A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects

Ries M, Weber F, Possart G, Steinmann P, Pfaller S (2022)


Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 161

Article Number: 107094

DOI: 10.1016/j.compositesa.2022.107094

Abstract

The enhanced mechanical behavior of polymer nanocomposites with spherical filler particles is attributed to the formation of matrix-filler interphases. The nano-scale leads to particularly high interphase volume fractions while rendering experimental investigations extremely difficult. Previously, we introduced a molecular dynamics-based interphase model capturing the crucial spatial profiles of elastic and inelastic properties inside the interphase. This contribution demonstrates that our model captures polymer nanocomposites’ essential characteristics reported from experiments. To this end, we thoroughly verify and validate the model before discussing the resulting local plastic strain distribution. Furthermore, we obtain a reinforcement in terms of the overall stiffness for smaller particles and higher filler contents, while the influence of particle spacing seems negligible, matching experimental observations in the literature. This paper proposes a methodology to unravel the underlying complex mechanical behavior of polymer nanocomposites and to translate the findings into engineering quantities accessible to a broader audience and technical applications.

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APA:

Ries, M., Weber, F., Possart, G., Steinmann, P., & Pfaller, S. (2022). A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects. Composites Part A-Applied Science and Manufacturing, 161. https://dx.doi.org/10.1016/j.compositesa.2022.107094

MLA:

Ries, Maximilian, et al. "A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects." Composites Part A-Applied Science and Manufacturing 161 (2022).

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