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• W4313576762 abstract "In this study, we promote a multi-scale modeling to predict the healing efficiencies (HEs) of incorporated polymers with self-healing microcapsules. The Python scripts were employed to generate three representative volume elements (RVEs) with randomly dispersed 5, 7.5, and 10% volume fraction (VF) of alginate microcapsules. Three VUSDFLD subroutines were codded and supplemented with ABAQUS/Explicit solver to obtain the maximum tensile stresses (Sut) of virgin, damaged, and healed samples followed by calculating HF of self-healing polymers. Based on the simulation results, more incorporation of self-healing microcapsules increased the tensile after impact HF, so that HFs were increased from 46.31% for RVEs containing 5% VF up to 65.41% and 84.84% for 7.5% and 10% VF, respectively. The presence of more self-healing microcapsules could improve the chance of rupturing more filled microcapsules with healing agents after crack propagation due to impact damages in the matrix. Thus, more damaged elements would be healed by spread healing agents. To evaluate the reliability of simulation results, the specimens containing electrosprayed multicore self-healing microcapsules were fabricated, and experimental HFs were calculated. The same trend was obtained for experimental results, as acquired in the simulation of RVEs. The error of healing efficiencies were only 6.82%, 2.81%, and 7.74 for incorporated specimens with 5, 7.5, and 10% VF of electrosprayed multicore microcapsules, respectively, indicating the accuracy of introduced multi-scale finite element modeling. The fabrication defects of experimental specimens can be the reason of simulation errors." @default.
• W4313576762 created "2023-01-06" @default.
• W4313576762 creator A5021315769 @default.
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• W4313576762 date "2023-01-05" @default.
• W4313576762 modified "2023-10-01" @default.
• W4313576762 title "Multi-scale modeling and experimental study on electrosprayed multicore microcapsule-based self-healing polymers" @default.
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• W4313576762 doi "https://doi.org/10.1080/15376494.2022.2162638" @default.
• W4313576762 hasPublicationYear "2023" @default.
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