NiO/ZnO Nanocomposites for Multimodal Intelligent MEMS Gas Sensors
Jiaqing Zhu, Lechen Chen, Wangze Ni, Weiwei Cheng, Zhi Yang, Shusheng Xu, Tao Wang, Bowei Zhang, Fuzhen Xuan
Abstract
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.