Aryanfar Asghar, Medlej Sajed, Tarhini Ali, Tehrani B Ali R
American University of Beirut, Riad El-Solh 1107, Lebanon.
Bahçesehir University, 4 Çırağan Cad, Besiktas, Istanbul 34353, Turkey.
Soft Matter. 2021 Mar 4;17(8):2081-2089. doi: 10.1039/d0sm01950j.
Graphene-based polymers exhibit a conductive microstructure formed by aggregates in a matrix which drastically enhances their transmitting properties. We develop a new numerical framework for predicting the electrical conductivity based on continuum percolation theory in a two dimensional stochastically-generated medium. We analyze the role of the flake shape and its aspect ratio and consequently predict the onset of percolation based on the particle density and the domain scale. Simultaneously, we have performed experiments and have achieved very high electrical conductivity for such composites compared to other film fabrication techniques, which have verified the results of computing the homogenized electrical conductivity. As well, the proximity to and a comparison with other analytical models and other experimental techniques are presented. The numerical model can predict the composite transmitting conductivity in a larger range of particle geometry. Such quantification is exceedingly useful for effective utilization and optimization of graphene filler densities and their spatial distribution during manufacturing.
基于石墨烯的聚合物呈现出由基质中的聚集体形成的导电微观结构，这极大地增强了它们的传输性能。我们基于二维随机生成介质中的连续渗流理论，开发了一种用于预测电导率的新数值框架。我们分析了薄片形状及其纵横比的作用，并据此根据颗粒密度和域尺度预测渗流的起始。同时，我们进行了实验，与其他薄膜制造技术相比，此类复合材料实现了非常高的电导率，这验证了计算均匀化电导率的结果。此外，还展示了与其他分析模型和其他实验技术的接近程度及比较情况。该数值模型可以在更大范围的颗粒几何形状中预测复合材料的传输电导率。这种量化对于在制造过程中有效利用和优化石墨烯填料密度及其空间分布极为有用。
