Hydrophobic PU fabric with synergistic conductive networks for boosted high sensitivity, wide linear-range wearable strain sensor

Strain sensing fabrics are able to sense the deformation of the outside world, bringing more accurate and real-time monitoring and feedback to users. However, due to the lack of clear sensing mechanism for high sensitivity and high linearity carbon matrix composites, the preparation of high performance strain sensing fabric weaving is still a major challenge. Here, an elastic polyurethane (PU)-based conductive fabric (GCPU) with high sensitivity, high linearity and good hydrophobicity is prepared by a novel synergistic conductive network strategy. The GCPU fabric consists of graphene sheets (GS)/carbon nanotubes (CNTs) elastic conductive layer and a PU elastic substrate. GS and CNTs can be constructed into a synergistic conductive network, and the fabric is endowed with high conductivity (1.193 S m-1). Simulated equivalent circuits show that GS in the conductive network will break violently under applied strain, making the GCPU fabric extremely sensitive (gauge factor 102). CNTs are spatially distributed in GS lamellae, avoiding the phenomenon that the constructed synergistic conductive network is violently fractured under the applied strain, which leads to the decrease of linearity (0.996). Styrene-ethylene-butylene-styrene (SEBS) was used as a dispersant and binder to uniformly disperse and closely bond GS and CNTs into PU fabrics. In addition, the hydrophobicity of SEBS makes the GCPU fabric resistant to water environment (The contact angle is 123 degrees). Due to the good mechanical stability of GCPU fabric, GCPU fabric has a wide strain range (0%-50%) and high cycle stability (over 1000 cycles). In practice, GCPU fabric can accurately simulate and detect the size and deformation motion of human body. Therefore, the successful construction of elastic fabrics with synergistic conductive networks provides a feasible path for the design and manufacture of wearable intelligent fabrics.