Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System

Hongyu Liu, Gang Meng, Zanhong Deng, Kazuki Nagashima, Shimao Wang, Tiantian Dai, Liang Li, Takeshi Yanagida, Xiaodong Fang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

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-, WO3-, and SnO2-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.

Original languageEnglish
Pages (from-to)4167-4175
Number of pages9
JournalACS sensors
Volume6
Issue number11
DOIs
Publication statusPublished - Nov 26 2021

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes

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