Molecular modeling of swelling and chain retention in temperature-sensitive composite hydrogels

Stimuli-responsive networks are of immense academic interest as an avenue toward smart, functional materials. These functions include drug delivery, artificial muscles, and soft robot actuators, among others, making use of a wide range of stimuli, including temperature, pH, and electric stimulus, to achieve improved control over drug release, as well as a variety of stretching and bending type motions in muscles and actuators. This exciting and rapidly expanding field inspired a computational study of temperature-sensitive composite hydrogels using a computationally efficient coarse-grained molecular dynamics simulation-based approach. This work investigates the impacts of material design parameters of cross-link density, polymer degree of polymerization, mol% thermoplastic filler, and polymer miscibility, on material morphology and phase separation, chain conformations and behavior, and swelling behavior and other response-based properties. A modeling protocol to study chain retention in composite hydrogels was also developed, something which to our knowledge has never been done in silico. This enabled a study of how the same material design parameters listed above impact the hydrogel network’s ability to retain the thermoplastic filler chains, preventing their leakage out of the network. Ultimately our simulations of both swelling and chain retention successfully replicated known experimental phenomena while also providing new nano-scale insights into the swelling behaviors and materials properties of these composite hydrogels, allowing for design recommendations relevant for soft actuators and drug delivery.