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  5. Self-Powered Acceleration Sensor Based on Liquid Metal Triboelectric Nanogenerator for Vibration Monitoring

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Article
en
2017

Self-Powered Acceleration Sensor Based on Liquid Metal Triboelectric Nanogenerator for Vibration Monitoring

0 Datasets

0 Files

en
2017
Vol 11 (7)
Vol. 11
DOI: 10.1021/acsnano.7b03818

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Binbin Zhang
Lei Zhang
Weili Deng
+8 more

Abstract

An acceleration sensor is an essential component of the vibration measurement, while the passivity and sensitivity are the pivotal features for its application. Here, we report a self-powered and highly sensitive acceleration sensor based on a triboelectric nanogenerator composed of a liquid metal mercury droplet (LMMD) and nanofiber-networked polyvinylidene fluoride (nn-PVDF) film. Due to the ultrahigh surface-to-volume ratio of nn-PVDF film and high surface tension, high mass density, high elastic as well as mechanical robustness of LMMD, the open-circuit voltage and short-circuit current reach up to 15.5 V and 300 nA at the acceleration of 60 m/s2, respectively. The acceleration sensor has a wide detection range from 0 to 60 m/s2 with a high sensitivity of 0.26 V·s/m2. Also, the output voltage and current show a negligible decrease over 200,000 cycles, evidently presenting excellent stability. Moreover, a high-speed camera was employed to dynamically capture the motion state of the acceleration sensor for insight into the corresponding work mechanism. Finally, the acceleration sensor was demonstrated to measure the vibration of mechanical equipment and human motion in real time, which has potential applications in equipment vibration monitoring and troubleshooting.

How to cite this publication

Binbin Zhang, Lei Zhang, Weili Deng, Long Jin, Fengjun Chun, Hong Pan, Bing‐Ni Gu, Haitao Zhang, Zekai Lv, Weiqing Yang, Zhong Lin Wang (2017). Self-Powered Acceleration Sensor Based on Liquid Metal Triboelectric Nanogenerator for Vibration Monitoring. , 11(7), DOI: https://doi.org/10.1021/acsnano.7b03818.

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Publication Details

Type

Article

Year

2017

Authors

11

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.7b03818

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