(734i) Hammett Study: Combined Experimental-Computational Approach to Understanding Electronic Metal-Support Interaction | AIChE

(734i) Hammett Study: Combined Experimental-Computational Approach to Understanding Electronic Metal-Support Interaction

Authors 

Kumar, G. - Presenter, Pennsylvania State University
Chandler, B. D., Trinity University
Janik, M. J., Pennsylvania State University
Metal-support interactions may include electron transfer between the two components that alters catalytic activity; however, probing this phenomenon is challenging. Using a combined experimental-computational approach, we investigate how the underlying support affects the electronics of the active site of a supported Au catalyst. We employ a Hammett study using benzyl alcohol oxidation to show that reaction rate is dependent on the identity of benzyl alcohol ring substituents (â??OCH3, -CH3, -Cl, and â??CF3). Further, the changes in substrate reactivity are well correlated with the sigma+ Hammett parameter of the substituted alcohols. Hammett studies on Au/Al2O3, Au/SiO2, Au/TiO2 and Au/ZnO show that the slope of this correlation, ρ, changes significantly with the underlying support, ranging from -0.36 for Au/ZnO to -0.87 for Au/Al2O3. The slope ρ can be interpreted as a semi-quantitative measure of the degree of charge build-up in the transition state, and the degree of influence of the support on the electronics of the Au nanoparticle. We use Density Functional Theory (DFT) to further investigate these relationships. We calculate the oxidation reaction energetics (to benzaldehyde) of para-substituted (CF3 and OCH3) benzyl alcohols over unsupported Au clusters with varying charges. The ring substituents have less effect on the rate of oxidation (indicative of the low ρ value) on electron rich clusters, consistent with a rate determining step of a hydride transfer to the Au nanoparticle. We will also show support properties that correlate with the slope ρ, and methods to quantify these electronic interactions using our experimental/computational data.

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