Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics

Jianjun Luo, Ziming Wang, Liang Xu, Aurelia Chi Wang, Kai Han, Tao Jiang, Qingsong Lai, Yu Bai, Wei Tang, Feng Ru Fan () and Zhong Lin Wang ()
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Jianjun Luo: Chinese Academy of Sciences
Ziming Wang: Chinese Academy of Sciences
Liang Xu: Chinese Academy of Sciences
Aurelia Chi Wang: Georgia Institute of Technology
Kai Han: Chinese Academy of Sciences
Tao Jiang: Chinese Academy of Sciences
Qingsong Lai: Chinese Academy of Sciences
Yu Bai: Chinese Academy of Sciences
Wei Tang: Chinese Academy of Sciences
Feng Ru Fan: Purdue University, West Lafayette
Zhong Lin Wang: Chinese Academy of Sciences

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self-powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.

Date: 2019
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DOI: 10.1038/s41467-019-13166-6

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