Predicting the electrical conductivity in polymer carbon nanotube nanocomposites based on the volume fractions and resistances of the nanoparticle, interphase, and tunneling regions in conductive networks

电导率 相间 渗透(认知心理学) 材料科学 碳纳米管 量子隧道 渗流阈值 纳米复合材料 体积分数 导电体 电阻率和电导率 渗流理论 纳米管 凝聚态物理 纳米技术 复合材料 化学 物理 光电子学 生物 物理化学 神经科学 量子力学 遗传学
作者
Zhenling Liu,Wanxi Peng,Yasser Zare,David S.C. Hui,Kyong Yop Rhee
出处
期刊:RSC Advances [The Royal Society of Chemistry]
卷期号:8 (34): 19001-19010 被引量:54
标识
DOI:10.1039/c8ra00811f
摘要

Some limited models have been suggested to determine the conductivity of polymer carbon nanotube (CNT) nanocomposites (PCNTs). However, earlier models (e.g., the Kovacs model) cannot properly consider the roles of the interphase regions or tunneling properties on the percolation threshold and subsequent conductivity of PCNTs. In this paper, the Kovacs model is further developed by assuming that the CNT, interphase, and tunneling regions are separate phases. Also, some simple equations are provided to calculate the percolation threshold as well as the volume fractions and resistances of the CNT, interphase, and tunneling regions in conductive networks. The experimental conductivity results for several samples are compared with the predictions of the developed model. In addition, the calculations of the developed model at different parameter levels are explained and justified. The conductivity calculations show good agreement with the experimental data. Moreover, the developed model reasonably explains the roles of the different parameters on the conductivity. For example, long, thin, and straight CNTs efficiently improve the conductivity because they form large networks in the nanocomposites. Additionally, a thick interphase enlarges the conductive networks, resulting in a desirable conductivity. The conductivity of PCNTs only depends on the tunneling resistance; this is the case because the poor resistance/significant conductivity of the CNT and interphase regions do not influence the conductivity. The developed equations can replace conventional approaches for predicting the conductivity of nanocomposites.
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