Sodium alginate-based environmentally stable tough ionogel sensors integrated with machine learning for high-accuracy...

Gel materials with exceptional mechanical property, environmental stability, and reliable sensing performance are highly desirable for intelligent human-machine interfaces. However, conventional hydrogel systems generally prone to fail under load-bearing applications due to their poor mechanics, progressive dehydration, and freezing-induced performance deterioration. Herein, we report a mechanically tough ionogel with remarkable environmental stability via a facile solvent-replacement strategy: incorporating a non-volatile halometallate ionic liquid composed of CaBr₂ and 1-butyl-3-methylimidazolium bromide ([BMIM]Br) into a polyacrylamide/sodium alginate (PAAm/SA) hydrogel network. Synergistic coordination and ion-dipole interactions reconstructed the network structure and solvent-polymer microenvironment, facilitating efficient energy dissipation. Consequently, the optimized ionogel exhibited remarkable environmental stability and mechanical properties, including 2860% stretchability, 0.68 MPa tensile strength, 9.46 MJ/m³ toughness, and 16.02 kJ/m² fracture energy. Moreover, the ionogel demonstrated stable electromechanical response across a broad strain range (1-500%). Benefiting from these merits, the ionogel-based sensor enabled accurate monitoring of complex human motions and achieved 99.45% recognition accuracy for eight static gestures when integrated with a machine learning algorithm. Collectively, this work provides a facile strategy for constructing environmentally stable yet mechanic tough ionogels through coordination-ionic co-regulation paradigm, offering a viable pathway towards reliable and field-deployable gel systems for wearable electronics.