Quantitative Comparison of Atomistic Simulations with Experiment for a Cross-Linked Epoxy: A Specific Volume–Cooling Rate AnalysisClick to copy article linkArticle link copied!
Abstract
Cross-linked epoxy thermosets, like all glass-forming viscoelastic materials, show both a temperature and rate dependence in their thermomechanical properties. However, accounting for rate effects on these properties using molecular dynamics (MD) simulations and making quantitative comparison with experimental measurements has proven to be a difficult task due to the extreme mismatch between experimental and computationally accessible cooling rates. For this reason, the effect of cooling rate on material properties in glass-forming systems (including epoxy networks) has been mostly ignored in computational studies, making quantitative comparison with experimental data nebulous. In this work, we investigate a strategy for modeling rate effects in an epoxy network based on an approach that uses theoretically informed simulation and analysis protocols in combination with material specific time–temperature superposition (TTSP) data obtained from experimental measurements. To illustrate and test the strategy, we build and study an atomistic model of a cross-linked epoxy network. Molecular dynamics simulations are used to model the specific volume as a function of temperature across the glass transition from the rubbery to the glassy state using a total of five computationally accessible cooling rates. From the trends thus identified, we pinpoint the temperatures at which the models show rubbery and glassy behavior and use this information to calculate the values of the glass transition temperature (Tg) for each of the different cooling rates. Comparison with experimental data obtained from the literature (for the identical epoxy network) shows that our computations successfully predict the trends in specific volume in the rubbery and the glassy regions within 0.5%. We then compare the Tg values obtained from the data analysis with those calculated using the TTSP data obtained from the literature. Excellent agreement is found, and the Tg values from the two different methods are within 1.5% for all cooling rates. While our MD simulations do not replicate the experimental cooling rates, the agreement between these two sets of Tg values quantitatively relates the computational and experimental data sets. This agreement indicates that atomistic simulations can reliably capture the molecular mechanisms underlying viscoelasticity in this cross-linked epoxy (even when cooling rates differ by orders of magnitude) and that volume–rate analysis in conjunction with TTSP is a reliable method to computationally characterize certain classes of glass-forming materials. We believe that the general paradigm and protocols developed in this work should in principle be extensible as an efficacious means to integrate theory, computation, and experiment for the analysis of amorphous macromolecular materials.
Cited By
This article is cited by 35 publications.
- Igor V. Volgin, Maria V. Andreeva, Sergey V. Larin, Leonid I. Klushin, Sergey V. Lyulin. Solubility of Gases and Free Volume Evolution in R-BAPB Polyimide: Molecular Dynamics Simulations and Analytical Theory Insights into Cooling Velocity Effect. Macromolecules 2024, 57
(2)
, 586-596. https://doi.org/10.1021/acs.macromol.3c01657
- Chaofu Wu. Temperature-Transferable Coarse-Grained Models for Volumetric Properties of Poly(lactic Acid). The Journal of Physical Chemistry B 2024, 128
(1)
, 358-370. https://doi.org/10.1021/acs.jpcb.3c07026
- Prashik S. Gaikwad, Aaron S. Krieg, Prathamesh P. Deshpande, Sagar U. Patil, Julia A. King, Marianna Maiaru, Gregory M. Odegard. Understanding the Origin of the Low Cure Shrinkage of Polybenzoxazine Resin by Computational Simulation. ACS Applied Polymer Materials 2021, 3
(12)
, 6407-6415. https://doi.org/10.1021/acsapm.1c01164
- Levi M. J. Moore, Neil D. Redeker, Andrea R. Browning, Jeffrey M. Sanders, Kamran B. Ghiassi. Polycyanurates via Molecular Dynamics: In Situ Crosslinking from Di(Cyanate Ester) Resins and Model Validation through Comparison to Experiment. Macromolecules 2021, 54
(13)
, 6275-6284. https://doi.org/10.1021/acs.macromol.1c00207
- Lorena Alzate-Vargas, Nicolas Onofrio, Alejandro Strachan. Universality in Spatio-Temporal High-Mobility Domains Across the Glass Transition from Bulk Polymers to Single Chains. Macromolecules 2020, 53
(21)
, 9375-9385. https://doi.org/10.1021/acs.macromol.0c00853
- Alessandro Perego, Fardin Khabaz. Thermodynamics, Dynamics, and Rheology of Fuel Surrogates: Application of the Time–Temperature Superposition Principle in Molecular Dynamics Simulations. Energy & Fuels 2020, 34
(9)
, 10631-10640. https://doi.org/10.1021/acs.energyfuels.0c01183
- Chaofu Wu. Free Surface-Induced Glass-Transition Temperature Suppression of Simulated Polymer Chains. The Journal of Physical Chemistry C 2019, 123
(14)
, 9237-9246. https://doi.org/10.1021/acs.jpcc.9b01253
- Jingjing Chen, Bo Li, Gen Li, Bohong Gu, Baozhong Sun. Compressive behaviors and deformation mechanism of carbon fiber reinforced epoxy composites: A microscopic and molecular dynamics perspective. Polymer Composites 2024, https://doi.org/10.1002/pc.29135
- Jiaxian Zhang, Hongxia Guo. Constructing a temperature transferable coarse-grained model of cis-1,4-polyisoprene with the structural and thermodynamic consistency aided by machine learning. Polymer 2024, 311 , 127516. https://doi.org/10.1016/j.polymer.2024.127516
- Adegbola Balogun, Rajesh Khare. Structure and Dynamics of Ions in a Poly(ethylene oxide) Matrix Near a Graphite Surface. Macromolecular Theory and Simulations 2024, 33
(5)
https://doi.org/10.1002/mats.202400029
- Chaofu Wu. Process dependent properties of glassy polymer films revealed by molecular dynamics simulations. Computational Materials Science 2024, 244 , 113252. https://doi.org/10.1016/j.commatsci.2024.113252
- Fernando J. Carmona Esteva, Yong Zhang, Edward J. Maginn, Yamil J. Colón. Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations. The Journal of Chemical Physics 2024, 161
(1)
https://doi.org/10.1063/5.0207835
- Qiuyu Tang, Jie Jiang, Jinjin Li, Ling Zhao, Zhenhao Xi. Effects of Chemical Composition and Cross-Linking Degree on the Thermo-Mechanical Properties of Bio-Based Thermosetting Resins: A Molecular Dynamics Simulation Study. Polymers 2024, 16
(9)
, 1229. https://doi.org/10.3390/polym16091229
- Qiuyue Ding, Ning Ding, Xiangfeng Chen, Wenyue Guo, Fahmi Zaïri. Effect of grain boundaries, cure, and temperature on the thermomechanical properties of epoxy/graphene composites. Polymer Composites 2024, 45
(4)
, 3406-3421. https://doi.org/10.1002/pc.27998
- Sagar U. Patil, Aaron S. Krieg, Leif K. Odegard, Upendra Yadav, Julia A. King, Marianna Maiaru, Gregory M. Odegard. Simple and convenient mapping of molecular dynamics mechanical property predictions of bisphenol-F epoxy for strain rate, temperature, and degree of cure. Soft Matter 2023, 19
(35)
, 6731-6742. https://doi.org/10.1039/D3SM00697B
- In‐Chul Yeh, Alex J. Hsieh. Molecular dynamics simulation study of thermomechanical properties and hydrogen bonding structures of two‐component polyurethanes. Journal of Polymer Science 2023, 115 https://doi.org/10.1002/pol.20230347
- Aigul Shamsieva, Irina Piyanzina, Benoit Minisini. Amorphous cis-1,4-polybutadiene P–V-T properties from atomistic simulations. Journal of Molecular Modeling 2023, 29
(8)
https://doi.org/10.1007/s00894-023-05658-6
- O. V. Arzhakova, M. S. Arzhakov, E. R. Badamshina, E. B. Bryuzgina, E. V. Bryuzgin, A. V. Bystrova, G. V. Vaganov, V. V. Vasilevskaya, A. Yu. Vdovichenko, M. O. Gallyamov, R. A. Gumerov, A. L. Didenko, V. V. Zefirov, S. V. Karpov, P. V. Komarov, V. G. Kulichikhin, S. A. Kurochkin, S. V. Larin, A. Ya. Malkin, S. A. Milenin, A. M. Muzafarov, V. S. Molchanov, A. V. Navrotskiy, I. A. Novakov, E. F. Panarin, I. G. Panova, I. I. Potemkin, V. M. Svetlichny, N. G. Sedush, O. A. Serenko, S. A. Uspenskii, O. E. Philippova, A. R. Khokhlov, S. N. Chvalun, S. S. Sheiko, A. V. Shibaev, I. V. Elmanovich, V. E. Yudin, A. V. Yakimansky, A. A. Yaroslavov. Polymers for the future. Russian Chemical Reviews 2022, 91
(12)
, RCR5062. https://doi.org/10.57634/RCR5062
- Gregory M. Odegard, Sagar U. Patil, Prashik S. Gaikwad, Prathamesh Deshpande, Aaron S. Krieg, Sagar P. Shah, Aspen Reyes, Tarik Dickens, Julia A. King, Marianna Maiaru. Accurate predictions of thermoset resin glass transition temperatures from all-atom molecular dynamics simulation. Soft Matter 2022, 18
(39)
, 7550-7558. https://doi.org/10.1039/D2SM00851C
- Rakesh Kumar Giri, Narasimhan Swaminathan. Role of mapping schemes on dynamical and mechanical properties of coarse-grained models of cis-1,4-polyisoprene. Computational Materials Science 2022, 208 , 111309. https://doi.org/10.1016/j.commatsci.2022.111309
- Ming Huang, Nicolas J. Alvarez, Giuseppe R. Palmese, Cameron Abrams. The effect of network topology on material properties in vinyl-ester/styrene thermoset polymers using molecular dynamics simulations and time–temperature superposition. Computational Materials Science 2022, 207 , 111264. https://doi.org/10.1016/j.commatsci.2022.111264
- Yan Mao, Hong Liu, Feng Long Gu, Ming-Xing Wu, Yan Wang. The molecular design of performance-enhanced intraocular lens composites. Biomaterials Science 2022, 10
(6)
, 1515-1522. https://doi.org/10.1039/D1BM01919H
- Andrea Giuntoli, Nitin K. Hansoge, Anton van Beek, Zhaoxu Meng, Wei Chen, Sinan Keten. Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization. npj Computational Materials 2021, 7
(1)
https://doi.org/10.1038/s41524-021-00634-1
- Ketan S. Khare, Cameron F. Abrams. Atomistic simulation of volumetric properties of epoxy networks: effect of monomer length. Soft Matter 2021, 17
(43)
, 9957-9966. https://doi.org/10.1039/D1SM01128F
- Yan Wang, Han-Lin Gan, Chi-Xin Liang, Zhong-Yan Zhang, Mo Xie, Ji-Yuan Xing, Yao-Hong Xue, Hong Liu. Network structure and properties of crosslinked bio-based epoxy resin composite: An in-silico multiscale strategy with dynamic curing reaction process. Giant 2021, 7 , 100063. https://doi.org/10.1016/j.giant.2021.100063
- Lilian C. Johnson, Frederick R. Phelan. Dynamically consistent coarse-grain simulation model of chemically specific polymer melts via friction parameterization. The Journal of Chemical Physics 2021, 154
(8)
https://doi.org/10.1063/5.0034910
- Michael M. Henry, Stephen Thomas, Mone’t Alberts, Carla E. Estridge, Brittan Farmer, Olivia McNair, Eric Jankowski. General-Purpose Coarse-Grained Toughened Thermoset Model for 44DDS/DGEBA/PES. Polymers 2020, 12
(11)
, 2547. https://doi.org/10.3390/polym12112547
- Chaofu Wu. Tacticity Effects on Polymer Glass Transition Revisited by Coarse‐Grained Simulations. Macromolecular Theory and Simulations 2020, 29
(3)
https://doi.org/10.1002/mats.202000001
- Sanjib C. Chowdhury, Robert M. Elder, Timothy W. Sirk, John W. Gillespie. Epoxy resin thermo-mechanics and failure modes: Effects of cure and cross-linker length. Composites Part B: Engineering 2020, 186 , 107814. https://doi.org/10.1016/j.compositesb.2020.107814
- Ketan S. Khare, Frederick R. Phelan. Integration of Atomistic Simulation with Experiment Using Time−Temperature Superposition for a Cross‐Linked Epoxy Network. Macromolecular Theory and Simulations 2020, 29
(2)
https://doi.org/10.1002/mats.201900032
- Eric Jankowski, Neale Ellyson, Jenny W. Fothergill, Michael M. Henry, Mitchell H. Leibowitz, Evan D. Miller, Mone’t Alberts, Samantha Chesser, Jaime D. Guevara, Chris D. Jones, Mia Klopfenstein, Kendra K. Noneman, Rachel Singleton, Ramon A. Uriarte-Mendoza, Stephen Thomas, Carla E. Estridge, Matthew L. Jones. Perspective on coarse-graining, cognitive load, and materials simulation. Computational Materials Science 2020, 171 , 109129. https://doi.org/10.1016/j.commatsci.2019.109129
- Hassan Ghermezcheshme, Hesam Makki, Mohsen Mohseni, Morteza Ebrahimi, Gijsbertus de With. MARTINI-based simulation method for step-growth polymerization and its analysis by size exclusion characterization: a case study of cross-linked polyurethane. Physical Chemistry Chemical Physics 2019, 21
(38)
, 21603-21614. https://doi.org/10.1039/C9CP03407B
- Jeffrey DeFelice, Jane E. G. Lipson. Different metrics for connecting mobility and glassiness in thin films. Soft Matter 2019, 15
(7)
, 1651-1657. https://doi.org/10.1039/C8SM02355G
- Chaofu Wu. A multiscale scheme for simulating polymer Tg. Journal of Molecular Modeling 2018, 24
(12)
https://doi.org/10.1007/s00894-018-3867-5
- Chaofu Wu. Multiscale modeling of glass transition in polymeric films: Application to stereoregular poly(methyl methacrylate)s. Polymer 2018, 146 , 91-100. https://doi.org/10.1016/j.polymer.2018.05.036
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.
Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.
The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.