Metal-Independent Correlations for Site-Specific Binding Energies of Relevant Catalytic Intermediates.

Establishing energy correlations among different metals can accelerate the discovery of efficient and cost-effective catalysts for complex reactions. Using a recently introduced coordination-based model, we can predict site-specific metal binding energies (Δ ) that can be used as a descriptor for chemical reactions. In this study, we have examined a range of metals including Ag, Au, Co, Cu, Ir, Ni, Os, Pd, Pt, Rh, and Ru and found linear correlations between predicted Δ and adsorption energies of CH and O (Δ and Δ ) at various coordination environments for all the considered metals.

Unveiling the Stability of Encapsulated Pt Catalysts Using Nanocrystals and Atomic Layer Deposition.

Platinum exhibits desirable catalytic properties, but it is scarce and expensive. Optimizing its use in key applications such as emission control catalysis is important to reduce our reliance on such a rare element. Supported Pt nanoparticles (NPs) used in emission control systems deactivate over time because of particle growth in sintering processes.

Oxidizing Role of Cu Cocatalysts in Unassisted Photocatalytic CO Reduction Using p-GaN/AlO/Au/Cu Heterostructures.

Photocatalytic CO reduction to CO under unassisted (unbiased) conditions was recently demonstrated using heterostructure catalysts that combine p-type GaN with plasmonic Au nanoparticles and Cu nanoparticles as cocatalysts (p-GaN/AlO/Au/Cu). Here, we investigate the mechanistic role of Cu in p-GaN/AlO/Au/Cu under unassisted photocatalytic operating conditions using Cu K-edge X-ray absorption spectroscopy and first-principles calculations. Upon exposure to gas-phase CO and HO vapor reaction conditions, the composition of the Cu nanoparticles is identified as a mixture of Cu and Cu oxide, hydroxide, and carbonate compounds without metallic Cu.

Prediction of Feasibility of Polaronic OER on the (110) Surface of Rutile TiO.

The front cover artwork is provided by Dr. Hori Pada Sarker from Dr. Frank Abild-Pedersen's research group at the SLAC National Accelerator Laboratory.

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations.

Prediction of Feasibility of Polaronic OER on (110) Surface of Rutile TiO.

The polaronic effects at the atomic level hold paramount significance for advancing the efficacy of transition metal oxides in applications pertinent to renewable energy. The lattice-distortion mediated localization of photoexcited carriers in the form of polarons plays a pivotal role in the photocatalysis. This investigation focuses on rutile TiO, an important material extensively explored for solar energy conversion in artificial photosynthesis, specifically targeting the generation of green H through photoelectrochemical (PEC) HO splitting.

Toward Carbon Monoxide Methanation at Mild Conditions on Dual-Site Catalysts.

The catalytic carbon monoxide (CO) methanation is an ideal model reaction for the fundamental understanding of catalysis on the gas-solid interface and is crucial for various industrial processes. However, the harsh operating conditions make the reaction unsustainable, and the limitations set by the scaling relations between the dissociation energy barrier and dissociative binding energy of CO further increase the difficulty in designing high-performance methanation catalysts operating under milder conditions. Herein, we proposed a theoretical strategy to circumvent the limitations elegantly and achieve both facile CO dissociation and C/O hydrogenation on the catalyst containing a confined dual site.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors.

Atom-Specific Probing of Electron Dynamics in an Atomic Adsorbate by Time-Resolved X-Ray Spectroscopy.

The electronic excitation occurring on adsorbates at ultrafast timescales from optical lasers that initiate surface chemical reactions is still an open question. Here, we report the ultrafast temporal evolution of x-ray absorption spectroscopy (XAS) and x-ray emission spectroscopy (XES) of a simple well-known adsorbate prototype system, namely carbon (C) atoms adsorbed on a nickel [Ni(100)] surface, following intense laser optical pumping at 400 nm. We observe ultrafast (∼100  fs) changes in both XAS and XES showing clear signatures of the formation of a hot electron-hole pair distribution on the adsorbate.

Symmetry-resolved CO desorption and oxidation dynamics on O/Ru(0001) probed at the C K-edge by ultrafast x-ray spectroscopy.

We report on carbon monoxide desorption and oxidation induced by 400 nm femtosecond laser excitation on the O/Ru(0001) surface probed by time-resolved x-ray absorption spectroscopy (TR-XAS) at the carbon K-edge. The experiments were performed under constant background pressures of CO (6 × 10 Torr) and O (3 × 10 Torr). Under these conditions, we detect two transient CO species with narrow 2π* peaks, suggesting little 2π* interaction with the surface.

Catalytic Performance and Near-Surface X-ray Characterization of Titanium Hydride Electrodes for the Electrochemical Nitrate Reduction Reaction.

The electrochemical nitrate reduction reaction (NORR) on titanium introduces significant surface reconstruction and forms titanium hydride (TiH, 0 < x ≤ 2). With grazing-incidence X-ray diffraction (GIXRD) and X-ray absorption spectroscopy (XAS), we demonstrated near-surface TiH enrichment with increasing NORR applied potential and duration. This quantitative relationship facilitated electrochemical treatment of Ti to form TiH/Ti electrodes for use in NORR, thereby decoupling hydride formation from NORR performance.

Vernier template synthesis of molecular knots.

Molecular knots are often prepared using metal helicates to cross the strands. We found that coordinatively mismatching oligodentate ligands and metal ions provides a more effective way to synthesize larger knots using Vernier templating. Strands composed of different numbers of tridentate 2,6-pyridinedicarboxamide groups fold around nine-coordinate lanthanide (III) ions to generate strand-entangled complexes with the lowest common multiple of coordination sites for the ligand strands and metal ions.

Colloidal Platinum-Copper Nanocrystal Alloy Catalysts Surpass Platinum in Low-Temperature Propene Combustion.

Low-temperature removal of noxious environmental emissions plays a critical role in minimizing the harmful effects of hydrocarbon fuels. Emission-control catalysts typically consist of large quantities of rare, noble metals (e.g.

Modeling Potential-Dependent Electrochemical Activation Barriers: Revisiting the Alkaline Hydrogen Evolution Reaction.

Accurate theoretical simulation of electrochemical activation barriers is key to understanding electrocatalysis and guides the design of more efficient catalysts. Providing a detailed picture of proton transfer processes encounters several challenges: the constant potential requirement during charge transfer, the different time scales involved in the processes, and the thermal fluctuation of the solvent. Hence, it is prohibitively expensive computationally to apply density functional theory (DFT) calculations in modeling the potential-dependent activation barrier at the electrode-solvent interface, and the results are dubious.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces.

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials.

Guiding the Catalytic Properties of Copper for Electrochemical CO Reduction by Metal Atom Decoration.

Tuning bimetallic effects is a promising strategy to guide catalytic properties. However, the nature of these effects can be difficult to assess and compare due to the convolution with other factors such as the catalyst surface structure and morphology and differences in testing environments. Here, we investigate the impact of atomic-scale bimetallic effects on the electrochemical CO reduction performance of Cu-based catalysts by leveraging a systematic approach that unifies protocols for materials synthesis and testing and enables accurate comparisons of intrinsic catalytic activity and selectivity.

Achieving industrial ammonia synthesis rates at near-ambient conditions through modified scaling relations on a confined dual site.

The production of ammonia through the Haber-Bosch process is regarded as one of the most important inventions of the 20th century. Despite significant efforts in optimizing the process, it still consumes 1 to 2% of the worldwide annual energy for the high working temperatures and pressures. The design of a catalyst with a high activity at milder conditions represents another challenge for this reaction.

Ultrafast Adsorbate Excitation Probed with Subpicosecond-Resolution X-Ray Absorption Spectroscopy.

We use a pump-probe scheme to measure the time evolution of the C K-edge x-ray absorption spectrum from CO/Ru(0001) after excitation by an ultrashort high-intensity optical laser pulse. Because of the short duration of the x-ray probe pulse and precise control of the pulse delay, the excitation-induced dynamics during the first picosecond after the pump can be resolved with unprecedented time resolution. By comparing with density functional theory spectrum calculations, we find high excitation of the internal stretch and frustrated rotation modes occurring within 200 fs of laser excitation, as well as thermalization of the system in the picosecond regime.

Identification of earth-abundant materials for selective dehydrogenation of light alkanes to olefins.

Selective ethane dehydrogenation (EDH) is an attractive on-purpose strategy for industrial ethylene production. Design of an effective, stable, and earth-abundant catalyst to replace noble metal Pt is the main obstacle for its large-scale application. Herein, we report an experimentally validated theoretical framework to discover promising catalysts for EDH, which combines descriptor-based microkinetic modeling, high-throughput computations, machine-learning concepts, and experiments.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights.

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution.

Predicting metal-metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities.

Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. Herein, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity.