(404j) Predicting Viscoelasticity of Dynamically Associating Polymer Networks Using Brachiation Theory

Engineering polymer networks with dynamically associating groups to achieve a desired viscoelasticity can be a long, iterative process. Ideally, viscoelasticity of these supramolecular networks can be modeled to predict the desired polymer network design, but theoretical models that have good agreement with experimental rheological data through fitting (e.g. sticky Rouse, sticky reptation) face a crucial pitfall: the parameters are not directly experimentally relevant. Brachiation theory fills that gap by incorporating experimentally controllable molecular-level parameters (polymer concentration, chain length, number of association units per chain, unbinding rate) to fully capture the rheological behavior of physically associating gels. Hyaluronic acid chains modified with host–guest motifs were selected as a prototypical supramolecular network with a wide range of viscoelastic properties for validation of the Brachiation model. The viscoelasticity of a subset of host–guest networks was measured using dynamic light scattering microrheology, and the theory parameters were fitted to the data. Following this, the theory was used to accurately predict the rheological behavior of several new host-guest networks. The resulting agreement between the predictions and the measured rheology demonstrates the utility of Brachiation theory as a valuable tool in future design principles of dynamically associating polymer materials.