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  5. Smart Floor with Integrated Triboelectric Nanogenerator As Energy Harvester and Motion Sensor

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Article
en
2017

Smart Floor with Integrated Triboelectric Nanogenerator As Energy Harvester and Motion Sensor

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en
2017
Vol 9 (31)
Vol. 9
DOI: 10.1021/acsami.7b08526

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Chuan He
Weijun Zhu
Baodong Chen
+6 more

Abstract

A smart floor is demonstrated by integrating a square-frame triboelectric nanogenerator (SF-TENG) into a standard wood floor. The smart floor has two working modes based on two pairs of triboelectric materials: one is purposely chosen polytetrafluoroethylene films and aluminum (Al) balls, and the other is the floor itself and the objects that can be triboelectrically charged, such as basketball, shoe soles, and Scotch tape, etc. Utilizing the Al balls enclosed inside shallow boxes, the smart floor is capable of harvesting vibrational energy and, hence, provides a nonintrusive way to detect sudden falls in elderly people. In addition, when the basketball is bounced repeatedly on the floor, the average output voltage and current are 364 ± 43 V and 9 ± 1 μA, respectively, and 87 serially connected light-emitting diodes can be lit up simultaneously. Furthermore, the friction between the triboelectrically chargeable objects and the floor can also induce an alternating current output in the external circuit without the vibration of the Al balls. Normal human footsteps on the floor produce a voltage of 238 ± 17 V and a current of 2.4 ± 0.3 μA. Therefore, this work presents a smart floor with built-in SF-TENG without compromising the flexibility and stability of the standard wood floor and also demonstrates a way to harvest ambient energy solely by using conventional triboelectric materials in our daily life.

How to cite this publication

Chuan He, Weijun Zhu, Baodong Chen, Liang Xu, Tao Jiang, Chang Bao Han, Guang Qin Gu, Dichen Li, Zhong Lin Wang (2017). Smart Floor with Integrated Triboelectric Nanogenerator As Energy Harvester and Motion Sensor. , 9(31), DOI: https://doi.org/10.1021/acsami.7b08526.

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Publication Details

Type

Article

Year

2017

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsami.7b08526

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