Zhu Jiaqing, Chen Lechen, Ni Wangze, Cheng Weiwei, Yang Zhi, Xu Shusheng, Wang Tao, Zhang Bowei, Xuan Fuzhen
School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
ACS Sens. 2025 Apr 25;10(4):2531-2541. doi: 10.1021/acssensors.4c02789. Epub 2025 Mar 24.
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.
为模式识别设计的气体传感器阵列在对多种挥发性有机化合物（VOC）实现高灵敏度和选择性方面面临持续挑战，尤其是在变化的环境条件下。为解决这些限制，我们通过精确调整NiO和ZnO组分的纳米复合材料比例，开发了多模式智能微机电系统（MEMS）气体传感器。这些传感器在不同工作温度下对乙二醇（EG）和柠檬烯（LM）表现出增强的响应，展现出材料特异性选择性。此外，采用多任务深度学习模型对VOC进行实时定量检测，准确预测其浓度和类型。这些结果展示了将材料优化与先进算法相结合用于实际VOC检测的有效性，推动了气味分析工具领域的发展。
