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Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
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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.

ACS Applied Materials & Interfaces
|September 17, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a scalable, high-sensitivity fabric pressure sensor using interlaced triboelectric yarns. Optimized weave densities and a novel circuit model enable precise, crosstalk-free pressure mapping for wearable health monitoring.

Keywords:
equivalent circuit model (FO-ECM)scalable productiontriboelectric fabric pressure sensorstriboelectric fabric sensing arrayswearable health monitoring

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Area of Science:

  • Materials Science
  • Textile Engineering
  • Sensor Technology

Background:

  • Scalable manufacturing of all-textile triboelectric pressure sensors for high-sensitivity and crosstalk-free pressure mapping is a significant challenge.
  • Existing methods often struggle with integration, scalability, and interference in sensor arrays.

Purpose of the Study:

  • To develop a scalable triboelectric fabric pressure mapping sensor with high sensitivity and no crosstalk.
  • To establish a predictive model for sensor output based on fabric parameters.
  • To demonstrate the practical application of the sensor in real-time health monitoring.

Main Methods:

  • Direct interlacing of polytetrafluoroethylene/stainless steel (PTFE-SS) and polyamide 66/stainless steel (PA66-SS) braided yarns into a plain derivative weave fabric on an industrialized loom.
  • Investigating the effect of warp/weft densities on electrical output and developing the first equivalent circuit model (FO-ECM) for prediction.
  • Designing a triboelectric fabric sensor array (TFSA) with structural spacing to eliminate crosstalk by analyzing fringe electric fields.

Main Results:

  • Reducing warp/weft densities significantly boosted electrical output, with optimized parameters achieving high sensitivity (2.942 V·kPa⁻¹, 0-1 kPa).
  • The FO-ECM achieved a 0.978 fitting coefficient for predicting output under varying weave densities, enabling digital parametric design.
  • The developed TFSA demonstrated interference-free, high-spatial-resolution pressure mapping, validated by monitoring human body activities.

Conclusions:

  • A facile, integrated approach enables scalable fabrication of high-performance triboelectric fabric pressure sensors.
  • The optimized sensor design and predictive modeling advance the development of wearable, self-powered health monitoring systems.
  • Elimination of crosstalk and high sensitivity pave the way for advanced, real-time pressure distribution mapping.