Free-atom-like d states beyond the dilute limit of single-atom alloys

Recent advances in single-atom alloys: preparation methods and applications in heterogeneous catalysis

Single-atom alloys (SAAs) are a different type of alloy where a guest metal, usually a noble metal (e.g., Pt, Pd, and Ru), is atomically dispersed on a relatively more inert (e.g., Ag and Cu) host metal. As a type of atomic-scale catalyst, single-atom alloy catalysts have broad application prospects in the field of heterogeneous catalysis for hydrogenation, dehydrogenation, oxidation, and other reactions. Numerous experimental and characterization results and theoretical calculations have confirmed that the resultant electronic structure caused by charge transfer between the host metal and guest metal and the special geometric structure of the guest metal are responsible for the high selectivity and catalytic activity of SAA catalysts. In this review, the common methods for the preparation of single-atom alloys in recent years are introduced, including initial wet impregnation, physical vapor deposition, and laser ablation in liquid technique. Afterwards, the applications of single-atom alloy catalysts in selective hydrogenation, dehydrogenation, oxidation reactions, and hydrogenolysis reactions are emphatically reviewed. Finally, several challenges for the future development of SAA catalysts are proposed.

Modulating paired Ir-O-Ir via electronic perturbations of correlated Ir single atoms to overcome catalytic selectivity

Single-atom catalysts have been extensively utilized for electrocatalysis, in which electronic metal-support interactions are typically employed to stabilize single atoms. However, this neglects the metal-metal interactions of adjacent atoms, which are essential for the fine-tuning of selective sites. Herein, the high-loading of Ir single atoms (Ir SAs) (8.9 wt%) were adjacently accommodated into oxygen vacancy-rich Co3O4 nanosheets (Ir SAs/Co3O4). Electronic perturbations for both Ir single atoms and Co3O4 supports were observed under electronic metal-support and metal-metal interactions, thus generating Ir-O-Co/Ir units. Electrons were transferred from Co and Ir to O atoms, inducing the depletion of 3d/5d states in Co/Ir and the occupation of 2p states in O atoms to stabilize the Ir SAs. Moreover, the O atoms of Ir-O-Ir functioned as the main active sites for the electrocatalysis of As(iii), which reduced the energy barrier for the rate-determining step. This was due to the stronger electronic affinities for intermediates from reduction of As(iii), which were completely distinct from other coordinated O atoms of Co3O4 or IrO2. Consequently, the resultant Ir SAs/Co3O4 exhibited far more robust electrocatalytic activities than IrO2/Co3O4 and Co3O4 in the electrocatalysis of As(iii). Moreover, there was a strong orbital coupling effect between the coordinated O atoms of Ir SAs and the -OH of H3AsO3, thus exhibiting superior selectivity for As(iii) in contrast to other common heavy metal cations. This work offers useful insights into the rational design of intriguing SACs with high selectivity and stability for the electrocatalysis and electrochemical analysis of pollutants on an electronic level.

A Quantum-Chemical Bonding Database for Solid-State Materials

An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.