Demir Eyup Can, McDermott Mark T, Kim Chun Ll, Ayranci Cagri
Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada.
Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada.
J Compos Mater. 2023 Mar;57(6):1087-1104. doi: 10.1177/00219983221149122. Epub 2023 Jan 3.
The stiffness of polymeric materials can be improved dramatically with the addition of nanoparticles. In theory, as the nanoparticle loading in the polymer increases, the nanocomposite becomes stiffer; however, experiments suggest that little or no stiffness improvement is observed beyond an optimal nanoparticle loading. The mismatch between the theoretical and experimental findings, particularly at high particle loadings, needs to be understood for the effective use of nanoparticles. In this respect, we have recently developed an analytical model to close the gap in the literature and predict elastic modulus of nanocomposites. The model is based on a three-phase Mori-Tanaka model coupled with the Monte-Carlo method, and satisfactorily captures the experimental results, even at high nanoparticle loadings. The developed model can also be used to study the effects of agglomeration in nanocomposites. In this paper, we use this model to study the effects of agglomeration and related model parameters on the stiffness of nanocomposites. In particular, the effects of particle orientation, critical distance, dispersion state and agglomerate property, and particle aspect ratio are investigated to demonstrate capabilities of the model and to observe how optimal particle loading changes with respect these parameters. The study shows that the critical distance defining agglomerates and the properties of agglomerates are the key design parameters at high particle loadings. These two parameters rule the optimal elastic modulus with respect to particle loading. The findings will allow researchers to form design curves and successfully predict the elastic moduli of nanocomposites without the exhaustive experimental undertakings.
通过添加纳米颗粒，聚合物材料的刚度可得到显著提高。理论上，随着聚合物中纳米颗粒负载量的增加，纳米复合材料会变得更硬；然而，实验表明，超过最佳纳米颗粒负载量后，几乎观察不到刚度的提高。为了有效利用纳米颗粒，需要理解理论和实验结果之间的差异，尤其是在高颗粒负载量的情况下。在这方面，我们最近开发了一个分析模型，以弥合文献中的差距并预测纳米复合材料的弹性模量。该模型基于三相森田模型与蒙特卡罗方法相结合，即使在高纳米颗粒负载量下也能令人满意地捕捉实验结果。所开发的模型还可用于研究纳米复合材料中的团聚效应。在本文中，我们使用该模型研究团聚以及相关模型参数对纳米复合材料刚度的影响。特别是，研究了颗粒取向、临界距离、分散状态和团聚体性质以及颗粒长径比的影响，以展示该模型的能力，并观察最佳颗粒负载量如何随这些参数变化。研究表明，定义团聚体的临界距离和团聚体的性质是高颗粒负载量下的关键设计参数。这两个参数决定了相对于颗粒负载量的最佳弹性模量。这些发现将使研究人员能够形成设计曲线，并成功预测纳米复合材料的弹性模量，而无需进行详尽的实验。
