Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations
Abstract
Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that the fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.
- Publication Date:
- Research Org.:
- Ford Motor Company, Detroit, MI (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1431024
- Grant/Contract Number:
- EE0006867
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Macromolecules
- Additional Journal Information:
- Journal Volume: 49; Journal Issue: 24; Journal ID: ISSN 0024-9297
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; Epoxy; fracture energy; molecular dynamics simulation; continuum model; conversion degree; component ratio; glass transition temperature
Citation Formats
Meng, Zhaoxu, Bessa, Miguel A., Xia, Wenjie, Kam Liu, Wing, and Keten, Sinan. Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations. United States: N. p., 2016.
Web. doi:10.1021/acs.macromol.6b01508.
Meng, Zhaoxu, Bessa, Miguel A., Xia, Wenjie, Kam Liu, Wing, & Keten, Sinan. Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations. United States. https://doi.org/10.1021/acs.macromol.6b01508
Meng, Zhaoxu, Bessa, Miguel A., Xia, Wenjie, Kam Liu, Wing, and Keten, Sinan. Tue .
"Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations". United States. https://doi.org/10.1021/acs.macromol.6b01508. https://www.osti.gov/servlets/purl/1431024.
@article{osti_1431024,
title = {Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations},
author = {Meng, Zhaoxu and Bessa, Miguel A. and Xia, Wenjie and Kam Liu, Wing and Keten, Sinan},
abstractNote = {Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that the fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.},
doi = {10.1021/acs.macromol.6b01508},
journal = {Macromolecules},
number = 24,
volume = 49,
place = {United States},
year = {Tue Dec 06 00:00:00 EST 2016},
month = {Tue Dec 06 00:00:00 EST 2016}
}
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