Trends in C-O and N-O bond scission on rutile oxides described using oxygen vacancy formation energies

Thermal Decomposition Mechanism of Ammonium Nitrate on the Main Crystal Surface of Ferric Oxide: Experimental and Theoretical Studies

Understanding the decomposition process of ammonium nitrate (AN) on catalyst surfaces is crucial for the development of practical and efficient catalysts in AN-based propellants. In this study, two types of nano-Fe2O3 catalysts were synthesized: spherical particles with high-exposure (104) facets and flaky particles with high-exposure (110) facets. Through thermal analysis and particle size analysis, it was found that the nanosheet-Fe2O3 catalyst achieved more complete AN decomposition despite having a larger average particle size compared to nanosphere-Fe2O3. Subsequently, the effects of AN pyrolysis on the (110) and (104) facets were investigated by theoretical simulations. Through studying the interaction between AN and crystal facets, it was determined that the electron transfer efficiency on the (110) facet is stronger compared to that on the (104) facet. Additionally, the free-energy step diagrams for the reaction of the AN molecule on the two facets were calculated with the DFT + U method. Comparative analysis led us to conclude that the (110) facet of α-Fe2O3 is more favorable for AN pyrolysis compared to the (104) facet. Our study seeks to deepen the understanding of the mechanism underlying AN pyrolysis and present new ideas for the development of effective catalysts in AN pyrolysis.

Trends in Surface Oxygen Formation Energy in Perovskite Oxides

Perovskite oxides comprise an important class of materials, and some of their applications depend on the surface reactivity characteristics. We calculated, using density functional theory, the surface O vacancy formation energy (E _Ovac) for perovskite-structure oxides, with a transition metal (Ti-Fe) as the B-site cation, to estimate the catalytic reactivity of perovskite oxides. The E _Ovac value correlated well with the band gap and bulk formation energy, which is a trend also found in other oxides. A low E _Ovac value, which is expected to result in higher catalytic activity via the Mars-van Krevelen mechanism, was found in metallic perovskites such as CaCoO₃, BaFeO₃, and SrFeO₃. On the other hand, titanates had high E _Ovac values, typically exceeding 4 eV/atom, suggesting that these materials are less reactive when O vacancy formation is involved in the reaction mechanism.

General embedded cluster protocol for accurate modeling of oxygen vacancies in metal-oxides

The O vacancy (Ov) formation energy, EOv, is an important property of a metal-oxide, governing its performance in applications such as fuel cells or heterogeneous catalysis. These defects are routinely studied with density functional theory (DFT). However, it is well-recognized that standard DFT formulations (e.g., the generalized gradient approximation) are insufficient for modeling the Ov, requiring higher levels of theory. The embedded cluster method offers a promising approach to compute EOv accurately, giving access to all electronic structure methods. Central to this approach is the construction of quantum(-mechanically treated) clusters placed within suitable embedding environments. Unfortunately, current approaches to constructing the quantum clusters either require large system sizes, preventing application of high-level methods, or require significant manual input, preventing investigations of multiple systems simultaneously. In this work, we present a systematic and general quantum cluster design protocol that can determine small converged quantum clusters for studying the Ov in metal-oxides with accurate methods, such as local coupled cluster with single, double, and perturbative triple excitations. We apply this protocol to study the Ov in the bulk and surface planes of rutile TiO2 and rock salt MgO, producing the first accurate and well-converged determinations of EOv with this method. These reference values are used to benchmark exchange–correlation functionals in DFT, and we find that all the studied functionals underestimate EOv, with the average error decreasing along the rungs of Jacob’s ladder. This protocol is automatable for high-throughput calculations and can be generalized to study other point defects or adsorbates.

Finding Key Factors for Efficient Water and Methanol Activation at Metals, Oxides, MXenes, and Metal/Oxide Interfaces

Activating water and methanol is crucial in numerous catalytic, electrocatalytic, and photocatalytic reactions. Despite extensive research, the optimal active sites for water/methanol activation are yet to be unequivocally elucidated. Here, we combine transition-state searches and electronic charge analyses on various structurally different materials to identify two features of favorable O–H bond cleavage in H₂O, CH₃OH, and hydroxyl: (1) low barriers appear when the charge of H moieties remains approximately constant during the dissociation process, as observed on metal oxides, MXenes, and metal/oxide interfaces. Such favorable kinetics is closely related to adsorbate/substrate hydrogen bonding and is enhanced by nearly linear O–H–O angles and short O–H distances. (2) Fast dissociation is observed when the rotation of O–H bonds is facile, which is favored by weak adsorbate binding and effective orbital overlap. Interestingly, we find that the two features are energetically proportional. Finally, we find conspicuous differences between H₂O/CH₃OH and OH activation, which hints toward the use of carefully engineered interfaces.

Understanding and controlling the formation of surface anion vacancies for catalytic applications

Systematic computational efforts aimed at calculating surface anion vacancy formation energies as important descriptors of catalytic performance are summarized.

Factors determining surface oxygen vacancy formation energy in ternary spinel structure oxides with zinc

Spinel oxides are an important class of materials for heterogeneous catalysis including photocatalysis and electrocatalysis. The surface O vacancy formation energy (E_Ovac) is a critical quantity for catalyst performance because the surface of metal oxide catalysts often acts as a reaction site, for example, in the Mars-van Krevelen mechanism. However, experimental evaluation of E_Ovac is very challenging. We obtained the E_Ovac for (100), (110), and (111) surfaces of normal zinc-based spinel oxides ZnAl₂O₄, ZnGa₂O₄, ZnIn₂O₄, ZnV₂O₄, ZnCr₂O₄, ZnMn₂O₄, ZnFe₂O₄, and ZnCo₂O₄. The most stable surface is (100) for all compounds. The smallest E_Ovac for a surface is the largest in the (100) surface except for ZnCo₂O₄. For (100) and (110) surfaces, there is a good correlation, over all spinels, between the smallest E_Ovac for the surface and bulk formation energy, while the ionization potential correlates well in (111) surfaces. Machine learning over E_Ovac of all surface sites in all orientations and for all compounds to find the important factors, or descriptors, that decide the E_Ovac revealed that bulk and surface-dependent descriptors are the most important, namely the bulk formation energy, a Boolean descriptor of whether the surface is (111) or not, and the ionization potential, followed by geometrical descriptors that are different in each O site.

Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials

Metal oxides are widely used in the fields of chemistry, physics and materials science. Oxygen vacancy formation energy is a key parameter to describe the chemical, mechanical, and thermodynamic properties of metal oxides. How to acquire quickly and accurately oxygen vacancy formation energy remains a challenge for both experimental and theoretical researchers. Herein, we propose a machine learning model for the prediction of oxygen vacancy formation energy via data-driven analysis and the definition of simple descriptors. Starting with the database containing oxygen vacancy formation energies for 1750 metal oxides with enough structural diversity, new descriptors that effectively avoid the defects of molecular fingerprints, molecular graphic descriptors and site descriptors are defined. The descriptors have obvious physical meanings and wide practicability. Multiple linear regression analysis is then used to screen important features for machine learning model development, and two strongly associated features are obtained. The selected descriptors are used as input for the training of 21 machine learning models to select and develop the most accurate machine learning model. Finally, it is shown that the least squares support vector regression method exhibits the best performance for accurate prediction of the targeted oxygen vacancy formation energy through systematic error analysis, and the prediction accuracy is also verified by the external dataset. Our work establishes a novel and simple computational approach for accurate prediction of the oxygen vacancy formation energy of metal oxides and highlights the availability of data-driven analysis for metal oxide material research.

A synergetic effect between a single Cu site and S vacancy on an MoS2 basal plane for methanol synthesis from syngas

Compared to MoS₂(001), the synergetic effect between the single Cu site and S vacancy on Cu/MoS₂(001) destabilizes O, which not only increases the CO hydrogenation rate by 5 orders of magnitude, but leads to the selectivity switch from CH₄ to CH₃OH.