed approach is systematic because the approach is based on system identification which is one of systems and control theories. Firstly estimation problems of protein networks are defined. Here it is assumed that some concentrations of proteins are given in advance. These proteins can be considered as the state describing cell cycle systems. Since the state is available, it is possible to apply system identification techniques to the defined estimation problems. In the proposed approach, state space models are estimated by using least squares method which is one of system identification techniques by using data concerning about given concentrations of proteins. Considering each components of the matrix derived from the estimated state space model, a protein network is estimated for cell cycle. To demonstrate the efficacy of the proposed approach, 6 concentrations of proteins are given in advance and a protein network for the 6 dimensional cell cycle system is estimated.

2 Least-Squares Method for Linear State Space Model Consider an identification problem of the following linear state space model x( k + 1) = Ax ( k )

(1)

based on the collected data x(k ) ∈ R n for the discrete time k = 1,2, " N + 1 . The problem to identify the matrix A denotes to find out an optimal relationship x( k + 1) and x(k ) from the collected data of N samples. The state space model (1) can be described as the following linear regressive model.

Y (k ) = ΘΦ(k ) ,

(2)

where the matrix Θ = A is unknown and the following conditions are satisfied.

Y ( k ) = x( k + 1) Φ(k ) = x(k ) Assuming that x( k ), k = 1,2, " , N is obtained, it is possible to estimate the unknown matrix Θ by using the least squares method. Here the criterion for the least squares method is defined as follows, J (Θ ) =

1 N

N

2

∑ (Y (k ) − ΘΦ (k )) .

(3)

k =1

ˆ to minimize the criterion (3) is given as the following Then the optimum solution Θ form.

A New Approach to Estimation of Protein Networks for Cell Cycle N N ˆ = ⎛⎜ ∑ Y (k )Φ (k )T ⎞⎟⎛⎜ ∑ Φ (k )Φ (k )T ⎞⎟ Θ ⎝ k =1 ⎠⎝ k =1 ⎠

323

−1

(4)

This is easy to derive by calculating the next condition. dJ (Θ) =0 dΘ ˆ = A is included in the space R n× x , the least Now by considering that the matrix Θ squares estimated matrix satisfying the state space model (1) is given as follows,

⎡ a11 ⎢a A = ⎢ 21 ⎢ # ⎢ ⎣a n1

a12 a 22 # a n2

" a1n ⎤ " a 2 n ⎥⎥ . % # ⎥ ⎥ " a nn ⎦

(5)

This matrix (5) can be easily computed based on the equation (4) because the data Y (k ), Φ (k ), k = 1,2, " , N is obtained in advance of computation. Remark 1: The least-squares method is a basic technique for optimization problems. For the dynamical system described in (1), the other techniques seem applicable however it does not make clear that those techniques are really effective. If the collected data is periodic, the least-squares method has some advantages which are proved in the paper [12].

3 Estimation of Protein Networks for 6-Dimensional Cell Cycle System Assume that concentrations of proteins are obtained and the number of proteins is 6. Then the estimation problem of protein networks for cell cycle is defined as follows. Estimation problem of protein networks Estimate protein networks with 6 nodes by using obtained concentrations of 6 proteins. For this problem, a new approach is proposed based on the least squares method in this article. Because concentrations of proteins are obtained and the number of proteins is 6, the following state vector can be defined.

x(k ) = [x1 (k ) x 2 (k ) x3 (k ) x 4 (k ) x5 (k ) x 6 (k )]

T

where

xi (k ), i = 1,2," 6 denotes the obtained time-series data of each protein

concentration. By using the least squares method explained in the previous section, the state space model is identified in form of the following state space model.

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⎡ x1 (k + 1) ⎤ ⎡ a11 ⎢ x (k + 1)⎥ ⎢a ⎥ ⎢ 21 ⎢ 2 ⎢ x 3 (k + 1) ⎥ ⎢ a 31 ⎥=⎢ ⎢ ⎢ x 4 (k + 1)⎥ ⎢a 41 ⎢ x 5 (k + 1) ⎥ ⎢ a 51 ⎥ ⎢ ⎢ ⎣⎢ x 6 (k + 1)⎦⎥ ⎣⎢ a 61

a12

a13

a14

a15

a 22 a 32 a 42 a 52 a 62

a 23 a 33 a 43 a 53 a 63

a 24 a 34 a 44 a 54 a 64

a 25 a 35 a 45 a 55 a 65

a16 ⎤ ⎡ x1 (k ) ⎤ a 26 ⎥⎥ ⎢⎢ x 2 (k )⎥⎥ a 36 ⎥ ⎢ x3 (k ) ⎥ ⎥ ⎥⎢ a 46 ⎥ ⎢ x 4 (k )⎥ a 56 ⎥ ⎢ x5 (k ) ⎥ ⎥ ⎥⎢ a 66 ⎦⎥ ⎣⎢ x 6 (k )⎦⎥

(6)

The first equation of the state space model (6) is described as

x1 (k + 1) = a11 x1 (k ) + a12 x 2 (k ) + " + a16 x6 (k ) . Then it can be possible to consider that the coefficient aij denotes the binding intensity between the i protein and the j protein. Thus the network path between the i protein and the j protein is existing if the absolute value of the coefficient aij is large. Note that we assume that the diagonal elements in the matrix are degradation terms of proteins and the network paths do not appear in the estimated protein network. For example, if the matrix A is estimated as follows,

⎡1.0 ⎢0.0 ⎢ ⎢1.0 A=⎢ ⎢0.0 ⎢0.0 ⎢ ⎢⎣0.6

0.9 1.0 0.0 0.7 0.0 0.0

0.0 0.0 1.0 0.0 0.9 1.0

0 .0 0.7 1 .0 1.0 0 .0 0.0

1.0 0.0 0.0 0.6 1.0 1.0

0.7 ⎤ 0.0 ⎥⎥ 0.0 ⎥ , ⎥ 0.0 ⎥ 0.0 ⎥ ⎥ 1.0 ⎥⎦

Fig. 1. An example of the estimated network. The circles denote each protein and the arrows denote network paths or chemical interactions between each protein.

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then the estimated protein network can be illustrated in Fig. 1. The circles denote 6 proteins and the arrowed lines denote relations between each protein. Because diagonal elements in the matrix denotes degradation of each protein, the arrowed lines does not appear in the network. Remark 2: In the proposed approach, we assume that the relationships with each protein are time-invariant. Considering experiments in molecular biology, the proposed approach has some advantages of easy understanding of the relationships with each protein and easy application to experiments. Since it seems that the relationships with each protein in cell are time-varying, it is necessary to extend the proposed approach to time-varying cases. However there are not effective techniques of system identification for linear time-varying systems.

4 An Estimation Result of Protein Networks for 6-Dimensional Cell Cycle Systems To demonstrate the approach proposed in the previous section, estimation problems of 6 dimensional protein networks are considered. We assume that 6 concentrations of proteins are obtained in Fig. 2. The proteins shown in Fig. 2 are experimentally known to relate cell cycle in budding yeast. C2 (t ), C p (t ), pM (t ), M (t ), Y (t ), Yp (t ) are Cdh1 protein, phosphorylated Cdh1 protein, pre maturation promoting factor(MPF), active MPF, Cyclin protein and phosphorylated Cyclin protein respectively.

(a) C2 (t )

(d) M (t )

(b) C p (t )

(c) pM (t )

(d) Y (t )

(e) Yp (t )

Fig. 2. Concentrations of 6 proteins for cell cycle in budding yeast (Total time =100[min]).

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Since each figure in Fig. 2 is continuous time signal, we sampled continuous signals of the figures and obtained discrete time data. The sampling time is 0.01 min and the number of data is 10000 for each proteins. Thus the following state was obtained.

[

x( k ) = C ( k ) C p ( k )

]

pM (k ) M ( k ) Y ( k ) Y p ( k ) , k = 1,2, " ,10001 T

The number of data is important because the accuracy of the solution of the least squares method partially depends on data quantity. However the huge number of data causes the numerical error. Thus the adequate number of data is needed to obtain better results.. Remark 3: Concentrations of protein in Fig. 2 are obtained via computer simulation by using the following nonlinear system and the parameters [7].

dC2 (t ) = k6 M (t ) − k8 PC2 (t ) + k9C p (t ) dt dC p (t ) = − k3C p (t )Y (t ) + k8C2 (t ) − k9 PC p (t ) dt dpM (t ) = k3C p (t )Y (t ) − pM (t ) F ( M (t )) + k5 PM (t ) dt dM (t ) = pM (t ) F ( M (t )) − k5 PM (t ) − k6 M (t ) dt dY (t ) = k1aa − k 2Y (t ) − k3C p (t )Y (t ) dt dYp (t ) = k6 M (t ) − k7Yp (t ) dt M 2 (t ) F ( M (t )) = k4' + k4 CT 2 This nonlinear system describes a dynamics of a 6 dimensional cell cycle system based on a protein network of cell cycle but it is difficult to estimate the structure of this nonlinear system from concentrations of proteins in Fig. 2. Moreover the original protein network is difficult to estimate from Fig. 2. The advantage of the proposed approach is to estimate the structure of the protein network from Fig. 2 directly. Applying the least squares method to the data, the following matrix A was computed. − 3.97e − 4⎤ 9.08e − 2 − 5.67 e − 4 9.66e − 1 3.35 ⎡ 9.09e − 1 ⎢ 9.08e − 1 − 4.23e − 2⎥⎥ 9.03e − 1 − 1.16e − 2 8.19e − 2 47.9 ⎢ ⎢ 1.45e − 3 9.11e − 2 1.22 4.90e − 2 − 1.06e + 3 9.31e − 1 ⎥ A= ⎢ ⎥ 1.00e + 3 − 8.85e − 2 ⎥ ⎢− 3.03e − 6 − 8.53e − 2 − 2.09e − 1 8.59e − 1 ⎢ 8.26e − 5 − 5.18e − 6 7.66e − 6 − 7.67e − 6 9.65e − 1 3.01e − 6 ⎥ ⎢ ⎥ 5.03e + 1 9.37e − 1 ⎦⎥ ⎢⎣ − 1.70e − 6 − 4.26e − 3 − 1.05e − 2 9.02e − 2

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Because this matrix has larger elements than the other elements, this matrix is normalized by using the maximum values of each row. From this matrix, it is known that the diagonal elements are almost same as 1. This means that there exist self-feedback loops for each protein and these self-feedback loops can be interpreted as degradation terms. Here it is obvious that the maximum values of each protein are different in Fig. 2. Thus the matrix is normalized by using those maximum values. Then the following matrix is obtained. 4.09e − 4 − 6.29e − 5⎤ 8.24e − 2 − 1.76e − 4 1.87e − 2 ⎡ 7.96e − 2 ⎢ 7.95e − 2 8.20e − 1 − 3.61e − 3 1.58e − 2 5.85e − 3 − 6.70e − 4⎥⎥ ⎢ ⎢ 1.27e − 4 8.27e − 2 3.79e − 1 9.48e − 3 − 1.29e − 1 1.48e − 2 ⎥ A1 = ⎢ ⎥ 1.23e − 1 − 1.40e − 2 ⎥ ⎢− 2.65e − 6 − 7.74e − 2 − 6.50e − 2 1.66e − 1 ⎢ 7.23e − 7 − 4.70e − 6 2.38e − 6 − 1.48e − 6 1.18e − 4 4.47 e − 7 ⎥ ⎥ ⎢ 6.14e − 3 1.49e − 1 ⎥⎦ ⎢⎣ − 1.49e − 7 − 3.87 e − 3 − 3.26e − 3 1.74e − 2

Finally some elements, which are smaller than about centesimal of each maximum value in each row element, are ignored and the the following protein network shown in Fig. 3 was estimated.

Fig. 3. The estimated protein network by using the proposed approach. Circles mean each protein.

Fig.3 can be illustrated by using the notation proposed in the papers [12][13] and considering a previous result [7]. Then Fig. 4 is derived. In the figure, the arrowed and dashed lines were new network paths estimated by using the proposed approach. The other networks were known in the previous result [7]. This figure demonstrates the efficacy of the proposed approach and shows that the proposed approach provides a possibility to find out unknown network paths for cell cycle in budding yeast.

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Fig. 4. The estimated protein network based on chemical reactions. The solid lines are known in the paper [7] and the dashed lines are newly estimated by using the proposed approach.

5 Discussion about the Proposed Approach in View of the Number of Data In this section, the influence of the number of data is discussed about the proposed approach. First the influence of the number of data is considered. Assume that concentrations of 6 proteins shown in Fig. 5 are obtained. The total time is 40 min and about 1 cycle data can be used. The sampling period is 0.01 min and the number of data is 4000. By using the proposed approach, the following matrix was derived after normalization of the first computed matrix. 8.34e − 2 3.40e − 4 5.66e − 2 − 5.35e − 4 1.03e − 5 ⎤ ⎡ 9.06e − 2 ⎢ 9.09e − 2 8.25e − 1 − 4.49e − 4 1.34e − 3 5.60e − 4 − 1.56e − 5⎥⎥ ⎢ ⎢ 8.25e − 5 3.75e − 3 3.13e − 1 − 1.32e − 3 − 5.58e − 3 1.08e − 3 ⎥ A2 = ⎢ ⎥ 5.55e − 3 − 1.08e − 3⎥ ⎢ − 2.04e − 6 − 3.59e − 3 − 2.83e − 3 1.93e − 1 ⎢ 6.95e − 5 − 4.73e − 6 1.27e − 6 4.71e − 7 1.20e − 4 4.17e − 8 ⎥ ⎢ ⎥ 2.77e − 5 1.58e − 1 ⎥⎦ ⎢⎣ − 1.02e − 8 − 1.79e − 5 − 1.41e − 5 1.93e − 3

Based on this matrix, the estimated protein network is shown in Fig. 6. It is easily found out that Fig.6 (a) and (b) are almost same as Fig.3 and Fig. 4 respectively but there does not exist the network path between the Cyclin protein and the phosphorylated Cyclin protein in Fig. 6. The results indicate that 1 cycle data is necessary to estimate protein networks in the proposed approach. Thus the large number of data is not needed. This result is theoretically proved in the paper [12]. Moreover the proposed approach can suggest possibility of existences of unknown network paths concerning about cell cycle by using the less number of data. This result is significant to apply the proposed approach to various experiments in molecular biology.

A New Approach to Estimation of Protein Networks for Cell Cycle

(a) C2 (t )

(d) M (t )

(b) C p (t )

(c) pM (t )

(d) Y (t )

(e) Yp (t )

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Fig. 5. Concentrations of 6 proteins for cell cycle in budding yeast (Total time =40[min]).

(a) The network structure

(b) The chemical reaction

Fig. 6. The protein network estimated by using the less number of data.

Remark 4: The dashed arrows are estimated and new network paths in Fig. 4 or Fig. 6. The next and important problems are to identify the roles of the estimated network paths in viewpoint from systems biology. We guess that some network paths are related with robustness of the 6 dimensional cell-cycle system. In the paper [15], the role of the estimated network paths is discussed based on the following nonlinear model for Fig. 4 and some estimated network paths are concerning about sensitivity of the 6 dimensional cell cycle system, that is robustness of cell cycle.

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dC 2 (t ) = k 6 M (t ) − k 8 PC 2 (t ) + k 9 C p (t ) dt dC p (t ) b1C p (t ) M (t ) = − k 3 C p (t )Y (t ) + k 8 C 2 (t ) − k 9 PC p (t ) − dt J 1 + C p (t ) b2 p M (t )Y p (t ) dp M (t ) = k 3 C p (t )Y (t ) − p M (t ) F ( M (t )) + k 5 PM (t ) − J 2 + p M (t ) dt b3 M (t )C p (t ) dM (t ) =− + p M (t ) F (M (t )) − k 5 PM (t ) − k 6 M (t ) dt J 3 + M (t ) −

b4 M (t )Y (t ) b5 M (t )Y p (t ) + J 4 + M (t ) J 5 + M (t )

b Y (t ) p M (t ) dY (t ) = k1 a a − k 2 Y (t ) − k 3 C p (t )Y (t ) − 6 J 6 + Y (t ) dt dY p (t ) dt

= k 6 M (t ) − k 7Y p (t )

F ( M (t )) = k 4' + k 4

M 2 (t ) CT 2

6 Conclusion In this article, a new approach to estimate protein networks for cell cycle has been proposed. The proposed approach is based on system identification techniques and least squares method plays an important role in the proposed approach. A numerical example has been shown to demonstrate the efficacy of the proposed approach and a new protein network has been estimated. The estimated protein network includes known network paths and unknown network paths.

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Author Index

Adachi, Shuichi 321 Al-Shamma’a, A.I. 15 Assouar, B. 169 Azuma, Takehito 321

Md Yunus, M.A. 39 Mukhopadhyay, S.C. 39, 253 Murthy, Garmiela Rama 305 Nadi, M. 169 Nahire, S.B. 157 Neumayer, M. 65

Battaglini, L. 107 Brun, Alain Le 1 Burrascano, P. 107

Olejnik, R. Canova, A. 107 Carnegie, D.A. 133 Chavan, D.N. 157 Claudel, J. 169 Dorrington, A.A. 133 Drayton, B. 133 Ekanayake, E.M.I. Mala 279 Enokizono, Masato 293 Ficili, F. Fuchs, A.

107 65

Gaikwad, V.B. 123, 157 Gir˜ ao, P. Silva 191 Govindarajulu, Regeti 305 Ibrahim, M. 169 Ikezawa, Satoshi 207 Iyengar, S. Sitharama 305 Iyer, Vasanth 305 Jain, Gotan H. 123, 157 Jayasundera, K.P. 253 Jongenelen, A.P.P. 133

233

Patil, G.E. 123, 157 Pawar, N.K. 123 Pawlat, Joanna 207 Payne, A.D. 133 Pereira, J.M. Dias 191 Postolache, O. 191 Preethichandra, D.M.G. Qu´eff´elec, Patrick 1 Quendo, C´edric 1 Ricci, M. Riha, P. Rossi, D.

107 233 107

Sciacca, F. 107 Shaw, A. 15 Shinde, S.D. 123, 157 Slobodian, P. 233 Srinathan, Kannan 305 Srinivas, Mandalika B. 305 Syaifudin, A.R. Mohd 253 Takahashi, Noriko 321 Talbot, Philippe 1

Kajale, D.D. 123, 157 Kerouedan, Julien 1 Kourtiche, D. 169 Kurata, Masachika 321

Ueda, Toshitsugu

Luo, Jiaoliang

Yamada, Koji

293

Mason, A. 15 McClymont, J.R.K.

133

279

207

Wakamatsu, Muneaki Watzenig, D. 65

207

293

Zangl, H. 65 Zimin, Yury L’vovich

207