Open AccessMonte Carlo modelling of percolation and conductivity in carbon filled polymer nanocomposites
Author(s) -
Vas Joseph Vimal,
Joy Thomas M.
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0093
Subject(s) - materials science , conductivity , composite material , silicone rubber , percolation (cognitive psychology) , nanocomposite , monte carlo method , filler (materials) , composite number , polymer , thermal conductivity , polymer nanocomposite , percolation threshold , electromagnetic shielding , carbon nanotube , electrical resistivity and conductivity , physics , statistics , mathematics , quantum mechanics , neuroscience , biology
Conducting polymer composites are attractive alternatives for applications such as electromagnetic shielding in which metals are conventionally used. They have unique advantages like light weight, ease of processing and good strength to weight ratio. This makes them economically viable especially in industries like avionics where weight is an important criterion. However, a major challenge in using conducting polymer composites in many of the applications is its limited conductivity. Even though many models exist to describe the conductivity of the composites at the macro level taking into account the interparticle conductivity, very few studies are conducted to understand the conductivity of the composites directly from basic principles. In this study, Monte Carlo (MC) simulations are carried out to study the conductivity in such composites. The results obtained using the MC model showed good agreement with the Mamunya model and the experimental measurements. The effect of various parameters like filler diameter and filler conductivity on the final composite conductivity is studied for spherical and fibrous carbon fillers in silicone rubber. The method can be easily adopted for any other filler–polymer combination and can also be used as a predictive tool for designing simple composites of required electrical properties.