期刊
NANO ENERGY
卷 100, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.nanoen.2022.107509
关键词
Self-powered sensing; Triboelectric nanogenerator; Liquid-solid interface; Ti3C2Tx MXene; SO(2 )sensor
类别
资金
- National Natural Science Foundation of China [51777215]
- Original Innovation Special Project of Science and Technology Plan of Qingdao West Coast New Area [2020-85]
A wave-driven liquid-solid triboelectric nanogenerator (TENG) was developed to construct a self-powered sensing system for marine environmental monitoring. The TENG harvested wave energy and powered a SO2 gas sensor with excellent response. The temperature and humidity error correction of the sensor was achieved through data integration and processing.
Triboelectric nanogenerators (TENGs) have great application prospects in self-powered detection system. In this paper, a wave-driven liquid-solid TENG was developed to construct a self-powered sensing system for marine environmental monitoring. The TENG was constructed with ethylene chlorotrifluoroethylene (ECTFE) film and ionic hydrogel electrodes to harvest wave energy. The peak-to-peak value of open-circuit voltage and powered density from the TENG can reach up to 332 V and 1.85 W/m(2), respectively. The self-powered MXene/TiO2/SnSe sensor driven by the TENG was prepared for SO2 gas detection, which has an excellent response (Delta U/U-a = 170 % @ 30 ppm) and is 14 times larger than that of the resistive sensor. The gas-sensing improvement mechanism was discussed in detail using ternary p-m-n heterojunction and density functional theory simulation. A self-powered marine environment monitoring system was developed to further demonstrate the application potential of the prepared TENG and sensor. Sensor signals from the self-powered system can be transmitted to smartphones and upper modules to monitor temperature, humidity, SO2 concentration, water surface height and other environmental factors in real time. In addition, the temperature and humidity error correction of the gas sensor was realized by integrating and processing the data of the sensor system with the back propagation neural network model.
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