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  5. Charge-Accumulation-Enhanced Triboelectric Nanogenerator with Multilayer Stacked Electrodes for Self-Powered Monitoring System

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Article
en
2023

Charge-Accumulation-Enhanced Triboelectric Nanogenerator with Multilayer Stacked Electrodes for Self-Powered Monitoring System

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en
2023
Vol 5 (11)
Vol. 5
DOI: 10.1021/acsaelm.3c00864

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Xiaoyu Zhu
Jie Yang
Xia Cao
+2 more

Abstract

As an emerging power technology, triboelectric nanogenerator (TENGs) promote the development of self-powered portable electronic systems because they can harvest ambient mechanical energy with a variety of working modes. Nevertheless, the practical application of TENGs is still limited by their low output and poor mechanical durability, and it is still a challenge to fabricate fully sustainable TENGs with high performance. Herein, a spring-supported TENG based on multilayer stacked triboelectric electrodes is proposed, which has good mechanical stability and output performance because of the introduction of the stacked sponge buffer layer and the particle vibration layer. The as-obtained TENG shows remarkable output performance enhancement, and a peak open-circuit voltage of 1.85 kV and a maximal short circuit current of 90 μA are realized. The peak power reaches up to 26 mW, which can light up 475 LEDs. Additionally, the electric signals can also be utilized to monitor environmental stimuli, such as vibration and human movement; thus, a sustainable self-powered monitoring system may be developed. Furthermore, the device could also be used to develop smart homes on the basis of human–machine interaction, thus showing great potential for applications in the Internet of Things.

How to cite this publication

Xiaoyu Zhu, Jie Yang, Xia Cao, Ning Wang, Zhong Lin Wang (2023). Charge-Accumulation-Enhanced Triboelectric Nanogenerator with Multilayer Stacked Electrodes for Self-Powered Monitoring System. , 5(11), DOI: https://doi.org/10.1021/acsaelm.3c00864.

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Publication Details

Type

Article

Year

2023

Authors

5

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsaelm.3c00864

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