Machine learning-assisted wearable sensor array for comprehensive ammonia and nitrogen dioxide detection in wide relative humidity range  

作　　者：Yiwen Li Shuai Guo Boyi Wang Jianguo Sun Liupeng Zhao Tianshuang Wang Xu Yan Fangmeng Liu Peng Sun John Wang Swee Ching Tan Geyu Lu 

机构地区：[1]State Laboratory on Integrated Optoelectronics,College of Electronic Science and Engineering,Jilin University,Changchun,the People's Republic of China [2]Department of Materials Science and Engineering,National University of Singapore,Singapore,Singapore [3]International Center of Future Science,Jilin University,Changchun,the People's Republic of China

出　　处：《InfoMat》2024年第6期95-109,共15页信息材料（英文）

基　　金：National Key Research and Development Program of China,Grant/Award Number:2022YFB3205500;National Nature Science Foundation of China,Grant/Award Numbers:61833006,61831011;Fundamental Research Funds for the Central Universities;Ministry of Education Academic Research Fund Tier 1,Grant/Award Number:A-0009304-00-00。

摘　　要：The fast booming of wearable electronics provides great opportunities for intelligent gas detection with improved healthcare of mining workers,and a variety of gas sensors have been simultaneously developed.However,these sensing systems are always limited to single gas detection and are highly susceptible to the inference of ubiquitous moisture,resulting in less accuracy in the analysis of gas compositions in real mining conditions.To address these challenges,we propose a synergistic strategy based on sensor integration and machine learning algorithms to realize precise NH_(3) and NO_(2) gas detections under real mining conditions.A wearable sensing array based on the graphene and polyaniline composite is developed to largely enhance the sensitivity and selectivity under mixed gas conditions.Further introduction of backpropagation neural network(BP-NN)and partial least squares(PLS)algorithms could improve the accuracy of gas identification and concentration prediction and settle the inference of moisture,realizing over 99%theoretical prediction level on NH_(3) and NO_(2) concentrations within a wide relative humidity range,showing great promise in real mining detection.As proof of concept,a wireless wearable bracelet,integrated with sensing arrays and machine-learning algorithms,is developed for wireless real-time warning of hazardous gases in mines under different humidity conditions.

关 键 词：bismuth oxyselenide device applications preparation methods properties two-dimensional material 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]