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  5. Thermal Insulating Textile Based Triboelectric Nanogenerator for Outdoor Wearable Sensing and Interaction

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Preprint
en
2023

Thermal Insulating Textile Based Triboelectric Nanogenerator for Outdoor Wearable Sensing and Interaction

0 Datasets

0 Files

en
2023
DOI: 10.2139/ssrn.4576207

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Haishuang Jiao
Xiangde Lin
Yao Xiong
+7 more

Abstract

People have shown increasing interests in outdoor activities in recent years. However, the development of smart outdoor clothing for sophisticated health monitoring and multifunctional interaction remains challenging. Here, we develop a thermal-insulating textile based TENG (TI-TENG) composed of multiple functional layers, including the crucial triboelectrification layer, Ag-coated Nylon electrodes, windproof outer textile, and inner textile lining. The triboelectric output properties of the TI-TENG can be enhanced by material modification and structural optimization. The prepared TI-TENG is demonstrated with multifunctionality (e.g., thermal insulation and antibacterial properties) and intelligences (e.g., sensing and energy harvesting function). In addition, an intelligent human motion monitoring as well as an outdoor wireless signal transmission system are developed by employing the TI-TENGs as self-powered wearable sensors and human-machine interactive interfaces, which are ready for one-button call for emergency rescue during the outdoor activities. The available large-scale production of TI-textile also promise TENG with great commercial potentials, offering a new research direction for multifunctional wearable devices toward self-powered sensing, energy harvesting, and human-machine interaction.

How to cite this publication

Haishuang Jiao, Xiangde Lin, Yao Xiong, Jing Han, Yang Liu, Jiahong Yang, Shishuo Wu, Tao Jiang, Zhong Lin Wang, Qijun Sun (2023). Thermal Insulating Textile Based Triboelectric Nanogenerator for Outdoor Wearable Sensing and Interaction. , DOI: https://doi.org/10.2139/ssrn.4576207.

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Publication Details

Type

Preprint

Year

2023

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.2139/ssrn.4576207

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