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  5. A Dual-Mode Triboelectric Nanogenerator for Wind Energy Harvesting and Self-Powered Wind Speed Monitoring

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Article
en
2022

A Dual-Mode Triboelectric Nanogenerator for Wind Energy Harvesting and Self-Powered Wind Speed Monitoring

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0 Files

en
2022
Vol 16 (4)
Vol. 16
DOI: 10.1021/acsnano.1c11658

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Lixia He
Chuguo Zhang
Baofeng Zhang
+7 more

Abstract

The triboelectric nanogenerator shows a broad application potential in wind energy collection and wind speed sensing. However, it is difficult to realize wind energy collection and real-time wind speed monitoring in one simple device without external power support. Here, a high-performance dual-mode triboelectric nanogenerator is proposed to simultaneously collect wind energy efficiently and monitor wind speed in real time, which is composed by an alternating current triboelectric nanogenerator (AC-TENG) and a direct-current triboelectric nanogenerator (DC-TENG). Based on the material optimization, the charge density of the AC-TENG improves by a factor of 1 compared with previous works. Moreover, benefiting from the elastic structure and material optimization to realize a low friction force, the AC-TENG shows an excellent durability and obtains a retention of 87% electric output after 1 200 000 operation cycles. Meanwhile, thanks to the high charge density and low friction force, the energy-harvesting efficiency of the AC-TENG is doubled. In addition, the DC-TENG not only displays an excellent real-time sensing performance but also can provide gale warning. Our finding exhibits a strategy for efficiently collecting wind energy and achieving fully self-powered and real-time wind speed monitoring.

How to cite this publication

Lixia He, Chuguo Zhang, Baofeng Zhang, Yang Ou, Wei Yuan, Linglin Zhou, Zhihao Zhao, Zhiyi Wu, Jie Wang, Zhong Lin Wang (2022). A Dual-Mode Triboelectric Nanogenerator for Wind Energy Harvesting and Self-Powered Wind Speed Monitoring. , 16(4), DOI: https://doi.org/10.1021/acsnano.1c11658.

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Publication Details

Type

Article

Year

2022

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.1c11658

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