Eliminating common biases in modelling the electrical conductivity of carbon nanotube–polymer nanocomposites

Linh Trong Hoang; Siu Ning Leung; Zheng Hong Zhu

doi:10.1039/C8CP01715H

Eliminating common biases in modelling the electrical conductivity of carbon nanotube–polymer nanocomposites

Linh Trong Hoang,^a Siu Ning Leung*^b and Zheng Hong Zhu^b

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* Corresponding authors

^a Department of Physics & Astronomy, York University, Toronto, Canada

^b Department of Mechanical Engineering, York University, Toronto, Canada
E-mail: leungsun@yorku.ca

Abstract

Modelling carbon nanotube–polymer nanocomposites to predict their electrical conductivity demands high computational power. Past research has led to the assumption that conductive networks follow a periodic pattern; however, the impact of the underlying biases had never been investigated. This work provides insights into evaluating such biases and eliminating them to improve simulation accuracy.

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Publication details

The article was received on 16 Mar 2018, accepted on 12 Apr 2018 and first published on 12 Apr 2018

Article type: Communication

DOI: 10.1039/C8CP01715H

Citation: Phys. Chem. Chem. Phys., 2018,20, 13118-13121

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Eliminating common biases in modelling the electrical conductivity of carbon nanotube–polymer nanocomposites

L. T. Hoang, S. N. Leung and Z. H. Zhu, Phys. Chem. Chem. Phys., 2018, 20, 13118
DOI: 10.1039/C8CP01715H
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Physical Chemistry Chemical Physics

Eliminating common biases in modelling the electrical conductivity of carbon nanotube–polymer nanocomposites

Abstract

Supplementary files

Publication details

Eliminating common biases in modelling the electrical conductivity of carbon nanotube–polymer nanocomposites

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