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Ruddlesden-Popper (RP) transition-metal oxides belong to a versatile class of functional materials whose electronic properties can be tuned by varying chemical composition in a wide range. However, the relation between chemical composition and electronic properties of multi-metal RP oxides, in particular taking into account different distributions of metal ions in the lattice, has not been investigated systematically so far. In this work, we use density-functional theory (DFT) to explore the compositional dependence of electronic, magnetic, and structural properties of La 2 - x Sr x Co 1 / 2 Fe 1 / 2 O 4 oxide series for x = 0, 1, 2. To account for localized nature of transition-metal d-orbitals, self-consistent DFT+U approach Agapito-Curtarolo-Buongiorno-Nardelli (ACBN0) is employed. We show that widely used electronic-structure-based descriptors of oxygen-defect formation energies, oxygen transport, and catalytic activity strongly depend on the distribution of transition metal ions. Nevertheless, the distribution-averaged and ground-state descriptor values for La 2 - x Sr x Co 1 / 2 Fe 1 / 2 O 4 are found to depend monotonically on the composition x. In addition, we showed that in complex systems such as multi-metal Ruddlesden-Popper phases, Hubbard U values for the same species vary significantly between crystallographically non-equivalent sites. [ABSTRACT FROM AUTHOR]

Ruddlesden-Popper (RP) transition-metal oxides belong to a versatile class of functional materials whose electronic properties can be tuned by varying chemical composition in a wide range. However, the relation between chemical composition and electronic properties of multi-metal RP oxides, in particular taking into account different distributions of metal ions in the lattice, has not been investigated systematically so far. In this work, we use density-functional theory (DFT) to explore the compositional dependence of electronic, magnetic, and structural properties of La 2 - x Sr x Co 1 / 2 Fe 1 / 2 O 4 oxide series for x = 0, 1, 2. To account for localized nature of transition-metal d-orbitals, self-consistent DFT+U approach Agapito-Curtarolo-Buongiorno-Nardelli (ACBN0) is employed. We show that widely used electronic-structure-based descriptors of oxygen-defect formation energies, oxygen transport, and catalytic activity strongly depend on the distribution of transition metal ions. Nevertheless, the distribution-averaged and ground-state descriptor values for La 2 - x Sr x Co 1 / 2 Fe 1 / 2 O 4 are found to depend monotonically on the composition x. In addition, we showed that in complex systems such as multi-metal Ruddlesden-Popper phases, Hubbard U values for the same species vary significantly between crystallographically non-equivalent sites. [ABSTRACT FROM AUTHOR]

Urea oxidation reaction (UOR) is a crucial process for the efficiency of urea-based energy conversion technologies, including electrocatalytic water splitting and urea fuel cells. These technologies are considered alternatives to the traditional hydrogen production methods due to lower overpotentials and higher cost-effectiveness. However, sluggish UOR kinetics currently limit the practical applications of these technologies. In this study, we investigated the influence of electrolyte composition and temperature on the performance of Ruddlesden-Popper complex oxides in UOR. Our findings demonstrate that the formation of a carbonate layer at the electrode surface is a critical factor limiting UOR kinetics. This layer is mainly composed of carbonates of alkali metal from the solution and can be eliminated at elevated temperatures due to increased solubility. Specifically, we found that LaSrNi0.5Co0.5O4±δ catalyst exhibited superior electrocatalytic activity (6 mA cm−2 at 1.45 V vs. RHE with loading 100 µg cm−2) operating at 60 °C in 5 M KOH. Moreover, changing the solution to cesium hydroxide, we were able to avoid the creation of a non-conductive layer due to higher solubility of the cesium carbonate, thus stabilizing the catalytic reaction. [ABSTRACT FROM AUTHOR]

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials Science". We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. [ABSTRACT FROM AUTHOR]

Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO2 conversion. Here the authors demonstrate an artificial-intelligence based approach to identify catalytic materials features that correlate with mechanisms that trigger, facilitate, or hinder CO2 catalytic reactions. [ABSTRACT FROM AUTHOR]

Here we discuss effect of spin-polarization on plasmon excitation in deep ultra-violet spectral range for Co nanoparticles with a single-domain magnetic structure. Structural, magnetic, and optical characterizations of Co nanoparticles shine a light on a mechanism of the magneto-plasmonic response [ABSTRACT FROM AUTHOR]

The halide ions of organic-inorganic hybrid perovskites can strongly influence the interaction between the central organic moiety and the inorganic metal halide octahedral units and thus their lattice vibrations. Here, we report the halide-ion-dependent vibrational coherences in formamidinium lead halide (FAPbX3, X = Br, I) perovskite nanocrystals (PNCs) via the combination of femtosecond pump–probe spectroscopy and density functional theory calculations. We find that the FAPbX3 PNCs generate halide-dependent coherent vibronic wave packets upon above-bandgap non-resonant excitation. More importantly, we observe several higher harmonics of the fundamental modes for FAPbI3 PNCs as compared to FAPbBr3 PNCs. This is likely due to the weaker interaction between the central FA moiety and the inorganic cage for FAPbI3 PNCs, and thus the PbI64− unit can vibrate more freely. This weakening reveals the intrinsic anharmonicity in the Pb-I framework, and thus facilitating the energy transfer into overtone and combination bands. These findings not only unveil the superior stability of Br–based PNCs over I–based PNCs but are also important for a better understanding of their electronic and polaronic properties. Using a combination of femtosecond pump-probe spectroscopy and first-principles calculations, Debnath et al. elucidated the halide-dependence of the excited state vibrational coherences in hybrid organic-inorganic perovskite nanocrystals. The study revealed an intrinsic anharmonicity of lead-halide framework, which correlates with perovskite stability and is influenced by the interaction between the framework and the organic molecules. [ABSTRACT FROM AUTHOR]

Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications. Single-atom metal alloys attract considerable interest as alternative metal hydrogenation catalysts. Here the authors combine first-principles calculations with compressed-sensing data-analytics approaches to develop stability and activity's descriptors for screening single atom alloy catalysts. [ABSTRACT FROM AUTHOR]