Proton exchange membrane fuel cells are attractive power sources because they are highly efficient and do not pollute the environment. However, the use of Pt-based catalysts in present fuel cell technologies is not optimal: Pt is rare and expensive, and even the best commercial Pt cathodes have high overpotentials due to slow oxygen reduction kinetics. As a result, much effort has gone toward developing cheaper, more effective catalysts.
Nanoparticles are attractive because they have different catalytic properties than analogous bulk systems, require less material, and have tunable reactivities based on their composition and size. It is important to perform detailed studies of nanoparticle catalysts since composition and size effects are poorly understood. Computational simulations of such materials can provide useful insights and potentially aid in the design of new catalysts.
Here, I examine composition and size effects in nanoparticle catalysts using computational methods. Two bimetallic systems are investigated to explore composition effects: Pd-shell particles with several different core metals, and Pd/Cu random alloy particles. Depending on how the two metals are mixed (core-shell or random alloy), charge transfer and strain due to alloying are found to have different contributions to the catalytic activity. Size effects are studied for pure Pt particles, where corner and edge sites are found to play an important role. The binding geometries of molecular oxygen to corner and edge sites lead to peroxide formation instead of water on small Pt particles. Results form these calculations can provide useful information for designing novel catalysts in the future. By changing the composition and/or size of nanoparticles in the proper way, the interaction between the adsorbate and catalyst can be optimized, and better catalysts can be obtained. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-05-775 |
| Date | 16 September 2010 |
| Creators | Tang, Wenjie, 1982- |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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