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<p>Smart textiles are emerging as key enablers in wearable electronics, health monitoring, and flexible sensing systems, where electrical performance must be carefully engineered through material design. This study presents a comprehensive analysis of how varying concentrations of conductive nanomaterials affect the sheet resistance of cotton-based fabrics. Four materials—PEDOT: PSS, single-walled carbon nanotubes (SWCNTs), multi-walled carbon nanotubes (MWCNTs), and graphene—were investigated using a dual-model framework: second-degree polynomial regression for graphene, and log–log linear regression for the remaining materials. The results revealed material-specific threshold concentrations at which conductivity improves sharply: 1.97 wt% for SWCNTs, 2.83 wt% for MWCNTs, 3.20 wt% for PEDOT: PSS, and approximately 65.5 wt% for graphene. Log–log regression achieved strong predictive performance (R² = 0.903–0.946) for SWCNTs, MWCNTs, and PEDOT: PSS, while a second-degree model captured the complex non-linearity in graphene with R² = 0.932. At optimal concentrations, sheet resistance dropped as low as 0.034 Ω/sq for SWCNTs, 5.49 Ω/sq for PEDOT: PSS, 21.83 Ω/sq for MWCNTs, and 90.66 Ω/sq for graphene. Sensitivity analysis further showed PEDOT: PSS to be the most responsive to concentration changes (sensitivity coefficient = − 3.22), while MWCNTs provided more stable behavior (–1.52). These findings offer a novel, data-driven modeling approach to optimize the electrical properties of smart textiles. By combining threshold analysis, log–log regression, and sensitivity profiling across multiple nanomaterials, this work provides practical insights for material selection and predictive design in wearable electronics.</p>

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Optimizing electrical performance in smart textiles: modeling conductivity across nanomaterial-infused cotton fabrics

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Fahad Alhashmi Alamer,
  • Khalid Althagafy

摘要

Smart textiles are emerging as key enablers in wearable electronics, health monitoring, and flexible sensing systems, where electrical performance must be carefully engineered through material design. This study presents a comprehensive analysis of how varying concentrations of conductive nanomaterials affect the sheet resistance of cotton-based fabrics. Four materials—PEDOT: PSS, single-walled carbon nanotubes (SWCNTs), multi-walled carbon nanotubes (MWCNTs), and graphene—were investigated using a dual-model framework: second-degree polynomial regression for graphene, and log–log linear regression for the remaining materials. The results revealed material-specific threshold concentrations at which conductivity improves sharply: 1.97 wt% for SWCNTs, 2.83 wt% for MWCNTs, 3.20 wt% for PEDOT: PSS, and approximately 65.5 wt% for graphene. Log–log regression achieved strong predictive performance (R² = 0.903–0.946) for SWCNTs, MWCNTs, and PEDOT: PSS, while a second-degree model captured the complex non-linearity in graphene with R² = 0.932. At optimal concentrations, sheet resistance dropped as low as 0.034 Ω/sq for SWCNTs, 5.49 Ω/sq for PEDOT: PSS, 21.83 Ω/sq for MWCNTs, and 90.66 Ω/sq for graphene. Sensitivity analysis further showed PEDOT: PSS to be the most responsive to concentration changes (sensitivity coefficient = − 3.22), while MWCNTs provided more stable behavior (–1.52). These findings offer a novel, data-driven modeling approach to optimize the electrical properties of smart textiles. By combining threshold analysis, log–log regression, and sensitivity profiling across multiple nanomaterials, this work provides practical insights for material selection and predictive design in wearable electronics.