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Machine Learning-Driven Gas Identification in Gas Sensors

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Machine Learning for Advanced Functional Materials

Abstract

Gas identification plays a critical role in characterizing our (chemical) environment. It allows to warn of hazardous gases and may help to diagnose medical conditions. Miniaturized gas sensors, and especially those based on chemiresistive detection mechanisms, have gained rapid development and commercialization in the past decades due to their numerous advantageous characteristics, such as simple fabrication, easy operation, high sensitivity, ability to detect a wide range of gases, and compatibility with miniaturization as well as integration for portable applications. However, they suffer from a remarkable limitation, namely their low selectivity. Recently, machine learning-driven approaches to enhance the selectivity of gas sensors have attracted considerable interest in the community of gas sensors, increasing the analyte gas identification ability. In this chapter, firstly, we introduce the general approaches to enhance the selectivity of gas sensors implemented by machine learning techniques, which consists of the architecture scheme design of gas sensors (sensor array and single sensor architecture), the selection of gas sensing response features (steady-state feature and transient-state feature), and the utilization of gas sensing signal modulation techniques (sensing materials modulation, concentration modulation, and temperature modulation). Afterward, a specific application case using a machine learning-enabled smart gas sensor for the identification of industrial gases (PH3 and NH3) is presented, which is based on a single-channel device and utilizes multiple transient features of the response. We believe machine learning in combination with efficient sensing signal modulation techniques could be a feasible way to gain the gas identification capability of gas sensors.

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Acknowledgements

The authors appreciate the below funding projects: VolkswagenStiftung (grant no. 96632, 9B396) project, EU project “Smart Electronic Olfaction for Body Odor Diagnostics” (SMELLODI, grant no. 101046369), 6G-life project (Federal Ministry of Education and Research of Germany in the programme of “Souverän. Digital. Vernetzt.”, project identification no. 16KISK001K), as well as Sächsische Aufbaubank (SAB) project (project no. 100525920).

Author information

Authors and Affiliations

  1. Institute for Materials Science and Max Bergmann Center for Biomaterials, TU Dresden, 01062, Dresden, Germany

    Shirong Huang, Bergoi Ibarlucea & Gianaurelio Cuniberti

  2. Institute for Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany

    Alexander Croy

  3. Dresden Center for Computational Materials Science (DCMS), TU Dresden, 01062, Dresden, Germany

    Gianaurelio Cuniberti

Authors
  1. Shirong Huang
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  2. Alexander Croy
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  3. Bergoi Ibarlucea
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  4. Gianaurelio Cuniberti
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Corresponding authors

Correspondence to Shirong Huang or Gianaurelio Cuniberti .

Editor information

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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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Huang, S., Croy, A., Ibarlucea, B., Cuniberti, G. (2023). Machine Learning-Driven Gas Identification in Gas Sensors. 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_2

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