Date of Award

1-1-2013

Document Type

Open Access Thesis

Department

Mechanical Engineering

First Advisor

Sarah C Baxter

Second Advisor

Juan M Caicedo

Abstract

Polymer nanocomposites (PNCs) represent a radical alternative to conventional filled polymers or polymer blends. In contrast to conventional composites, where the included phase is on the order of micrometers, PNCs are defined as those that have discrete constituents on the order of a few hundred nanometers. The value of PNCs is not solely based on tailoring mechanical properties, as in traditional composite design and manufacture, but rather on the potential for the design and optimization of multi-functional properties. There is major interest in these polymeric materials embedded with a conductive nanoscale filler. This is due to the possibility of designing a composite that retains the easy processing of plastics but can take advantage of a conductive nanoscale phase; providing electrical conductivity in additional to structural reinforcement.

The challenge to modeling multi-functional properties of PNCs is that, traditionally, different models have been applied to model different properties. Mechanical properties are most often modeled using mean-field models from micromechanics; properties depend on the microstructural arrangement of the included phase, phase properties and volume fraction. Electrical conductivity has primarily been modeled using percolation theory and power-law models; properties depend on theoretical or simulation based estimates of a percolation threshold, phase properties and volume fraction. However, models of both properties should ideally be built on specific microstructural descriptions as well as include a probabilistic framework to capture percolation effects.

In this work a modeling framework, developed for predicting composite mechanical properties, is investigated for its applicability in modeling effective electrical composite properties. The basis for using a micromechanics approach for predictions of conductivity is presented as well as how the model is adapted to model conductivity. A comparison of the adapted and original micromechanics approach is presented for deterministic microstructures. The modeling framework is subsequently used to predict the effective electrical conductivity of a model PNC. Using the micromechanical parameters of interface thickness and properties, the model can be adjusted to correspond with observed experimental and is useful in suggesting underlying mechanisms.

Rights

© 2013, Neelima Yellepeddi

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