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At least 415 records · Page 23

Activation of N 2 on Manganese Nitride-Supported Ni 3 and Fe 3 Clusters and Relevance to Ammonia Formation

Dual-site models were constructed to represent manganese nitride (Mn 4 N)-supported Ni 3 and Fe 3 clusters for NH 3 synthesis. Density functional theory calculations produced an energy barrier of approximately 0.55 eV for N–N bond activation at the interfacial nitrogen vacancy sites (N v ); also, the hydrogenation and removal of interfacial N is promoted by earth-abundant Ni and Fe metals. Steady-state microkinetic modeling revealed that the turnover frequencies of NH 3 production follow an order of Fe 3 @Mn 4 N ≈ Ni 3 @Mn 4 N > Mn 4 N > Fe >> Ni. Moreover, we present clear evidence that, before NH 3 formation, NH migrates from N v onto the metallic sites. Using N binding energy (BE N ) and the transition-state energy of N 2 activation (E TS ) as descriptors, we concluded that the beneficial effects owing to interfacial N v sites are the most pronounced when BE N is either too strong or too weak while E TS is high; otherwise, excessive N v sites may hinder catalyst performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Further, models trained through this workflow achieved an error (mean absolute error of 0.43 J mol –1 K –1 ) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Direct Observation of Hydroxyls Formed from Water and Oxygen on Ag(100)

The interaction of oxygen with silver is a key descriptor of the catalytic reactivity of silver nanoparticles which are ubiquitous in large-scale partial oxidation reactions like ethylene epoxidation. Despite Ag(100) being proposed as the most selective facet, it is less studied than (111) and (110) surfaces. Using scanning tunneling microscopy and synchrotron X-ray photoelectron spectroscopy, we report that, in addition to the well-known O adatoms formed from O 2 dissociation on Ag(100), hydroxyl groups (OH), at a binding energy of ∼ 531 eV, are also present. The O/OH ratio depends on exposure to water and surface temperature. These assignments are consistent with our density functional theory calculations, which indicate that the formation of two OH groups from an O atom and H 2 O molecule is exothermic. These results indicate that, in addition to O, OH is present even under ultrahigh vacuum conditions and therefore should be considered in proposed catalytic pathways.

36 MATERIALS SCIENCE↗

Surface Nanostructure Control and Thermodynamic Stability Analysis of Femtosecond Laser-Ablated CuCoMn 1.75 NiFe 0.25 Nanoparticles

Surface nanostructure control is the key to functionalizing nanomaterials. This paper presents a characterization with thermodynamic stability analysis of CuCoMn 1.75 NiFe 0.25 high-entropy alloy (HEA) nanoparticles synthesized by femtosecond laser ablation in ethanol and liquid nitrogen (LN2). Using multimodal electron microscopy and spectroscopy, we examine phase, particle size, defect structure, chemical distribution, and surface composition and relate them to HEA stability. Elemental distributions are uniform in both media, but LN2 produces smaller particles with a narrower size distribution and mainly single- or few-domain interiors, whereas ethanol yields larger particles built from 2–4 nm crystallites with domain aggregation. Edge defects appear in both but energy-dispersive X-ray spectroscopy (EDS) is broadly uniform with local fluctuations in ethanol. X-ray photoelectron spectroscopy (XPS), supported by an attenuation model, indicates an ∼1 nm oxide overlayer that suppresses Mn 2p intensity; correcting for it returns Mn toward the bulk value. UV–NIR and photoluminescent spectra independently support a thin oxide shell. Composition-based thermodynamic descriptors place LN2 closer to bulk mixing parameters, while ethanol raises ΔH_mix and lowers Ω. Cooling simulations are consistent (LN2 ∼ 0.1 μs quench, ethanol ∼1 μs). In conclusion, these results connect solvent-controlled kinetics and thermodynamics to crystalline state and surface chemistry, informing surface control of HEA nanoparticles.

Femtosecond Laser Ablation↗

Composition and Configuration Dependence of Glass-Transition Temperature in Binary Copolymers and Blends of Polyhydroxyalkanoate Biopolymers

Polyhydroxyalkanoates (PHAs), a promising class of biomaterials, have gained considerable attention to replace petroleum-based plastics owing to their excellent biocompatibility and biodegradability. Homopolymers of PHA suffer from poor tunability in thermal and mechanical properties. Going from homopolymers to copolymers, the design space can be substantially enhanced by combining two or more monomers in different compositions (i.e., relative ratios of the different monomers) and configurations (i.e., relative positions of the different monomers in the polymer backbone) leading to a substantially large chemical space where application-specific optimization for the targeted functionality can be performed. However, this composition and configuration dependence of properties in the vast PHA copolymer chemical space remains largely unexplored. In this contribution, further building on our past work with PHA homopolymers, we systematically explore these chemical trends for glass-transition temperature (Tg) in PHA copolymers and blends. Overall, our molecular dynamics simulations, utilizing a previously validated force field for PHAs, suggest that these trends are largely governed not only by the homopolymer Tg values but also configuration-dependent interchain interactions in the copolymer system. In particular, our results indicate that the configuration-dependent variation in the target property at a fixed composition can be significant in the presence of hydrogen-bond-forming monomers. These qualitative observations are further rationalized by quantitatively analyzing various closely related atomic level descriptors of copolymers and blends such as monomer mobility, number of hydrogen bonds, and pair correlation functions. The findings presented in this work help to develop a deeper atomistic-level understanding of thermomechanical behavior of PHA-based copolymers and can potentially guide the rational design of biopolymers as environmental-friendly functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Anisotropic Coarse-Grained Model for Conjugated Polymers: Investigations into Solution Morphologies

The optoelectronic properties of conjugated polymers are dictated by the interplay of multiscale structural features, including intrachain dihedrals, interchain pi-pi stacking, and the complex mesoscale morphology. While much is known about the structures of polymers with isotropically interacting monomers, little is known about polymers with strongly anisotropic backbone monomers, a class of materials to which conjugated polymers belong. Fundamental understanding is further complicated when the semiflexible and molecularly heterogeneous nature of conjugated polymers is taken into account. We present an anisotropic coarse-grained (CG) model for conjugated polymers that incorporates key molecular features (monomer anisotropy, intermonomer dihedrals, and side chains). Finally, the model is employed to characterize the single-chain conformational properties of conjugated polymers, revealing a rich temperature-dependent conformational landscape. These studies provide a critical link between CG molecular descriptors and conformational ordering in conjugated polymers.

36 MATERIALS SCIENCE↗

Molecular Dynamics Investigation of Oil Wetting on Synthetic Polymer Substrates

Understanding the interaction of polymer surfaces with nonpolar, low surface tension liquids, or whether a substrate is oleophobic versus oleophilic, is critical for applications ranging from antifingerprint coatings to oil–water separation membranes and oil spill remediation. Despite its technological importance, the molecular mechanisms underpinning polymer–oil wetting are not well characterized. Here, we employ molecular dynamics simulations to investigate the behavior of n-hexadecane in contact with chemically distinct polymer surfaces, spanning eight constitutional unit chemistries as well as amorphous and crystalline morphologies. This permits a critical examination of both thermodynamic and dynamic descriptors of oleophobicity, including oil contact angle, dewetting free energy, interfacial diffusivity, and a proposed “ghost probe energy,” which does not require explicit simulation of oil–polymer interactions. We find that the oil contact angle does not reliably distinguish oleophobic behavior across polymer chemistries, whereas other metrics provide clearer and more consistent differentiation. Analysis of the results reveals that polymer–oil wetting behavior is primarily governed by interfacial van der Waals interactions and modulated by surface flexibility and morphology. Collectively, this work establishes a computational framework for characterizing oil wetting and provides additional insight into what molecular-level factors dictate trends in oleophobicity.

lipids↗

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗

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↗