文献类型：期刊文献

英文题名：NiO/ZnO Nanocomposites for Multimodal Intelligent MEMS Gas Sensors

作者：Zhu, Jiaqing[1];Chen, Lechen[2];Ni, Wangze[2];Cheng, Weiwei[1];Yang, Zhi[2];Xu, Shusheng[1];Wang, Tao[3];Zhang, Bowei[3];Xuan, Fuzhen[3]

机构：[1]Shanghai Univ Engn Sci, Sch Mat Sci & Engn, Shanghai 201620, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Natl Key Lab Adv Micro & Nano Manufacture Technol, Shanghai 200240, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China

年份：2025

卷号：10

期号：4

起止页码：2531

外文期刊名：ACS SENSORS

收录：;EI(收录号：20251318122129);WOS:【SCI-EXPANDED(收录号:WOS:001450928100001)】；

基金：This work was supported by the National Natural Science Foundation of China (62301314 and 62241406), and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (52321002).

语种：英文

外文关键词：gas sensor; NiO/ZnO nanocomposite; electronicnose; deep learning; real-time detection

摘要：Gas sensor arrays designed for pattern recognition face persistent challenges in achieving high sensitivity and selectivity for multiple volatile organic compounds (VOCs), particularly under varying environmental conditions. To address these limitations, we developed multimodal intelligent MEMS gas sensors by precisely tailoring the nanocomposite ratio of NiO and ZnO components. These sensors demonstrate enhanced responses to ethylene glycol (EG) and limonene (LM) at different operating temperatures, demonstrating material-specific selectivity. Additionally, a multitask deep learning model is employed for real-time, quantitative detection of VOCs, accurately predicting their concentration and type. These results showcase the effectiveness of combining material optimization with advanced algorithms for real-world VOCs detection, advancing the field of odor analysis tools.