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Recent Advances in Machine Learning for Electrochemical, Optical, and Gas Sensors

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Machine Learning for Advanced Functional Materials

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Abstract

Machine learning is increasingly used in the analysis of distinct types of data for clinical diagnosis and monitoring the environment, particularly because of the large amounts of data generated in sensing and biosensing methods. In this chapter, we discuss the usage of machine learning for electrochemical sensors, with emphasis on colorimetric principles of detection

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Acknowledgments

 This work was supported by CNPq (402816/2020-0,102127/2022-0,115857/2022-2), CAPES, INEO, and FAPESP (2018/22214-6, 2021/08387-8).

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Authors and Affiliations

  1. Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos, SP, 13566-590, Brazil

    Elsa M. Materón, Filipe S. R. Silva Benvenuto & Emanuel Carrilho

  2. Instituto Nacional de Ciência e Tecnologia de Bioanalítica-INCTBio, Campinas, SP, 13083-970, Brazil

    Elsa M. Materón & Emanuel Carrilho

  3. São Carlos Institute of Physics, University of São Paulo, P.O Box 369, São Carlos, SP, 13560-970, Brazil

    Elsa M. Materón, Nirav Joshi, Odemir Martinez Bruno & Osvaldo N. Oliveira

  4. Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, São José Do Rio Preto, SP, 15054-000, Brazil

    Lucas C. Ribas

  5. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, 13566-590, Brazil

    Odemir Martinez Bruno

Authors
  1. Elsa M. Materón
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  2. Filipe S. R. Silva Benvenuto
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  3. Lucas C. Ribas
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  4. Nirav Joshi
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  5. Odemir Martinez Bruno
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  6. Emanuel Carrilho
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  7. Osvaldo N. Oliveira
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Corresponding author

Correspondence to Elsa M. Materón .

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

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Materón, E.M. et al. (2023). Recent Advances in Machine Learning for Electrochemical, Optical, and 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_6

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