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Enhanced performance of triboelectric mechanical motion sensor via continuous charge supplement and adaptive signal processing
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作者 Zitang Yuan Xiaosong Zhang +4 位作者 Hengyu Li Ping Shen Jianming Wen Zhong Lin Wang Tinghai Cheng 《Nano Research》 SCIE EI CSCD 2023年第7期10263-10271,共9页
The development of automation industry is inseparable from the progress of sensing technology.As a promising self-powered sensing technology,the durability and stability of triboelectric sensor(TES)have always been in... The development of automation industry is inseparable from the progress of sensing technology.As a promising self-powered sensing technology,the durability and stability of triboelectric sensor(TES)have always been inevitable challenges.Herein,a continuous charge supplement(CCS)strategy and an adaptive signal processing(ASP)method are proposed to improve the lifetime and robustness of TES.The CCS uses low friction brushes to increase the surface charge density of the dielectric,ensuring the reliability of sensing.A triboelectric mechanical motion sensor(TMMS)with CCS is designed,and its electrical signal is hardly attenuated after 1.5 million cycles after reasonable parameter optimization,which is unprecedented in linear TESs.After that,the dynamic characteristics of the CCS-TMMS are analyzed with error rates of less than 1%and 2%for displacement and velocity,respectively,and a signal-to-noise ratio of more than 35 dB.Also,the ASP used a signal conditioning circuit for impedance matching and analog-to-digital conversion to achieve a stable output of digital signals,while the integrated design and manufacture of each hardware module is achieved.Finally,an intelligent logistics transmission system(ILTS)capable of wirelessly monitoring multiple motion parameters is developed.This work is expected to contribute to automation industries such as smart factories and unmanned warehousing. 展开更多
关键词 triboelectric mechanical motion sensor continuous charge supplement adaptive signal processing durability and stability multiple motion parameters monitoring
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