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  5. Boosting the Power and Lowering the Impedance of Triboelectric Nanogenerators through Manipulating the Permittivity for Wearable Energy Harvesting

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Article
en
2021

Boosting the Power and Lowering the Impedance of Triboelectric Nanogenerators through Manipulating the Permittivity for Wearable Energy Harvesting

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0 Files

en
2021
Vol 15 (4)
Vol. 15
DOI: 10.1021/acsnano.1c00914

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Hai Lu Wang
Zi Hao Guo
Guang Zhu
+2 more

Abstract

Triboelectric nanogenerators (TENGs), which hold great promise for sustainably powering wearable electronics by harvesting distributed mechanical energy, are still severely limited by their unsatisfactory power density, small capacitance, and high internal impedance. Herein, a materials optimization strategy is proposed to achieve a high performance of TENGs and to lower the matching impedance simultaneously. A permittivity-tunable electret composite film, i.e., a thermoplastic polyurethane (TPU) matrix with polyethylene glycol (PEG) additives and polytetrafluoroethylene (PTFE) nanoparticle inclusions, is employed as the triboelectric layer. Through optimizing the dielectric constant of the composite, the injected charge density and internal capacitance of the TENG are significantly enhanced, thus synergistically boosting the output power and reducing the impedance of the TENG. The optimal output power reaches 16.8 mW at an external resistance of 200 kΩ, showing a 17.3 times enhancement in output power and a 90% decline in matching impedance. This work demonstrates a significant progress toward the materials optimization of a triboelectric generator for its practical commercialization.

How to cite this publication

Hai Lu Wang, Zi Hao Guo, Guang Zhu, Xiong Pu, Zhong Lin Wang (2021). Boosting the Power and Lowering the Impedance of Triboelectric Nanogenerators through Manipulating the Permittivity for Wearable Energy Harvesting. , 15(4), DOI: https://doi.org/10.1021/acsnano.1c00914.

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Publication Details

Type

Article

Year

2021

Authors

5

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.1c00914

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