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Low-cost, environmentally friendly and high-performance cellulose-based triboelectric nanogenerator for self-powered human motion monitoring

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Abstract

Cellulose-based triboelectric nanogenerators (TENGs) can provide power for various monitoring devices and are environmentally friendly and sustainable. Chemical functional modification is a common method to improve the electrical output performance of cellulose-based TENGs. In this work, an environmentally friendly high-performance triboelectric nanogenerator based on a polydopamine/cellulose nanofibril (PDA/CNF) composite membrane and fluorinated ethylene propylene was developed. Dopamine generates polydopamine nanoparticles through oxidative self-polymerization and adheres to the surface of nanofibers. The synergistic effect of amino group introduction and membrane surface microstructure effectively enhanced the output performance of TENGs to a certain extent. The effects of PDA content, CNF composite film thickness and different working conditions on the electrical output were systematically investigated. The optimized PDA/CNF-TENGs exhibited an enhanced electrical output performance with voltage, current, and power density values of ≈205 V, ≈20 µA, and ≈48.75 μW·cm−2, respectively. The PDA/CNF-TENGs exhibited stable and identifiable signals when used as a self-powered sensor for human motion monitoring, showing the potential prospects of cellulose materials for TENGS and other electronic applications.

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Acknowledgments

We thank the Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control for providing the technology and financial support. The authors would like to thank shiyanjia lab for the support of XPS analysis.

Funding

This project was supported by the Guangxi Natural Science Foundation of China (2018GXNSFAA281336) and Guangxi Ba-Gui Scholars Program2019A33.

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Correspondence to Lianxin Luo.

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Zhu, Q., Wang, T., Wei, Y. et al. Low-cost, environmentally friendly and high-performance cellulose-based triboelectric nanogenerator for self-powered human motion monitoring. Cellulose 29, 8733–8747 (2022). https://doi.org/10.1007/s10570-022-04800-6

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