Chiral Photonic Ionic Skin for Ultrafast, Hysteresis-Free Mechanosensing

摘要

Biological skins integrate optical and electrical signaling for dynamic environmental interactions, inspiring the development of artificial photonic ionic skins. However, existing synthetic systems often lack mechanical robustness, fast response, and dual-mode sensing capabilities of their natural counterparts. Here, we present a bioinspired photonic ionic skin that synergistically combines cellulose nanocrystal (CNC)-based chiral nanostructures with an ionically conductive network to achieve high-performance mechano-optical-electrical coupling. The system features an interpenetrating double-network architecture comprising a rigid chiral CNC framework embedded within a flexible hydrogel matrix, endowing the material with exceptional mechanical robustness and resilience. The resulting composite ionogel exhibits significantly enhanced strength, modulus, and toughness that are 2.8-fold, 2.6-fold, and 7.4-fold greater than those of the pristine hydrogels, respectively, while maintaining near-zero hysteresis and excellent cyclic stability. Additionally, dual-mode sensing capabilities have also been realized in the ionogel system through stress-dependent modulation of both its photonic bandgap and ionic conductivity. Mechanical deformation induces dynamic shifts in structural coloration across the visible to near-infrared spectrum while simultaneously altering the electrical properties. Notably, the integration of mechanical robustness and ultralow hysteresis endows the ionogel sensor with an ultrafast response/recovery time of 0.3/1.4 ms (corresponding to 520 Hz) and stable signal output under high-frequency stimulation, outperforming conventional sensing materials. Capitalizing on its high-frequency detection capability, the ionogel sensor demonstrates efficient texture classification and recognition when integrated with a machine learning model. This work establishes a robust platform for the development of next-generation wearable sensors, soft robotics, and human-machine interfaces that require skin-like multifunctionality.