Low-index Surfaces Research Articles

Ab-Initio study of water molecule adsorption on monoclinic Scheelite-Type BiVO4 surfaces

Herein, we present a study on the adsorption of one water molecule (bonded to either Bi or V sites) and two water molecules (bonded to both Bi and V sites) onto seven low-index surfaces (001), (010), (011), (100), (101), (110), and (111) as well as one high-index surface (211) of a monoclinic scheelite-type BiVO4 crystal structure using ab initio calculations. By predicting the adsorption energies for different facets, we find that water adsorption is more likely to occur on Bi sites. However, for the (001) and (211) surfaces, water adsorption is more likely on the V sites. Furthermore, we find that the studied low-index facets can be grouped into four distinct categories. Facets within the same group exhibit similar water adsorption energies. These groups are ((001)), ((010), (100)), ((110)), and ((011), (101), (111)). For low-index surfaces, favorable adsorption occurs on the (001) surface on the V sites.

Digging Its Own Site: Linear Coordination Stabilizes a Pt1/Fe2O3 Single-Atom Catalyst.

Determining the local coordination of the active site is a prerequisite for the reliable modeling of single-atom catalysts (SACs). Obtaining such information is difficult on powder-based systems and much emphasis is placed on density functional theory computations based on idealized low-index surfaces of the support. In this work, we investigate how Pt atoms bind to the (11̅02) facet of α-Fe2O3; a common support material in SACs. Using a combination of scanning tunneling microscopy, X-ray photoelectron spectroscopy, and an extensive computational evolutionary search, we find that Pt atoms significantly reconfigure the support lattice to facilitate a pseudolinear coordination to surface oxygen atoms. Despite breaking three surface Fe-O bonds, this geometry is favored by 0.84 eV over the best configuration involving an unperturbed support. We suggest that the linear O-Pt-O configuration is common in reactive Pt-based SAC systems because it balances thermal stability with the ability to adsorb reactants from the gas phase. Moreover, we conclude that extensive structural searches are necessary to determine realistic active site geometries in single-atom catalysis.

Exploring the low-index surfaces of D52-La2O3 from the first-principles calculations

The surface relaxations, surface stability, electronic structures, and equilibrium morphology of D52-La2O3 were analyzed by means of first-principles calculations. The stoichiometric surfaces of D52-La2O3 possess thermodynamic energies of the following order: (001) < (110) < (100). Changes in temperature and the partial pressure of oxygen were employed to determine the energy of the non-stoichiometric surfaces. The results indicated that the energies of the (ns-1La1O)-terminated (100) and (ns-1La)-terminated (001) surfaces increased with increasing oxygen partial pressures and decreased with temperatures, whereas the (ns-1O)-terminated (001) and (ns-1O)-terminated (100) surfaces exhibited the reverse rule. According to the calculated density of states, surface relaxations primarily impact the surface electronic structures. The Gibbs-Wulff model was used to forecast the equilibrium morphology of D52-La2O3, which followed in comparison with other’s experimental findings.

Evaluating the selectivity of CO2 reduction reaction on elementary metal particles with DFT calculations

Electrocatalytic CO2 reduction reaction (CO2RR) towards two-electron products is a competent technique to convert renewable electricity into chemical energy at ambient conditions. Poly-crystalline metals are well-known electrocatalysts for CO2RR. However, no theoretical research was reported to date that evaluates the selectivity of two-electron CO2RR on metal particles. In this work, we proposed a paradigm to evaluate the product selectivity of CO2RR on metal particles. The product selectivity on three low-index surfaces of six metals (In, Pb, Ag, Au, Ni, and Pt) was firstly determined based on the detailed reaction mechanisms established for CO2RR to products of HCOOH and CO. Then, the product selectivity of CO2RR on the first four metal particles was evaluated using the effective free energy barrier which accounts for the facet dependence of CO2RR on the metal particles. The computational results well rationalize the experimental observations, namely preference of HCOOH to CO on the In and Pb particles with higher HCOOH selectivity on the Pb particle, while CO preferred to HCOOH on the Ag and Au particles with higher CO selectivity on the Au particle.

Full prediction of band potentials in semiconductor materials

A machine learning (ML) framework to predict the physical band potentials for a range of semiconductor materials, from metal sulfide, oxide, and nitride, to oxysulfide and oxynitride, is hereby described. A valence band maximum (VBM) model was established via the transfer learning of a large dataset of 2D materials (1382 samples, but with incorrect VBM potentials) onto a much smaller dataset of physically measured VBM for bulk 3D materials (87 samples) on a crystal graph convolutional neural network. This resulted in predictions with experimental accuracy (RMSE = 0.27 eV), which is a 3-fold improvement compared with ML trained on the physical dataset without transfer learning (RMSE = 0.75 eV). When combined with the bandgap prediction model (RMSE = 0.29 eV), a full prediction of conduction and valence band potentials can be made, which to the best of our knowledge, is the first for any ML framework. The variation of band potentials across low-index surfaces was predicted correctly and verified with reported physical potentials. In fact, the framework is able to capture variation in band potentials associated with minor atomic position alterations. Based on this, a general trend between surface atomic displacement and VBM shift was observed across various semiconductor materials. The model is not yet able to cope with major rearrangement of atomic sequence on surface layers, i.e., surface reconstructions, since it was not trained with such data but can be easily done so with specifically designed dataset. As an example application, the ML framework was used for the screening of potential photocatalytic materials for visible light water splitting. A total of 824 materials was successfully identified, including those experimentally-verified in the literature.

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces

Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases.

Atomic-level into the microscopic mechanism of gold capture by FeS during gold collection in matte process

Understanding the microscopic mechanisms involved in gold capture by FeS at the atomic level is crucial for optimizing the matte process and improving the gold recovery from gold concentrate containing arsenic and antimony. Herein, the DFT method was employed to explore the adsorption structure and electronic characteristics of a gold (Au) atom on the FeS surface. Furthermore, the effects of Fe/S vacancy and As/Sb dopant on Au adsorption were also investigated. The results reveal that the FeS(100) with S termination exhibits the smallest surface energy (0.56 J/m2) and possesses the highest stability amongst the low-index surfaces. Furthermore, the Au atom exhibits a preference for adsorption on the Fe-bridge site of FeS(100) through chemisorption due to the formation of a strong Au-Fe metallic bond, large adsorption energy (−5.40 eV), and small adsorption distance. Moreover, the presence of S vacancy enhances the Au capture capability of FeS(100) with an adsorption energy of −6.35 eV, while the Fe vacancy slightly weakens it. Additionally, the doping of As and Sb enhances the adsorption of Au atoms on FeS(100), and the Sb doping showcases a more distinct enhancement in Au capture efficiency. The findings of this work contribute to the advancement of matte processing technology, enabling enhanced gold recovery and potentially opening new avenues for the treatment of refractory gold ores.

Surface Energy Calculation for FCC Metals with Negative Cauchy’s Discrepancy using the GEAM

The three low-index surfaces of fcc metals with negative Cauchy’s discrepancy, strontium (Sr) and Iridium (Ir) are here investigated using the generalized embedded-atom method (GEAM), a model developed by Oni-Ojo et al. (2007) and the corresponding surface energies calculated. The low-index surface energies studied are: , and , with having the lowest and having the highest energy value. The predicted values are in good agreement with the experimental values.

Comparison of Inter-atomic Pair Potential Curves of different GEAM iterated values for Sr and Ir

In this study, the inter-atomic pair potential curve of Sr and Ir are compared with the potential curve of Rose et al. (1984) using the values predicted in our earlier study (The surface energy calculation for fcc metals with negative Cauchy’s discrepancy using the GEAM), utilizing an EAM model developed by Oni-Ojo et al. (2007) that ha demonstrated efficacy in predicting the low index surface energies of Sr and Ir, both of which have negative Cauchy’s discrepancy.

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

Water adsorption on surfaces of calcium aluminosilicate crystal phase of stone wool: a DFT study

Stone wool is widely used as an efficient thermal insulator within the construction industry; however, its performance can be significantly impacted by the presence of water vapor. By altering the material’s characteristics and effective thermo-physical properties, water vapor can reduce overall efficacy in various environmental conditions. Therefore, understanding water adsorption on stone wool surfaces is crucial for optimizing insulation properties. Through the investigation of interaction between water molecules and calcium aluminosilicate (CAS) phase surfaces within stone wool using density functional theory (DFT), we can gain insight into underlying mechanisms governing water adsorption in these materials. This research aims to elucidate the molecular-level interaction between water molecules and CAS surfaces, which is essential for understanding fundamental properties that govern their adsorption process. Both dissociative and molecular adsorptions were investigated in this study. For molecular adsorption, the adsorption energy ranged from -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document} 84 to -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document} 113 kJ mol-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} depending on surface orientation. A wider range of adsorption energy (-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document} 132 to -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document} 236 kJ mol-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) was observed for dissociative adsorption. Molecular adsorption was energetically favored on (010) surfaces while dissociative adsorption was most favorable on (111) surfaces. This DFT study provides valuable insights into the water adsorption behavior on low index surfaces of CAS phase in stone wool, which can be useful for designing effective strategies to manage moisture-related issues in construction materials. Based on these findings, additional research on the dynamics and kinetics of water adsorption and desorption processes of this thermal isolation material is suggested.

Comment on “surface characterization of cerium oxide catalysts using deep learning with infrared spectroscopy of CO”

We demonstrate that the predicted distribution of ceria facets in CeO2 nanorods, derived from the analysis of the measured IR spectrum of CO by fitting it using experimentally or DFT(HSE) calculated CO vibrational frequencies collected on all three low-index ceria surfaces, does not align with that derived using deep learning methods [1]. The latter utilizes a dataset based on extensive DFT(PBE) + U calculations, which are erroneous and have thus been scaled to the experimental values.

Natural-like generation of grain boundary models and the combined effects of microstructural elements and lithiation on the plastic behavior of TiO2: A computational study

Microstructural features of ceramics have a significant effect on mechanical properties and ion transport in different technologies ranging from optical materials to renewable energy generation and storage. Atomistic modeling is usually done on postulated grain boundaries which may not account for the distributions of different grain boundaries in real materials. We generate microstructural features of TiO2 with molecular dynamics (MD) by simulating a heat treatment; in this way, a natural-like distribution of grain sizes and boundaries is obtained. We used a model system with about 545,000 atoms and obtained structures with average grain sizes ranging about 6 to 12 nm and trending with the annealing temperature. While the model is nano- rather than micro-meter structured, it present key microstructural features. We observe ‘clean’ grain boundaries as well as boundaries with an amorphous interlayer. The clean boundaries feature low-index surfaces. When an amorphous interlayer is present, higher-index surfaces can be observed. The resulting structures show significant softening (reduction of maximum stress) vs. ideal crystalline titania, with isotropy of mechanical properties. Elastic constants, besides significant isotropy, show moderate stiffening due to the presence of grains. When introducing lithium, we observe segregation at grain boundaries, and lithiation of grainy structures leads to further softening.

Local Chemical Environment Dependent Nitrate-Reduction-to-Ammonia Performance on Cu-Based Electrocatalysts.

The active component of copper-based materials for electrocatalytic nitrate reduction to ammonia (NRA) remains unclear due to the susceptibility of oxidation of copper. Using density functional theory calculations, NRA pathways are evaluated on low-index crystal surfaces Cu2O (111), CuO (111), and Cu (111) at different pH. Cu2O (111), with abundant undercoordinated Cu atoms on the surface, shows easier adsorption of NO3- than Cu (111) or CuO (111). NRA on CuO (111) is hindered by the large ΔG of adsorption of NO3- and hydrogenation of *NO. Thus, Cu (111) and Cu2O (111) contribute most to the NRA activity while CuO (111) is inert. Three key steps of NRA on copper-based catalysts are identified: adsorption of NO3-, *NO → *NOH/*NHO, and *NH3 desorption, as the three can be rate-determining steps depending on the local environment. Moreover, previous experimentally detected NH2OH on copper-based catalysts may come from the NRA on Cu2O (111) as the most probable pathway on Cu2O (111) is NO3- → *NO3 → *NO2 → *NO → *NHO → *NHOH → *NH2OH → *NH2 → *NH3 → *NH3(g). At high reduction potential, CuOx would be reduced into Cu, so the effective active substance for NRA in a strong reduction environment is Cu.

Band-offsets scaling of low-index Ge/native-oxide heterostructures

We investigate, through XPS and AFM, the pseudo layer-by-layer growth of Ge native oxide across Ge(001), (110) and (111) surfaces in ambient environment. More significantly, our study reveals a universal set of valence and conduction band offset (VBO and CBO) values observed for Ge(001), Ge(110), and Ge(111) surfaces as a function of Ge-oxide concentration. We find that the band offsets appear to be the same across these low-index Ge surfaces i.e., for Ge-oxide/Ge heterostructures with the same Ge-oxide overlayer concentration or thickness. In contrast, different oxidation rates for Ge(001), Ge(110), and Ge(111) surfaces were observed, where the oxidation rate is fastest for Ge(001), compared to Ge(110) and Ge(111). This can be attributed to the different number of unsatisfied Ge dangling bonds (2 vs 1) associated to the respective ideal Ge surface in forming Ge-oxide. Thus, at any given oxidation time, the oxide concentration or thickness for each type of low index Ge surface will be different. This in turn will lead to different band offset value observed for each type of Ge surface. More significantly, we show that while oxidation rates can differ from different Ge surface-types, the band offset values can be estimated simply based on the Ge-oxide concentration regardless of Ge surface type.

Study of the effect of rare earth elements X(Pm, Ce, Pr, La) on the modification of NbN/Fe interface using first-principles calculations

This study presents an investigation of the basic properties of bulk phase NbN and bulk phase Fe as well as the impact of doping with rare earth elements (Pm, Ce, Pr, La) on the degree of bonding at the NbN/Fe interface using first-principles calculations. For each low-index surface of the two bulk phases, calculations were performed and analyzed. Based on the analyses, the selected NbN (211) and Fe (110) surfaces were then combined as the most suitable interface. The analysis reveals a reduced N–Nb bond length at the interface, with an attachment work of −0.21292 J/m2 for the NbN(211)/Fe(110) interface. Furthermore, the interfacial energy is 0.89769 J/m2, and the combination of the two leads to a tighter bonding at the interface. The four rare earth elements are doped into the interface, and the calculation results of the rare earth element atom substitution for Nb are as follows: γ-Ce is −5.02865 J/m2, γ-Pr is −3.91418 J/m2, γ-Pm is −4.65489 J/m2, γ-La is −4.95645 J/m2; the rare earth element atom substitutions for Fe are as follows: γ-Ce is −5.12654 J/m2, γ-Pr is −6.59452 J/m2, γ-Pm is −4.98143 J/m2, and γ-La is −5.01213 J/m2. The addition of the four rare earth elements can bring the interface closer. Furthermore, substituting rare earth elements for Fe atoms considerably enhances the interface. Notably, the lowest value of doping formation energy is −0.12086 J/m2, which is observed when Pr replaces Fe, and the lowest value of doping solvation energy is −3.08754 eV. Among the four rare-earth elements, replacing Fe atoms with Pr demonstrates the most significant enhancement of interfacial bonding.

Formation of oxygen vacancy at surfaces of ZnO by trimethylaluminum

The two-dimensional electron gas (2DEG) is a group of electrons that can move freely in horizontal dimensions but are confined in the third direction. It has been reported that atomic layer deposition (ALD) of Al2O3 on various reducible n-type oxides can lead to the formation of 2DEG at the heterojunction interfaces, among which ZnO is known to provide promising properties. In this study, we have performed a theoretical analysis using density functional theory calculations combined with experimental investigations to elucidate the surface reactions of Al2O3 ALD on low-index nonpolar ZnO surfaces, specifically focusing on the formation of oxygen vacancies (VO). The trimethylaluminum precursor was observed to undergo sequential dissociation of CH3 ligands, leading to the removal of surface oxygen of ZnO in the form of dimethyl ether. In addition, by examining the electronic structure after the removal of oxygen, the localization of the charge density at the surface was confirmed. Experimentally, the carrier density of the 2DEG at the Al2O3/ZnO interface showed a strong dependence on the ALD process temperature of Al2O3, confirming the endothermic nature of the formation of the 2DEG. By examining the characteristics of the 2DEG induced by VO, insights into the fundamental comprehension of oxide-based 2DEG systems are provided.

Toward High-Energy-Density Fuels for Direct Liquid Organic Hydrogen Carrier Fuel Cells: Electrooxidation of 1-Cyclohexylethanol.

Electrochemically active liquid organic hydrogen carriers (EC-LOHCs) can be used directly in fuel cells; so far, however, they have rather low hydrogen storage capacities. In this work, we study the electrooxidation of a potential EC-LOHC with increased energy density, 1-cyclohexylethanol, which consists of two storage functionalities (a secondary alcohol and a cyclohexyl group). We investigated the product spectrum on low-index Pt single-crystal surfaces in an acidic environment by combining cyclic voltammetry, chronoamperometry, and in situ infrared spectroscopy, supported by density functional theory. We show that the electrooxidation of 1-cyclohexylethanol is a highly structure-sensitive reaction with activities Pt(111) ≫ Pt(100) > Pt(110). Most importantly, we demonstrate that 1-cyclohexylethanol can be directly converted to acetophenone, which desorbs from the electrode surface. However, decomposition products are formed, which lead to poisoning. If the latter side reactions could be suppressed, the electrooxidation of 1-cyclohexylethanol would enable the development of EC-LOHCs with greatly increased hydrogen storage capacities.

Mineral dissolution mechanism of alite polymorphs from ReaxFF molecular dynamics and 29Si NMR investigations

The hydration of tricalcium silicate (C3S, C = CaO, S = SiO2) played a critical role in the mechanical property improvement in cement-based cementitious materials. In this study, we investigated the hydration mechanism of M3- and T1-C3S polymorphs using molecular dynamics simulation with the ReaxFF force field. Six surfaces were selected by their low-index surface energy and surface areas of the Wulff structure to study their hydration process, including M3-C3S (100), (001) and (111) surfaces, as well as T1-C3S (001), (010) and (111) surfaces. Bonding formation kinetics and radial distribution functions were analyzed. Results show that water dissociation degrees on the studied surfaces were comparable, independent of the crystal polymorphs. The final bond formation percent of Ca-OwH and Os/Oi-H on all the surfaces exhibited little difference. However, we observed the formation of pentacoordinate silicon atoms with a [SiO5]6- trigonal bipyramid structure, occurring earlier in M3-C3S than in T1-C3S. Furthermore, the M3-C3S exhibited a higher abundance of pentacoordinate silicon atoms compared to the T1 crystal, suggesting greater reactivity of M3-C3S. This study provided valuable insights into enhancing the quality of cement clinker and activating other silicate minerals in supplementary cementitious materials.

Surface energy calculation for some transition metal using the GEAM

The three low index surface energies of fcc metals Cu, Ag, and Au are here calculated using the generalized embedded-atom method (GEAM). The low-index surface energies {, and }, with having the lowest and having the highest energy value. The anticipated values accord well with the experimental values.