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A multiscale model for predicting mechanical properties of polymer composites.

Authors :
Yin, Jinping
Wang, Xuan
Tang, Weiqiang
Xu, Xiaofei
Zhao, Shuangliang
Xuan, Fu-zhen
Source :
Chemical Engineering Science. Dec2023, Vol. 282, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

[Display omitted] • A multiscale method to predict mechanical properties of polymer composite is proposed. • The contributions include polymer matrix, particle filling and surface adhesion. • The comparisons between predictions and experimental measurements validate the model. • Further theoretical analysis on matrix composite and particle size distribution is performed. Owing to the multiscale structural characteristics and complex internal coupling, the evaluation of mechanical properties of a polymer composite upon its microstructural information is challenging. Herein, we propose a multiscale method for predicting the mechanical properties of polymer composites by accounting for the contributions of the polymer matrix and those from particle filling and particle–matrix interactions. The former contributions are addressed with the reference polymer matrix by means of molecular dynamic simulation, while the latter are described by a revised continuum model with the input from basic experimental data. The proposed model, validated with the corresponding experimental measurements, indicates that a small difference in the matrix composition can lead to a significant deviation of 60% in the ultimate stress at room temperature, and that the particle size distribution has a synergistic effect, resulting in a nonmonotonic dependence of the mechanical properties on the average particle size. This study provides a feasible engineering tool for evaluating the mechanical properties of polymer composites. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00092509
Volume :
282
Database :
Academic Search Index
Journal :
Chemical Engineering Science
Publication Type :
Academic Journal
Accession number :
173234493
Full Text :
https://doi.org/10.1016/j.ces.2023.119352