The effect of network topology on material properties in vinyl-ester/styrene thermoset polymers using molecular dynamics simulations and time–temperature superposition

Ming Huang, Nicolas J. Alvarez, Giuseppe R. Palmese, Cameron Abrams

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The time–temperature superposition principle (TTSP) is a promising tool for filling the time-scale gap between molecular dynamics simulation and experiments in polymer systems. Here we propose a molecular dynamics approach to generate vinyl-ester (VE)/styrene (ST) thermoset modulus master curves that are comparable to experiments. Using this approach we generate modulus master curves for VE/ST systems which were generated with various relative reactivity ratios and therefore with vastly different network architectures. The master curves show that, while network architecture is irrelevant for glassy-state moduli measured either at low temperature or high frequencies, it can strongly determine rubbery-state behavior measured at high temperatures or low frequencies. The most important aspects of network architecture are the length of dangling chains and the molecular weight between crosslinks Mc, both of which depend on the relative reactivity of vinyl ester to styrene. We show that the modulus in the transition region between the glassy and rubbery states decreases with increasing Mc at constant monomer composition. Our results provide an explanation for the observation that processing conditions that can affect relative reactivity, such as the processing temperature, can influence Mc and dictate material properties through specific topological features of the cured networks.

Original languageEnglish (US)
Article number111264
JournalComputational Materials Science
Volume207
DOIs
StatePublished - May 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics

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