Unraveling the formation of oxygen vacancies on the surface of transition metal-doped ceria utilizing artificial intelligence.

Ceria has been extensively utilized in different fields, with surface oxygen vacancies playing a central role. However, versatile oxygen vacancy regulation is still in its infancy. In this work, we propose an effective strategy to manipulate the oxygen vacancy formation energy via transition metal doping by combining first-principles calculations and analytical learning. We elucidate the underlying mechanism driving the formation of oxygen vacancies using combined symbolic regression and data analytics techniques. The results show that the Fermi level of the system and the electronegativity of the dopants are the paramount parameters (features) influencing the formation of oxygen vacancies. These insights not only enhance our understanding of the oxygen vacancy formation mechanism in ceria-based materials to improve their functionality but also potentially lay the groundwork for future strategies in the rational design of other transition metal oxide-based catalysts.

Unveiling the Structure of Oxygen Vacancies in Bulk Ceria and the Physical Mechanisms behind Their Formation.

Understanding the structures of oxygen vacancies in bulk ceria is crucial as they significantly impact the material's catalytic and electronic properties. The complex interaction between oxygen vacancies and Ce3+ ions presents challenges in characterizing ceria's defect chemistry. We introduced a machine learning-assisted cluster-expansion model to predict the energetics of defective configurations accurately within bulk ceria. This model effectively samples configurational spaces, detailing oxygen vacancy structures across different temperatures and concentrations. At lower temperatures, vacancies tend to cluster, mediated by Ce3+ ions and electrostatic repulsion, while at higher temperatures, they distribute uniformly due to configurational entropy. Our analysis also reveals a correlation between thermodynamic stability and the band gap between occupied O 2p and unoccupied Ce 4f orbitals, with wider band gaps indicating higher stability. This work enhances our understanding of defect chemistry in oxide materials and lays the groundwork for further research into how these structural properties affect ceria's performance.

Boosting Up Electrosynthesis of Urea with Nitrate and Carbon Dioxide via Synergistic Effect of Metallic Iron Cluster and Single-Atom.

Electrocatalytic conversion of nitrates and carbon dioxide to urea under ambient conditions shows promise as a potential substitute for traditional urea synthesis processes characterized by high consumption and pollution. In this study, a straightforward one-pot method is employed to prepare a highly efficient FeNC-Fe1N4 electrocatalyst, consisting of atomically dispersed Fe1N4 sites and metallic Fe clusters (FeNC) with particle size of 4-7 nm. The FeNC-Fe1N4 catalyst exhibits remarkable electrocatalytic activity for urea synthesis from nitrate anion (NO3 -) and carbon dioxide (CO2), achieving a urea production rate of 38.2 mmol gcat -1 h-1 at -0.9 V (vs RHE) and a Faradaic efficiency of 66.5% at -0.6 V (vs RHE). Both experimental and theoretical results conclusively demonstrate that metallic Fe clusters and Fe1N4 species provide active sites for the adsorption and activation of NO3 - and CO2, respectively, and the synergistic effect between Fe1N4 and metallic Fe clusters significantly enhances the electrochemical efficiency of urea synthesis. In all, this work contributes to the rational design and comprehensive synthesis of a dual-active site iron-based electrocatalyst, facilitating efficient and sustainable urea synthesis.

Direct in-situ insights into the asymmetric surface reconstruction of rutile TiO2 (110).

The reconstruction of rutile TiO2 (110) holds significant importance as it profoundly influences the surface chemistry and catalytic properties of this widely used material in various applications, from photocatalysis to solar energy conversion. Here, we directly observe the asymmetric surface reconstruction of rutile TiO2 (110)-(1×2) with atomic-resolution using in situ spherical aberration-corrected scanning transmission electron microscopy. Density functional theory calculations were employed to complement the experimental observations. Our findings highlight the pivotal role played by repulsive electrostatic interaction among the small polarons -formed by excess electrons following the removal of neutral oxygen atoms- and the subsequent surface relaxations induced by these polarons. The emergence and disappearance of these asymmetric structures can be controlled by adjusting the oxygen partial pressure. This research provides a deeper understanding, prediction, and manipulation of the surface reconstructions of rutile TiO2 (110), holding implications for a diverse range of applications and technological advancements involving rutile-based materials.

Revealing Temperature-Dependent Oxidation Dynamics of Ni Nanoparticles via Ambient Pressure Transmission Electron Microscopy.

Understanding the oxidation mechanism of metal nanoparticles under ambient pressure is extremely important to make the best use of them in a variety of applications. Through ambient pressure transmission electron microscopy, we in situ investigated the dynamic oxidation processes of Ni nanoparticles at different temperatures under atmospheric pressure, and a temperature-dependent oxidation behavior was revealed. At a relatively low temperature (e.g., 600 °C), the oxidation of Ni nanoparticles underwent a classic Kirkendall process, accompanied by the formation of oxide shells. In contrast, at a higher temperature (e.g., 800 °C), the oxidation began with a single crystal nucleus at the metal surface and then proceeded along the metal/oxide interface without voids formed during the whole process. Through our experiments and density functional theory calculations, a temperature-dependent oxidation mechanism based on Ni nanoparticles was proposed, which was derived from the discrepancy of gas adsorption and diffusion rates under different temperatures.

Statistical analysis of the performance of a variety of first-principles schemes for accurate prediction of binary semiconductor band gaps.

Standard density functional theory (DFT) approximations tend to strongly underestimate band gaps, while the more accurate GW and hybrid functionals are much more computationally demanding and unsuitable for high-throughput screening. In this work, we have performed an extensive benchmark of several approximations with different computational complexity [G0W0@PBEsol, HSE06, PBEsol, modified Becke-Johnson potential (mBJ), DFT-1/2, and ACBN0] to evaluate and compare their performance in predicting the bandgap of semiconductors. The benchmark is based on 114 binary semiconductors of different compositions and crystal structures, for about half of which experimental band gaps are known. Surprisingly, we find that, compared with G0W0@PBEsol, which exhibits a noticeable underestimation of the band gaps by about 14%, the much computationally cheaper pseudohybrid ACBN0 functional shows a competitive performance in reproducing the experimental data. The mBJ functional also performs well relative to the experiment, even slightly better than G0W0@PBEsol in terms of mean absolute (percentage) error. The HSE06 and DFT-1/2 schemes perform overall worse than ACBN0 and mBJ schemes but much better than PBEsol. Comparing the calculated band gaps on the whole dataset (including the samples with no experimental bandgap), we find that HSE06 and mBJ have excellent agreement with respect to the reference G0W0@PBEsol band gaps. The linear and monotonic correlations between the selected theoretical schemes and experiment are analyzed in terms of the Pearson and Kendall rank coefficients. Our findings strongly suggest the ACBN0 and mBJ methods as very efficient replacements for the costly G0W0 scheme in high-throughput screening of the semiconductor band gaps.

CuCeOx/CuO Catalyst Derived from the Layered Double Hydroxide Precursor: Catalytic Performance in NO Reduction with CO in the Presence of Water and Oxygen.

Valencies of metal species and lattice defects, such as oxygen vacancies, play a pivotal role in metal oxide-catalyzed reactions. Herein, we report a promising synthetic strategy for preparing CuO-supported CuCeOx catalysts (CuCeOx/CuO) by calcination of a hydrotalcite precursor [Cu6Ce2(OH)16]CO3·nH2O. The structural and chemical properties of catalysts were characterized by XRD, ICP-AES, TEM, TPR, NH3-TPD, XPS, Raman spectroscopy, and N2 adsorption, which revealed that the thermal pretreatment in an oxidative atmosphere caused segregation and reconstitution processes of the precursor, resulting in a mesoporous catalyst consisting of well-dispersed CuO-supported CuCeOx clusters of 1.8-3.2 nm in size with a high population of oxygen vacancies. The as-prepared catalyst shows excellent catalytic performance in the reduction of NO by CO in the absence as well as in the presence of water and oxygen. This behavior is attributed to its high oxygen defect concentration facilitating the interplay of the redox equilibria between Cu2+ and reduced copper species (Cu+/Cu0) and (Ce4+/Ce3+). The high surface population of oxygen vacancies and in situ-generated metallic copper species have been evidenced by Raman spectroscopy and X-ray photoelectron spectroscopy. The layered double hydroxide-derived CuCeOx/CuO also showed good water tolerance and long-term stability. In situ infrared spectroscopy investigations indicated that adsorbed hyponitrite species are the main reaction intermediates of the NO conversion as also corroborated by theoretical simulations.

Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection.

Symbolic regression offers a promising avenue for describing the structure-property relationships of materials with explicit mathematical expressions, yet it meets challenges when the key variables are unclear because of the high complexity of the problems. In this work, we propose to solve the difficulty by automatically searching for important variables from a large pool of input features. A new algorithm that integrates symbolic regression with iterative variable selection (VS) was designed for optimization of the model with a large amount of input features. Using the recent method SISSO for symbolic regression and random search for variable selection, we show that the VS-assisted SISSO (VS-SISSO) can effectively manage even hundreds of input features that the SISSO alone was computationally hindered, and it fastly converges to (near) optimal solutions when the model complexity is not high. The efficiency of this approach for improving the accuracy of symbolic regression in materials science was demonstrated in the two showcase applications of learning approximate equations for the band gap of inorganic halide perovskites and the stability of single-atom alloy catalysts.

Revealing Surface Restraint-Induced Hexagonal Pd Nanocrystals via In Situ Transmission Electron Microscopy.

Achieving metal nanocrystals with metastable phase draws much attention due to their anticipated fascinating properties, wheras it is still challenging because their polymorphism nature and phase transition mechanism remain elusive. Here, phase stability of face-centered cubic (fcc) Pd nanocrystals was studied via in situ spherical aberration (Cs)-corrected transmission electron microscopy (TEM). By constructing a well-defined Pd/C composite structure, Pd nanocrystals encapsulated by graphite, the dispersion process of fcc Pd was observed through a nucleation and growth process. Interestingly, Cs-corrected scanning TEM analysis demonstrated that the newly formed Pd nanocrystals could adopt a metastable hexagonal phase, which was considered challenging to obtain. Accordingly, formation mechanism of the hexagonal Pd nanocrystals was proposed, which involved the combined effect of two factors: (1) templating of graphite and (2) size effect. This work is expected to offer new insight into the polymorphism of Pd nanocrystals and pave the way for the future design of metastable metal nanomaterials.

Effect of the Configuration of Copper Oxide-Ceria Catalysts in NO Reduction with CO: Superior Performance of a Copper-Ceria Solid Solution.

Various copper-ceria-based composites have attracted attention as efficient catalysts for the reduction of NO with CO. In this comparative study, we have examined the catalytic potential of different configurations of copper oxide-ceria catalysts, including catalysts based on a copper-ceria solid solution, copper oxide particles supported on ceria, and ball-milled copper oxide-ceria. The structurally different interfaces between the constituents of these catalysts afforded very different catalytic performances. The solid solution catalyst outperformed the corresponding ceria-supported and ball-milled CuO-CeO2 catalysts. The copper cations incorporated into the ceria lattice strongly improved the activity, N2 selectivity, and water vapor tolerance compared to the other catalyst configurations. The experimental observations are supported by first-principles density functional theory (DFT) studies of the reaction pathway, which indicate that the incorporation of Cu cations into the ceria matrix lowers the energy required for activating the lattice oxygen, thereby enhancing the formation and healing of oxygen vacancies, and thus promoting NO reduction with CO.

In Situ Resolving the Atomic Reconstruction of SnO2 (110) Surface.

Understanding surface reconstruction of nanocrystals is of great importance to their applications, however it is still challenging due to lack of atomic-level structural information under reconstruction conditions. Herein, through in situ spherical aberration corrected scanning transmission electron microscopy (STEM), the reconstruction of nanocrystalline SnO2 (110) surface was studied. By identifying the precise arrangements of surface/subsurface Sn and O columns through both in situ bright-field and high-angle annular dark-field STEM images, an unexpected added Sn2O model was determined for SnO2 (110)-(1 × 2) surface. The protruded Snδ+ of this surface could act as the active sites for activating O2 molecules according to our density functional theory (DFT) calculations. On the basis of in situ observation of atomic-level reconstruction behaviors and DFT calculations, an energy-driven reconstruction process was also revealed. We anticipate this work would help to clarify the long-standing debate regarding the reconstruction of SnO2 (110) surface and its intrinsic property.

The dynamic interplay between water and oxygen vacancy at the near-surface of ceria.

Water, even at trace concentrations, strongly increases the CO oxidation activities of the reducible metal oxide supported noble-metal catalysts, where the transfer of proton plays a key role. In this paper, we performed a thorough investigation of the interplay between water molecules and the reduced CeO2(111) surface. It was found that water molecules can induce the migration of oxygen vacancies which in turn results in the formation of surface protons. The proton then entangles with the near-surface polaron to form polaron-proton pair due to their mutual attractive interactions. The hopping of the polaron can easily trigger the long-range or short-range diffusion of protons mediated by water molecules at the CeO2(111) surface. These findings provide new insights into the key roles of oxygen vacancies and polarons in reducible oxide based heterogeneous catalysis, which is beneficial for the understanding of the increased activity of reducible oxide supported metal nanoparticles in the presence of water.

Coherent vibrational dynamics reveals lattice anharmonicity in organic-inorganic halide perovskite nanocrystals.

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.

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence.

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.

Crystallographic Orientation Dependence of Surface Segregation and Alloying on PdCu Catalysts for CO2 Hydrogenation.

The influence of the crystallographic orientation on surface segregation and alloy formation in model PdCu methanol synthesis catalysts was investigated in situ using near-ambient pressure X-ray photoelectron spectroscopy under CO2 hydrogenation conditions. Combined with scanning tunneling microscopy and density functional theory calculations, the study showed that submonolayers of Pd undergo spontaneous alloy formation on Cu(110) and Cu(100) surfaces in vacuum, whereas they do not form an alloy on Cu(111). Upon heating in H2, inward diffusion of Pd into the Cu lattice is favored, facilitating alloying on all Cu surfaces. Under CO2 hydrogenation reaction conditions, the alloying trend becomes stronger, promoted by the reaction intermediate HCOO*, especially on Pd/Cu(110). This work demonstrates that surface alloying may be a key factor in the enhancement of the catalytic activity of PdCu catalysts as compared to their monometallic counterparts. Furthermore, it sheds light on the hydrogen activation mechanism during catalytic hydrogenation on copper-based catalysts.

Synergistic Effects of Ternary PdO-CeO2-OMS-2 Catalyst Afford High Catalytic Performance and Stability in the Reduction of NO with CO.

We developed a robust ternary PdO-CeO2-OMS-2 catalyst with excellent catalytic performance in the selective reduction of NO with CO using a strategy based on combining components that synergistically interact leading to an effective abatement of these toxic gases. The catalyst affords 100% selectivity to N2 at the nearly full conversion of NO and CO at 250 °C, high stability in the presence of H2O, and a remarkable SO2 tolerance. To unravel the origin of the excellent catalytic performance, the structural and chemical properties of the PdO-CeO2-OMS-2 nanocomposite were analyzed in the as-prepared and used state of the catalyst, employing a series of pertinent characterization methods and specific catalytic tests. The experimental as well as theoretical results, based on density-functional theory calculations suggest that CO and NO follow different reaction pathways, CO is preferentially adsorbed and oxidized at Pd sites (PdII and Pd0), while NO decomposes on the ceria surface. Lattice oxygen vacancies at the interfacial perimeter of PdO-CeO2 and PdO-OMS-2, and the diffusion of oxygen and oxygen vacancies are proposed to play a critical role in this multicenter reaction system.

Water-Assisted Homolytic Dissociation of Propyne on a Reduced Ceria Surface.

The emergence of ceria (CeO2 ) as an efficient catalyst for the selective hydrogenation of alkynes has attracted great attention. Intensive research effort has been devoted to understanding the underlying catalytic mechanism, in particular the H2 -CeO2 interaction. Herein, we show that the adsorption of propyne (C3 H4 ) on ceria, another key aspect in the hydrogenation of propyne to propene, strongly depends on the degree of reduction of the ceria surface and hydroxylation of the surface, as well as the presence of water. The dissociation of propyne and the formation of methylacetylide (CH3 CC-) have been identified through the combination of infrared reflection absorption spectroscopy (IRAS) and DFT calculations. We demonstrate that propyne undergoes heterolytic dissociation on the reduced ceria surface by forming a methylacetylide ion on the oxygen vacancy site and transferring a proton to the nearby oxygen site (OH group), while a water molecule that competes with the chemisorbed methylacetylide at the vacancy site assists the homolytic dissociation pathway by rebounding the methylacetylide to the nearby oxygen site.

Corrigendum: The Structure of Oxygen Vacancies in the Near-Surface of Reduced CeO2 (111) Under Strain.

[This corrects the article DOI: 10.3389/fchem.2019.00436.].

The Structure of Oxygen Vacancies in the Near-Surface of Reduced CeO2 (111) Under Strain.

Strain has been widely recognized as important for tuning the behavior of defects in metal oxides since properties such as defect configuration, electronic structure, excess charge localization, and local atomic distortions may be affected by surface strain. In CeO2, the most widely used promoter in three-way catalysts and solid state electrolyte in fuel cells, the behaviors of oxygen vacancies, and associated Ce3+ polarons are crucial in applications. Recent STM and AFM investigations as well as DFT-based calculations have indicated that in the near-surface of CeO2 (111), at low temperatures and vacancy concentrations, subsurface oxygen vacancies are more stable than surface ones, and the Ce3+ ions are next-nearest neighbors to both types of vacancies, which can be explained by the better ability of the system to relax the lattice strain induced by vacancy formation as well as by the excess charge localization. The results also revealed that the interaction between first-neighbor vacancies is repulsive. In this work, the relative stability of surface and subsurface oxygen vacancies at the CeO2 (111) surface under in-plane strain is investigated by means of DFT+U calculations. The tensile strain favors isolated surface vacancies with next nearest neighbor polarons, whereas isolated subsurface vacancies with nearest neighbor polarons are energetically favored under compressive strain. In addition, the formation of both surface and subsurface dimers is favored over having corresponding isolated species under compressive strain, which implies the possibility of controlling the formation of vacancy clusters using strain. In many applications, ceria is employed as a supported thin film or within a heterostructure in which ceria can be strained, and this study shows that strain can be a useful handle to tune properties of such materials.

Surface Segregation in CuNi Nanoparticle Catalysts During CO2 Hydrogenation: The Role of CO in the Reactant Mixture.

Surface segregation and restructuring in size-selected CuNi nanoparticles were investigated via near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) at various temperatures in different gas environments. Particularly in focus were structural and morphological changes occurring under CO2 hydrogenation conditions in the presence of carbon monoxide (CO) in the reactant gas mixture. Nickel surface segregation was observed when only CO was present as adsorbate. The segregation trend is inverted in a reaction gas mixture consisting of CO2, H2, and CO, resulting in an increase of copper concentration on the surface. Density functional theory calculations attributed the inversion of the segregation trend to the formation of a stable intermediate on the nanocatalyst surface (CH3O) in the CO-containing reactant mixture, which modifies the nickel segregation energy, thus driving copper to the surface. The promoting role of CO for the synthesis of methanol was demonstrated by catalytic characterization measurements of silica-supported CuNi NPs in a fixed-bed reactor, revealing high methanol selectivity (over 85%) at moderate pressures (20 bar). The results underline the important role of intermediate reaction species in determining the surface composition of bimetallic nanocatalysts and help understand the effect of CO cofeed on the properties of CO2 hydrogenation catalysts.