(508h) Investigating the Effect of Topology on the Shape Memory Properties of Thermoset Shape Memory Polymers Using Coarse-Grained Molecular Dynamics Simulations

Thermoset shape memory polymers (TSMPs) are materials that can change shape when exposed to heat, making them useful for various applications in fields like biomedicine, aerospace, automotive, robotics, and smart textiles. This study focuses on how manipulating the topology of TSMPs can improve their properties and potential for use in additive manufacturing. Via a coarse-grained model, we investigated various ways to quantify network quality (i.e., degree of topological perfection), and demonstrated that network quality has important effects on rubbery elastic modulus, recovery stress, and recovery ratio. We also found that our best parameter to describe network quality (the topological score) is a better predictor of network properties than crosslinking fraction. Additionally, higher hardener functionality also improved material properties, especially when the topological score is also high. These results demonstrate that topology significantly affects the mechanical and shape memory properties of TSMPs, and such an effect can be quantitatively characterized in terms of network perfection and quantified by a simple parameter. This study suggests that increasing crosslinking fraction and hardener functionality, combined with methods to improve network quality, can lead to significant improvements in the shape memory properties of TSMPs.