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Vol. 16 (2024)

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

https://doi.org/10.1007/s40820-024-01489-z
Haixia Mei
Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun 130022, People’s Republic of China
Jingyi Peng
Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun 130022, People’s Republic of China
Tao Wang
Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
Tingting Zhou
State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, People’s Republic of China
Hongran Zhao
State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, People’s Republic of China
Tong Zhang
State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, People’s Republic of China
Zhi Yang
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

Corresponding Author: Zhi Yang

zhiyang@sjtu.edu.cn

Nano-Micro Letters, Vol. 16 (2024), Article Number: 269

Abstract

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.


Highlights:
1 The types, working principles, advantages and limitations of pattern recognition methods based on chemiresistive gas sensor array are reviewed and discussed comprehensively.
2 Outstanding and novel advancements in the application of machine learning methods for gas recognition in different important areas are compared, summarized and evaluated.
3 The current challenges and future prospects of machine learning methods in artificial olfactory systems are discussed and justified.

Keywords

Pattern recognition Sensor array Chemiresistive gas sensor Cross-sensitivity Artificial olfactory
Mei, Haixia, Jingyi Peng, Tao Wang, Tingting Zhou, Hongran Zhao, Tong Zhang, and Zhi Yang. 2024. “Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array”. Nano-Micro Letters 16 (August):269. https://doi.org/10.1007/s40820-024-01489-z.
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