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

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 CaBr 2 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/m 3 toughness, and 16.02 kJ/m 2 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. Download: Download high-res image (118KB) Download: Download full-size image