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Review

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Department of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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Author to whom correspondence should be addressed.
Academic Editors: Laura Pigani and Rosalba Calvini
Sensors 2021, 21(8), 2877; https://doi.org/10.3390/s21082877
Received: 29 March 2021 / Revised: 13 April 2021 / Accepted: 15 April 2021 / Published: 20 April 2021
(This article belongs to the Special Issue Electronic Tongues, Electronic Noses, and Electronic Eyes)
Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors. View Full-Text
Keywords: chemiresistive and FET sensors; carbon materials; 2D TMDCs; metal/metal oxide; density function theory (DFT); selectivity; drift compensation; machine learning; data processing; feature extraction; smart sensors; smart breath analyzers; PCA; classifiers chemiresistive and FET sensors; carbon materials; 2D TMDCs; metal/metal oxide; density function theory (DFT); selectivity; drift compensation; machine learning; data processing; feature extraction; smart sensors; smart breath analyzers; PCA; classifiers
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MDPI and ACS Style

Yaqoob, U.; Younis, M.I. Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review. Sensors 2021, 21, 2877. https://doi.org/10.3390/s21082877

AMA Style

Yaqoob U, Younis MI. Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review. Sensors. 2021; 21(8):2877. https://doi.org/10.3390/s21082877

Chicago/Turabian Style

Yaqoob, Usman, and Mohammad I. Younis 2021. "Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review" Sensors 21, no. 8: 2877. https://doi.org/10.3390/s21082877

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