Bound Rubber as a Transferable Structural Descriptor: Connecting MD-Derived Interfacial Scaling to Continuum Reinforcement Models

Filled elastomers often exhibit a low-frequency power-law storage modulus (G-prime), yet quantitative links between molecular interfacial structure and macroscopic reinforcement remain unresolved. This gap is addressed using a hierarchical multiscale framework that integrates coarse-grained molecular dynamics (MD) and dynamic mechanical analysis (DMA). Overall, MD contributes transferable structural descriptors rather than direct macro-rheology prediction. MD simulations yield a bound-layer scaling relation for chain length N=50 in coarse-grained simulations serving as a structural probe. For EPDM master curves, the single-phase fractional Maxwell model is statistically preferred (Delta AICc > 147, n = 56), reflecting limited statistical power; larger datasets (e.g., PC/ABS, n = 952) favor the dual-phase formulation. For cross-scale prediction, an MD-derived effective-volume-fraction baseline (MAPE = 54.1%) provides a structural prior; the regime-partitioned bridge model absorbs relaxation physics not resolved at the MD scale, reducing error to 7.3% (blocked-CV MAPE = 9.5%, with a 2.3% fold-to-fold spread). Linear-viscoelastic constraints improve nonlinear PTT calibration, reducing die-swell error by 87%.

Authors

Institutions

Publication Details

Journal
Polymers
Published
2026-02-26
DOI
https://doi.org/10.3390/polym18050565
Primary Topic
Block Copolymer Self-Assembly
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bound Rubber as a Transferable Structural Descriptor: Connecting MD-Derived Interfacial Scaling to Continuum Reinforcement Models

Wenzhong Deng, Dianming Chu, Ranran Jian, Yancai Sun et al.
Polymers
Block Copolymer Self-Assembly
article

Bound Rubber as a Transferable Structural Descriptor: Connecting MD-Derived Interfacial Scaling to Continuum Reinforcement Models

Wenzhong Deng, Dianming Chu, Ranran Jian, Yancai Sun, Wenjuan Bai, Haoran Wang, Yan He, Peiwu Hou
article en

Abstract

Filled elastomers often exhibit a low-frequency power-law storage modulus (G-prime), yet quantitative links between molecular interfacial structure and macroscopic reinforcement remain unresolved. This gap is addressed using a hierarchical multiscale framework that integrates coarse-grained molecular dynamics (MD) and dynamic mechanical analysis (DMA). Overall, MD contributes transferable structural descriptors rather than direct macro-rheology prediction. MD simulations yield a bound-layer scaling relation for chain length N=50 in coarse-grained simulations serving as a structural probe. For EPDM master curves, the single-phase fractional Maxwell model is statistically preferred (Delta AICc > 147, n = 56), reflecting limited statistical power; larger datasets (e.g., PC/ABS, n = 952) favor the dual-phase formulation. For cross-scale prediction, an MD-derived effective-volume-fraction baseline (MAPE = 54.1%) provides a structural prior; the regime-partitioned bridge model absorbs relaxation physics not resolved at the MD scale, reducing error to 7.3% (blocked-CV MAPE = 9.5%, with a 2.3% fold-to-fold spread). Linear-viscoelastic constraints improve nonlinear PTT calibration, reducing die-swell error by 87%.

PolymersVol. 18(5)
Qingdao University (CN), Qingdao University of Science and Technology (CN), Guilin University of Aerospace Technology (CN), Guilin University of Technology (CN), Marine Biology Institute of Shandong Province (CN), China National Chemical Engineering (China) (CN)
Natural Science Foundation of Guangxi Province
Openalex Percentile: Top 43%
Block Copolymer Self-Assembly
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.