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  5. All‐Fabric Direct‐Current Triboelectric Nanogenerators Based on the Tribovoltaic Effect as Power Textiles

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Article
en
2023

All‐Fabric Direct‐Current Triboelectric Nanogenerators Based on the Tribovoltaic Effect as Power Textiles

0 Datasets

0 Files

en
2023
Vol 13 (29)
Vol. 13
DOI: 10.1002/aenm.202301178

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Peng Xiao
Kai Dong
Zhong Lin Wang
+6 more

Abstract

Abstract The tribovoltaic effect is the direct‐current (DC) output that results from sliding a p‐type semiconductor on top of an n‐type semiconductor, and it is caused by the electron–hole pairs generated However, the rigid structure of traditional semiconductor limits its potential application in wearable fields. Here, p–type and n–type fabric with semiconductor properties are prepared by doping small organic molecules of cetyltrimethylammonium bromide and sodium dodecylbenzene sulfonate on the carbon atoms of single–wall carbon nanotubes (SWCNTs), and three all‐fabric direct‐current triboelectric nanogenerators based on the tribovoltaic effect (AFDC‐TENG) are developed , which exhibit high flexibility, satisfactory comfort, and stable DC output. In addition, the effects of structural parameters and environmental factors on the electrical output of AFDC‐TENG are systematically discussed. The output voltage, current, and power density of p‐type AFDC‐TENG can reach 0.2 V, 0.29 µA, and 45.5 mV m −2 at a maximum speed of 0.2 m s −1 and a sliding frequency of 1 Hz, respectively. This work proposes a simple and scalable design form for all‐fabric DC power supply devices, which has potential applications in the future micro/nano energy or self‐powered flexible sensors.

How to cite this publication

Peng Xiao, Kai Dong, Zhong Lin Wang, Tianmei Lv, Renwei Cheng, Chuanhui Wei, Erming Su, Tao Jiang, Feifan Sheng (2023). All‐Fabric Direct‐Current Triboelectric Nanogenerators Based on the Tribovoltaic Effect as Power Textiles. , 13(29), DOI: https://doi.org/10.1002/aenm.202301178.

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Publication Details

Type

Article

Year

2023

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.202301178

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