Bioinspired triple-synergistic enhancement and machine learning optimization for ultra-strong polyvinyl alcohol hydrogels

Hydrogel-based soft materials show broad prospects in soft robotics, flexible electronics, and artificial skin, but their relatively poor mechanical performance limits practical applications. Moreover, achieving both high extensibility and fracture strength in hydrogels remains challenging. To tackle this issue, this study innovatively integrates a triple synergistic enhancement strategy with data-driven optimization to develop ultra-strong polyvinyl alcohol hydrogels. The synergistic sequential strategy draws inspiration from structural reinforcement principles commonly observed in natural load-bearing systems. It includes: directional freezing to induce an anisotropic, reinforced framework through oriented physical crosslinking; mechanical training in a glycerol-water solvent to dynamically reorganize weak intermolecular interactions and strengthen the preformed framework, similar to muscle strengthening; and salting out, a process inspired by the solidification of spider silk, which further densifies hydrogen-bond networks and locks the reinforced structure formed in the previous steps. In addition, a Bayesian optimization framework integrating random forest models is used to map preparation parameters to mechanical properties, enabling rapid prediction of optimal conditions. The resulting hydrogels exhibit exceptional performance: tensile strength 14.59 MPa, Young's modulus 1.77 MPa, elongation at break 1419%, and toughness 102.74 MJ/m 3 . This work advances hydrogel mechanical regulation via bioinspired structural reinforcement, synergistic reinforcement, and data-driven optimization, supporting applications in cartilage repair and artificial skin. • A biomimetic triple-synergistic strategy constructs robust anisotropic hydrogel architecture. • The hydrogels exhibit exceptional mechanical properties both in strength and toughness. • Data-drive Bayesian optimization efficiently identifies optimal hydrogel preparation conditions.