Mesoscale modeling of Controlled Degradation and Erosion of Polymer Networks

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NONTECHNICAL SUMMARY This award supports the development of a computational framework that captures the controlled degradation of polymer networks at the mesoscale. Understanding and controlling degradation of polymer networks plays a vital role in a multitude of applications from wound dressings and tissue adhesives to the delivery of drugs and the growth regulation of neural networks. Of particular interest is light-induced degradation with its potential to spatially resolve the control over physical and chemical properties of degrading polymeric materials. Current models, often analytic and continuum approaches that inform our understanding of polymer network degradation, focus on small or large length scales. Small and large length scales reach down to the level of individual atoms and extend to and beyond visible distances, respectively. Our understanding on intermediate length scales, from a few nanometers to tens or hundreds of micrometers that are known as the mesoscale, remains limited. The development of an efficient computational approach for modeling the dynamics of degrading polymer networks on the mesoscale is expected to advance fundamental understanding and help establish guidelines for efficient design of degradable materials. The project includes ample research and educational opportunities for graduate and undergraduate students; strong emphasis will be placed on recruiting students from underrepresented groups. Some of the outcomes of the research will be incorporated into courses taught by the PI, and relevant educational materials will be made available via a science and engineering gateway that is part of the Network for Computational Nanotechnology. TECHNICAL SUMMARY This award supports the development of a computational modeling framework that captures photo-controlled degradation of polymer networks at the mesoscale. A suitable Dissipative Particle Dynamics approach capturing erosion, reverse gelation, and elasticity of the degrading network will be developed and validated. To establish a reliable model of degradation, a modified Segmental Repulsive Potential will be used to prevent unphysical topological crossings of bonded polymer chains. As a model system, hydrogels formed by the end-linking of four-arm polyethylene glycol (PEG) macromolecular precursors, often referred to as tetra-PEG gels, are chosen. The four-arm PEG precursors can be modified during their synthesis by including photocleavable functional groups to enable controlled degradation. The applicability of the proposed model can be extended to various network architectures and used to study their behavior under externally imposed conditions. To demonstrate the utility of the developed mesoscale approach, it will be used to capture the effects of controlled degradation on spreading and reconstruction of nanogel particles at liquid-liquid interfaces. An original multiscale model of early stages of gel degradation will also be developed, where the mesoscale model will be effectively scaled up into the respective continuum model. The development of an efficient computational approach for modeling the dynamics of degrading polymer networks will advance fundamental knowledge by accounting for the specifics of network architecture, diffusion of all the involved species, hydrodynamic interactions, network heterogeneities, and reaction rate constants that are locally controlled by the solvent quality. Proposed simulation methods will allow to probe the applicability of relevant network theories at the mesoscale and may establish guidelines for future design of novel degrading materials with dynamically and spatially controlled properties. This project will train graduate and undergraduate students and support diversity. Research outcomes will be incorporated into courses taught by the PI, and relevant educational materials will be made available through the NanoHub portal.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.