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  5. Robust Swing‐Structured Triboelectric Nanogenerator for Efficient Blue Energy Harvesting

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Article
en
2020

Robust Swing‐Structured Triboelectric Nanogenerator for Efficient Blue Energy Harvesting

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en
2020
Vol 10 (23)
Vol. 10
DOI: 10.1002/aenm.202000064

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Tao Jiang
Hao Pang
Jie An
+5 more

Abstract

Abstract Ocean wave energy is a promising renewable energy source, but harvesting such irregular, “random,” and mostly ultra‐low frequency energies is rather challenging due to technological limitations. Triboelectric nanogenerators (TENGs) provide a potential efficient technology for scavenging ocean wave energy. Here, a robust swing‐structured triboelectric nanogenerator (SS‐TENG) with high energy conversion efficiency for ultra‐low frequency water wave energy harvesting is reported. The swing structure inside the cylindrical TENG greatly elongates its operation time, accompanied with multiplied output frequency. The design of the air gap and flexible dielectric brushes enable mininized frictional resistance and sustainable triboelectric charges, leading to enhanced robustness and durability. The TENG performance is controlled by external triggering conditions, with a long swing time of 88 s and a high energy conversion efficiency, as well as undiminished performance after continuous triggering for 4 00 000 cycles. Furthermore, the SS‐TENG is demonstrated to effectively harvest water wave energy. Portable electronic devices are successfully powered for self‐powered sensing and environment monitoring. Due to the excellent performance of the distinctive mechanism and structure, the SS‐TENG in this work provides a good candidate for harvesting blue energy on a large scale.

How to cite this publication

Tao Jiang, Hao Pang, Jie An, Pinjing Lu, Yawei Feng, Xi Liang, Wei Zhong, Zhong Lin Wang (2020). Robust Swing‐Structured Triboelectric Nanogenerator for Efficient Blue Energy Harvesting. , 10(23), DOI: https://doi.org/10.1002/aenm.202000064.

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Publication Details

Type

Article

Year

2020

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.202000064

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