Review–Modern Data Analysis in Gas Sensors

Autor:	Md. Samiul Islam Sagar, Noah Riley Allison, Harikrishnan Muraleedharan Jalajamony, Renny Edwin Fernandez, Praveen Kumar Sekhar
Rok vydání:	2022
Předmět:	Renewable Energy Sustainability and the Environment Materials Chemistry Electrochemistry Condensed Matter Physics Surfaces Coatings and Films Electronic Optical and Magnetic Materials
Zdroj:	Journal of The Electrochemical Society. 169:127512
ISSN:	1945-7111 0013-4651
DOI:	10.1149/1945-7111/aca839
Popis:	Development in the field of gas sensors has witnessed exponential growth with multitude of applications. The diverse applications have led to unexpected challenges. Recent advances in data science have addressed the challenges such as selectivity, drift, aging, limit of detection, and response time. The incorporation of modern data analysis including machine learning techniques have enabled a self-sustaining gas sensing infrastructure without human intervention. This article provides a birds-eye view on data enabled technologies in the realm of gas sensors. While elaborating the prior developments in gas sensing related data analysis, this article is poised to be an entrant for enthusiast in the domain of data science and gas sensors.
Databáze:	OpenAIRE
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