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  5. Triboelectric Nanogenerator as a Self-Powered Communication Unit for Processing and Transmitting Information

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Article
en
2016

Triboelectric Nanogenerator as a Self-Powered Communication Unit for Processing and Transmitting Information

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en
2016
Vol 10 (4)
Vol. 10
DOI: 10.1021/acsnano.5b07407

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Aifang Yu
Xiangyu Chen
Rui Wang
+11 more

Abstract

In this paper, we demonstrate an application of a triboelectric nanogenerator (TENG) as a self-powered communication unit. An elaborately designed TENG is used to translate a series of environmental triggering signals into binary digital signals and drives an electronic-optical device to transmit binary digital data in real-time without an external power supply. The elaborately designed TENG is built in a membrane structure that can effectively drive the electronic-optical device in a bandwidth from 1.30 to 1.65 kHz. Two typical communication modes (amplitude-shift keying and frequency-shift keying) are realized through the resonant response of TENG to different frequencies, and two digital signals, i.e., "1001" and "0110", are successfully transmitted and received through this system, respectively. Hence, in this study, a simple but efficient method for directly transmitting ambient vibration to the receiver as a digital signal is established using an elaborately designed TENG and an optical communication technique. This type of the communication system, as well as the implementation method presented, exhibits great potential for applications in the smart city, smart home, password authentication, and so on.

How to cite this publication

Aifang Yu, Xiangyu Chen, Rui Wang, Jingyu Liu, Jianjun Luo, Libo Chen, Yang Zhang, Wei Wu, Caihong Liu, Hongtao Yuan, Mingzeng Peng, Weiguo Hu, Junyi Zhai, Zhong Lin Wang (2016). Triboelectric Nanogenerator as a Self-Powered Communication Unit for Processing and Transmitting Information. , 10(4), DOI: https://doi.org/10.1021/acsnano.5b07407.

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Publication Details

Type

Article

Year

2016

Authors

14

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.5b07407

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