USING MULTISCALE MODELING TO PREDICT MATERIAL RESPONSE OF POLYMERIC MATERIALS

Presented is a multiscale modeling method applied to light activated shape memory polymers (LASMP). LASMP are a new class of shape memory polymer (SMP) being developed for applications where a thermal stimulus is undesired. Rotational Isomeric State (RIS) theory is used to build a molecular scale model of the polymer chain yielding a list of distances between the predicted cross-link locations, or r-values. The r-values are then fit with Johnson probability density functions and used with Boltzmann statistical mechanics to predict stress as a function of strain of the phantom network. Junction constraint theory is then used to calculate the stress contribution due to interactions with neighboring chains, resulting in previously unattainable numerically accurate Young's modulus predictions based on the molecular formula of the polymer. The system is modular in nature and thus lends itself well to being adapted for specific applications. The results of the model are presented with experimental data for confirmation of correctness along with discussion of the potential of the model to be used to computationally adjust the chemical composition of LASMP to achieve specified material characteristics, greatly reducing the time and resources required for formula development.