A Molecular-Level Theory for Predicting Rheological Behavior of Dynamically Associating Polymer Networks

Polymer materials with dynamic associations are ubiquitous in materials applications, from tissue engineering to flexible electronics, due to their tunable physical behavior. Designing new dynamic polymer networks can involve many rounds of synthesis and characterization and would greatly benefit from the ability to predict the mechanical behavior of hypothetical materials. To that end, we present a new theory that incorporates experimentally controllable molecular-level parameters (concentration, chain length, unbinding rate) to fully capture the rheological behavior of physically associating gels. Hyaluronic acid, a biopolymer used in many biomedical applications, serves as the basis for studying biologically relevant polymers and are modified with guest-host molecules to form a dynamic supramolecular network, exhibiting tunable physical properties. Rheological measurements using dynamic light scattering microrheology are compared to theoretical predictions using only molecular level parameters across 6 decades in frequency without the assumption of time-temperature superposition. The resulting fit demonstrates the utility of our theory as a key tool in future design principles of dynamically associating polymer materials.