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  5. Grating‐Structured Freestanding Triboelectric Nanogenerator for Self‐Powered Acceleration Sensing in Real Time

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Article
en
2022

Grating‐Structured Freestanding Triboelectric Nanogenerator for Self‐Powered Acceleration Sensing in Real Time

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en
2022
Vol 8 (1)
Vol. 8
DOI: 10.1002/admt.202200746

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Yufeng Liu
Ding Li
Yu Hou
+1 more

Abstract

Abstract Acceleration sensors have wide applications in earthquake warning, human motion recognition, vehicle restraint system, etc. However, the existing commercial acceleration sensors have some limitations on needing an external power supply, high fabrication cost, and small signal when self‐powered. Here a grating‐structured freestanding triboelectric nanogenerator (GF‐TENG) capable of sensing displacements, velocities, and accelerations in real‐time is presented with self‐powered, low cost, and sufficiently large signal. The slider with grating‐structured electrodes of GF‐TENG sliding over the stator with another grating‐structured electrode generates the periodic open‐circuit voltage due to electrostatic induction. By recognizing the shape of open‐circuit voltage, it could sense the acceleration in real time even at scales down to hundred microns through systematic optimization of simulations and experiments. Furthermore, the acceleration sensing range could be expanded to the desired ranges, such as 5.0 to 45.0 m s –2 , by assembling springs and GF‐TENG into a grating‐structured TENG‐based acceleration sensor (GTAS). Moreover, GTAS is demonstrated to sense the vehicle motion and be a part of the vehicle restraint system on a model car. This work reports a new self‐powered acceleration sensor with sufficiently large outputs for real‐time motion sensing and collision detection, which could be further applicated for robotics and human motion recognition.

How to cite this publication

Yufeng Liu, Ding Li, Yu Hou, Zhong Lin Wang (2022). Grating‐Structured Freestanding Triboelectric Nanogenerator for Self‐Powered Acceleration Sensing in Real Time. , 8(1), DOI: https://doi.org/10.1002/admt.202200746.

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Publication Details

Type

Article

Year

2022

Authors

4

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/admt.202200746

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