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At least 469 records · Page 26

Microscopic Theory of Plasmons in Substrate-Supported Borophene

Here, we compute the dielectric properties of freestanding and metal-supported borophene from first-principles time-dependent density functional theory. We find that both the low- and high-energy plasmons of borophene are fully quenched by the presence of a metallic substrate at borophene–metal distances smaller than ≃9 Å. Based on these findings, we derive an electrodynamic model of the interacting, momentum-dependent polarizability for a two-dimensional metal on a model metallic substrate, which quantitatively captures the evolution of the dielectric properties of borophene as a function of metal–borophene distance. Applying this model to a series of metallic substrates, we show that maximizing the plasmon energy detuning between borophene and substrate is the key material descriptor for plasmonic performance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Automation-Accelerated Electrolyte Design Mitigates Solubility Competition between Redox-Active Molecules and Supporting Salts

In nonaqueous redox-flow batteries (NRFBs), redox-active organic molecules (ROMs) and supporting salts compete for solvation sites, limiting achievable energy density. We combine automated high-throughput experimentation (HTE) with camera-based saturation monitoring and quantitative NMR to measure paired (ROM, salt) solubilities across single and mixed organic solvents. Using 2,1,3-benzothiadiazole (BTZ) with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as a model system, we find that a binary m-xylene/acetonitrile mixture dissolves ≈3 M of both BTZ and LiTFSI─surpassing the previously reported 2 M ceiling for neat acetonitrile─by leveraging complementary solvation (MX is BTZ-philic and salt-phobic; ACN stabilizes LiTFSI). A random-forest model (RMSE ≈ 0.24) trained on solvent descriptors highlights log P and salt concentration as dominant predictors and predicts MX/ACN ≈0.3/0.7 (v/v) to be near-optimal. These formulations retain practical viscosity and ∼5 mS·cm –1 conductivity at high loading. In conclusion, the workflow provides a reproducible, data-centric route to NRFB electrolyte design and motivates an open, standardized dual-solute solubility resource for accelerated electrolyte discovery.

Electrolytes↗

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

The design of multinary solid-state material systems that undergo reversible phase changes via changes in temperature and pressure provides a potential means of safely storing hydrogen. However, fully mapping the stabilities of known or newly targeted compounds relative to competing phases at reaction conditions has previously required many stringent experiments or computationally demanding calculations of each compound’s change in Gibbs energy with respect to temperature, G(T). Here, we have extended the approach of constructing chemical potential phase diagrams based on ΔG f (T) to enable the analysis of phase stability at non-zero temperatures. We first performed density functional theory calculations to compute the formation enthalpies of binary, ternary, and quaternary compounds within several compositional spaces of current interest for solid-state hydrogen storage. Temperature effects on solid compound stability were then accounted for using our recently introduced machine learned descriptor for the temperature-dependent contribution G δ (T) to the Gibbs energy G(T). From these Gibbs energies, we evaluated each compound’s stability relative to competing compounds over a wide range of conditions and show using chemical potential and composition phase diagrams that the predicted stable phases and H2 release reactions are consistent with experimental observations. This demonstrates that our approach rapidly computes the thermochemistry of hydrogen release reactions for compounds at sufficiently high accuracy relative to experiment to provide a powerful framework for analyzing hydrogen storage materials. This framework based on G(T) enables the accelerated discovery of active materials for a variety of technologies that rely on solid-state reactions involving these materials.

08 HYDROGEN↗

The Bonding Nature and Adhesion of Polyacrylic Acid Coating on Li-Metal for Li Dendrite Prevention

The success of polyacrylic acid (PAA) to suppress Li dendrite growth suggests that the mechanical properties of polymer-based coatings, including the modulus, toughness, and interfacial adhesion are important design criteria. However, the measurement of the adhesion of thin PAA, as well as other polymer coatings to the reactive Li-metal anode surface is limited experimentally and challenging computationally. In this paper, a strategy was proposed to estimate the adhesion and delamination of the PAA(polymer)/Li interface, based on the bonding nature at the simpler PAA (oligomer)/Li interfaces using density functional theory calculations. It has been shown that the carboxylic acid groups in PAA reacted strongly with metallic Li, which significantly enhances the interfacial adhesion through the Li–O bonds formation, Li ionization and its incorporation into PAA, and –H or –OH termination of Li after decomposition of the COOH functional group. During delamination, it was found that the most likely PAA delamination route involved breaking partial Li–O bonds and lifting some ionized Li atoms from the Li-metal, especially for the Li atoms that showed a charge closer to +1 or are bonded with two O atoms from PAA. Based on the average bonding energies from PAA(oligomer)/Li interface delamination calculations, the work of separation, W sep , of the PAA(polymer)/Li interface was estimated to be ~1.0 (J/m 2 ). The high W sep of PAA (polymer)/Li was comparable with the Li 2 O/Li interface and higher than Li 2 CO 3 /Li and LiF/Li interfaces. This order correlated well with the areal density of Li–O bonds, which can serve as a descriptor for the interfacial adhesion. Furthermore, this computational approach can be applied to other interfaces with polymer-based coatings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improved Rate for the Oxygen Reduction Reaction in a Sulfuric Acid Electrolyte using a Pt(111) Surface Modified with Melamine

The feasible commercialization of alkaline, phosphoric acid and polymer electrolyte membrane fuel cells depends on the development of oxygen reduction reaction (ORR) electrocatalysts with improved activity, stability, and selectivity. The rational design of surfaces to ensure these improved ORR catalytic requirements relies on the so-called "descriptors" (e.g., the role of covalent and noncovalent interactions on platinum surface active sites for ORR). Here, we demonstrate that through the molecular adsorption of melamine onto the Pt(111) surface [Pt(111)-M ad ], the activity can be improved by a factor of 20 compared to bare Pt(111) for the ORR in a strongly adsorbing sulfuric acid solution. Additionally, the M ad moieties act as "surface-blocking bodies," selectively hindering the adsorption of (bi)sulfate anions (well-known poisoning spectator of the Pt(111) active sites) while the ORR is unhindered. This modified surface is further demonstrated to exhibit improved chemical stability relative to Pt(111) patterned with cyanide species (CN ad ), previously shown by our group to have a similar ORR activity increase compared to bare Pt(111) in a sulfuric acid electrolyte, with Pt(111)-M ad retaining a greater than ninefold higher ORR activity relative to bare Pt(111) after extensive potential cycling as compared to a greater than threefold higher activity retained on a CN ad -covered Pt(111) surface. We suggest that the higher stability of the Pt(111)-M ad interface stems from melamine's ability to form intermolecular hydrogen bonds, which effectively turns the melamine molecules into larger macromolecular entities with multiple anchoring sites and thus more difficult to remove.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning

Predicting lithium-ion battery performance is hindered by microscale electrode heterogeneities invisible to conventional diagnostics. Here, we combine electrochemical fluorescence microscopy (EFM), which maps electronic connectivity by visualizing an electrofluorophore reaction distribution, with a multitask ElasticNet regression to forecast discharge capacity from spatial heterogeneity. Analyzing 196 images from six pilot-scale LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes with varying carbon loadings, we extract 62 descriptors that capture morphology and texture. A compact five-feature model predicts capacity across eight discharge rates, achieving a per-target R 2 of up to 0.63 and an overall R 2 of 0.92, with a mean absolute percentage error of less than 2%. This performance rivals impedance-based approaches while avoiding their reliance on postformation data and incomplete electronic network information. Our facile and rapid, image-driven method may enable electrode quality control upstream of costly cell assembly to offer a transformative tool for data-driven battery research and manufacturing.

battery electrodes↗

Heterolytic Hydrogen Activation: Understanding Support Effects in Water–Gas Shift, Hydrodeoxygenation, and CO Oxidation Catalysis

Identifying the role of oxide supports in transition metal catalysis is critical toward our understanding of heterogeneous catalysis. The water-gas shift (WGS) reaction is a prototypical example where oxide support dictates catalytic activity, yet the cause for this remains uncertain. Herein, we show that a single descriptor—the equilibrium constant for hydroxyl formation—relates the WGS turnover frequency across disparate oxide supports. The dissimilar equilibrium constant, or oxophilicity, between early and late transition metals exemplify the utility of metal-support interfacial sites to circumvent adsorption-energy scaling restrictions, thereby providing bifunctional gains for the WGS reaction class. In relation, the equilibrium constant for hydroxyl formation is equivalent to the equilibrium constant for the formal heterolytic dissociation of hydrogen, and therefore, reflects the ability of the metal-support interface to participate in hydrogen heterolysis. The ubiquitous coexistence, yet divergent chemical behavior of homo- and heterolytically activated hydrogen renders oxide support identity central toward our understanding of hydrogenation catalysis. This work was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences and performed at the Environmental Molecular Sciences Laboratory in (EMSL), which is a DOE Office of Science User Facility located at the Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory operated for DOE by Battelle. Computational resources were provided by a user proposal at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility. N.N. would like to thank Oliver Y. Gutierrez for critical feedback during the final stage of manuscript preparation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mechanistic Similarities and Differences for Hydrogenation of Aromatic Heterocycles and Aliphatic Carbonyls on Sulfided Ru Nanoparticles

This study establishes the contrasting reactivity trends for the hydrogenation of aromatic hydrocarbons (AHCs = C n H m X, X = N, S, O, and C) and aliphatic carbonyls [R a C(O)R b ; R a = alkyl group, R b = CH 3 or H] on sulfided Ru clusters arising from the difference in the elementary proton and hydride attack catalytic sequence. Both reactions require sequential additions of a proton from either Ru n+ –(SH 2 ) or S 2 –(H δ+ ) species and a hydride from Ru n+ –(H δ– ) species to the unsaturated C=X bonds. For the five-membered-ring aromatic heterocycles (AHCs = pyrrole, thiophene, and furan), an initial proton addition limits the catalytic turnovers; thus, their hydrogenation reactivity increases with increasing gas-phase proton affinities of the AHCs. Pyridine as the more basic six-membered N-AHC is more susceptible to protonation; therefore, it is more reactive, and its initial proton addition is quasi-equilibrated, followed by the kinetically relevant hydride addition. Conversely, aliphatic carbonyls prefer to undergo hydrogen additions in a reverse sequence, where a Ru n+ –(H δ– ) hydride initially attacks the electron-deficient carbonyl C atom as the kinetically relevant step before a subsequent rapid S 2 –(H δ+ ) proton addition on the electron-rich O atom, as confirmed by isotopic exchange studies with butanal-D 2 and 1-butanol-D 2 mixtures and density functional theory calculations on S-deficient RuS 2 (100) surfaces. For these reasons, their hydrogenation reactivity increases with increasing gas-phase hydride affinities of the carbonyls. On metallic Ru surfaces without sulfur, hydrogen adatoms (H*) are the only reactive hydrogen species; the reactivity of their attack on aromatic heterocycles increases with increasing reactant proton affinities much more sensitively than that on sulfided Ru surfaces. This work illustrates the distinct catalytic roles of the diverse hydrogen species in hydrogenation—the interplay between proton and hydride additions has marked catalytic consequences in shaping the free energy landscape of the reactions, which in turn leads to the observed kinetic dependences, kinetic parameters, reactivity trends, and scaling relations between the measured barriers and the appropriate kinetic descriptors, that is, proton affinity of AHCs and hydride affinity of carbonyls in hydrogenation catalysis. Here, these mechanistic similarities and differences provide explanations of the observed reactivity trends and thus have profound implications for industrial hydrogenation and hydrotreating catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sustainable Oxide Electrocatalyst for Hydrogen- and Oxygen-Evolution Reactions

In this work, an electrocatalyst for water splitting based on earth-abundant metals is reported. This perovskite-oxide catalyst, CaSrFe 0.75 Co 0.75 Mn 0.5 O 6-δ (CSFCM), is examined using both experimental and computational methods. It demonstrates a combination of properties, which include (a) very high activity for the oxygen-evolution reaction with an overpotential of eta = 0.19 V at 10 mA/cm 2 , (b) high stability over 1000 cycles of catalysis, (c) the ability to catalyze the hydrogen-evolution reaction effectively in both acidic and basic conditions, and (d) catalytic activity as a single-phase bulk material without the need for any additional processing, multicomponent composite preparation, or nanofabrication. Therefore, the catalytic activity of CSFCM is intrinsic, making it a good benchmark compound for future studies of electrocatalytic parameters. This work also highlights the impact of systematic structural design on electrocatalytic activity. Results from density functional theory calculations indicate that in addition to an optimal e g occupancy of similar to 1, an additional descriptor, i.e., maximizing the number of free e g carriers, correlates with the electrocatalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Limits of Detection for EXAFS Characterization of Heterogeneous Single-Atom Catalysts

Single-atom catalysts (SACs), consisting of individual metal atoms dispersed on a support, attract attention due to their unique reactivity, efficient use of precious metals, and precise chemical tunability. Characterization of the metal species is crucial to substantiate structure–function relationships. Authors often use—and referees often require—X-ray absorption spectroscopy (XAS) data to prove the absence of clustered metal (or metal oxide) structures after pre-treatment and under in situ or operando conditions. However, there has been no critical assessment of the limitations of XAS in substantiating such conclusive statements, which is particularly important given the potential outsized influence of minority catalyst structures in dictating catalytic activity. Here, in this article, we quantitatively assess the detection limits of XAS to identify metal (or metal oxide) clusters in samples containing predominantly single atoms by modeling the extended X-ray absorption fine structure (EXAFS) of mixtures of structures. We identified that a significant fraction of clusters can coexist with SAC active sites (e.g., ~10% metallic Pt or ~40% oxidized Pt clusters in Pt/CeO 2 SACs), while eluding detection via EXAFS with any statistical significance. To generalize these conclusions, a descriptor-based screening of bulk metal oxides using a continuous Cauchy wavelet transform was proposed that suggests certain materials for which differentiating atomically dispersed metal species and metal oxide clusters would be infeasible by EXAFS (e.g., ReO x ). Based on this analysis, we suggest best practices for the study of SACs using EXAFS and provide recommendations to ensure that conclusions do not outpace the evidence used to support them. In this rapidly expanding research area, rigorous characterization will lead to greater understanding of the behavior of SACs and ultimately improved catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Revised Nitrogen Reduction Scaling Relations from Potential-Dependent Modeling of Chemical and Electrochemical Steps

The electrochemical nitrogen reduction reaction (NRR) is a promising route to enable carbon-free ammonia production. However, this reaction is limited by the poor activity and selectivity of current catalysts. The rational design of superior NRR electrocatalysts requires a detailed mechanistic understanding of current material limitations to inform how these might be overcome. The current understanding of how scaling limits NRR on metal catalysts is predicated on a simplified reaction pathway that considers only proton-coupled electron transfer (PCET) steps. Here, we apply grand-canonical density functional theory to investigate a more comprehensive NRR mechanism that includes both electrochemical and chemical steps on 30 metal surfaces in solvent under an applied potential. We applied ..phi..max, a grand-canonical adaptation of the Gmax thermodynamic descriptor, to evaluate trends in catalyst activity. This approach produces a ..phi..max "volcano" diagram for NRR activity scaling on metals that qualitatively differs from the scaling relations identified when only PCET steps are considered. NH3* desorption was found to limit the NRR activity for materials at the top of the volcano and truncate the volcano's peak at increasingly reducing potentials. These revised scaling relations may inform the rational design of superior NRR electrocatalysts. This approach is transferable to study other materials and reaction chemistries where both electrochemical and chemical steps are modeled under an applied potential.

electrocatalysis↗

Classification of Adsorbed Hydrocarbons Based on Bonding Configurations of the Adsorbates and Surface Site Stabilities

The design of heterogeneous catalysts can be accelerated by identifying relevant descriptors that accurately and effectively link the binding and activation energies to reactivity. Herein, we investigated scaling relations between binding energies of various hydrocarbon-based adsorbates on three different Pt surfaces and metal binding energies estimated via the recently developed α-scheme model. In this study, we find that the scaling slopes are similar for certain groups of adsorbates, which then can be classified based on their spatial and electronic structure enabling fast description of binding strengths for each member of the class. Hence, our findings show that the binding energies of simple hydrocarbons CH x , x = {0,1,2,3,4}, and CHCH 2 can be used to identify the binding energies of more complex hydrocarbon-based adsorbates. We introduce this classification to establish a generalizable scheme in which complex hydrogenation/dehydrogenation processes of higher hydrocarbons can be predicted via the binding energies of simpler hydrocarbon-based species and ultimately through surface site stabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Calorimetric Energies and Chemical Potentials of Metal Atoms in Catalytic Nanoparticles on Oxide and Carbon Supports: Improved Size Dependencies and Adhesion Energies

The chemical potential of metal atoms, μM, in supported metal nanoparticles is an important descriptor related to both the catalytic activity and the stability of the nanoparticles. Here, we derive an expression relating μM to the radius of the particle’s contact area with the support and the adhesion energy at the metal/support interface (E adh ) that assumes the particles have the shape of spherical caps but of arbitrary contact angle with the support (θ c ) and includes an empirical correction for the increase in metal surface energy and adhesion energy with decreasing radius of curvature. We then show that, at any assumed contact angle, we can simultaneously fit previously reported measurements of both calorimetric μ M (from heats of metal vapor adsorption during nanoparticle growth by vapor deposition) versus metal coverage data and the He + low-energy ion scattering (LEIS) intensities for the metal and/or support versus metal coverage (using our recently developed spherical cap model for quantitative LEIS intensities), to determine the particle size versus coverage and E adh . Only one choice of contact angle gives a pair of values for contact angle and E adh , which is consistent with the Young–Dupré equation for the equilibrium shape of a spherical particle. At this equilibrium shape, we then applied this spherical cap model (SCM) to reanalyze microcalorimetric metal chemical potentials and LEIS signals versus coverage data for nine metal/support combinations that were previously analyzed by assuming that the particles had the shape of hemispherical caps, i.e., with a contact angle of 90°. We show that this revised approach gives close agreement with the calorimetric and LEIS data; the best-fit contact angles vary from 64 to 84°, correcting the earlier assumption of 90°. Furthermore, these results provide significant accuracy improvements in particle size versus coverage, metal chemical potential versus size and coverage, metal/support adhesion energies and contact angles for Cu, Ag and Au on CeO 2 (111), Ni on MgO(100), Ag on Fe 3 O 4 (111) and TiO 2 (100), and Ag, Ni and Pd on Ni-supported graphene. This revised approach is much more broadly applicable than the earlier hemispherical cap model (HCM).

99 GENERAL AND MISCELLANEOUS↗

Correlating Experimentally Determined Hydrogen Binding Energy with Hydrogen Evolution Activity over Metal Monolayers on Molybdenum Nitride

It is well established that hydrogen binding energy (HBE) is a key descriptor for hydrogen evolution reaction (HER) activity, and such a relationship is a useful tool for searching efficient and cost-effective HER catalysts. However, in almost all cases, the HBE values are obtained from density functional theory (DFT) calculations. Here in this study, temperature programmed desorption (TPD) was used to experimentally determine the HBE values of metal monolayers supported on molybdenum nitride (Mo 2 N), and electrochemical measurements were performed on the same surfaces. Combined DFT and kinetic Monte Carlo (kMC) simulations were used to validate the trend observed with TPD and the electrochemical HER activity. Depositing one monolayer of Pt on Mo 2 N led to similar HBE values seen for bulk Pt, and electrochemical measurements showed that monolayer Pt on Mo 2 N had similar HER activity to that of bulk Pt. Similar studies were also performed for monolayer Pd on Mo 2 N to confirm the correlation. This work also demonstrates that Mo 2 N is a promising support to reduce precious metal loading in HER catalysts.

08 HYDROGEN↗

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

The complex reaction network of catalytic biomass conversions often involves hundreds of surface intermediates and thousands of reaction steps, greatly hindering the rational design of metal catalysts for these conversions. Here, we present a framework of machine learning (ML)-accelerated first-principles studies for the hydrodeoxygenation (HDO) of propanoic acid over transition metal surfaces. The microkinetic model (MKM) is initially parametrized by ML-predicted energies and iteratively improved by identifying the rate-determining species and steps (RDS), computing their energies by density functional theory (DFT), and reparameterizing the MKM until all the RDS are computed by DFT. The Gaussian process (GP) model performs significantly better than the linear ridge regression model for predicting both the adsorption free energies and transition state free energies. Parameterized with energies from the GP model, only 5–20% of the full reaction network has to be computed by DFT for the MKM to possess DFT-level accuracy for the TOF and dominant reaction pathway. While the linear ridge regression model performs worse than the GP model, its performance is greatly improved when only transition states are predicted by the regression model and adsorption energies are computed by DFT. Overall, we find that a high accuracy in adsorption free energies is more important for a reliable MKM than a high accuracy in TS free energies. Lastly, based on the GP model with GOH and GCHCHCO as catalyst descriptors, we build two-dimensional volcano plots in activity and selectivity that can help design promising alloy catalysts for HDO reactions of organic acids.

adsorption↗

Nitrate Reduction Modeling under Acidic Conditions with Late Transition Metals

The electrochemical reduction of nitrate (NO 3 R) to ammonia is a bold yet conceivable way of producing ammonia using renewable electricity. However, serious challenges remain in finding optimal electrocatalysts for the process. An atomistic understanding of the surface energetics behind the NO 3 R is needed in order to design an efficient catalyst. Herein, we combine energetics from density functional theory and microkinetic modeling to demonstrate how surface descriptors can help simplify the search for efficient NO 3 R electrocatalysts. We illustrate the strong correlations between transition-state energetics and O* binding energies for adsorbed nitrate and nitrite on transition metals. For intermediates from NO* and beyond, we compare the benefits of using either the N* or H* binding energies to predict reduction onset potentials. These insights enable us to develop a simple microkinetic model that elucidates the surface coverages of intermediates and the product selectivity of NO 3 R across a range of potentials and transition metals. As a result, we show that the model adequately corroborates with quasi-steady-state rates observed experimentally.

ammonia↗