journal article·2020-01

Reduced percolation threshold of multi-walled carbon nanotubes/polymer composites by filling aligned ferromagnetic particles

Shuai Dong, Xuan Wu, Erhua Wang, Xiaojie Wang

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Abstract

Conductive polymer composites, consisting of multi-walled carbon nanotubes and a small amount of carbonyl iron particles, are fabricated under an ordinary magnetic field, to form anisotropic microstructures. The alignment of carbonyl iron particles will change the structure of a multi-walled carbon nanotube network and consequently the electrical properties of conductive polymer composites. In this research, we focus on the effect of the anisotropic microstructures on the electrical properties of the composites, especially on the percolation threshold and electrical resistivity. Monte Carlo simulations for three-dimensional stick percolation systems are performed to predict the percolation threshold of the anisotropic conductive polymer composites in terms of orientation distribution of multi-walled carbon nanotubes. In addition, an eight-chain model is proposed to investigate the influence of the anisotropic distribution of multi-walled carbon nanotubes on the electrical resistivity of the composites. It is predicted that the percolation threshold could be reduced from 0.70 vol% for the isotropic composites to 0.49 vol% for the anisotropic composites. Meanwhile, the electrical resistivity of the anisotropic composites is about 10%–20% of that of the isotropic composites when the volume fraction of multi-walled carbon nanotubes is higher than the percolation threshold. The simulation results are compared with the experimental study results that show a very similar behavior although there are some deviations in the values.
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Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Carbon Nanotubes in Composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou, China

School of Mechanical and Electrical Engineering, Changzhou College of Information Technology, Changzhou, China

Funding

National Natural Science Foundation of China · Key Laboratory of Human Factors Engineering · Changzhou Key Laboratory of High Technology

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