Computationally Driven Design of Soft Materials with Tissue-like Mechanical Properties

Abstract

Mimicking the mechanical properties of soft materials and biological tissues is crucial for novel materials development for medical implants, tissue engineering, soft robotics, and wearable electronics. Unfortunately, the required combination of softness, strength, and toughness is difficult to replicate in synthetic materials. Modern design strategies are predominantly Edisonian in nature and are based on exploratory mixing of assorted polymers, variation in cross-linking schemes, and solvents. However, it was recently demonstrated that it is possible to encode mechanical properties of soft tissues in solvent free synthetic elastomers by varying architecture of the network strands. This approach is based on the theoretical and computational studies of correlations between mechanical properties and architecture of networks with brush-like strands. Different types of graft polymers such as combs and bottlebrushes were modeled as ideal chains or filamets with effective Kuhn length. This representation of graft polymers allows for a precise mapping of network’s mechanical properties in both linear and nonlinear deformation regimes into molecular architecture of the network strands. This approach to materials design was tested by reproducing mechanical properties of assorted biological gels and tissues using poly(dimethylsiloxane) (PDMS) and poly(n-butyl acrylate) (PBA) graft polymer elastomers. This technique lays the foundation for a computationally driven materials design that will be capable of encoding mechanical properties of soft materials in solvent free elastomers.