Computational modeling of electrically conductive networks formed by graphene nanoplatelet–carbon nanotube hybrid particles
Computational modeling of electrically conductive networks formed by graphene nanoplatelet–carbon nanotube hybrid particles
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DOI：
10.1088/1361-651x/aaab7a
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发表时间：
2018-02
影响因子：
1.8
通讯作者：
A. Mora;F. Han;G. Lubineau
中科院分区：
文献类型：
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作者：
A. Mora;F. Han;G. Lubineau
确保聚合物复合材料中的纳米填料网络以低体积分数渗滤的一种策略是促进分离。在分离结构中，纳米填料的浓度在样品的某些区域中保持较低。反过来，剩余区域中的浓度远高于样品的平均浓度。纳米填料的这种选择性放置确保了在低平均浓度下的渗滤。一种促进分离的原始策略是通过调整纳米填料的形状。我们使用计算的方法来研究所形成的导电网络的混合颗粒通过生长碳纳米管（CNT）的石墨烯纳米片（GNP）。本研究的目的是（1）表明这些复合材料的较高电导率是由于混合颗粒形成了偏析结构，以及（2）了解定义混合颗粒的参数决定了偏析的效率。我们构建了一个微观结构来观察导电路径，并确定是否确实已经在复合材料内部形成了隔离结构。基于有助于导电网络的纳米填料的分数提出了效率的量度。然后，将混合颗粒网络的效率与其中不使用混合颗粒的碳基纳米填料的三个其他网络的效率进行比较：仅CNT、仅GNP以及CNT和GNP的混合物。最后，研究了杂化粒子的一些参数：GNP上的CNT密度，以及CNT和GNP的几何形状。我们还提出了建议，为进一步改善复合材料的导电性的基础上，这些参数。
One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in the remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite’s conductivity based on these parameters.