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  5. Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect

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Article
en
2024

Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect

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en
2024
DOI: 10.1021/acsami.4c03113

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Yuqi Wang
Qi Gao
Wenkai Liu
+5 more

Abstract

It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.

How to cite this publication

Yuqi Wang, Qi Gao, Wenkai Liu, Changcheng Bao, Hengyu Li, Yingting Wang, Zhong Lin Wang, Tinghai Cheng (2024). Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect. , DOI: https://doi.org/10.1021/acsami.4c03113.

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Publication Details

Type

Article

Year

2024

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsami.4c03113

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