Sewable coaxial dual-mode fibers for rapid identification in fire rescue

A smart textile integrating bimodal sensing with flame retardancy and thermal insulation for harsh condition servicing. • The thermal insulation barrier protects functional components, enabling bimodal signaling capability at high temperatures. • A machine learning-assisted rescue system with precise motion recognition and visual-assisted guidannce was developed. Multimodal sensors have attracted considerable attention due to their great potential in complex scenarios. However, achieving the synergistic integration of multimodal sensing in flexible intelligent textiles while which could maintain functional response under harsh conditions remains a formidable challenge. Herein, a fiber-scale integration strategy based on coaxial wet spinning is proposed to fabricate a bimodal core-sheath fiber (denoted as PLMOB) featuring integrated flame-retardant thermal insulation, long afterglow luminescence, and self-powered sensing. By constructing a synergistic thermal insulation barrier of hexagonal boron nitride (h-BN) and silicon dioxide (SiO 2 ) in the sheath, the thermal conductivity is reduced to 61.28 mW·m −1 ·K −1 . Owing to the thermal insulation effect and the response performances of functional contents, the fiber maintains stable conductivity and persistent blue afterglow emission (>1200 s). Through embroidery process, PLMOB fibers are integrated into firefighting garment, enabling visual guidance. Furthermore, we developed a machine learning-based system which is capable of realizing accurate recognition of limb movements (97% accuracy) and luminescence. This work offers a promising strategy for next-generation intelligent textiles in fire scenarios. Download: Download high-res image (400KB) Download: Download full-size image