(750d) Morphology Dynamics of Precious Metal Catalysts for Use in Steam Reformation of Oxygenated Fuels

The advancement in liquid fuel production from biological sources will result in oxygenated fuel compositions. While oxygen is beneficial for combustion purposes, its presence within the chemical makeup of the fuel is detrimental to the fuel’s energy density.  In understanding how oxygenated fuel may be reformed to remove the oxygen functional groups, we have been exploring ethanol reforming as a test case. Recently, initial results show for the first time that the oxidation state of catalytic Rh nanoparticles changes dynamically under reaction conditions with ethanol and that the extent of Rh oxidation appears to control catalyst activity and the rate of deactivation. Catalyst deactivation is one of the major challenges faced in fuel reforming and there exists a need not only to determine the primary causes, but model them in order to take preventative measures. Through the use of common analytical techniques such as CO chemisorption and X-ray Diffraction (XRD), the surface morphology of the catalysts has been examined. Each of three formulations was calcined at temperatures ranging from 550°C to 950°C. In addition, X-ray Diffraction (XRD) has been performed in order to detect any conformational changes in the crystal structure of the catalysts as a function of calcination temperature. The extent of sintering of the precious metal catalysts as well as any changes in oxidation state, catalyst poisons adsorbed to the catalyst, and burrowing of the precious metals into the support are suspected to result in lower catalytic activity. This presentation will correlate and explain the active metal distribution and catalytic activity as well as the time dependence of the distribution during the reaction. In this way, we can create a complete profile of the molecular composition of the catalyst surface, which could lead to better overall performance.  It is anticipated that these results will provide an important basis for DFT and mechanistic modeling to further elucidate the underpinnings of the observed performance.