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  5. Charge self-shuttling triboelectric nanogenerator based on wind-driven pump excitation for harvesting water wave energy

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Article
en
2024

Charge self-shuttling triboelectric nanogenerator based on wind-driven pump excitation for harvesting water wave energy

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en
2024
Vol 11 (3)
Vol. 11
DOI: 10.1063/5.0225737

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Shijie Liu
Xi Liang
Jiajia Han
+3 more

Abstract

The most important ocean energy sources are wind energy and water wave energy, both of which are significant to carbon neutrality. Due to uneven distribution and random movement, the conversion efficiency from the two energies into electrical energy is limited, so the coupling of them is necessary. However, the current energy harvesting technologies generally target one certain type, or are simple mechanical coupling. Here, we propose a composite water wave energy harvesting scheme with wind excitation based on triboelectric nanogenerators (TENGs). A rotation TENG driven by wind is introduced as a pump to inject charges into the main TENG. For the main TENG driven by water waves, a specially designed charge self-shuttling mode is applied (CSS-TENG). Under the pump excitation, the shuttling charge amount is increased by 11.8 times, and the peak power density reaches 33.0 W m−3, with an average power density of 2.4 W m−3. Furthermore, the CSS-TENG is expanded into an array by parallel connection, and the practical applications are demonstrated. This work organically couples the wind and water wave energy in the ocean scene, through the charge pumping and self-shuttling mode, providing a new pathway for the synergistic development of clean and renewable energy sources.

How to cite this publication

Shijie Liu, Xi Liang, Jiajia Han, Yuxue Duan, Tao Jiang, Zhong Lin Wang (2024). Charge self-shuttling triboelectric nanogenerator based on wind-driven pump excitation for harvesting water wave energy. , 11(3), DOI: https://doi.org/10.1063/5.0225737.

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Publication Details

Type

Article

Year

2024

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1063/5.0225737

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