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  5. Shape-Adaptive, Self-Healable Triboelectric Nanogenerator with Enhanced Performances by Soft Solid–Solid Contact Electrification

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Article
en
2019

Shape-Adaptive, Self-Healable Triboelectric Nanogenerator with Enhanced Performances by Soft Solid–Solid Contact Electrification

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en
2019
Vol 13 (8)
Vol. 13
DOI: 10.1021/acsnano.9b02690

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Yanghui Chen
Xiong Pu
Mengmeng Liu
+7 more

Abstract

The viable application of soft electronics/robotics relies on the development of power devices which are desired to be flexible, deformable, or even self-healable. We report here a shape-adaptive, self-healable triboelectric nanogenerator (SS-TENG) for harvesting biomechanical energies. The use of a viscoelastic polymer, normally known as Silly Putty, as the electrification material and as the matrix of a carbon-nanotube-filled composite (CNT-putty) electrode endows the SS-TENG the capability of adapting to arbitrary irregular surfaces and instantaneous healing from mechanical damage (almost completely recovered in 3 min without extra stimuli). Furthermore, the output performances of the SS-TENG have also been significantly improved because (i) the ideal soft contact is achieved at the solid-solid interfaces for more effective contact electrification and (ii) the introduced cation dopants make the putty even more tribo-negative than polytetrafluoroethylene. The SS-TENG can be adhered to any curvy surface, tailored, and reshaped into arbitrary configurations and utilized as a power supply for small electronics, suggesting promising applications in soft electronics/robotics in the future.

How to cite this publication

Yanghui Chen, Xiong Pu, Mengmeng Liu, Shuangyang Kuang, Panpan Zhang, Qilin Hua, Zifeng Cong, Wenbin Guo, Weiguo Hu, Zhong Lin Wang (2019). Shape-Adaptive, Self-Healable Triboelectric Nanogenerator with Enhanced Performances by Soft Solid–Solid Contact Electrification. , 13(8), DOI: https://doi.org/10.1021/acsnano.9b02690.

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Publication Details

Type

Article

Year

2019

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.9b02690

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