Date of Award
Doctor of Philosophy (PhD)
This work presents a probabilistic approach to model the electrical transport properties of carbon nanotube composite materials. A pseudo-random generation method is presented with the ability to generate 3-D samples with a variety of different configurations. Periodic boundary conditions are employed in the directions perpendicular to transport to minimize edge effects. Simulations produce values for drift velocity, carrier mobility, and conductivity in samples that account for geometrical features resembling those found in the lab. All results show an excellent agreement to the well-known power law characteristic of percolation processes, which is used to compare across simulations. The effect of sample morphology, like nanotube waviness and aspect ratio, and agglomeration on charge transport within CNT composites is evaluated within this model. This study determines the optimum simulation box-sizes that lead to minimize size-effects without rendering the simulation unaffordable. In addition, physical parameters within the model are characterized, involving various density functional theory calculations within Atomistix Toolkit. Finite element calculations have been performed to solve Maxwell's Equations for static fields in the COMSOL Multiphysics software package in order to better understand the behavior of the electric field within the composite material to further improve the model within this work. The types of composites studied within this work are often studied for use in electromagnetic shielding, electrostatic reduction, or even monitoring structural changes due to compression, stretching, or damage through their effect on the conductivity. However, experimental works have shown that based on various processing techniques the electrical properties of specific composites can vary widely. Therefore, the goal of this work has been to form a model with the ability to accurately predict the conductive properties as a function physical characteristics of the composite material in order to aid in the design of these composites.
Tarlton, Taylor Warren, "" (2016). Dissertation. 91.