Inspired by adipose tissue, mechanically robust and reprocessable LM-based composites for ultra-sensitive flexible pressure sensors

Abstract

The self-healing conductive elastomers can self-healing when subjected to external damage, making them popular candidate for flexible electronic device materials. However, their insufficient mechanical and self-healing properties limit practical applications. Therefore, it is urgent to develop self-healing conductive elastomer materials with mechanical stability. In this word, inspired by the two-phase structure of adipose tissue, a self-healing elastomer (SBS-TA-7%) with excellent mechanical properties was prepared by simple grafting modification of styrene-butadiene-styrene block copolymer (SBS). The synergistic effect of hydrogen bonding, imine bonding and π-π stacking enhances the interactions of the polymer network, enabling rapid repair after damage and improving the self-healing ability and mechanical properties of the material. The SBS-TA-7% elastomers exhibit excellent self-healing capabilities, reaching a self-healing efficiency of 97.38 %. Compared to unmodified SBS, the strength and toughness of SBS-TA-7% were increased by 42 times (4.19 MPa) and 13.5 times (20.65 MJ/m³), respectively. On this basis, self-healing conductive elastomers (SBS-TA-7%/LM) with excellent mechanical and conductive properties were further prepared by introducing liquid metal (LM) into SBS-TA-7%. The SBS-TA-7%/LM based self-healing pressure sensors offer high response values (536), fast response/recovery speeds (60/130 ms) and low pressure detection (0.03 kPa).The SBS-TA-7%/LM-based pressure sensing array monitors the pressure distribution of objects and plantar pressure distribution in different postures. The 8 different walking postures were identified by machine learning, and the recognition rate was as high as 98.1 %. This study provides valuable insights into the design of self-healing elastomers and expands the application field of self-healing conductive elastomers in green pressure sensors.