期刊
CHEMICAL ENGINEERING JOURNAL
卷 446, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2022.136914
关键词
Self-powered sensing; Hybrid nanogenerator; Ti 3 C 2 T X MXene; Ethanol sensor; Flexible electronics
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]
The development of flexible electronics has led to a demand for self-powered nanogenerators. In this study, a hybrid nanogenerator powered by tires was developed for gas and movement monitoring. The nanogenerator can power a light and charge a smartphone, and MXene/Ag-based sensors were fabricated for ethanol detection and joint activity monitoring.
The flexible electronics has triggered a huge demand for self-powered nanogenerators, which has great potential to convert mechanical energy into electrical energy. In this work, a triboelectric-electromagnetic hybrid nanogenerator (HNG) driven by tires was developed for self-powered gas and movement monitoring. The output performance of the triboelectric nanogenerator (TENG) and electromagnetic generator (EMG) increases with increasing vehicle speed, and the voltages of the TENG and EMG are 581 V and 62 V at the 15 km/h vehicle speed, respectively. The HNG can turn on a light (3 W) and charge a smartphone. A Ti3C2Tx MXene/Ag-based sensor driven by HNG was fabricated for ethanol detection. Density functional theory (DFT) simulations and bulk electrosensitive measurements show that MXene/Ag nanocomposites have excellent sensitivity to ethanol. The high response(Delta Uab/Uab(gas) = 204% @ 100 ppm) of the self-powered ethanol sensor is 24.5 times larger than that of the resistive sensor (Delta R/Ra = 8.3%). The self-powered sensor could be used to assess the levels of drinking and ensure cyclists ride safely. MXene/Ag-based flexible sensors were fabricated using a microelectronic printer and electrospinning apparatus to monitor articular activity. The self-powered monitor system can be used to access the motion data of cyclists and provide cyclists with better training and race strategies.
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