| Literature DB >> 31772189 |
Jianjun Luo1,2, Ziming Wang1,2, Liang Xu1,2, Aurelia Chi Wang3, Kai Han1,2, Tao Jiang1,2, Qingsong Lai1,2, Yu Bai1,2, Wei Tang1,2, Feng Ru Fan4, Zhong Lin Wang5,6,7.
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.Entities:
Mesh:
Year: 2019 PMID: 31772189 PMCID: PMC6879608 DOI: 10.1038/s41467-019-13166-6
Source DB: PubMed Journal: Nat Commun ISSN: 2041-1723 Impact factor: 14.919
Fig. 1Fabrication and schematic of the flexible wood-based TENG and smart ping-pong table. a Diagram of the process for fabricating a flexible W-TENG. b Schematic illustration of the natural wood-based smart ping-pong table. c Scanning electron microscopy (SEM) image of the treated wood surface. Scale bar, 20 μm. d Photograph of the as-prepared W-TENG devices, demonstrating its excellent flexibility. Scale bar, 2 cm. e Weight of the W-TENG device. Scale bar, 3 cm
Fig. 2Evolution of natural balsa wood upon chemical treatment and hot-pressing. a Graphical illustration of the structural evolution of natural wood upon chemical treatment. b, c Cross-sectional SEM images of the natural wood and treated wood. Scale bar, 100 μm. d FTIR spectra of the natural wood and treated wood. e Tensile stress-strain curves of the natural wood and treated wood (before and after bending 1000 cycles). f Comparison of flexibility between natural wood and treated wood. (i) Natural wood film breaks upon bending. (ii) Treated wood shows high flexibility upon bending. Scale bar, 1 cm. g Coefficient of friction evolution of natural wood and treated wood. h Interferometer images of wear traces on natural wood and treated wood after 5000 cycles of rubbing, showing notable decrease of wear depth of the treated wood. Scale bar, 500 μm. i Compression thickness of the natural wood and treated wood after hot-pressing. Error bars indicate standard deviations for 5 sets of data points. j Interferometer image showing the surface topography of treated wood after hot-pressing using 600-grit sand paper as the mold
Fig. 3Working mechanism and output performance of W-TENG in single-electrode mode. a Schematics of the operating principle for the W-TENG. b Potential simulation by COMSOL to elucidate the working principle. c, d Open-circuit voltage, short-circuit current of the W-TENG based on treated wood. e Comparison of the output performance between W-TENGs based on natural wood and treated wood. f Dependence of the output voltage and peak power of the W-TENG based on treated wood on the resistance of external load. g Stability and robustness measurement of the W-TENG, where the transferred charge density was recorded for over 20,000 cycles at a frequency of 1 Hz
Fig. 4Application of the W-TENG in a self-powered falling point distribution statistical system. a Scheme diagram of the W-TENG based self-powered falling point distribution statistical system. b Demonstration of the self-powered falling point distribution statistical system. Scale bar, 6 cm. c Screenshot showing the real-time statistical result of the self-powered system. d The measured output voltage of the TENG under the impact of ping-pong balls with variable velocities. e The summarized relationship and linear fitting between output voltages and velocities. Error bars indicate standard deviations for five sets of data points. f Graphical illustration showing the moving path of the ping-pong ball above a 4 × 2 W-TES array. g Real-time output voltage signals when a ping-pong ball impacted the surface of a 4 × 2 W-TES array along a path: 1-A → 2-A → 3-B → 4-B. h 2D mapping figure showing the distribution of statistical percentage of the falling points
Fig. 5Application of the W-TENG in a self-powered edge ball judgement system. a Scheme diagram of the W-TENG based self-powered edge ball judgement system. b Photograph of two W-TENGs attached on the edge of the ping-pong table. Scale bar, 2 cm. c–e Demonstration of the self-powered edge ball judgement system at the moment of top edge ball appeared: c Photograph, d output signals of two W-TENGs, and e screenshot of the real-time judgement result showing a ping-pong ball impacted on the top edge of the table. Scale bar, 2 cm. f–h Demonstration of the self-powered edge ball judgement system at the moment of side edge ball appeared: f Photograph, g output signals of two W-TENGs, and h screenshot of the real-time judgement result showing a ping-pong ball impacted on the side edge of the table. Scale bar, 2 cm