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

Literature Information

Publication Date 2018-04-12
DOI 10.1039/C8CP01715H
Impact Factor 3.676
Authors

Linh Trong Hoang, Siu Ning Leung, Zheng Hong Zhu


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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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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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