Selective Sensing of Volatile Organic Compounds Using an Electrostatically Formed Nanowire Sensor Based on Automatic Machine Learning

Xiaokai Yang, Anwesha Mukherjee, Min Li, Jiuhong Wang, Yong Xia*, Yossi Rosenwaks*, Libo Zhao, Linxi Dong*, Zhuangde Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With the development of Internet of Things technology, various sensors are under intense development. Electrostatically formed nanowire (EFN) gas sensors are multigate Si sensors based on CMOS technology and have the unique advantages of ultralow power consumption and very large-scale integration (VLSI) compatibility for mass production. In order to achieve selectivity, machine learning is required to accurately identify the detected gas. In this work, we introduce automatic learning technology, by which the common algorithms are sorted and applied to the EFN gas sensor. The advantages and disadvantages of the top four tree-based model algorithms are discussed, and the unilateral training models are ensembled to further improve the accuracy of the algorithm. The analyses of two groups of experiments show that the CatBoost algorithm has the highest evaluation index. In addition, the feature importance of the classification is analyzed from the physical meaning of electrostatically formed nanowire dimensions, paving the way for model fusion and mechanism exploration.

Original languageEnglish
Pages (from-to)1819-1826
Number of pages8
JournalACS Sensors
Volume8
Issue number4
DOIs
StatePublished - 28 Apr 2023

Funding

FundersFunder number
Chongqing Natural Science Basic Research Projectcstc2021jcyj-msxmX0801
National Natural Science Foundation of ChinaU1909221, 51890884
National Key Research and Development Program of China2021YFB2012500

    Keywords

    • automatic learning
    • electrostatically formed nanowires
    • machine learning
    • selectivity
    • sensor
    • volatile organic compounds

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