Abstract
In this work, we used coarse-grained molecular dynamics simulation methods to investigate the dispersion and percolation behavior of nanoparticles in polymer nanocomposite. Our aim was to investigate the correlation between particle arrangement in nearby layers and the stretching performance in composite systems by exploring the stress transfer processes during different stages of the stretching process. The machine learning technique of linear regression was used to quantitatively measure the efficiency of stress transfer between particles nearby. According to our research, increasing the strength of attraction can significantly enhance the particle dispersion and affect the percolation threshold. We also noticed a non-monotonic relationship between the interaction strength and the tensile stress. Additionally, we quantified the efficiency of nanoparticles and polymers at transferring stress to nearby nanoparticles. As a result, the stress value provided by each particle in the aggregation body is significantly increased by the aggregation behavior of nanoparticles. The non-monotonic behavior is caused by two variables: the rapid disintegration of aggregates and the improved stress transfer efficiency from polymers to nanoparticles. Significantly, it was discovered that the structural rearrangement of nanoparticles during stretching is the main reason that causes the yield-like behavior seen in poorly dispersed systems.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos.21833008 and 52293471) and the National Key R&D Program of China (No.2022YFB3707303).
We are grateful for the essential support of the Network and Computing Center, CIAC, CAS.
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SUN Zhaoyan is a youth executive editorial board member for Chemical Research in Chinese Universities and was not involved in the editorial review or the decision to publish this article. The authors declare no conflicts of interest.
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Guan, J., Sun, Z. Stress Transfer in Polymer Nanocomposites: A Coarse-grained Molecular Dynamics Study. Chem. Res. Chin. Univ. 39, 741–749 (2023). https://doi.org/10.1007/s40242-023-3176-0
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DOI: https://doi.org/10.1007/s40242-023-3176-0