Zirconium Oxynitride Thin Films for Photoelectrochemical Water Splitting.

Transition metal oxynitrides are a promising class of functional materials for photoelectrochemical (PEC) applications. Although these compounds are most commonly synthesized via ammonolysis of oxide precursors, such synthetic routes often lead to poorly controlled oxygen-to-nitrogen anion ratios, and the harsh nitridation conditions are incompatible with many substrates, including transparent conductive oxides. Here, we report direct reactive sputter deposition of a family of zirconium oxynitride thin films and the comprehensive characterization of their tunable structural, optical, and functional PEC properties. Systematic increases of the oxygen content in the reactive sputter gas mixture enable access to different crystalline structures within the zirconium oxynitride family. Increasing oxygen contents lead to a transition from metallic to semiconducting to insulating phases. In particular, crystalline Zr2ON2-like films have band gaps in the UV-visible range and are n-type semiconductors. These properties, together with a valence band maximum position located favorably relative to the water oxidation potential, make them viable photoanode candidates. Using chopped linear sweep voltammetry, we indeed confirm that our Zr2ON2 films are PEC-active for the oxygen evolution reaction in alkaline electrolytes. We further show that high-vacuum annealing boosts their PEC performance characteristics. Although the observed photocurrents are low compared to state-of-the-art photoanodes, these dense and planar thin films can offer a valuable platform for studying oxynitride photoelectrodes, as well as for future nanostructuring, band gap engineering, and defect engineering efforts.

Tuning Carbon Dioxide Reduction Reaction Selectivity of Bi Single-Atom Electrocatalysts with Controlled Coordination Environments.

Control over product selectivity of the electrocatalytic CO2 reduction reaction (CO2RR) is a crucial challenge for the sustainable production of carbon-based chemical feedstocks. In this regard, single-atom catalysts (SACs) are promising materials due to their tunable coordination environments, which could enable tailored catalytic activities and selectivities, as well as new insights into structure-activity relationships. However, direct evidence for selectivity control via systematic tuning of the SAC coordination environment is scarce. In this work, we have synthesized two differently coordinated Bi SACs anchored to the same host material (carbon black) and characterized their CO2RR activities and selectivities. We find that oxophilic, oxygen-coordinated Bi atoms produce HCOOH, while nitrogen-coordinated Bi atoms generate CO. Importantly, use of the same support material assured that alternation of the coordination environment is the dominant factor for controlling the CO2RR product selectivity. Overall, this work demonstrates the structure-activity relationship of Bi SACs, which can be utilized to establish control over CO2RR product distributions, and highlights the promise for engineering atomic coordination environments of SACs to tune reaction pathways.

The rise of electrochemical NAPXPS operated in the soft X-ray regime exemplified by the oxygen evolution reaction on IrOx electrocatalysts.

Photoelectron spectroscopy offers detailed information about the electronic structure and chemical composition of surfaces, owing to the short distance that the photoelectrons can escape from a dense medium. Unfortunately, photoelectron based spectroscopies are not directly compatible with the liquids required to investigate electrochemical processes, especially in the soft X-ray regime. To overcome this issue, different approaches based on photoelectron spectroscopy have been developed in our group over the last few years. The performance and the degree of information provided by these approaches are compared with those of the well established bulk sensitive spectroscopic approach of total fluorescence yield detection, where the surface information gained from this approach is enhanced using samples with large surface to bulk ratios. The operation of these approaches is exemplified and compared using the oxygen evolution reaction on IrOx catalysts. We found that all the approaches, if properly applied, provide similar information about surface oxygen speciation. However, using resonant photoemission spectroscopy, we were able to prove that speciation is more involved and complex than previously thought during the oxygen evolution reaction on IrOx based electrocatalysts. We found that the electrified solid-liquid interface is composed of different oxygen species, where the terminal oxygen atoms on iridium are the active species, yielding the formation of peroxo species and, finally, dioxygen as the reaction product. Thus, the oxygen-oxygen bond formation is dominated by peroxo species formation along the reaction pathway. Furthermore, the methodologies discussed here open up opportunities to investigate electrified solid-liquid interfaces in a multitude of electrochemical processes with unprecedented speciation capabilities, which are not accessible by one-dimensional X-ray spectroscopies.

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., platinum and palladium), which are expensive and environmentally damaging metals to extract. Alloying with cheaper base metals offers the potential to boost catalytic activity while optimizing the use of noble metals. In this work, we show that PtxCu100-x catalysts prepared from colloidal nanocrystals are more active than the corresponding Pt catalysts for complete propene oxidation. By carefully controlling their composition while maintaining nanocrystal size, alloys with dilute Cu concentrations (15-30% atomic fraction) demonstrate promoted activity compared to pure Pt. Complete propene oxidation was observed at temperatures as low as 150 °C in the presence of steam, and five to ten times higher turnover frequencies were found compared to monometallic Pt catalysts. Through DFT studies and structural and catalytic characterization, the remarkable activity of dilute PtxCu100-x alloys was related to the tuning of the electronic structure of Pt to reach optimal binding energies of C* and O* intermediates. This work provides a general approach toward investigation of structure-property relationships of alloyed catalysts with efficient and optimized use of noble metals.

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. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

Revealing the structure of a catalytic combustion active-site ensemble combining uniform nanocrystal catalysts and theory insights.

Supported metal catalysts are extensively used in industrial and environmental applications. To improve their performance, it is crucial to identify the most active sites. This identification is, however, made challenging by the presence of a large number of potential surface structures that complicate such an assignment. Often, the active site is formed by an ensemble of atoms, thus introducing further complications in its identification. Being able to produce uniform structures and identify the ones that are responsible for the catalyst performance is a crucial goal. In this work, we utilize a combination of uniform Pd/Pt nanocrystal catalysts and theory to reveal the catalytic active-site ensemble in highly active propene combustion materials. Using colloidal chemistry to exquisitely control nanoparticle size, we find that intrinsic rates for propene combustion in the presence of water increase monotonically with particle size on Pt-rich catalysts, suggesting that the reaction is structure dependent. We also reveal that water has a near-zero or mildly positive reaction rate order over Pd/Pt catalysts. Theory insights allow us to determine that the interaction of water with extended terraces present in large particles leads to the formation of step sites on metallic surfaces. These specific step-edge sites are responsible for the efficient combustion of propene at low temperature. This work reveals an elusive geometric ensemble, thus clearly identifying the active site in alkene combustion catalysts. These insights demonstrate how the combination of uniform catalysts and theory can provide a much deeper understanding of active-site geometry for many applications.

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. Strain-based dependencies will increase the model's accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal-metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.

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. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision.

Bimetallic nanoparticles present a vastly tunable structural and compositional design space rendering them promising materials for catalytic and energy applications. Yet it remains an enduring challenge to efficiently screen candidate alloys with atomic level specificity while explicitly accounting for their inherent stabilities under reaction conditions. Herein, by leveraging correlations between binding energies of metal adsorption sites and metal-adsorbate complexes, we predict adsorption energies of typical catalytic descriptors (OH*, CH3*, CH*, and CO*) on bimetallic alloys with site-specific resolution. We demonstrate that our approach predicts adsorption energies on top and bridge sites of bimetallic nanoparticles having generic morphologies and chemical environments with errors between 0.09 and 0.18 eV. By forging a link between the inherent stability of an alloy and the adsorption properties of catalytic descriptors, we can now identify active site motifs in nanoalloys that possess targeted catalytic descriptor values while being thermodynamically stable under working conditions.

How to control selectivity in alkane oxidation?

The well-defined particle morphology of crystalline MnWO4 catalysts investigated in the present study facilitates obtaining insight into the origin of selectivity limitations in alkane oxidation. Hydrothermal synthesis at variable pH values granted access to a series of phase-pure MnWO4 catalysts with particles ranging from cube-like (aspect ratio 1.5) to rod- or needle-like (aspect ratio 6.8) shapes. Kinetic studies reveal a strong dependence of the propane consumption rate on the particle shape. The true origin of the structure sensitivity was unraveled by comprehensive bulk and surface analysis using nitrogen adsorption, XRD, SEM, ADF-STEM, STEM-EELS, XPS, multi-laser excitation Raman and DRIFT/operando FTIR spectroscopies, temperature-programmed oxidation (TPO), in situ NEXAFS, and DFT calculations. The active phase is composed of a thin manganese oxy-hydroxide layer formed on the surface of crystalline MnWO4. The differences in catalytic performance within the series clearly illustrate that the structural motif as the most popular descriptor in oxidation catalysis is not essential, since all MnWO4 catalysts in the series under study exhibit the same bulk crystal structure and bulk chemical composition and are phase pure and homogenous. The variable particle shape serves as a proxy that reflects the formation of varying abundance of redox active Mn2+/Mn3+ surface sites, which correlates with catalytic activity. Operando FTIR spectroscopy directly confirms the formation of Mn-OH surface species by abstraction of hydrogen atoms from the propane molecule on nucleophilic oxygen atoms and suggests that active site regeneration occurs via oxidative dehydrogenation of Mn-OH species indicating a single-site nature of the active sites that does not allow four-electron reduction of molecular oxygen. Instead, intermediates are created that cause side reactions and lower the selectivity. The findings highlight fundamental design criteria that may be applied to advance the development of new alkane oxidation catalysts with improved selectivity.