Protein-Based Shape Memory Polymer Metamaterials with Strain-Induced Remodeling

Mechanical deformation of a polymer network is transferred from the macroscale to nanoscale to cause molecular-level motions and bond scission that ultimately lead to material failure. Understanding how to mitigate polymer disentanglement and bond scission is a significant challenge, especially in the development of active and shape-morphing materials. We report the additive manufacturing of hierarchically designed mechanical metamaterial lattices made with a protein-based polymer network that undergoes a unique strain learning behavior that combines mechanical remodeling with shape memory. At the molecular level, protein mechanophores unfold in the presence of a mechanical force to release its “stored length”, thereby stiffening in the direction of applied load after undergoing a healing cycle. Incomplete refolding of proteins during shape recovery affords a network with enhanced stiffness. At the macroscale, architected lattices distribute stress across a 3D printed structure to mitigate damage and enable complete shape recovery, and the efficiency of this process varies with the lattice architecture. The combined hierarchical responses cause a mechano-activated remodeling of folded proteins in the network to afford up to a 2 to 3-fold improvement in the mechanical properties. These bio-inspired materials offer a unique opportunity to develop novel materials that can autonomously remodel under an arbitrary applied load.