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  5. Robust Triboelectric Nanogenerator with Ratchet‐like Wheel‐Based Design for Harvesting of Environmental Energy

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Article
en
2019

Robust Triboelectric Nanogenerator with Ratchet‐like Wheel‐Based Design for Harvesting of Environmental Energy

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0 Files

en
2019
Vol 5 (1)
Vol. 5
DOI: 10.1002/admt.201900801

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Qi Gao
Yikang Li
Zhijie Xie
+6 more

Abstract

Abstract In this study, a ratchet‐like wheel‐based and spring‐assisted sustainable triboelectric nanogenerator (RS‐TENG) is fabricated for an efficient harvesting of rotational motion. The multitooth structure of the ratchet‐like wheel and force of the spring are used to convert the rotational motion induced by mechanical energy in the environment (such as wind energy) into a reciprocating linear motion of the movable parts, making two triboelectric layers of each sub‐triboelectrification unit produce contact–separation motion to achieve power output. The power output of one subunit is ≈0.37 mW at a uniform speed of 60 rpm, and the power output can increase to 6.05 mW by parallel connection of six subunits. The RS‐TENG with parallel connection of six subunits exhibits an open‐circuit voltage of 320 V throughout the 8 h of continuous operation (≈345 600 contact–separation events), reflecting its excellent durability and robustness. The thermometer can be stably powered at a wind speed of 15.5 m s −1 by the rectifier circuit and a 330 µF commercial capacitor. The above experiments show that RS‐TENG can not only harvest the mechanical energy around the environment for self‐powered sensing, but also exhibit an improved output performance and outstanding robustness.

How to cite this publication

Qi Gao, Yikang Li, Zhijie Xie, Weixiong Yang, Zheng Wang, Mengfei Yin, Xiaohui Lu, Tinghai Cheng, Zhong Lin Wang (2019). Robust Triboelectric Nanogenerator with Ratchet‐like Wheel‐Based Design for Harvesting of Environmental Energy. , 5(1), DOI: https://doi.org/10.1002/admt.201900801.

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Publication Details

Type

Article

Year

2019

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/admt.201900801

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