| Title: | Selective Sensing of Volatile Organic Compounds Using an Electrostatically Formed Nanowire Sensor Based on Automatic Machine Learning |
| Author(s): | Yang X; Mukherjee A; Li M; Wang J; Xia Y; Rosenwaks Y; Zhao L; Dong L; Jiang Z; |
| Address: | "State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University (Yantai) Research Institute for Intelligent Sensing Technology and System, Xi'an Jiaotong University, Xi'an 710049, China. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai 265503, China. Department of Physical Electronics, School of Electrical Engineering, Tel Aviv University, Ramat Aviv 69978, Israel. Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems, College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China" |
| DOI: | 10.1021/acssensors.3c00147 |
| ISSN/ISBN: | 2379-3694 (Electronic) 2379-3694 (Linking) |
| 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" |
| Keywords: | *Volatile Organic Compounds *Nanowires Algorithms Machine Learning Internet automatic learning electrostatically formed nanowires selectivity sensor volatile organic compounds; |
| Notes: | "MedlineYang, Xiaokai Mukherjee, Anwesha Li, Min Wang, Jiuhong Xia, Yong Rosenwaks, Yossi Zhao, Libo Dong, Linxi Jiang, Zhuangde eng Research Support, Non-U.S. Gov't 2023/04/13 ACS Sens. 2023 Apr 28; 8(4):1819-1826. doi: 10.1021/acssensors.3c00147. Epub 2023 Apr 12" |
