Catalytic activity predictions and the identification of active sites rely on precisely determining the dominant reaction mechanism. The activity governing mechanism and products could vary with the catalyst material, which can be described by material descriptor(s), typically the binding strength(s) of key intermediate species. Density functional theory calculations can be used to identify dominant reaction mechanisms. However, the dominant reaction mechanism is sensitive to choice of the exchange correlation functional. Here, we demonstrate using the example case of chlorine evolution reaction on rutile oxides, which can occur through at least three reaction mechanisms each mediated by different surface intermediates and active sites. We utilize Bayesian error estimation capabilities within the BEEF-vdW exchange correlation (XC) functional to quantify the uncertainty associated with predictions of the operative reaction mechanism by systematically propagating the uncertainty originating from DFT-computed adsorption free energies. We construct surface Pourbaix diagrams based on the calculated adsorption free energies for rutile oxides of Ru, Ir, Ti, Pt, V, Sn and Rh. We utilize confidence-value (c-value) to determine the degree of confidence in the predicted surface phase diagrams. Using the scaling relations between the adsorption energies of intermediates we construct a generalized Pourbaix diagram showing the stable surface composition as a function of potential and the oxygen binding energy on the cus site (∆E O c ). This is used to consistently determine activity volcano relationships. We incorporate the uncertainty in linear scaling relations to quantify the confidence in generalized Pourbaix diagram and the associated activity. This allows us to compute the expectation limiting potential as a function of ∆E O c , which provides a more appropriate activity measure incorporating DFT uncertainty. We show that the confidence in the classification problem of identifying the active reaction mechanism is much better than the prediction problem of determining catalytic activity. We believe that such a systematic approach is needed for accurate determination of activities and reaction pathways for multi-electron electrochemical reactions such as N 2 and CO 2 reduction.

Alternative approaches for producing ammonia are necessary to reduce the environmental impact of its production. The lithium-mediated electrochemical nitrogen reduction reaction (LM-NRR) is one attractive alternative method for producing ammonia at small scales in a distributed process. This process requires a proton donor in the electrolyte to produce ammonia from nitrogen, but the role of the proton donor in selective ammonia production is not well understood. In this work, we experimentally tested several classes of proton donors for the ability to promote LM-NRR. We found that a wide array of alcohols can promote nitrogen reduction and that n-butanol leads to the highest ammonia Faradaic efficiencies. Among the tested proton donors, even slight changes in the proton donor structure can significantly affect the yield of ammonia. In addition, most active proton donors exhibit a thresholding behavior as a function of their concentration, where the selectivity toward ammonia increases dramatically above a certain concentration of the proton donor. We found evidence to imply that these effects could be due to the proton-donor-induced changes in the properties of the solid electrolyte interphase (SEI), which lead to changes in the diffusion of relevant species through the SEI to the reactive electrode. By selectively allowing for diffusion of nitrogen over the proton donor to the electrode, the SEI can promote selective nitrogen reduction to ammonia. A coupled kinetic transport model of the process was proposed to explain the observed trends and to predict ammonia production as a function of operating conditions.

Selective two-electron oxygen reduction reaction (ORR) offers a promising route for hydrogen peroxide synthesis, and defective sp2-carbon-based materials are attractive, low-cost electrocatalysts for this process. However, due to a wide range of possible defect structures formed during material synthesis, the identification and fabrication of precise active sites remain a challenge. Here, we report a graphene edge-based electrocatalyst for two-electron ORRnanowire-templated three-dimensional fuzzy graphene (NT-3DFG). NT-3DFG exhibits notable efficiency [onset potential of 0.79 ± 0.01 V vs reversible hydrogen electrode (RHE)], high selectivity (94 ± 2% H2O2), and tunable ORR activity as a function of graphene edge site density. Using spectroscopic surface characterization and density functional theory calculations, we find that NT-3DFG edge sites are readily functionalized by carbonyl (CO) and hydroxyl (C–OH) groups under alkaline ORR conditions. Our calculations indicate that multiple functionalized configurations at both armchair and zigzag edges may achieve a local coordination environment that allows selective, two-electron ORR. We derive a generalized geometric descriptor based on the local coordination environment that provides activity predictions of graphene surface sites within ∼0.1 V of computed values. We combine synthesis, spectroscopy, and simulations to improve active site characterization and accelerate carbon-based electrocatalyst discovery.

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG. We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

First-row transition metal oxides and chalcogenides have been found to rival or exceed the performance of precious metal-based catalysts for the interconversion of water and O2, central reactions that underlie renewable electricity storage and utilization. However, the high lability of the first-row transition metal ions leads to surface dynamics under the conditions of catalysis and results in active site structures distinct from those expected by surface termination of the bulk lattice. While these surface transformations have been well-characterized on many metal oxides, the surface dynamics of heavier chalcogenides under electrocatalytic conditions are largely unknown. We recently reported that the heazlewoodite Ni3S2 bulk phase supports efficient ORR catalysis under benign aqueous conditions and exhibits excellent tolerance to electrolyte anions such as phosphate which poison Pt. Herein, we combine electrochemistry, surface spectroscopy and high resolution microscopy to characterize the surface dynamics of Ni3S2 under ORR catalytic conditions. We show that Ni3S2 undergoes self-limiting oxidative surface restructuring to form an approximately 2 nm amorphous surface film conformally coating the Ni3S2 crystallites. The surface film has a nominal NiS stoichiometry and is highly active for ORR catalysis. Using DFT simulations we show that, to a first approximation, the catalytic activity of nickel sulfides is determined by the Ni-S coordination numbers at surface exposed sites through a simple geometric descriptor. In particular, we find that the surface sites formed dynamically on the surface of amorphous NiS during surface restructuring provide an optimal energetic landscape for ORR catalysis. This work provides a systematic framework for characterizing the rich surface chemistry of metal-chalcogenides and provides principles for the development of structure-energy-activity descriptors leading to a broader understanding of electrocatalysis mediated by amorphous materials.The interconversion of water and oxygen is a central chemistry underlying the storage of renewable electricity in energy-dense chemical bonds (Lewis and Nocera, 2006). The oxidation of H2O to O2 is the efficiency limiting half reaction for the splitting of water to generate H2 fuel, whereas the reduction of O2 to H2O is the efficiency limiting cathode reaction in low temperature fuel cells (Katsounaros et al., 2014). Platinum group metals and their corresponding oxides and chalcogenides are well-known catalysts for these reactions(Matsumoto and Sato, 1986) - (Gasteiger et al., 2005), but recent studies have uncovered a diversity of earth abundant first-row transition metal oxides(Kanan and Nocera, 2008) - (Long et al., 2014) and chalcogenides (Gao et al., 2012) , (Gao et al., 2013) that, depending on the reaction conditions, rival the activity of their precious metal analogs.Unlike their precious metal congeners, first-row transition metal ions are labile, Merbach, 1999, 2005) and as a result, the surfaces of these materials are expected to ...