Return to search

Percolation Study Of Nano-composite Conductivity Using Monte Carlo Simulationpercolation

A Monte Carlo model is developed for predicting electrical conductivity of carbon nanofiber composite materials. The conductive nanofibers are models as both 2D and 3D network of finite sites that are randomly distributed. The percolation behavior of the network is studied using the Monte Carlo method, which leads to the determination of the percolation threshold. The effect of the nanofiber aspect ratio on the critical nanofiber volume rate is investigated in the current model, each of the nanofibers needs five independent geometrical parameters (i.e., three coordinates in space and two orientation angles) for its identification. There are three controlling parameters for each nanofiber, which includes the nanofiber length, the nanofiber diameter, and the nanofiber aspect ratio. The simulation results reveal a relationship between the fiber aspect ratio and the percolation threshold: the higher the aspect ratio, the lower the threshold. With the simulation results obtained from the Monte Carlo model, the effective electrical conductivity of the composite is then determined by assuming the conductivity is proportional to the ratio of the number of nanofibers forming the largest cluster to the total number of nanofibers. The numerical results indicate that as the volume rate reaches a critical value, the conductivity starts to rise sharply. These obtained simulation results agree fairly with experimental and numerical data published earlier by others. In addition, we investigate the convergence of the current percolation model. We also find the tunneling effect does not affect the critical volume rate greatly. We propose that the percolation model is not scalable as well.

Identiferoai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5099
Date01 January 2009
CreatorsBai, Jing
PublisherSTARS
Source SetsUniversity of Central Florida
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceElectronic Theses and Dissertations

Page generated in 0.0015 seconds