Future progress in soft robotics, wearable electronics, and personalized medicine requires synthesis of soft materials that are capable of mimicking mechanical properties of biological tissue. These materials have a broad range of the mechanical properties which is reflected in their complex response to the external deformation. In particular, biological tissues are strain adaptive with a strong nonlinear dependence of the deformation on the applied stress. A variety of polymeric systems (i.e. networks and gels) has been developed in an attempt to replicate mechanical properties of tissues. However, the applications of these materials are limited since the mapping of the mechanical properties requires simultaneous variation of multiple parameters such as solvent fraction, crosslinking scheme, and chemical compositions. We develop a computationally driven approach for design of soft materials with solvent free network architecture. This approach is based on the theoretical and computational studies of the mechanical properties of networks made of graft polymers such as combs and bottlebrushes. In particular, it is demonstrated that the graft polymers in a melt behave as ideal chains with effective Kuhn length. This model of graft polymers is applied to model mechanical properties of networks of graft polymers and to correlate network’s mechanical response with architectural and chemical structure of the network strands.  This approach is verified by replicating elastic materials with mechanical properties of jellyfish, lung, and arterial tissue in PDMS networks of combs and bottlebrushes. This technique lays the foundation for a computationally driven materials design that will enable encoding of a broad range of mechanical properties of soft materials in solvent free elastomers.<br/><br/>

Brush-like polymers and computationally driven design of soft materials

2019 Spring National Meeting

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