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  5. Achieving Ultrahigh Effective Surface Charge Density of Direct‐Current Triboelectric Nanogenerator in High Humidity

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Article
en
2022

Achieving Ultrahigh Effective Surface Charge Density of Direct‐Current Triboelectric Nanogenerator in High Humidity

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en
2022
Vol 18 (24)
Vol. 18
DOI: 10.1002/smll.202201402

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Lu Liu
Zhihao Zhao
Yanhong Li
+7 more

Abstract

As an emerging energy-harvesting technology, the triboelectric nanogenerator (TENG) is considered a powerful driving force toward the new-era of Internet of Things and artificial intelligence, but its output performance is dramatically influenced by environmental humidity. Herein, a direct current TENG (DC-TENG) based on the triboelectrification effect and electrostatic breakdown is reported to address the problem of output attenuation in high humidity environments for the conventional TENGs. It is found that high humidity not only enhances the sliding triboelectrification effect of hydrophobic triboelectric materials, but also promotes the electrostatic breakdown process for DC-TENG, thus contributing to the improvement of DC-TENG output. Furthermore, taking poly(vinyl chloride) film as the friction layer, the effective surface charge density of DC-TENG with microstructure-designed electrode achieves a milestone value of ≈2.97 mC m-2 under 90% relative humidity, which is almost 1.42-fold larger than that under 30% RH. This work not only establishes an effective methodology to boost the output performance of TENG in a high humidity environment, but also establishes a foundation for its practical applications in large-scale energy harvesting.

How to cite this publication

Lu Liu, Zhihao Zhao, Yanhong Li, Xinyuan Li, Di Liu, Shaoxin Li, Yikui Gao, Linglin Zhou, Jie Wang, Zhong Lin Wang (2022). Achieving Ultrahigh Effective Surface Charge Density of Direct‐Current Triboelectric Nanogenerator in High Humidity. , 18(24), DOI: https://doi.org/10.1002/smll.202201402.

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Publication Details

Type

Article

Year

2022

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/smll.202201402

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