Date of Award

12-2010

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Chemical Engineering

Advisor

Kitchens, Christopher L

Committee Member

Hirt , Douglas E

Committee Member

Husson , Scott M

Committee Member

Vertegel , Alexey A

Abstract

Wet chemical synthesis techniques offer the ability to control various nanoparticle characteristics including size, shape, dispersibility in both aqueous and organic solvents, and tailored surface chemistries appropriate for different applications. Large quantities of stabilizing ligands or surfactants are often required during synthesis to achieve these nanoparticle characteristics. Unfortunately, excess reaction byproducts, surfactants, and ligands remaining in solution after nanoparticle synthesis can impede application, and therefore post-synthesis purification must be employed. A liquid-liquid solvent/anti-solvent pair (typically ethanol/toluene or ethanol/hexane for gold nanoparticles, GNPs) can be used to both purify and size-selectively fractionate hydrophobically modified nanoparticles. Alternatively, carbon dioxide may be used in place of a liquid anti-solvent, a ―green‖ approach, enabling both nanoparticle purification and size-selective fractionation while simultaneously eliminating mixed solvent waste and allowing solvent recycle. We have used small-angle neutron scattering (SANS) to investigate the ligand structure and composition response of alkanethiol modified gold and silver nanoparticles at varying anti-solvent conditions (CO2 or ethanol). The ligand lengths and ligand solvation for alkanethiol gold and silver NPs were found to decrease with increased anti-solvent concentrations directly impacting their dispersibility in solution. Calculated Flory-Huggins interaction parameters support our SANS study for dodecanethiol dispersibility in the mixed organic solvents. This research has led to a greater understanding of the liquid-liquid precipitation process for metal nanoparticles, and provides critical results for future interaction energy modeling.

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