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Scalable-Manufacturing Triboelectric Fabric Sensor for Pressure Distribution Mapping during Human Body Activities
Qingyun Tao1, Cong Zheng1, Ruihua Wang1
1Key Laboratory of Textile Science & Technology, Ministry of Education, Donghua University, Shanghai 201620, China.
Abstract:
The scalable manufacturing of all-textile triboelectric pressure sensors capable of high-sensitivity and crosstalk-free pressure distribution mapping remains challenging. This study develops a scalable triboelectric fabric pressure mapping sensor with high sensitivity and free crosstalk by directly interlacing polytetrafluoroethylene/stainless steel (PTFE-SS) braided yarns (negative materials) and the polyamide 66/stainless steel (PA66-SS) braided yarn (positive materials) into the plain derivative weave fabric on an industrialized loom, enabling scalable production. Crucially, the results demonstrate that reducing warp/weft densities significantly boosts electrical output. We further establish the first equivalent circuit model (FO-ECM) for the triboelectric woven fabric pressure mapping sensor, achieving a 0.978 fitting coefficient to predict output under varying weave densities, thereby enabling digital parametric design. Significantly, parameter optimization of warp/weft densities identified an optimal configuration that enhances electrical output, achieving high sensitivity (2.942 V·kPa-1, 0-1 kPa). Furthermore, the triboelectric woven fabric pressure sensor was extended to develop an interference-free triboelectric fabric sensor array (TFSA) through structural spacing design, eliminating crosstalk by elucidating the dominant role of fringe electric fields and quantitatively modeling their spatial decay, thereby enabling high-spatial-resolution pressure mapping. Finally, the distributed pressure mapping of typical human body activities demonstrated the practical performance of the developed triboelectric fabric sensor. This facile integrated approach─from scalable fabrication to interference-free arrays─advances real-time wearable self-powered health monitoring.
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