A Machine-Learning-Algorithm Enhanced Multi-Functional Gas Sensor for Self-Humidity Compensation and Partial Discharge Detection

作者：Yutong Han, Haozhe Zhuang, Ziyang Yin, Ze Long, Ting Li, Yu Yao, Qibin Zheng, Zhigang Zhu · 发表于：ACS Sensors · 年份：2025 · DOI：10.1021/acssensors.5c01214 · 被引用次数：17 · 研究领域：Gas Sensing Nanomaterials and Sensors、Advanced Chemical Sensor Technologies、Analytical Chemistry and Sensors

Gas-Insulated switchgear (GIS) is prone to partial discharges (PDs) in high electric field environments, and the concentration of generated NO 2 is an essential indicator for determining the PD types and severity of faults. Notably, environmental humidity greatly influences the insulation performance of gas-insulated switchgear and the signals of NO 2 gas sensors. Thus, the simultaneous detection of humidity and NO 2 and the decoupling of signals has practical importance. Herein, a groundbreaking sensor is developed to achieve self-calibrated sensing of humidity and NO 2 gas, which is realized by a multifunctional WS 2 /ZnO sensitive material with an innovative self-humidity compensation algorithm of DF-MT1DCL. This synergistic system delivers dynamic, real-time humidity adaptive calibration and also enables precise recognition of partial discharge types. The sensor exhibited simultaneous response and a wide detection range (100 ppb–10 ppm of NO 2, 10.8–94.3% RH) exposed to NO 2 and humidity at room temperature. As a result, simultaneous monitoring and decoupling of signals can be realized. Further, a multitask deep learning model DF-MT1DCL combined 1D-CNN with LSTM was proposed to complete the humidity adaptive calibration based on a single WS 2 /ZnO sensor, which realizes the simultaneous prediction of humidity and NO 2 concentration, with R 2 values of 99.1% and 93.5% respectively. The WS 2 /ZnO sensor with excellent humidity and NO 2 sensing performance and the DF-MT1DCL ...