Chemosensor Arrays. Fundamental Pattern Recognition, Development and Applications

As demand has increased for innovative methods in simple everyday monitoring, chemists are focusing on chemical sensors based on molecular recognition chemistry. Conventional approaches involved the development of selective chemical sensors to specifically detect species of interest, whilst more recent studies have revealed the potential of cross-reactive molecular recognition. Such approaches, combined with pattern recognition, are a powerful technique to gain much qualitative and quantitative information. However, pattern recognition is frequently misunderstood and misinterpreted, leading to inaccurate interpretation of results.

Chemosensor Arrays: Fundamental Pattern Recognition, Development and Applications aims to help researchers in the design and application of chemosensor arrays, and the accurate interpretation of data patterns in molecular recognition chemistry. Beginning with an introduction to chemosensing, the principles of chemosensors, and the fundamentals of computational principles and methods for chemical sensing, the book goes on to explore some key design strategies and examples with chapters on a biological nose, artificial nose and artificial tongue. Applications to real world scenarios are then discussed, followed by consideration of future needs and developments in the field. Based on the cross-disciplinary experience of its expert authors, Chemosensor Arrays: Fundamental Pattern Recognition, Development and Applications provides practical advice for all those interested in pattern recognition-driven chemical sensing.

1. Introduction to Chemosensing
2. Introduction to Intermolecular Interactions
3. Principles of Chemosensors
4. Computational Principles & Methods for Chemical Sensing
5. Design Strategy for Artificial Chemosensor Array
6. Biological Nose For Chemical Sensing
7. Artificial Nose For Chemical Gas Sensing
8. Artificial tongue For Chemical Solution Sensing
9. Applications in Real Scenarios
10. Multivariate Analysis
11. Future Needs and Developments

Authors

Tsuyoshi Minami Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, Japan. Tsuyoshi Minami obtained his PhD in Engineering from Tokyo Metropolitan University, Japan, under the direction of Prof. Yuji Kubo. Between 2011 and 2013, he was a postdoctoral research associate at Bowling Green State University, working with Prof. Pavel Anzenbacher, Jr. In 2013, he was appointed as a research assistant professor at the same university. Then, in 2014, he moved to Yamagata University, Japan, as an assistant professor. From 2016 to 2019, he was a lecturer at The University of Tokyo, Japan. Meanwhile, he was selected as an excellent young independent researcher at The University of Tokyo. Since 2019, he has been an associate professor at the same university. He is also a visiting professor at The University of Technology of Compi�gne, France, and Airlangga University, Indonesia. His research interests include supramolecular materials and their applications to chemical sensors. Tetsuya Kobayashi Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, Japan. Tetsuya J. Kobayashi is a Professor at the Institute of Industrial Science, The University of Tokyo. He received his Ph.D. in Science from the University of Tokyo in 2005, where his doctoral research focused on the mathematical foundations of intracellular chemical reaction networks. After completing postdoctoral research at RIKEN's Center for Developmental Biology, he joined the University of Tokyo, where he has held faculty positions since 2008 and was promoted to full professor in 2023. Professor Kobayashi's research lies at the intersection of systems biology, biophysics, and applied mathematics. His work addresses the quantitative principles of cellular information processing, development, immunity, and evolution, with particular emphasis on stochastic processes, dynamical systems, and thermodynamics in biological systems for which he received the Early Career Award in Biophysics from the Biophysical Society of Japan. He is actively involved in the interdisciplinary scientific community of quantitative biology, theoretical biology and biophysics. Yui Sasaki Institute of Industrial Science, The University of Tokyo, Tokyo, Japan. Dr. Yui Sasaki received her Ph.D. degree from the University of Tokyo in 2020 under the supervision of Associate Professor Tsuyoshi Minami. Between 2018 and 2021, she was a JSPS Research Fellow for Young Scientists (DC1) and JSPS postdoctoral fellow (PD) at the University of Tokyo. Currently, she is a project researcher at the same university. During her Ph.D. course, she worked with Professor Yang Tian of East China Normal University (China) in 2019 and Professor Karsten Haupt of University of Technology of Compi�gne (France) in 2020 on collaborative projects. Her research interests include molecular self-assembled systems.

Table of Contents

Authors

Related Topics

Related Products

Introduction to Statistical Machine Learning. Edition No. 2

Multimodal Remote Sensing Fusion and Classification. Algorithms and Applications. Earth Observation

Neural Network Algorithms and Their Engineering Applications

Machine Learning and Artificial Intelligence in Chemical and Biological Sensing

Deep Learning for Earth Observation and Climate Monitoring