A morphological investigation of conductive networks in polymers loaded with carbon nanotubes
KAUST DepartmentComposite and Heterogeneous Material Analysis and Simulation Laboratory (COHMAS)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2017-01-13
Print Publication Date2017-04
Permanent link to this recordhttp://hdl.handle.net/10754/622802
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AbstractLoading polymers with conductive nanoparticles, such as carbon nanotubes, is a popular approach toward improving their electrical properties. Resultant materials are typically described by the weight or volume fractions of their nanoparticles. Because these conductive particles are only capable of charge transfer over a very short range, most do not interact with the percolated paths nor do they participate to the electrical transfer. Understanding how these particles are arranged is necessary to increase their efficiency. It is of special interest to understand how these particles participate in creating percolated clusters, either in a specific or in all directions, and non-percolated clusters. For this, we present a computational modeling strategy based on a full morphological analysis of a network to systematically analyse conductive networks and show how particles are arranged. This study provides useful information for designing these types of materials and examples suitable for characterizing important features, such as representative volume element, the role of nanotube tortuosity and the role of tunneling cutoff distance.
CitationLubineau G, Mora A, Han F, Odeh IN, Yaldiz R (2017) A morphological investigation of conductive networks in polymers loaded with carbon nanotubes. Computational Materials Science 130: 21–38. Available: http://dx.doi.org/10.1016/j.commatsci.2016.12.041.
SponsorsWe thank SABIC for providing funds for this research. In particular, we gratefully acknowledge research support from Dr. Amit Tevtia (SABIC CRD Saudi Arabia). This research was also supported by funding from King Abdullah University of Science and Technology (KAUST).
JournalComputational Materials Science