Forensics and Design of Polymer Networks and Gels

During the last decade, our understanding of soft matter has been transformed through a combination of theory, computer simulations, and experiments into a materials design engine. This dissertation describes how this approach has guided synthesis towards programmable molecular architectures to yield novel functional networks and gels with well-controlled physical properties. The developed nonlinear network deformation model serves as a foundation for a forensic method that allows accurate quantification of the most essential network structural elements including crosslink density, strand flexibility, and fraction of stress-supporting strands for multiple classes of polymer networks. For the first time, it is possible to identify a percolation-like transition from entanglement-controlled to crosslink-controlled network elasticity. The decoded structural blueprint enables a universal classification of polymer networks according to the effectiveness of the stress distribution between network strands.Furthermore, this dissertation demonstrates how this approach empowers the design of gels possessing different values of shear modulus at a constant equilibrium solvent fraction, producing a set of materials that can mimic the mechanical properties of a variety of biological tissues. This indicates a breakdown of the theory developed by Flory and Rehner for linear chains, which states that the modulus of a swollen gel scales with the polymer volume fraction. This design-by-architecture approach for biomimicking gels is made possible by the additional stiffening of the polymer backbone by the swollen side chains. We quantify this effect by implementing a scaling-based approach to evaluate the conformations of polymer chains in solution. A framework for independently analyzing the conformations of uncrosslinked solvated chains is demonstrated on semidilute polyelectrolyte solutions by estimating the correlation length with the concentration dependence of specific viscosity.