Computational design of shape memory polymer nanocomposites

The goal of this work is to understand the underlying mechanisms that govern shape memory of polymer nanocomposites at the molecular level and to utilize them for novel synthetic shape memory polymer (SMP) materials for reconfigurable structures. In this study, we have performed coarse-grained molecular dynamics simulations of buckypaper (BP)/epoxy nanocomposites with a focus on their mechanical and shape memory performances, specifically on prediction of the Young's modulus of the material as a function of carbon nanotube (CNT) loading. Our results demonstrate that the Young's modulus linearly increases with CNT volume fraction below 0.16 (40 wt%) followed by a sharp upsurge of the modulus at higher loading where the onset of entanglements of nanotubes was determined. Additionally, we found a significantly greater increase of the modulus at T>T_g compared with the values below the glass transition temperature T_g for all considered systems. The simulation suggests that incorporation of BP restricts relaxation of network strands of the polymer matrix and leads to resistance in the recovery process of composites.