文献类型：期刊文献

英文题名：Water-Based SRR Sensor With Machine Learning Algorithms for Simultaneous Temperature and Pressure Detection

作者：Tao, Shunzhen[1];Gao, Yang[1];Li, Bo[1];Zhang, Jianrui[1];Qian, Min[2];Xuan, Fuzhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Key Lab Pressure Syst & Safety, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Phys, Shanghai 200237, Peoples R China

年份：2025

卷号：25

期号：3

起止页码：5470

外文期刊名：IEEE SENSORS JOURNAL

收录：;EI(收录号：20250217648044);WOS:【SCI-EXPANDED(收录号:WOS:001416196200011)】；

基金：This work was supported in part by the National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China under Grant 32327801; in part by the National Natural Science Foundation of China Innovation Research Group Project under Grant 52321002; in part by the National Natural Science Foundation of China under Grant 52275146, Grant 61804054, Grant 12411530109, and Grant 12174102; in part by the State Key Laboratory of New Textile Materials and Advanced Processing Technologies, under Grant FZ2022006; and in part by the Space Application System of China Manned Space Program.

语种：英文

外文关键词：Sensors; Temperature sensors; Temperature; Water resources; Temperature measurement; Q-factor; Microwave antennas; Antennas; Wireless sensor networks; Wireless communication; Temperature-pressure decoupling; water resonance ring; wireless passive sensor

摘要：Water resources are vital for both human beings and the Earth's ecosystem, serving as a crucial asset for survival while also being a low-cost, environmentally friendly resource. However, in the field of electronic devices, water often signifies corrosion and failure, and very few of studies has investigated the feasibility of developing water-based devices. In this study, a wireless and powerless water-based split ring resonator (WSRR) sensor with machine learning (ML) algorithms was developed for simultaneous temperature and pressure detection. The sensor is fabricated by filling conductive water into a flexible substrate with a resonant ring structure. Within the operational frequency range of the sensor, the dielectric properties of sensor materials vary with temperature, which in turn affects the sensor's output. Through a combination of simulation and experimental methods, it has been demonstrated that the parameter $\vert {S}_{{11}}\vert $ in the output frequency curve can be used to measure temperature changes. Additionally, the fluidity of the conductive water, coupled with the flexibility of the substrate material, allows environmental pressure to be converted into deformation of the conductive material. Similarly, we have verified that the parameter ${f}_{r}$ in the output frequency curve can be used to detect pressure changes. The device has a temperature and pressure sensitivity of $3.4\times 10<^>{-{5}}$ dB/degrees C and 83 kHz/kPa, respectively. ML algorithms are used to eliminate the mutual interference of dual-parameter measurements for temperature and pressure. The trained model achieves an accuracy of 98.95% in predicting pressure, and the accuracy for temperature prediction is 88.67%.