Metal-ion coordination-regulated TA@rGO dual-network hydrogels for synergistic mechanical-electrical enhancement and machine-learning-assisted motion recognition

Metal-ion coordination jointly regulates hydrogel and TA@rGO networks. • PTGS-Fe 3+ reaches 1.10 MPa tensile and 3.33 MPa compressive strength. • Conductivity increases to 17.8 S m −1 despite stronger crosslinking. • Machine learning recognizes hydrogel states and nine human motions. The development of flexible sensors requires hydrogel materials with high mechanical strength, electrical conductivity, and multifunctional adaptability. However, achieving simultaneous enhancement of mechanical and electrical properties remains challenging due to the trade-off between network reinforcement and charge transport. Herein, a modular conductive hydrogel strategy based on metal-ion coordination regulation is proposed. A tannic acid-reduced graphene oxide (TA@rGO) conductive filler is incorporated into a polyacrylamide/sodium alginate (PAM/SA) double-network hydrogel, and the resulting PAM/TA@rGO/SA hydrogel is denoted as PTGS. Subsequently, dynamic coordination interactions between Cu 2+ /Fe 3+ ions and oxygen-containing groups of SA, as well as polyphenolic sites of TA, are utilized to prepare PTGS-Cu 2+ and PTGS-Fe 3+ hydrogels with tunable properties. Metal-ion coordination significantly improves the mechanical performance, with PTGS-Fe 3+ achieving tensile and compressive strengths of 1100 kPa and 3330 kPa, respectively. Unlike conventional ion-conducting hydrogels, where enhanced crosslinking generally suppresses conductivity, TA@rGO-mediated electronic pathways allow metal-ion coordination to simultaneously improve conductivity, reaching 17.8 S m −1 for PTGS-Fe 3+ . Moreover, different coordination states endow the hydrogels with distinct sensing characteristics, including wide-range strain detection, balanced sensitivity and detection range, and high sensitivity at low strain. Combined with machine learning algorithms, the hydrogels enable material-state recognition and accurate classification of diverse human motions. This work provides a modular strategy for designing multifunctional conductive hydrogels with tunable sensing capabilities.