Computational design of shape memory polymer nanocomposites

Yelena R. Sliozberg, Martin Kröger, Todd C. Henry, Siddhant Datta, Bradley D. Lawrence, Asha J. Hall, Aditi Chattopadhyay

Research output: Contribution to journalArticlepeer-review

Abstract

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>Tg compared with the values below the glass transition temperature Tg 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.

Original languageEnglish (US)
Article number123476
JournalPolymer
Volume217
DOIs
StatePublished - Mar 5 2021
Externally publishedYes

Keywords

  • Buckypaper (BP)/Epoxy nanocomposites
  • Coarse-grained molecular dynamics simulation
  • Shape memory polymers

ASJC Scopus subject areas

  • Organic Chemistry
  • Polymers and Plastics
  • Materials Chemistry

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