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  5. Swing‐Structured Triboelectric–Electromagnetic Hybridized Nanogenerator for Breeze Wind Energy Harvesting

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Article
en
2021

Swing‐Structured Triboelectric–Electromagnetic Hybridized Nanogenerator for Breeze Wind Energy Harvesting

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en
2021
Vol 6 (11)
Vol. 6
DOI: 10.1002/admt.202100496

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Pinjing Lu
Hao Pang
Jie Ren
+5 more

Abstract

Abstract Wind energy is one of the most promising renewable energy sources, but harvesting low frequency breeze wind energy is hardly achieved using traditional electromagnetic generators (EMGs). Triboelectric nanogenerators (TENGs) provide a new approach for large‐scale collection of distributed breeze wind energy (usually 3.4–5.4 m s −1 ). Herein, by coupling the TENG and EMG, a swing‐structured hybrid nanogenerator with improved performance and durability is designed. The dielectric brush and air gap designs can minimize the material wear and continuously supply the tribo‐charges. Under external triggering, systematic comparisons are made about the output characteristics of TENG and EMG. The rectified peak power and average power of TENG are respectively, 60 and 635 times higher than those of EMG at moderate coil turn numbers, showing that TENG is much more effective than EMG for harvesting low‐frequency distributed energy (high entropy energy). Furthermore, the hybrid nanogenerator and array device are hung on tree branches to demonstrate the effective harvesting of breeze wind energy, delivering total rectified peak power densities of 2.07 and 1.94 W m –3 for single and array devices, respectively. The applications of powering portable electronics reveal the huge prospects of hybrid nanogenerator in self‐powered environmental monitoring toward forest/park fire warning systems.

How to cite this publication

Pinjing Lu, Hao Pang, Jie Ren, Yawei Feng, Jie An, Xi Liang, Tao Jiang, Zhong Lin Wang (2021). Swing‐Structured Triboelectric–Electromagnetic Hybridized Nanogenerator for Breeze Wind Energy Harvesting. , 6(11), DOI: https://doi.org/10.1002/admt.202100496.

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Publication Details

Type

Article

Year

2021

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/admt.202100496

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