Facebook Reality Labs, Redmond, WA, USA, 98052.
Department of Materials Science and Engineering, Cornell University, Ithaca, NY, 14850, USA.
Nat Commun. 2020 Aug 10;11(1):4000. doi: 10.1038/s41467-020-17816-y.
Additive manufacturing permits innovative soft device architectures with micron resolution. The processing requirements, however, restrict the available materials, and joining chemically dissimilar components remains a challenge. Here we report silicone double networks (SilDNs) that participate in orthogonal crosslinking mechanisms-photocurable thiol-ene reactions and condensation reactions-to exercise independent control over both the shape forming process (3D printing) and final mechanical properties. SilDNs simultaneously possess low elastic modulus (E < 700kPa) as well as large ultimate strains (dL/L up to ~ 400 %), toughnesses (U ~ 1.4 MJ·m), and strengths (σ ~ 1 MPa). Importantly, the latent condensation reaction permits cohesive bonding of printed objects to dissimilar substrates with modulus gradients that span more than seven orders of magnitude. We demonstrate soft devices relevant to a broad range of disciplines: models that simulate the geometries and mechanical properties of soft tissue systems and multimaterial assemblies for next generation wearable devices and robotics.
增材制造允许具有微米分辨率的创新软设备架构。然而，加工要求限制了可用材料的选择，并且化学性质不同的组件的连接仍然是一个挑战。在这里，我们报告了硅酮双网络（SilDN），它们参与正交交联机制 - 光固化硫醇 - 烯反应和缩合反应 - 对形状形成过程（3D 打印）和最终机械性能进行独立控制。SilDN 同时具有低弹性模量（E < 700kPa）和大极限应变（dL/L 高达400%）、韧性（U1.4 MJ·m）和强度（σ~1 MPa）。重要的是，潜伏缩合反应允许打印物体与具有跨越七个数量级以上的模量梯度的不同基底进行内聚粘合。我们展示了与广泛学科相关的软设备：模拟软组织系统的几何形状和机械性能的模型，以及下一代可穿戴设备和机器人的多材料组件。
