An Elastic Triboelectric Nanogenerator for Harvesting Random Mechanical Energy with Multiple Working Modes

Yuliang Chen, Ying Zhang, Taotao Zhan, Zhiming Lin, Steven L. Zhang, Haiyang Zou, Guobin Zhang, Chongwen Zou, Zhong Lin Wang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Because of the shortage of fossil energy and the rapid development of Internet of things nowadays, renewable and clean energy is becoming valuable and necessary. One renewable energy source, triboelectric nanogenerators (TENGs), has been proved to effectively harvest energy from vibration, walking, and even sphygmus. TENGs based on contact–separation mode are foremost and fundamental, and the input stimuli for TENGs are strictly limited by constant amplitude and uniaxial direction of motion, which limits many practical applications. Therefore, it is extremely meaningful to develop a kind of TENG to collect random energy, which ubiquitously exists in the ambient environment. Here, an elastic rubber is placed inside the structure of an elastic TENG (E-TENG). With this novel structure, the output signals of the TENG are almost independent of the amplitude of the external stimuli due to the elasticity of rubber. Moreover, the E-TENG shows good performance on angle detection without the limitation of the direction of stimuli. It is believed that the E-TENG will contribute to developing next-generation flexible TENGs and self-powered sensors in the future.
Original languageEnglish (US)
Pages (from-to)1900075
JournalAdvanced Materials Technologies
Volume4
Issue number7
DOIs
StatePublished - Apr 1 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-07
Acknowledgements: Y.L.C. and Y.Z. contributed equally to this work. The authors acknowledge support from King Abdullah University of Science and Technology (KAUST), National Natural Science Foundation of China (NSFC No. 51576161), and 111 project (B16038). Y.L.C. thanks China Scholarship Council for supplying oversea scholarship (201706340019). The authors thank Prof. Y.-C. Wang for the discussion and paper revisions.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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