Strain learning in protein-based mechanical metamaterials

材料科学 超材料 聚合物 复合材料 刚度 变形(气象学) 拉伤 纳米技术 光电子学 医学 内科学
作者
Naroa Sadaba,Eva Sanchez‐Rexach,Curt Waltmann,Shayna L. Hilburg,Lilo D. Pozzo,Mónica Olvera de la Cruz,Haritz Sardón,Lucas R. Meza,Alshakim Nelson
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [Proceedings of the National Academy of Sciences]
卷期号:121 (45)
标识
DOI:10.1073/pnas.2407929121
摘要

Mechanical deformation of polymer networks causes molecular-level motion and bond scission that ultimately lead to material failure. Mitigating this strain-induced loss in mechanical integrity is a significant challenge, especially in the development of active and shape-memory materials. We report the additive manufacturing of mechanical metamaterials made with a protein-based polymer that undergo a unique stiffening and strengthening behavior after shape recovery cycles. We utilize a bovine serum albumin-based polymer and show that cyclic tension and recovery experiments on the neat resin lead to a ~60% increase in the strength and stiffness of the material. This is attributed to the release of stored length in the protein mechanophores during plastic deformation that is preserved after the recovery cycle, thereby leading to a “strain learning” behavior. We perform compression experiments on three-dimensionally printed lattice metamaterials made from this protein-based polymer and find that, in certain lattices, the strain learning effect is not only preserved but amplified, causing up to a 2.5× increase in the stiffness of the recovered metamaterial. These protein–polymer strain learning metamaterials offer a unique platform for materials that can autonomously remodel after being deformed, mimicking the remodeling processes that occur in natural materials.
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