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  5. Boosting the Performance on Scale‐Level of Triboelectric Nanogenerators by Controllable Self‐Triggering

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Article
en
2022

Boosting the Performance on Scale‐Level of Triboelectric Nanogenerators by Controllable Self‐Triggering

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en
2022
Vol 13 (6)
Vol. 13
DOI: 10.1002/aenm.202203707

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Qianwang Wang
Xin Yu
Jianlong Wang
+4 more

Abstract

Abstract The characteristics of low current and high output impedance of triboelectric nanogenerators (TENGs) constrain their development and application. Recently, corresponding energy management methods have been proposed to solve the problems. Among them, the switch plays an important role in energy management to improve output performance and reduce output impedance. Therefore, a self‐triggering switch controlled by TENGs is proposed for power management to achieve high‐performance output. It is comprised of an electronic logic switch and the triggering TENG to release the energy stored by the energy‐TENG. The experimental results demonstrate that the self‐triggering switch can adjust the duty cycle with the assistance of the inherent load characteristics of the TENG. In addition, the coupling output of the two TENGs increases the single‐cycle peak current by 137 times (from 32 µA to 4.32 mA) in the vertical contact‐separation mode and 5284 times (from 1.3 µA to 6.87 mA) in the horizontal contact mode. In application, the commercial sensor can be powered, and six 100 W lamps in parallel can be lit. The coupling output method of switch‐TENGs and energy‐TENGs provides important guidance for the development of power management and a new approach toward practical applications.

How to cite this publication

Qianwang Wang, Xin Yu, Jianlong Wang, Yang Yu, Zhenjie Wang, Zhong Lin Wang, Tinghai Cheng (2022). Boosting the Performance on Scale‐Level of Triboelectric Nanogenerators by Controllable Self‐Triggering. , 13(6), DOI: https://doi.org/10.1002/aenm.202203707.

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Publication Details

Type

Article

Year

2022

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.202203707

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