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Degree

Doctor of Philosophy in Engineering, University of Toledo, College of Engineering, 2011.

Abstract

The electrical conductivity of polymer nanocomposites follows a percolation-like behavior which is attributed to the formation of a connected network of conductive inclusions within the insulating polymer matrix. Percolation in polymer nanocomposites is of high interest due to the potential to create electrically conductive materials with an optimized conductivity and as low as possible a loading of conductive fillers. The development of powerful numerical models has enabled researchers to simulate the microstructure of composites and predict percolation in disordered systems. Most of the literature studies model fibers as fully interpenetrating objects into each other which presume that the electrical and geometric percolation thresholds occur simultaneously which is true only when in a composite the reinforcing particles coalesce so that a physically connected network is produced.

In this dissertation, an approach has been developed aimed at developing a qualitative and quantitative picture of the electrical behavior of particulate nanocomposite materials. The continuum connectedness model was applied to the systems of uniform and variable objects of a given geometry to simulate the realistic percolation in both nanosheet and fibrous systems (nanofibers, nanotubes) and predict how shape and size distribution influence the percolation thresholds. The relationship between percolation threshold and excluded volume in systems which obey finite size scaling theory was investigated using Monte Carlo simulations. Voltage-current studies were performed on the nanocomposite specimens to measure the electrical conductivity. The results were interpreted using both an analytical percolation theory model and numerical simulations. The agreement between the predictions and the experimental results establishes the effectiveness of the model in simulating the electrical percolation phenomenon in nanofibers or any conductive particles suspended in an electroconductive medium, demonstrating that the model can be used as a predictive tool for designing nanocomposite materials.

Subject Headings

Mechanical Engineering; Nanotechnology

Keywords

Percolation; Modeling; Nanocomposites; Conductivity

Committee / Advisors

Dr. Lesley Berhan, PhD (Advisor)
Dr. Maria Coleman, PhD (Committee Member)
Dr. Yong Gan, PhD (Committee Member)
Dr. Ahalapitiya Jayatissa, PhD (Committee Member)
Dr. Ioan Marinescu, PhD (Committee Member)

Pages

147p.

Document number: toledo1302196468
Permalink: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302196468

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