(638c) Elucidating the Role of Coadsorbed O/OH Species in the Selective Oxidation of Glycerol on Late Transition Metals | AIChE

(638c) Elucidating the Role of Coadsorbed O/OH Species in the Selective Oxidation of Glycerol on Late Transition Metals

Authors 

Roling, L., Iowa State University
The obstructive impact of fossil fuels worldwide has led to the exploration of environmentally friendly energy sources derived from plants or biomass. With biodiesel's vast production, glycerol, a significant by-product, is available in surplus but has less economic value due to its inefficient conversion into commercially valuable products through selective oxidation.1 In the search for an efficient chemo-selective process, computation-based catalyst design using density functional theory (DFT) calculations has emerged to elucidate the surface chemistry at an atomic level, which would be difficult to otherwise understand.

Here, we share density functional theory (DFT) calculations of glycerol oxidation mechanisms for the formation of glyceraldehyde and dihydroxyacetone on transition metal catalysts. Previous studies have shown the alcohol oxidation dependency on the reaction medium, particularly due to the presence of coadsorbed species on monometallic surfaces such as Au and Pt.2 Since such coadsorbed species are rarely considered in mechanistic studies of glycerol oxidation, we seek to elucidate their effects toward generating a more realistic catalyst model. Our results suggests that the adsorption of glycerol is stabilized in the presence of coadsorbed oxygenated (O/OH) species. These species mediate the product selectivity (DHA formation) from the secondary O-H bond scission rather than secondary C-H bond scission (favorable in the absence of co adsorbed species) with improved kinetics for the glycerol dehydrogenation on Pt(111), Pt(100), Au(100), and Au(111) surface structures. We also leverage the classical BEP relationship to correlate the glycerol dehydrogenation thermodynamics and kinetics with a good quality of prediction suggesting an efficient way to structure a kinetic model for the complex reaction networks.

References

  1. Katryniok, B. et al. Green Chem. 13, 1960–1979 (2011).
  2. Zope, B.N. et al. Science. 533, 74–79 (2010).