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<p>Flexible piezoresistive pressure sensors with high sensitivity, mechanical robustness, and wide detection range are critical for next-generation wearable and human–machine interface (HMI) applications. Conductive elastomer composite (CEC) materials have received a lot of attention because to their high sensitivity, wide operating range, and consistent output. Single conductive fillers placed in a polydimethylsiloxane (PDMS) elastomer matrix were shown to aggregate and/or agglomerate, necessitating a large filler quantity. In this study, a porous polydimethylsiloxane (pPDMS) composite incorporating conductive carbon black (CB) and semiconductive graphene oxide (GO) was developed to achieve tunable electromechanical performance. The hybridisation of CB and GO within the PDMS network establishes an efficient conductive architecture that combines the high electrical conductivity of CB with the interfacial modulation capability of GO. The semiconductive GO facilitates charge tunnelling and enhances contact resistance variation under compression, while CB provides continuous electron pathways, resulting in a synergistically improved piezoresistive response. The fabricated pPDMS + CB+GO sensor exhibited a dual-regime absolute sensitivity of 83.15 kΩ/kPa (0–3&#xa0;kPa) and normalised sensitivity of 0.35&#xa0;kPa⁻¹ (3–80&#xa0;kPa), along with rapid response and recovery times (2.9/2.1&#xa0;s) and excellent cyclic stability. The porous structure further enhances deformability and stress distribution, enabling reliable detection of both subtle physiological signals and large-scale body movements. This study presents a simple, cost-effective, and scalable strategy for engineering hybrid conductive–semiconductive networks in elastomeric matrices, offering a promising platform for high-performance, flexible pressure sensors in wearable health monitoring and interactive electronics. All measurements gave reliable data and were compared with literature data.</p> Graphical abstract <p></p>

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Fabrication and characterization of porous PDMS-graphene oxide conductive composite based resistance pressure sensor for human motion detection

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Vimala allam,
  • Krishnamoorti Chinthagumpala,
  • Suresh Kumar Vandrangi

摘要

Flexible piezoresistive pressure sensors with high sensitivity, mechanical robustness, and wide detection range are critical for next-generation wearable and human–machine interface (HMI) applications. Conductive elastomer composite (CEC) materials have received a lot of attention because to their high sensitivity, wide operating range, and consistent output. Single conductive fillers placed in a polydimethylsiloxane (PDMS) elastomer matrix were shown to aggregate and/or agglomerate, necessitating a large filler quantity. In this study, a porous polydimethylsiloxane (pPDMS) composite incorporating conductive carbon black (CB) and semiconductive graphene oxide (GO) was developed to achieve tunable electromechanical performance. The hybridisation of CB and GO within the PDMS network establishes an efficient conductive architecture that combines the high electrical conductivity of CB with the interfacial modulation capability of GO. The semiconductive GO facilitates charge tunnelling and enhances contact resistance variation under compression, while CB provides continuous electron pathways, resulting in a synergistically improved piezoresistive response. The fabricated pPDMS + CB+GO sensor exhibited a dual-regime absolute sensitivity of 83.15 kΩ/kPa (0–3 kPa) and normalised sensitivity of 0.35 kPa⁻¹ (3–80 kPa), along with rapid response and recovery times (2.9/2.1 s) and excellent cyclic stability. The porous structure further enhances deformability and stress distribution, enabling reliable detection of both subtle physiological signals and large-scale body movements. This study presents a simple, cost-effective, and scalable strategy for engineering hybrid conductive–semiconductive networks in elastomeric matrices, offering a promising platform for high-performance, flexible pressure sensors in wearable health monitoring and interactive electronics. All measurements gave reliable data and were compared with literature data.

Graphical abstract