Water‐Rich Biomimetic Composites with Abiotic Self‐Organizing Nanofiber Network

Load‐bearing soft tissues, e.g., cartilage, ligaments, and blood vessels, are made predominantly from water (65–90%) which is essential for nutrient transport to cells. Yet, they display amazing stiffness, toughness, strength, and deformability attributed to the reconfigurable 3D network from stiff collagen nanofibers and flexible proteoglycans. Existing hydrogels and composites partially achieve some of the mechanical properties of natural soft tissues, but at the expense of water content. Concurrently, water‐rich biomedical polymers are elastic but weak. Here, biomimetic composites from aramid nanofibers interlaced with poly(vinyl alcohol), with water contents of as high as 70–92%, are reported. With tensile moduli of ≈9.1 MPa, ultimate tensile strains of ≈325%, compressive strengths of ≈26 MPa, and fracture toughness of as high as ≈9200 J m−2, their mechanical properties match or exceed those of prototype tissues, e.g., cartilage. Furthermore, with reconfigurable, noncovalent interactions at nanomaterial interfaces, the composite nanofiber network can adapt itself under stress, enabling abiotic soft tissue with multiscale self‐organization for effective load bearing and energy dissipation.Water‐rich biomimetic composites from aramid nanofibers interlaced with poly(vinyl alcohol) emulate the collagen–proteoglycan network in load‐bearing soft tissues. The hydrogen bonding between stiff nanofibers and soft polymers affords synergistic stiffening and toughening, allowing the nanofiber network to self‐organize under stress for effective load bearing and energy dissipation. Their mechanics, biocompatibility, and high water content permit utilization as load‐bearing biomaterials and for other applications including durable high‐transport‐rate membranes, membranes in water desalination, fuel cells, and batteries.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141174/1/adma201703343-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141174/2/adma201703343_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141174/3/adma201703343.pd