High-performance MXene/CuNWs/MH-TPU piezoresistive strain sensors fabricated via template-assisted phase separation with integrated thermal-EMI dual protection for emergency rescue.

In recent years, complex and extreme fire environments have posed a severe threat to the safety of front-line firefighters, urgently requiring high-performance flexible strain sensors for real-time, accurate monitoring of their physiological and motion states. A one-step template-assisted solvent-induced phase separation (SIPS) process was innovatively developed to simultaneously construct a near-zero Poisson's ratio (NZPR) macroscopic structure, arrayed domed microstructures for crack regulation, and interconnected porous networks for efficient stress dissipation. By combining 45 wt% magnesium hydroxide (MH) flame-retardant modification of the TPU substrate with an SDS-mediated MXene/CuNWs bridging conductive network at a mass ratio of 5 : 4, an MH-TPU/MXene/CuNWs/TPU (MH-TMCT) flexible strain sensor was successfully fabricated, which exhibits high electrical conductivity and effectively delays oxidative degradation. The sensor integrates a wide strain range (0-85%) and high sensitivity (GFmax = 518.0), maintains excellent stability over 4000 stretching cycles, and exhibits outstanding thermal protection as well as electromagnetic interference shielding properties with an average electromagnetic interference shielding effectiveness of 51.72 dB in the X-band, which effectively resists sensing signal interference from on-site fire rescue communication equipment. Additionally, the MH-TMCT sensor shows an average infrared thermal radiation reflectivity of 76.3% over the 250-2000 nm range to reduce external thermal radiation absorption and a low matrix thermal conductivity of 0.1662 W m-1 K-1 to significantly block heat conduction. The as-fabricated MH-TMCT sensor enables accurate identification of abnormal physiological and motion states of firefighters. The MH-TMCT sensor realizes real-time graded early warning of fire safety risks when integrated with machine learning algorithms and a wireless transmission system, demonstrating great application prospects in the field of intelligent fire protection and emergency rescue equipment.