Three-Dimensional Binary-Conductive-Network Silver Nanowires@Thiolated Graphene Foam-Based Room-Temperature Self-Healable Strain Sensor for Human Motion Detection.

A lot of attention has recently been focused on wearable strain sensors because of their promising applications in the rising areas of human motion detection, health monitoring, and smart human-machine interaction. However, the design and fabrication of self-healable strain sensors with superior overall properties including stretchability, sensitivity, response ability, stability, and durability is still a huge challenge. Herein, we report an innovative self-healable strain sensor with exceptional overall performance constructed with three-dimensional binary-conductive-network silver nanowire-coated thiolated graphene foam (AgNWs@TGF) and room-temperature self-healing functionalized polyurethane (FPU) elastomer. Taking advantage of the good ductility and continuity of the AgNWs@TGF binary structure and the excellent resilience of the FPU, the strain sensor exhibits good stretchability (up to 60% strain), high sensitivity [gauge factor (GF) of 11.8 at 60% strain and detection limit of 0.1% strain], fast response ability (response/recovery time of 40/84 ms), and exceptional durability for 800 cycles of fatigue test. Besides, the highly flexible polydimethylsiloxane chains, strong intermolecular hydrogen bonding, and dynamic exchange reaction of aromatic disulfides ensure the sensor excellent recovery property of electrical conductivity, and the GF of sensor after self-healed only increases slightly. More importantly, the sensor is successfully applied for detecting a variety of human motions including pulse beats, voice recognitions, various joint movements, and handwriting. The method for preparing room-temperature self-healable strain sensor is facile, scalable, and cost-effective. The finds provide a new perspective on fabricating new-generation high-performance and functional strain sensors for health monitoring, wearable electronics, and intelligent robots.