Engineering Thermoresponsive Biointerfaces Using Graft-To Strategies: Connecting Polymer Architecture to...

Temperature-responsive substrates provide a promising strategy for addressing issues in biomaterials design, but their utility hinges on achieving predictable, tunable surface responses through deliberate thermoresponsive polymer design. Here, we investigate how varying polymer architecture, namely, surface attachment point density and chain length, influences wettability above and below the lower critical solution temperature (LCST) and the magnitude of temperature-driven contact angle change of polymer-grafted glass surfaces. Using random copolymers of thermoresponsive di-(ethylene glycol) methyl ether methacrylate (DEGMA) units and surface attachment point aminoethyl methacrylate (AEMA) units, we show that increasing polymer attachment point density from 2 to 30 mol % while holding the degree of polymerization constant decreases the magnitude of the temperature-driven contact angle change from 15.2 ± 0.5 to 5.7 ± 0.03°. This suggests that higher attachment point densities produce compact polymer loop structures on the surface that constrain thermoresponsive units and hinder hydration below the LCST. In contrast, increasing the degree of polymerization from 49 to 350 at a constant attachment point density increases the magnitude of the contact angle shift from 7.4 ± 4.4 to 22 ± 2.2°, likely attributed to enhanced chain mobility with increasing chain length. Notably, all surfaces converge to approximately the same contact angle above the LCST, regardless of polymer architecture, likely owing to the formation of similarly collapsed, dehydrated polymer layers that present comparable surface compositions to the interface. This work demonstrates that polymer architectures that allow greater chain mobility, achieved through lower attachment point density or longer chain length, produce larger thermal surface responses below the LCST, driven by enhanced hydration. By linking polymer loop structure and chain mobility to surface behavior, these insights provide a framework for designing biointerfaces with predictable and tunable properties.