Computational design of shape memory polymer nanocomposites

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