Robust Stitching Interface and Deep Learning Empowered Hydrogel Human-Machine Interface

Mechanical mismatch and weak interfacial adhesion between hydrogels and encapsulation layers, particularly polyethylene terephthalate (PET), remain the primary cause of signal distortion in dynamic sensing applications. We address this through a molecular design strategy that exploits synergistic carboxylate anion-quaternary ammonium interactions to simultaneously strengthen the hydrogel-PET interface and the bulk hydrogel network. The strategy operates through two coupled mechanisms. Fe^{3 +} ions bridge the -COO^- groups of sodium acrylate with the electronegative -C═O groups of PET, converting a mechanically mismatched contact into a chemically bonded interface that suppresses interfacial slippage under dynamic loading. Concurrently, electrostatic cross-linking within the polyacrylamide network reinforces bulk mechanical integrity. The resulting MA_2.5D₁GF hydrogel delivers a 9.2-fold increase in adhesion strength to PET and a 2.6-fold improvement in tensile stress at break relative to the unfunctionalized control-demonstrating that interfacial and bulk reinforcement are achieved within the same molecular architecture rather than as competing trade-offs. Integrated with deep learning signal processing, the stable hydrogel-PET interface enables consistent signal acquisition and reliable human-machine interaction under prolonged dynamic operation-establishing a molecularly programmable adhesion strategy with direct applicability to smart interface and wearable sensing platforms.