Sensors for Next-Generation Electronic Systems and Technologies

✍ Scribed by K. Venkata Lakshmi Narayana (editor), P. Uma Sathyakam (editor)

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 295
• Edition: 1
• Category: Library

✦ Synopsis

The text covers fiber optic sensors for biosensing and photo-detection, graphene and CNT-based sensors for glucose, cholesterol, and dopamine detection, and implantable sensors for detecting physiological, bio-electrical, biochemical, and metabolic changes in a comprehensive manner. It further presents a chapter on sensors for military and aerospace applications. It will be useful for senior undergraduate, graduate students, academic researchers in the fields of electrical engineering, electronics, and communication engineering.

The book

• Discusses implantable sensors for detecting physiological, bio-electrical, biochemical, and metabolic changes.

• Covers applications of sensors in diverse fields including healthcare, industrial flow, consumer electronics, and military.

• Includes experimental studies such as the detection of biomolecules using SPR sensors and electrochemical sensors for biomolecule detection.

• Presents artificial neural networks (ANN) based industrial flow sensor modeling.

• Highlights case studies on surface plasmon resonance sensors, MEMS-based fluidic sensors, and MEMS-based electrochemical gas sensors.

The text presents case studies on surface plasmon resonance sensors, MEMS-based fluidic sensors, and MEMS-based electrochemical gas sensors in a single volume. The text will be useful for senior undergraduate, graduate students, academic researchers in the fields of electrical engineering, electronics, and communication engineering.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Acknowledgements
Preface
About the editors
Contributors
Chapter 1: Fabrication and study of fluidic MEMS device for toxic heavy metal ion sensing in water
1.1 Introduction
1.2 Design of micromixer device
1.2.1 Simulation of Herringbone (HB) structure micromixer device
1.3 Experimental
1.3.1 Design of HB bent micromixer device
1.3.2 Fabricated HB bent micromixer device
1.3.3 Preparation of gold nanofluids
1.4 Sample fluid preparation
1.4.1 Detection process of metal ions using microfluidic device
1.5 Results and discussion
1.5.1 FTIR studies of sensing fluids
1.5.2 Fluorescence studies
1.6 Colorimetric method based heavy metal ions detection
1.6.1 Colorimetric analysis in conventional tube
1.6.2 Colorimetric analysis in microfluidic device
1.7 Conclusion
References
Chapter 2: The review of micro-electromechanical systems-based biosensor: A cellular base perspective
2.1 Introduction
2.2 The prologue of biological cells
2.2.1 The prologue of biological cell
2.2.2 Cell cycle and division
2.2.3 Mathematical modeling of Single cell
2.2.4 Growth model
2.2.5 Cancer prognosis
2.3 Techniques involved in detection of biophysical properties of the cell
2.3.1 Coulter devices
2.3.2 Fluorescent-based techniques
2.3.3 Flow cytometry
2.3.4 Mass spectrometry
2.3.5 Surface plasmon resonance
2.4 Micro electro mechanical systems based mass sensors
2.4.1 Technique of mass sensing using resonant mass sensor
2.4.2 Cantilever mechanics
2.5 Mass sensors reported in literature
2.5.1 Pedestal mass sensor
2.5.2 Suspended micro-resonating channel (SMR)
2.6 Fractal MEMS structures
2.6.1 Fractal tree geometry realization
References
Chapter 3: MEMS-based electrochemical gas sensor
3.1 Introduction
3.2 Gas sensors classification
3.2.1 Mass-sensitive gas sensors
3.2.2 Optical gas sensors
3.2.3 Thermometric sensor
3.2.4 Electrochemical sensors
3.3 Fabrication materials
3.3.1 Metal oxide semiconductors
3.3.2 Graphene
3.3.3 Composite materials (CM)
3.4 MEMS electrochemical gas sensor
3.5 Structure
3.6 Fabrication of MEMS gas sensors
3.7 Application
3.8 Conclusion
References
Chapter 4: Electrochemical biosensors
4.1 Introduction
4.2 Classifying of biosensors
4.3 Principles of electrochemical biosensors
4.3.1 Potentiometric method
4.3.2 Voltammetric method
4.3.3 Impedance method
4.3.4 Amperometric method
4.3.5 Electrochemical biosensor assay strategy: labeled vs. label-free
4.3.6 Biocatalytic/affinity electrochemical biosensors
4.3.6.1 Biocatalytic biosensors
4.3.6.2 Affinity biosensors
4.4 Electrochemical lab-on-a-chip systems
4.4.1 Design of LOC systems
4.4.2 Materials and fabrication
4.4.3 Microfluidics
4.4.3.1 The physics of microfluidics
4.4.3.2 Classification of microfluidic platforms
4.4.3.2.1 Channel microfluidics
4.4.3.2.2 Digital microfluidics (DMF)
4.4.3.2.3 Paper-based microfluidics
4.5 Wearable electrochemical biosensor
4.5.1 Target biofluids for the wearable electrochemical biosensors
4.5.1.1 Saliva analysis
4.5.1.2 Tear analysis
4.5.1.3 Sweat analysis
4.5.1.4 Interstitial Fluid (ISF) analysis
4.5.2 Template and non-template fabrication methods
4.6 Healthcare applications
4.6.1 Cancer detection
4.6.2 Infectious detection
4.6.3 Cardiac detection
4.7 Conclusion
References
Chapter 5: Graphene/carbon nanotubes-based biosensors for glucose, cholesterol, and dopamine detection
5.1 Introduction to carbon nanomaterials
5.1.1 Carbon nanotubes
5.1.2 Structure
5.1.3 Properties
5.1.4 Synthesis and functionalization of CNTs
5.1.5 Purification of CNTs
5.1.6 Graphene
5.2 Graphene synthesis methods
5.2.1 Chemical vapor deposition techniques
5.2.2 Exfoliation
5.2.3 Carbon nanomaterials for electrochemical detection
5.2.4 Glucose biosensors
5.2.5 CNT and graphene-based glucose biosensor
5.2.6 Non-enzymatic glucose biosensors
5.2.7 Cholesterol biosensors
5.2.8 Dopamine biosensors
5.2.9 Computational studies on graphene and carbon nanotube-based biosensors
5.2.10 Challenges in modeling and simulation of graphene and CNTs for sensing applications
5.2.11 Challenges and future trends
References
Chapter 6: Transition metal dichalcogenide based surface plasmon resonance for bio-sensing
6.1 Introduction
6.1.1 Basics of the surface plasmon resonance sensor
6.1.2 Interrogation approaches
6.1.3 Surface plasmon resonance sensor performance parameter
6.2 Two-dimensional materials
6.3 Biosensor
6.3.1 Optical biosensor
6.4 Advanced materials-based surface plasmon resonance biosensor
6.4.1 Graphene-based sensors
6.4.2 TMDs based sensors
6.5 Conclusion
References
Chapter 7: Graphene and carbon nanotube-based sensors
7.1 Introduction
7.2 Classification of graphene and its derivatives
7.2.1 Graphene
7.2.2 Graphene oxide
7.2.3 Reduced graphene oxide
7.3 Graphene based sensors
7.3.1 Graphene based SERS sensor
7.3.2 Graphene based electrochemical sensor
7.3.3 Graphene based fluorescence sensor
7.3.4 Graphene based FET sensor
7.4 Classification of carbon nanotubes
7.4.1 Multi–walled carbon nanotubes
7.4.2 Single–walled carbon nanotubes
7.5 Application of carbon nanotubes–based sensors
7.5.1 Carbon nanotubes-based SERS sensors
7.5.2 Carbon nanotubes-based electrochemical sensors
7.5.3 Carbon nanotubes-based fluorescence sensors
7.5.4 Carbon nanotubes-based FET sensor
7.6 Conclusion
Acknowledgements
References
Chapter 8: Intelligent flow sensor using artificial neural networks
8.1 Introduction: background of flow measurement and taxonomy
8.2 Theory of the control valve
8.3 The multilayer perceptron neural network
8.4 FPGA implementation
8.5 FPGA implementation of the sigmoid function
8.6 FPGA Implementation of the MLP neural network
8.7 Discussion
8.8 Summary
Bibliography
Chapter 9: Smart sensor systems for military and aerospace applications
9.1 Introduction
9.2 Requirement for an ideal design of smart sensor systems
9.3 HEMT for RADAR application
9.4 Photodetectors in military applications
9.4.1 Photoresistors
9.4.2 Photodiodes
9.4.3 Infrared sensors
9.5 Solar blind photodetectors for military applications
9.6 Infrared photodetectors as night vision devices
9.7 Conclusion
References
Chapter 10: Magnetic biosensors: Need and progress
10.1 Basics of biosensors
10.2 Existing technologies of biosensing
10.2.1 Hemagglutination Assay (HA)
10.2.2 Enzyme-linked immunosorbent assay (ELISA)
10.2.3 Polymerase Chain Reaction (PCR)
10.3 Advantages and limitations of the existing methods
10.3.1 Characteristics of a biosensor
10.3.2 Selectivity
10.3.3 Reproducibility
10.3.4 Stability
10.3.5 Sensitivity
10.3.6 Linearity
10.4 Principles of magnetic biosensing
10.4.1 Magnetic nanoparticles
10.4.2 Classification of MNPs based on their magnetic nature
10.4.3 Need for magnetic nanosensors
10.5 Types of biorecognition elements
10.5.1 Enzymatic biosensors
10.5.2 Biosensors using DNA and RNA
10.5.3 Biosensors using antibody
10.5.4 Aptasensors
10.5.5 Peptide based molecular sensors
10.6 Principles of magnetic nanosensors
10.6.1 Magnetoresistance
10.6.2 The Hall effect sensors
10.6.3 Anisotropic Magnetoresistance Sensors (AMR)
10.6.4 Giant Magnetoresistance Sensors (GMR)
10.6.5 Tunneling Magneto Resistance (TMR)
10.6.6 Magnetic nanosensors for biomedical applications
10.6.7 Magnetic Relaxation Immunoassays (MARIA) using Fluxgate Sensor
10.6.8 Planar Hall Magnetoresistive (PHR) aptasensor for thrombin detection
10.6.9 Anisotropic Magnetoresistance (AMR) biosensors
10.6.10 Eigen Diagnosis Platform (EDP) using giant magnetoresistance biosensors
10.6.11 Tunneling Magnetoresistance (TMR) biosensors
10.7 Conclusion
References
Index