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ACS Sens

Title:		Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System
Author(s):		Liu H; Meng G; Deng Z; Nagashima K; Wang S; Dai T; Li L; Yanagida T; Fang X;
Address:		"College of New Materials and New Energies, Shenzhen Technology University, Shenzhen 518118, China. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, China. Anhui Institute of Optics and Fine Mechanics, and Key Lab of Photovoltaic and Energy Conservation Materials, Chinese Academy of Sciences, Hefei 230031, China. Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan. School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou 215006, China"
Journal Title:		ACS Sens
Year:		2021
Volume:		20211104
Issue:		11
Page Number:		4167 - 4175
DOI:		10.1021/acssensors.1c01704
ISSN/ISBN:		2379-3694 (Electronic) 2379-3694 (Linking)
Abstract:		"Discriminating structurally similar volatile organic compounds (VOCs) molecules, such as benzene, toluene, and three xylene isomers (BTX), remains a significant challenge, especially, for metal oxide semiconductor (MOS) sensors, in which selectivity is a long-standing challenge. Recent progress indicates that temperature modulation of a single MOS sensor offers a powerful route in extracting the features of adsorbed gas analytes than conventional isothermal operation. Herein, a rectangular heating waveform is applied on NiO-, WO(3)-, and SnO(2)-based sensors to gradually activate the specific gas/oxide interfacial redox reaction and generate rich (electrical) features of adsorbed BTX molecules. Upon several signal preprocessing steps, the intrinsic feature of BTX molecules can be extracted by the linear discrimination analysis (LDA) or convolutional neural network (CNN) analysis. The combination of three distinct MOS sensors noticeably benefits the recognition accuracy (with a reduced number of training iterations). Finally, a prototype of a smart BTX recognition system (including sensing electronics, sensors, Wi-Fi module, UI, PC, etc.) based on temperature modulation has been explored, which enables a prompt, accurate, and stable identification of xylene isomers in the ambient air background and raises the hope of innovating the future advanced machine olfactory system"
Keywords:		Benzene/analysis *Environmental Monitoring Oxides Toluene/analysis *Xylenes/analysis BTX molecules deep learning algorithm smart sensing system temperature modulation xylene isomer classification;
Notes:		"MedlineLiu, Hongyu Meng, Gang Deng, Zanhong Nagashima, Kazuki Wang, Shimao Dai, Tiantian Li, Liang Yanagida, Takeshi Fang, Xiaodong eng Research Support, Non-U.S. Gov't 2021/11/05 ACS Sens. 2021 Nov 26; 6(11):4167-4175. doi: 10.1021/acssensors.1c01704. Epub 2021 Nov 4"