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Computational analysis of electrical conduction in hybrid nanomaterials with embedded non-penetrating conductive particles

机译:嵌入非穿透性导电粒子的杂化纳米材料中电导率的计算分析

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In this work, a comprehensive multi-resolution two-dimensional (2D) resistor network model is proposed to analyze the electrical conductivity of hybrid nanomaterials made of insulating matrix with conductive particles such as CNT reinforced nanocomposites and thick film resistors. Unlike existing approaches, our model takes into account the impenetrability of the particles and their random placement within the matrix. Moreover, our model presents a detailed description of intra-particle conductivity via finite element analysis, which to the authors' best knowledge has not been addressed before. The inter-particle conductivity is assumed to be primarily due to electron tunneling. The model is then used to predict the electrical conductivity of electrospun carbon nanofibers as a function of microstructural parameters such as turbostratic domain alignment and aspect ratio. To simulate the microstructure of single CNF, randomly positioned nucleation sites were seeded and grown as turbostratic particles with anisotropic growth rates. Particle growth was in steps and growth of each particle in each direction was stopped upon contact with other particles. The study points to the significant contribution of both intra-particle and inter-particle conductivity to the overall conductivity of hybrid composites. Influence of particle alignment and anisotropic growth rate ratio on electrical conductivity is also discussed. The results show that partial alignment in contrast to complete alignment can result in maximum electrical conductivity of whole CNF. High degrees of alignment can adversely affect conductivity by lowering the probability of the formation of a conductive path. The results demonstrate approaches to enhance electrical conductivity of hybrid materials through controlling their microstructure which is applicable not only to carbon nanofibers, but also many other types of hybrid composites such as thick film resistors.
机译:在这项工作中,提出了一个全面的多维二维(2D)电阻器网络模型,以分析由绝缘基质与导电颗粒(如CNT增强纳米复合材料和厚膜电阻器)构成的杂化纳米材料的电导率。与现有方法不同,我们的模型考虑了粒子的不可渗透性及其在矩阵中的随机放置。此外,我们的模型通过有限元分析提供了粒子内电导率的详细描述,据作者所知,以前从未解决过。假定粒子间电导率主要是由于电子隧穿。然后,该模型用于预测电纺碳纳米纤维的电导率,该电导率是微观结构参数(如涡轮层域排列和纵横比)的函数。为了模拟单个CNF的微观结构,将随机定位的成核位点播种并生长为具有各向异性生长速率的涡轮层颗粒。颗粒逐步生长,并且在与其他颗粒接触时停止每个方向上的每个颗粒的生长。研究指出,颗粒内和颗粒间电导率对混合复合材料的总电导率有重要贡献。还讨论了粒子排列和各向异性生长速率比对电导率的影响。结果表明,与完全对准相反,部分对准可以导致整个CNF的最大电导率。高度对准可以通过降低形成导电路径的可能性来不利地影响导电性。结果表明,通过控制混合材料的微观结构来增强其导电性的方法不仅适用于碳纳米纤维,而且适用于许多其他类型的混合复合材料,例如厚膜电阻器。

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