NiO/ZnO Nanocomposites for Multimodal Intelligent MEMS Gas Sensors

被引：0
|
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
来源：
ACS SENSORS | 2025年
基金：
中国国家自然科学基金;
关键词：
gas sensor; NiO/ZnO nanocomposite; electronicnose; deep learning; real-time detection;
D O I：
10.1021/acssensors.4c02789
中图分类号：
O6 [化学];
学科分类号：
0703 ;
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.
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页数：11
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