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  5. Triboelectric Nanogenerator Networks Integrated with Power Management Module for Water Wave Energy Harvesting

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Article
en
2019

Triboelectric Nanogenerator Networks Integrated with Power Management Module for Water Wave Energy Harvesting

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en
2019
Vol 29 (41)
Vol. 29
DOI: 10.1002/adfm.201807241

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Xi Liang
Tao Jiang
Guoxu Liu
+6 more

Abstract

Ocean waves are one of the most promising renewable energy sources for large-scope applications. Recently, triboelectric nanogenerator (TENG) network has been demonstrated to effectively harvest water wave energy possibly toward large-scale blue energy. However, the absence of effective power management severely restricts the practicability of TENGs. In this work, a hexagonal TENG network consisting of spherical TENG units based on spring-assisted multilayered structure, integrated with a power management module (PMM), is constructed for harvesting water wave energy. The output performance of the TENG network is found to be determined by water wave frequencies and amplitudes, as well as the wave type. Moreover, with the implemented PMM, the TENG network could output a steady and continuous direct current (DC) voltage on the load resistance, and the stored energy is dramatically improved by up to 96 times for charging a capacitor. The TENG network integrated with the PMM is also applied to effectively power a digital thermometer and a wireless transmitter. The thermometer can constantly measure the water temperature with the water wave motions, and the transmitter can send signals that enable an alarm to go off once every 10 s. This study extends the application of the power management module in the water wave energy harvesting.

How to cite this publication

Xi Liang, Tao Jiang, Guoxu Liu, Tianxiao Xiao, Liang Xu, Wei Li, Fengben Xi, Chi Zhang, Zhong Lin Wang (2019). Triboelectric Nanogenerator Networks Integrated with Power Management Module for Water Wave Energy Harvesting. , 29(41), DOI: https://doi.org/10.1002/adfm.201807241.

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Publication Details

Type

Article

Year

2019

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/adfm.201807241

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