Polymer nanocomposites (PNCs) have gained significant interest because they have outstanding performance that allows cost reduction, weight reduction, and product improvement. This research study focuses on the manufacture and characterization of PNCs in order to explore their potential in automotive applications. More specifically, polypropylene (PP) nanocomposites reinforced with xGnP and nanokaolin were fabricated by manufacturing methods that optimize their performances. Exfoliated graphite nanoplatelets (xGnP) are promising nanofillers that are cost effective and multifunctional with superior mechanical, thermo-mechanical and electrical properties. Nanokaolin is a newly introduced natural mineral mind in Georgia that has not been studied as of now. PNCs reinforced with these two nanofillers were characterized in terms of mechanical, thermo-mechanical, and various other properties, and then compared to talc- reinforced PP composites, which are the current state of the art for rear bumpers used by Honda Motor. Characterization results indicated that xGnP had better performance than talc and nanokaolin. Furthermore, the addition of xGnP introduces electrical conductivity in the PNCs, leading to more potential uses for PNCs in automotive applications such as the ability to be electrostatic painted. In order to fabricate PNCs with a desired conductivity value, there is need for a design tool that can predict electrical conductivity. Existing electrical conductivity models were examined in terms of model characteristics and parameters, and model predictions were compared to the experimental data. The percolation threshold is the most important parameter in these models, but it is difficult to determine experimentally, that is why a correlation between thermo-mechanical properties and electrical conductivity is also investigated in this study.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/37128 |
| Date | 03 November 2010 |
| Creators | Chu, Chun |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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