Glycerol is a byproduct of biodiesel production and an abundant feedstock that can be used for the synthesis of high-value chemicals. There are many approaches for glycerol valorization, but, due to the complicated reaction mechanism, controlling which products are produced is challenging. Here, we describe glycerol's chemical selectivity for different metallic catalysts using descriptors for carbon (mainly *C, *CH2OH) and oxygen (mainly *O, CH3O*). The quality of these descriptors and the weighted combinations thereof are validated based on their fit, via linear regression, to the binding energies of all reaction intermediates generated in the first two glycerol dehydrogenation steps on a number of close-packed Ru, Co, Rh, Ir, Ni, Pd, Pt, Cu, Ag, and Au surfaces. We show that *CH2OH is a better descriptor than *C for the studied carbon-bound intermediates, which is attributed to the observation that the adjacent *OH group interacts with the surface. This leads to a negative oxygen dependence, which can be generalized to similar alcohol-derived adsorbates. Furthermore, we show that CH3O* is a better oxygen descriptor than *0 for the studied intermediates. This is mainly attributed to the difference between the single and double bonds, as we show that *OH is closer to the accuracy of CH3O*. Multilinear regression with different combinations of *C, *O, and *OH is comparable in accuracy to that of *CH2OH and CH3O*. Scaling relationships are used to determine the selectivity map for glycerol dehydrogenation. The results show that the first dehydrogenation is selective toward two different intermediates (one bonded via the secondary carbon and the other via the secondary oxygen) depending on the relative bond strength of the carbon and oxygen descriptors. The second dehydrogenation step results in five intermediates, again depending primarily on the relative bond strength of carbon and oxygen to the surface. The selectivity maps can be used together with kinetic considerations and experimental data to find catalyst candidates for glycerol dehydrogenation.

Glycerol is a byproduct of biodiesel production and an abundant feedstock for the synthesis of high-value chemicals. One promising approach for valorization of glycerol is electrooxidation yielding hydrogen and value-added products. However, due to the vast amount of intermediary steps and possible products, the process is not fully understood. Here, the first two deprotonations of glycerol on close-packed transition metals (Ru, Co, Rh, Ir, Ni, Pd, Pt, Cu, Ag, and Au) are investigated using density functional theory calculations together with the computational hydrogen electrode. We find that the theoretical limiting potential for the studied reaction is close to 0 V vs the reversible hydrogen electrode, ranging from −0.12 V for ruthenium to +0.35 V for gold. Furthermore, the results show that Ru, Rh, Ir, Ag, and Au are selective toward dihydroxyacetone and its derivatives, while Pd and Pt are selective toward either dihydroxyacetone or glyceraldehyde and their derivatives, and that Cu, Co, and Ni are selective toward hydropyruvic acid. The results can be rationalized in terms of the relative bond strengths of carbon and oxygen on the metal. In addition, we find that solvent effects are generally small, the exceptions being the limiting potential on copper and the mechanism on rhodium. These results can be used to steer the selectivity toward more valuable products and thereby increase the economic yield of biodiesel production.

CO2 reduction reactions, which provide one route to limit the emission of this greenhouse gas, are commonly performed over Cu-based catalysts. Here, we use ambient pressure X-ray photoelectron spectroscopy together with density functional theory to obtain an atomistic understanding of the dissociative adsorption of CO2 on Cu(100). We find that the process is dominated by the presence of steps, which promote both a lowering of the dissociation barrier and an efficient separation between adsorbed O and CO, reducing the probability for recombination. The identification of steps as sites for efficient CO2 dissociation provides an understanding that can be used in the design of future CO2 reduction catalysts.