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A Review of the High-Performance Gas Sensors Using Machine Learning

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Machine Learning for Advanced Functional Materials

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Abstract

High-performance gas sensors are of great importance to accurately identify/detect pollutant gases and monitor their concentrations in the environment to ensure human safety in daily life and production. Machine-learning techniques have been used to successfully improve gas sensing performances of gas sensors leveraging large onsite data sets generated by them. A simple process is introduced to show the typical approach to collect the features from sensing response curves and conduct a machine-learning algorithm to further analyze the data set. The improved gas sensing performances of the machine-learning-enabled sensors reported recently are summarized and compared, especially regarding selectivity and long-term stability (drift compensation). Furthermore, the expanded applications of a gas sensor or sensor array under machine-learning algorithms were discussed and reviewed. In addition, the possible challenges/prospects are emphasized and discussed as well. Our review further indicated that machine-learning techniques are effective strategies to successfully improve the gas sensing behavior of a single gas sensor or sensor array.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant no. 51802109, 51972102, 52072115 and U21A20500), the Department of Education of Hubei Province (Grant no. D20202903) and the Department of Science and Technology of Hubei Province (Grant no. 2022CFB525).

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Authors and Affiliations

  1. Hubei Key Laboratory for Processing and Application of Catalytic Materials, School of Physics and Electronic Information, Huanggang Normal University, Huanggang, 438000, China

    Shulin Yang, Gui Lei, Huoxi Xu, Zhigao Lan & Haoshuang Gu

  2. Faculty of Physics and Electronic Sciences, Hubei University, Wuhan, 430062, China

    Shulin Yang, Gui Lei, Zhao Wang & Haoshuang Gu

Authors
  1. Shulin Yang
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  2. Gui Lei
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  3. Huoxi Xu
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  4. Zhigao Lan
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  5. Zhao Wang
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  6. Haoshuang Gu
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Correspondence to Shulin Yang or Haoshuang Gu .

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Editors and Affiliations

  1. University of Sao Paulo, Sao Carlos Institution of Physics, Grupo de Polimeros, São Paulo, Brazil

    Nirav Joshi

  2. Department of Civil Engineering Materials and Structures, Indian Institute of Technology Jammu, Jammu, India

    Vinod Kushvaha

  3. Proof of Concept and Innovation Group, Stanley Black and Decker (United States), Atlanta, GA, USA

    Priyanka Madhushri

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Yang, S., Lei, G., Xu, H., Lan, Z., Wang, Z., Gu, H. (2023). A Review of the High-Performance Gas Sensors Using Machine Learning. In: Joshi, N., Kushvaha, V., Madhushri, P. (eds) Machine Learning for Advanced Functional Materials. Springer, Singapore. https://doi.org/10.1007/978-981-99-0393-1_8

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