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  5. Self‐Powered Wireless Sensor Node Enabled by a Duck‐Shaped Triboelectric Nanogenerator for Harvesting Water Wave Energy

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Article
en
2016

Self‐Powered Wireless Sensor Node Enabled by a Duck‐Shaped Triboelectric Nanogenerator for Harvesting Water Wave Energy

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en
2016
Vol 7 (7)
Vol. 7
DOI: 10.1002/aenm.201601705

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Abdelsalam Ahmed
Zia Saadatnia
Islam Hassan
+5 more

Abstract

This paper presents a fully enclosed duck‐shaped triboelectric nanogenerator (TENG) for effectively scavenging energy from random and low‐frequency water waves. The design of the TENG incorporates the freestanding rolling mode and the pitch motion of a duck‐shaped structure generated by incident waves. By investigating the material and structural features, a unit of the TENG device is successfully designed. Furthermore, a hybrid system is constructed using three units of the TENG device. The hybrid system achieves an instantaneous peak current of 65.5 µA with an instantaneous output power density of up to 1.366 W m −2 . Following the design, a fluid–solid interaction analysis is carried out on one duck‐shaped TENG to understand the dynamic behavior, mechanical efficiency, and stability of the device under various water wave conditions. In addition, the hybrid system is experimentally tested to enable a commercial wireless temperature sensor node. In summary, the unique duck‐shaped TENG shows a simple, cost‐effective, environmentally friendly, light‐weight, and highly stable system. The newly designed TENG is promising for building a network of generators to harvest existing blue energy in oceans, lakes, and rivers.

How to cite this publication

Abdelsalam Ahmed, Zia Saadatnia, Islam Hassan, Yunlong Zi, Yi Xi, Xu He, Jean W. Zu, Zhong Lin Wang (2016). Self‐Powered Wireless Sensor Node Enabled by a Duck‐Shaped Triboelectric Nanogenerator for Harvesting Water Wave Energy. , 7(7), DOI: https://doi.org/10.1002/aenm.201601705.

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Publication Details

Type

Article

Year

2016

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.201601705

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