Smart Sensors for Environmental and Medical Applications / Edition 1 available in Hardcover, eBook
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Smart Sensors for Environmental and Medical Applications / Edition 1
- ISBN-10:
- 1119587344
- ISBN-13:
- 9781119587347
- Pub. Date:
- 05/12/2020
- Publisher:
- Wiley
![Smart Sensors for Environmental and Medical Applications / Edition 1](http://img.images-bn.com/static/redesign/srcs/images/grey-box.png?v11.11.4)
Smart Sensors for Environmental and Medical Applications / Edition 1
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$135.95Overview
This book presents a comprehensive overview of chemical sensors, ranging from the choice of material to sensor validation, modeling, simulation, and manufacturing. It discusses the process of data collection by intelligent techniques such as deep learning, multivariate analysis, and others. It also incorporates different types of smart chemical sensors and discusses each under a common set of sub-sections so that readers can fully understand the advantages and disadvantages of the relevant transducers—depending on the design, transduction mode, and final applications.
Smart Sensors for Environmental and Medical Applications covers all major aspects of the field of smart chemical sensors, including working principle and related theory, sensor materials, classification of respective transducer type, relevant fabrication processes, methods for data analysis, and suitable applications. Chapters address field effect transistors technologies for biological and chemical sensors, mammalian cell–based electrochemical sensors for label-free monitoring of analytes, electronic tongues, chemical sensors based on metal oxides, metal oxide (MOX) gas sensor electronic interfaces, and more.
Addressing the limitations and challenges in obtaining state-of-the-art smart biochemical sensors, this book:
- Balances the fundamentals of sensor design, fabrication, characterization, and analysis with advanced methods
- Categorizes sensors into sub-types and describes their working, focusing on prominent applications
- Describes instrumentation and IoT networking methods of chemical transducers that can be used for inexpensive, accurate detection in commercialized smart chemical sensors
- Covers monitoring of food spoilage using polydiacetylene- and liposome-based sensors; smart and intelligent E-nose for sensitive and selective chemical sensing applications; odor sensing system; and microwave chemical sensors
Smart Sensors for Environmental and Medical Applications is an important book for senior-level undergraduate and graduate students learning about this high-performance technology and its many applications. It will also inform practitioners and researchers involved in the creation and use of smart sensors.
Product Details
ISBN-13: | 9781119587347 |
---|---|
Publisher: | Wiley |
Publication date: | 05/12/2020 |
Series: | IEEE Press Series on Sensors |
Pages: | 240 |
Product dimensions: | 0.39(w) x 0.39(h) x 0.39(d) |
About the Author
Hadi Heidari is an Assistant Professor (Lecturer) in Electronics and Nanoscale Engineering and lead of the Microelectronics Lab (meLAB) at the University of Glasgow, UK. His research focuses on microelectronics and sensors for wearable and implantable devices. He has authored over 140 articles in top-tier peer reviewed journals and in international conferences. He is an IEEE Senior Member, an Associate Editor for 4 Journals and the General Chair of IEEE ICECS 2020 Conference. He is member of the IEEE Circuits and Systems Society Board of Governors, and Member-at-Large in IEEE Sensors Council. He has grant portfolio of +£1 million funded by major research councils and funding organizations including European Commission, UK's EPSRC, Royal Society and Scottish Funding Council.
Table of Contents
List of Contributors xiPreface xiii
About the Editors xvii
1 Introduction 1Hamida Hallil and Hadi Heidari
1.1 Overview 1
1.2 Sensors: History and Terminology 2
1.2.1 Definitions and General Characteristics 3
1.2.2 Influence Quantities 5
1.3 Smart Sensors for Environmental and Medical Applications 6
1.4 Outline 8
Reference 9
2 Field Effect Transistor Technologies for Biological and Chemical Sensors 11Anne-Claire Salaün, France Le Bihan, and Laurent Pichon
2.1 Introduction 11
2.2 FET Gas Sensors 12
2.2.1 Materials 12
2.2.1.1 Inorganic Semiconductors 12
2.2.1.2 Semiconductor Polymers 12
2.2.1.3 Nanostructured Materials 13
2.2.2 FET as Gas Sensors 13
2.2.2.1 Pioneering FET Gas Sensors 13
2.2.2.2 OFET Gas Sensors 13
2.2.2.3 Nanowires-Based FET Gas Sensors 14
2.3 Ion-Sensitive Field Effect Transistors Based Devices 18
2.3.1 Classical ISFET 18
2.3.2 Other Technologies 19
2.3.2.1 EGFET: Extended Gate FET 20
2.3.2.2 SGFET: Suspended Gate FFETs 20
2.3.2.3 DGFET: Dual-Gate FETs 20
2.3.2.4 Water Gating FET or Electrolyte Gated FET 21
2.3.2.5 Other FETs 23
2.3.3 BioFETs 23
2.3.3.1 General Considerations 23
2.3.3.2 DNA BioFET 23
2.3.3.3 Protein BioFET 25
2.3.3.4 Cells 25
2.4 Nano-Field Effect Transistors 25
2.4.1 Fabrication of Nano-Devices 25
2.4.1.1 Silicon Nano-Devices 25
2.4.1.2 Carbon Nanotubes Nano-Devices 28
2.4.2 Detection of Biochemical Particles by Nanostructures-Based FET 28
2.4.2.1 SiNW pH Sensor 29
2.4.2.2 DNA Detection Using SiNW-Based Sensor 30
2.4.2.3 Protein Detection 32
2.4.2.4 Detection of Bacteria and Viruses 33
References 34
3 Mammalian Cell-Based Electrochemical Sensor for Label-Free Monitoring of Analytes 43Md. Abdul Kafi, Mst. Khudishta Aktar, and Hadi Heidari
3.1 Introduction 43
3.2 State-of-the-Art Cell Chip Design and Fabrication 45
3.3 Substrate Functionalization Strategies at the Cell–Electrode Interface 48
3.4 Electrochemical Characterization of Cellular Redox 49
3.5 Application of Cell-Based Sensor 51
3.6 Prospects and Challenges of Cell-Based Sensor 54
3.7 Conclusion 56
References 56
4 Electronic Tongues 61Flavio M. Shimizu, Maria Luisa Braunger, Antonio Riul, Jr., and Osvaldo N. Oliveira, Jr.
4.1 Introduction 61
4.2 General Applications of E-tongues 63
4.3 Bioelectronic Tongues (bETs) 65
4.4 New Design of Electrodes or Measurement Systems 66
4.5 Challenges and Outlook 73
Acknowledgments 73
References 74
5 Monitoring of Food Spoilage Using Polydiacetylene‐ and Liposome‐Based Sensors 81Max Weston, Federico Mazur, and Rona Chandrawati
5.1 Introduction 81
5.2 Polydiacetylene for Visual Detection of Food Spoilage 82
5.2.1 Contaminant Detection 83
5.2.2 Freshness Indicators 85
5.2.3 Challenges, Trends, and Industrial Applicability in the Food Industry 87
5.3 Liposomes 88
5.3.1 Pathogen Detection 88
5.3.1.1 Escherichia coli 88
5.3.1.2 Salmonella spp. 90
5.3.1.3 Other Bacterium 90
5.3.1.4 Viruses, Pesticides, and Toxins 91
5.3.2 Stability of Liposome‐Based Sensors 93
5.3.3 Industrial Applicability of Liposomes 93
5.4 Conclusions 94
References 94
6 Chemical Sensors Based on Metal Oxides 103K. S. Shalini Devi, Aadhav Anantharamakrishnan, Uma Maheswari Krishnan, and Jatinder Yakhmi
6.1 Introduction 103
6.2 Classes of MOx-Based Chemical Sensors 104
6.3 Synthesis of MOx Structures 104
6.4 Mechanism of Sensing by MOx 105
6.5 Factors Influencing Sensing Performance 106
6.6 Applications of MOx-Based Chemical Sensors 109
6.6.1 MOx Sensors for Environmental Monitoring 109
6.6.2 MOx Sensors in Clinical Diagnosis 112
6.6.3 MOx Sensors in Pharmaceutical Analysis 113
6.6.4 MOx-Based Sensors in Food Analysis 116
6.6.5 MOx Sensors in Agriculture 117
6.6.6 MOx Sensors for Hazard Analysis 117
6.6.7 Flexible Sensors Based on MOx 118
6.6.8 MOx-Based Lab-on-a-Chip Sensors 118
6.7 Concluding Remarks 119
Acknowledgment 119
References 120
7 Metal Oxide Gas Sensor Electronic Interfaces 129Zeinab Hijazi, Daniele D. Caviglia, and Maurizio Valle
7.1 General Introduction 129
7.1.1 Gas Sensing System 129
7.1.2 Gas Sensing Technologies 130
7.2 MOX Gas Sensors 131
7.2.1 Principle of Operation 131
7.2.2 Assessment of Available MOX-Based Gas Sensors 132
7.3 System Requirements and Literature Review 134
7.3.1 System Requirements 134
7.3.2 Wide Range Resistance Interface Review 136
7.4 Resistance to Time/Frequency Conversion Architecture 137
7.4.1 Electronic Circuit Description 137
7.4.2 Specifications for Each Building Block to Preserve High Linearity 138
7.4.2.1 Resistance to Current Conversion (R-to-I) 138
7.4.2.2 Switches 141
7.4.2.3 Current to Voltage Conversion (I-to-V) 141
7.4.2.4 Voltage to Time/Period (V-to-T) Conversion 141
7.5 Power Consumption 141
7.5.1 Power Consumption of MOX Gas Sensor 141
7.5.2 Low Power Operating Mode 142
7.5.3 Power Consumption at Circuit Level 142
7.6 Conclusion 143
References 143
8 Smart and Intelligent E-nose for Sensitive and Selective Chemical Sensing Applications 149Saakshi Dhanekar
8.1 Introduction 149
8.1.1 The Human Olfactory System 150
8.1.2 The Artificial Olfactory System 150
8.1.2.1 Sensor Array 151
8.1.2.2 Multivariate Data Analysis 152
8.1.2.3 Pattern Recognition Methods 153
8.2 What is an Electronic Nose? 154
8.3 Applications of E-nose 155
8.3.1 Key Applications of E-nose 155
8.3.2 E-nose for Chemical Sensing 155
8.4 Types of E-nose 157
8.5 Examples of E-nose 158
8.6 Improvements and Challenges 165
8.7 Conclusion 165
References 166
9 Odor Sensing System 173Takamichi Nakamoto and Muis Muthadi
9.1 Introduction 173
9.2 Odor Biosensor 174
9.3 Prediction of Odor Impression Using Deep Learning 176
9.4 Establishment of Odor‐Source Localization Strategy Using Computational Fluid Dynamics 181
9.4.1 Background of Odor‐Source Localization 181
9.4.2 Sensor Model with Response Delay 182
9.4.3 Simulation of Testing Environment Using CFD 183
9.4.4 Simulation of Biologically Inspired Odor‐Source Localization 185
9.4.4.1 Odor Plume Tracking Strategy 185
9.4.4.2 Result 186
9.4.5 Summary of Odor Source Localization Strategy 187
9.5 Conclusion 188
Acknowledgments 189
References 189
10 Microwave Chemical Sensors 193Hamida Hallil and Corinne Dejous
10.1 Interests of Electromagnetic Transducer Gas Sensors at Microwave Frequencies 193
10.2 Operating Principle 193
10.2.1 Electromagnetic Transducers 193
10.2.2 The Case of Microwave Transducers 195
10.3 Theory of Microwave Transducers: Design, Methodology, and Approach 196
10.4 Microwave Structure‐Based Chemical Sensor 200
10.4.1 Manufacturing Techniques 200
10.4.2 Chemical Microwave Sensors 200
10.4.3 Wireless Interrogation Schemes 204
10.5 Multivariate Data Analysis and Machine Learning for Targeted Species Identification 207
10.6 Conclusion and Prospects 209
Acknowledgments 210
References 210
Index 217