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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. 

Abstract Stretchable conductive materials have attracted great attention due to their potential applications as strain sensors, wearable electronics, soft robotics, and medical devices. The fabrication of these materials with customized object geometries is desirable, but the methods to achieve them are still highly limited. Additive manufacturing via vat photopolymerization can generate sophisticated object geometries, but there is still a significant need to print with materials that afford improved conductivity, mechanical properties, elastic recovery, and durability. Herein, stretchable strain sensors with a range of 3D printed designs are reported using vat photopolymerization. Ionic liquid resins are optimized for their printability using Sudan‐I as a photoabsorber and used to fabricate 3D objects that are subjected to compression, stretching, and bending loads that are detected as real‐time changes in current. Additionally, the self‐adhesive nature of these materials enables mechanically damaged structures to be mended together to regain its function as a strain sensor. These ionic liquid resins are compatible with commercial 3D printers, which enhances their applicability for on‐demand production of customized devices. 

Abstract 4D printing is the 3D printing of objects that change chemically or physically in response to an external stimulus over time. Photothermally responsive shape memory materials are attractive for their ability to undergo remote activation. While photothermal methods using gold nanorods (AuNRs) are used for shape recovery, 3D patterning of these materials into objects with complex geometries using degradable materials is not addressed. Here, the fabrication of 3D printed shape memory bioplastics with photo‐activated shape recovery is reported. Protein‐based nanocomposites based on bovine serum albumin (BSA), poly (ethylene glycol) diacrylate (PEGDA), and AuNRs are developed for vat photopolymerization. These 3D printed bioplastics are mechanically deformed under high loads, and the proteins served as mechano‐active elements that unfolded in an energy‐dissipating mechanism that prevented fracture of the thermoset. The bioplastic object maintained its metastable shape‐programmed state under ambient conditions. Subsequently, up to 99% shape recovery is achieved within 1 min of irradiation with near‐infrared (NIR) light. Mechanical characterization and small angle X‐ray scattering (SAXS) analysis suggest that the proteins mechanically unfold during the shape programming step and may refold during shape recovery. These composites are promising materials for the fabrication of biodegradable shape‐morphing devices for robotics and medicine.