journal article·2025-11Open access

Molecular dynamics study on the synergistic effect of HFPT, Cu, and SiC nanoparticles on epoxy resin properties

Hang Zhang, Zhijin Zhang, Xingliang Jiang, Qin Hu, Hualong Zheng

Abstract

With the rapid development of ultra-high-voltage (UHV) power systems, epoxy resins (EP) are expected to meet increasingly stringent performance demands. Conventional single-filler strategies have shown limited effectiveness, while multi-filler synergistic approaches offer promising potential. This study explores a hybrid filler system comprising hexafluoropropylene trimer (HFPT), copper (Cu), and silicon carbide (SiC) nanoparticles. Molecular dynamics (MD) simulations are conducted to evaluate the impact of these fillers on key properties of epoxy composites. And a Particle Swarm Optimization Back-Propagation Neural Network Genetic Algorithm (PSO-BPNN-GA) is employed to determine the optimal filler content for enhancing the overall performance of the EP composites. Results reveal that the mean square displacement ( MSD ) decreases then increases with nanoparticle loading, while HFPT tends to raise MSD . The glass transition temperature ( Tg ) increases with total filler content, reaching 168.9 °C, 22.3% higher than that of pure epoxy. Young’s modulus ( E ) also improves, peaking at 4.13 MPa (21.8% increase), with HFPT content having little influence. Thermal conductivity ( TC ) rises with nanoparticle content, up to 0.214 W/m·K (26.6% increase), whereas HFPT content shows little effect. The dielectric constant ( ε ) declines with increasing HFPT, and more notably when Cu content exceeds 2%, reaching a minimum, 2.5. Cohesive energy density electrostatic component ( CEDe ) increases with HFPT and SiC but decreases with Cu. The optimized filler ratio is determined to be HFPT:Cu:SiC = 3.2795:2.8058:1.9441.
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Research context

OpenAlex topics: Railway Systems and Materials Science · Advanced materials and composites · Advanced ceramic materials synthesis

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References (38)

Show source-provided references
1. Interface thermal resistance and thermal conductivity of polymer composites at different types, shapes, and sizes of fillers: a review10.1002/pc.26029 ↗
2. Impact of temperature on the transient DC field distribution of ±1100 kV UHVDC Wall bushing10.1109/TPWRD.2020.3009121 ↗
3. Study on mechanical thermal and electrical characterizations of Nano-SiC/Epoxy composites10.1295/polymj.PJ2008173 ↗
4. Investigation of the dielectric and thermal conductive properties of core–shell structured HGM/hBN/PTFE composites10.1016/j.mseb.2018.12.015 ↗
5. Thermal, optical, mechanical, dielectric, and electrical properties of nanocomposites
6. Investigation of the mechanical, absorption, flammability and swelling properties of Graphene filled Sisal/glass fiber reinforced polymer hybrid nanocomposites10.1080/23311916.2024.2342433 ↗
7. Characterization of electrical and thermal properties of carbon nanotube/epoxy composites10.1016/j.compositesb.2013.08.032 ↗
8. Synergetic optimization of charge transport and breakdown strength of epoxy nanocomposites: realizing sandwich topological structure through constructing a SiC@SiO2/EP surface layer and m-BNNS/EP insert layer10.1016/j.mssp.2021.106430 ↗
9. Enhanced switching electric field and breakdown strength of epoxy composites with core‐shell silicon carbide nanoparticles10.1049/hve2.12247 ↗
10. N. H. J. A. Hasan, Mechanical and physical properties of epoxy/SiC composites simulated, Eng Res Express, Volume 6, Number 035523, DOI 10.1088/2631-8695/ad62af.10.1088/2631-8695/ad62af ↗
11. Effect of SiO2 particle size scale on the electrical performance of epoxy‐based nonlinear conductive composite10.1002/pc.27259 ↗
12. Study on electrical performance of nano-silicide filled epoxy resin composite materials10.1016/j.polymer.2025.128555 ↗
13. A study on the thermodynamic performance of nano-silicide filled epoxy resin composite materials10.1039/D5CP01471A ↗
14. Thermal, rheological and electrical analysis of MWCNTs/epoxy matrices10.1016/j.compscitech.2015.02.002 ↗
15. Characterization of electrical and thermal properties of carbon nanotube/epoxy composites10.1016/j.compositesb.2013.08.032 ↗
16. Molecular dynamics simulation of effect of nano-SiO2 Si3N4 doping on thermodynamic properties of high crosslinked epoxy resin
17. Molecular dynamics simulation of glass transition behavior of polymer based nanocomposites
18. Glass transition temperature of functionalized graphene epoxy composites using molecular dynamics simulation10.1080/10584587.2017.1370331 ↗
19. Determination of elastic constants of functionalized graphene-based epoxy nanocomposites: a molecular modeling and MD simulation study10.1007/s00894-022-05134-7 ↗
20. Molecular dynamics Study on thermal conductivity properties and dielectric behaviors of graphene-based epoxy resin nanocomposites10.3390/polym17010112 ↗
21. A molecular dynamics study on the interaction between epoxy and functionalized graphene sheets
22. Carbon nanotubes and silver flakes filled epoxy resin for new hybrid conductive adhesives10.1016/j.microrel.2011.03.020 ↗
23. Highly thermal conductive copper nanowire composites with ultralow loading: toward applications as thermal interface materials10.1021/am500009p ↗
24. Wu
25. Multi-functional epoxy resins performing high thermal resistance with good flow-ability based on dimeric naphthols10.1177/0954008309347638 ↗
26. Development of high Tg epoxy resin and mechanical properties of its fiber-reinforced composites10.1002/app.38040 ↗
27. Benchmarking machine learning models for polymer informatics: an example of glass transition temperature10.1021/acs.jcim.1c01031 ↗
28. Polymer genome: a data-powered polymer informatics platform for property predictions10.1021/acs.jpcc.8b02913 ↗
29. Machine learning-based epoxy resin property prediction10.1039/D4ME00060A ↗
30. Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning10.1016/j.matdes.2020.108932 ↗
31. Zhang
32. Modelling sorption and diffusion of NF3 and CF4 in Teflon AF perfluoropolymer membranes10.1016/j.memsci.2014.07.033 ↗
33. The structural characterization and properties of SiO2-Epoxy nanocomposites10.1177/0021998305055193 ↗
34. Molecular simulation and experimental analysis on thermal and mechanical properties of carbon nanotube/epoxy resin composites with different curing agents at high-low temperature
35. A method for predicting ground pressure in meihuajing coal mine based on improved BP neural network by immune algorithm-particle swarm optimization10.3390/pr12010147 ↗
36. W. Xu, L. Cheng, Y. Cui, H. Yan, H. Jiang, W. Jiao, An improved method of applying PSO-BP to optimize PAT energy efficiency based on entropy production theory, Energy Convers Manag, Volume 327, 2025, 119472, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2024.119472.10.1016/j.enconman.2024.119472 ↗
37. Optimisation investigation on hybrid battery thermal management system for electric aircraft based on genetic algorithm10.1080/19942060.2025.2517314 ↗
38. Multi-objective optimal planning of Rapid Charging Stations, distributed generators, and D-STATCOM in coupled networks considering waiting time. Energy sources