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dc.contributor.authorYaqoob, Usman
dc.contributor.authorYounis, Mohammad I.
dc.date.accessioned2021-04-29T08:40:52Z
dc.date.available2021-04-29T08:40:52Z
dc.date.issued2021-04-20
dc.date.submitted2021-03-29
dc.identifier.citationYaqoob, U., & Younis, M. I. (2021). Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review. Sensors, 21(8), 2877. doi:10.3390/s21082877
dc.identifier.issn1424-8220
dc.identifier.doi10.3390/s21082877
dc.identifier.urihttp://hdl.handle.net/10754/669010
dc.description.abstractNowadays, 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 includ-ing 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.
dc.description.sponsorshipThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/1424-8220/21/8/2877
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license .
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleChemical gas sensors: Recent developments, challenges, and the potential of machine learning—a review
dc.typeArticle
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentMechanical Engineering Program
dc.identifier.journalSensors
dc.eprint.versionPublisher's Version/PDF
dc.identifier.volume21
dc.identifier.issue8
dc.identifier.pages2877
kaust.personYaqoob, Usman
kaust.personYounis, Mohammad I.
dc.date.accepted2021-04-15
dc.identifier.eid2-s2.0-85104379405
refterms.dateFOA2021-04-29T08:42:04Z


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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license .
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