Engineering periodic dynamic polymer microstructure to design artificial muscles and multilayered, self-healing electronics

Dynamic polymer networks exhibit robust and tunable mechanical properties (e.g., tough, self-healable, stimuli-responsive). In nature, these networks are hierarchically-ordered and assemble via cooperative interactions of many weak bonds as opposed to the independent association of a few strong bonds. Here, we use these principles to design linear polymers with periodically-placed dynamic bonds that assemble into supramolecular nanofibers. We show that when the overall molecular weight (M_n) is below the polymer’s critical entanglement molecular weight (M_c), self-assembly of supramolecular nanofibers occurs, delaying the onset of terminal flow by more than 100°C. We then design a shape memory polymer with high energy density based on the formation of strain-induced supramolecular nanostructures. While initially, polymer chains adopt an amorphous structure (M_n > M_c), during strain the polymers form nanostructures that trap the elongated backbones. Finally, we use these dynamic polymers to demonstrate selective self-healing between layers in a multilayered device. When initially misaligned, this selective self-healing enables autonomous re-alignment and functional recovery of the multilayer device. We model the interface between layers using SCFT and CGMD and fit these predictions directly to data. These examples show that periodic dynamic polymers are a promising platform for autonomous self-assembly, where key material properties are controlled through polymer molecular design.