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  5. A Hybridized Triboelectric‐Electromagnetic Nanogenerator as a Power Supply of Monitoring Sensors for the Ventilation System

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Article
en
2022

A Hybridized Triboelectric‐Electromagnetic Nanogenerator as a Power Supply of Monitoring Sensors for the Ventilation System

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en
2022
Vol 12 (42)
Vol. 12
DOI: 10.1002/aenm.202201966

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Fangyan Zheng
Yuxuan Zhou
Shuangting Hu
+3 more

Abstract

Abstract With the improvement of the airtightness of modern buildings, installing ventilation systems indoors is becoming increasingly important, which works for a long time continuously and needs to be monitored in real‐time. However, the complex wiring of monitoring sensors makes assembly and maintenance more difficult. This work reports a hybridized triboelectric‐electromagnetic nanogenerator (HNG) as a power supply of a self‐powered wireless monitoring system for ventilation systems. The HNG integrates an air inlet cover, a stator with coils and interdigitated copper electrodes, a rotor with magnets, fluorinated ethylene propylene films, and optimized built‐in wind blades. The HNG scavenges wind energy from a ventilation system and serves as a power supply for electric applications. Under the wind speed of 6.5 m s −1 in the ventilation system, the maximum stabilized voltage and maximum instantaneous current of the HNG are 177.5 V and 0.049 A, which can charge smartphones and light up a bulb. Furthermore, the HNG realizes the self‐powered wireless transmission of temperature and humidity sensing nodes. This work demonstrates an effective wind energy harvester, providing an innovative strategy for monitoring the condition of the ventilation system and broadening the thoughts of prospective energy harvesting.

How to cite this publication

Fangyan Zheng, Yuxuan Zhou, Shuangting Hu, Ruonan Li, Zhong Lin Wang, Zhiyi Wu (2022). A Hybridized Triboelectric‐Electromagnetic Nanogenerator as a Power Supply of Monitoring Sensors for the Ventilation System. , 12(42), DOI: https://doi.org/10.1002/aenm.202201966.

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Publication Details

Type

Article

Year

2022

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.202201966

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