Neighboring Atoms Research Articles

Effect of Alloying Metal Elements on the Valence Band of β-Ga2O3: A First-Principles Study.

β-Ga2O3 is a candidate semiconductor material for high-power electronics due to its ultrawide bandgap and high Baliga's figure of merit. However, its p-type doping is extremely difficult because of its low and flat band dispersion at its valence band maximum (VBM). A few reports have predicted that the VBM of β-Ga2O3 can be enhanced via alloying a specific metal (M), which enables p-type conduction. To fully understand the M regulation on the valence band of β-Ga2O3, 49 different M-alloyed β-Ga2O3 i.e., β-(M0.125Ga0.875)2O3, are investigated in this work through first-principles calculations. The alloys' configurations and electronic structures are found dependent more on the group number of M. The β-(M0.125Ga0.875)2O3 members with Ms in groups 3, 9, 13, and 15 and the Ms of Be, Cr, and Fe are semiconductors. The VBMs' energies are enhanced by more than 1 eV in the β-(M0.125Ga0.875)2O3 for the Ms of Rh, Ir, Sb, and Bi because these VBMs are newly formed by the orbital hybridization of the Ms and neighboring oxygen atoms. The band dispersions at VBMs generally become steeper, especially for Ms in groups 13 and 15. The average hole effective mass in β-(Al0.125Ga0.875)2O3 is only 3.43% of that in the β-Ga2O3. It is believed that the reduced hole effective mass and increased VBMs' energies make the p-type doping easier in these alloys and the applications of the alloys wider.

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The molecular models of tin dioxide nanoparticles containing 1 – 10 metal atoms and can include coordinated or constitutive water were constructed. Their equilibrium spatial structure and electronic structure are calculated using the second-order Möller – Plesset perturbation theory with the SBKJC valence basis set. It is shown that the length of the Sn–O bond in nanoclusters does not depend on their size and the coordination number of Sn atoms, but is determined by the coordination type of neighboring oxygen atoms. Namely, the Sn–O(3) bond length (~ 2.10 Å) > the Sn–O(2) bond length (~ 1.98 Å). The obtained Sn–O(3) bond lengths are in good agreement with the experimental values for crystalline SnO2 samples (2.05 Å). The calculated atomization energy for SnO2 is 1661 kJ/mol and satisfactorily corresponds to the experimentally measured specific atomization energy of crystalline SnO2 (1381 kJ/mol). It was found that a satisfactory reproduction of the experimental characteristics of crystalline tin dioxide is possible when using clusters containing at least 10 tin atoms, for example, (SnO2)10×14H2O. Based on the analysis of the energy effects of coordination of water molecules and hydroxide ion, proton removal and proton transfer on the hydrated surface of tin dioxide, quantitative estimates of the acid-base characteristics of the active centers of the SnO2 surface were made. The dependence of the acidity of hydroxyl groups and coordinated water molecules on the coordination number of the oxygen atom and the neighboring tin atom, as well as on the size of the cluster model, was revealed. It has been shown that the acidity of proton and aproton centers naturally decreases with increasing coordination number of the tin atom. The methodology used in this work to calculate the рКа value of the smallest model of the SnO2×H2O composition allows us to reproduce the experimental data for stannous acids. The mechanisms of formation of the simplest nanostructures from the initial forms of stannous hydroxide Sn(OH)4 are proposed. It has been shown that the formation of a dimer (SnO2)2×4H2O by the association of two Sn(OH)4 molecules is energetically most advantageous. Further transformations of nanoparticles lead to an increase in their size, dehydration, and the formation of denser structures that have crystallinity features inherent in solid-phase SnO2.

Deciphering the Basis of Enhanced Na+ Storage Induced by Halogen Doping in Na2VTi(PO4)3

AbstractElemental doping is a widely adopted strategy to enhance the electrochemical performance of electrode materials, yet limited studies have explored the resulting variations in the bonding environments and the interactions between dopant atoms and their neighboring atoms. In this study, halogen‐doped (fluorine, chlorine, and bromine) polyanionic phosphates are synthesized to investigate the effects of halogen doping on the fine crystal structure, chemical micro‐environment, and electronic structure of Na2VTi(PO4)3 for Na ion storage. Density functional theory analysis reveals that halogen doping strengthens interactions at the Na sites while disrupting their symmetry, thereby promoting Na+ conductivity. Simultaneously, the increased electron density and expanded electron cloud potentially bridge the electron clouds previously isolated by [PO4] tetrahedra, facilitating electron transport. As a result, the doped samples demonstrate improved performance in rate capability, capacity, and cycling stability. This study provides deeper insights into the influence of elemental doping on electrode materials properties.

Molecular Design and Mechanism Study of Non-Activated Collectors for Sphalerite (ZnS) Based on Coordination Chemistry Theory and Quantum Chemical Simulation.

Sphalerite flotation is generally achieved by copper activation followed by xanthate collection. This study aims to propose a design idea to find novel collectors from the perspective of molecular design and prove the theoretical feasibility that the collector can effectively recover sphalerite without copper activation. To address this, 30 compounds containing different structures of sulfur atoms and different neighboring atoms were designed based on coordination chemistry. Twelve potential collectors were screened, and their properties and interactions with a hydrated sphalerite (110) surface were evaluated. Compound 27 (C2H4S22-) showed the greatest reactivity, suggesting that the double-coordination structure of two sulfhydryl groups is an effective molecular structure for direct sphalerite flotation. The DFTB+ and MD results demonstrate that 1,2-butanedithiol (C4H10S2), having a similar coordination structure to compound 27, has the potential to replace the traditional reagent scheme of sphalerite flotation. The strong reagent-surface interaction is attributed to the overlap of Zn 3d with S 3p orbitals, the most negative electrostatic potential, the relatively high EHOMO and low average local ionization energy, and the eliminated steric hindrance effect. It is expected that this study can provide a design idea for the targeted design and development of novel reagents for complex sulfide ore flotation.

Nonlocality and strength of interatomic interactions inducing the topological phonon phase transition

Understanding phonon behavior in semiconductors from a topological physics perspective offers opportunities to uncover extraordinary phenomena related to phonon transport and electron–phonon interactions. While various types of topological phonons have been reported in different crystalline solids, their microscopic origins remain quantitatively unexplored. In this study, analytical interatomic force constant (IFC) models are employed for wurtzite GaN and AlN to establish relationships between phonon topology and real-space IFCs. The results demonstrate that variations in the strength and nonlocality of IFCs can induce phonon phase transitions in GaN and AlN through band reversal, leading to the emergence of new Weyl phonons at the boundaries and within the Brillouin zones. Among the observed Weyl points, some remain identical in both materials under simple IFC modeling, while others exhibit variability depending on the specific case. Compared to the strength of the IFCs, nonlocal interactions have a significantly larger impact on inducing topological phonon phase transitions, particularly in scenarios modeled by the IFC model and the SW potential. The greater number of the third nearest neighbor atoms in wurtzite AlN provides more room for variations in the topological phonon phase than in GaN, resulting in more substantial changes in AlN.

Synergistic effects of interstitial and substitutional doping on the thermoelectric properties of PbS

Lead sulfide (PbS) is widely recognized as a promising n-type thermoelectric material for use in the middle-temperature range. Although it already exhibits favorable electronic and thermal properties, its thermoelectric performance could be further enhanced by addressing the disparity between the light and heavy bands in the conduction band, thereby optimizing electrical transport, and by modifying the strength of its chemical bonds to reduce lattice thermal conductivity. In this study, we demonstrate that introducing just small amounts of antimony (Sb) into PbS generates a unique combination of interstitial and substitutional doping that leads to a significant improvement in both directions. Substitutional doping enhances the degeneracy between the light and heavy bands, increasing carrier mobility. At the same time, interstitial doping introduces a new resonance state near the Fermi level, providing an additional channel for electron transport while boosting carrier concentration. These synergistic effects lead to a marked increase in the power factor of PbS, achieving an average power factor (PFavg) of 1.07 mW m−1 K−2 across the temperature range of 320–873 K. Moreover, Sb substitution for Pb induces a shift in the surrounding S atoms toward Sb, weakening their bonds with neighboring Pb atoms. This shift results in a coexistence of strong and weak chemical bonds, which effectively reduces lattice thermal conductivity. Additionally, the defect structures introduced by Sb doping effectively scatter phonons, further lowering lattice thermal conductivity. As a result, PbS doped with 0.5% Sb exhibits a figure of merit (ZT) of 0.73 at 873 K, which is approximately three times higher than that of undoped PbS.

Large-Scale DFT Calculations on Size and Site Dependences of Atomic and Electronic Structures in Metallic Nanoparticle Catalysts

Size control of Pt nanoparticle catalysts is one of the key challenges in fuel cell development. Miniaturization of Pt nanoparticles is expected to increase activity and save the amount of Pt, while very small particles (e.g. sub-nano particles or clusters) have different electronic structures and stabilities from “nano” particles. To investigate the atomic and electronic structures of materials, first-principles density functional theory (DFT) calculation is a powerful tool. However, because of its high computational cost, the DFT applications have been limited mainly for small particles and clusters of tens to hundreds of atoms. In this study, by using our own large-scale DFT calculation method, we investigate the size and site dependences of atomic and electronic structures in Pt nanoparticles with the diameters of several nanometers, which are comparable to the sizes of the particles in practical use.The computational cost of the conventional DFT calculation methods scale cubically to the system size, i.e., the number of the atoms in the target system and the number of local functions for each atom, which are used to express electronic density. We have developed the multi-site support function (MSSF) method [1,2], in which local functions are constructed as the linear combinations of the atomic orbital functions belonging to not only the target atom but also its neighboring atoms. The MSSF method enables us to reduce the number of local functions to be the minimal-basis size, leading the dramatical computational cost reduction. The MSSF method is implemented in our large-scale DFT code CONQUEST [3,4].We optimized the structures of Pt nanoparticles with the diameters of 0.5 nm (13 atoms) - 5.5 nm (3871 atoms) and found that the Pt-Pt bond lengths inside the nanoparticle converge to those in bulk Pt when the nanoparticle size becomes large. The similar trend is found also in the electronic structures as shown in the figure. In the presentation, we compare the size dependence of the stability and atomic and electronic structures of Pt nanoparticle with other metallic nanoparticles. Site dependence of the electronic structures and reactivity with molecules are also investigated by using large-scale DFT together with statistical analysis.[1] A. Nakata, D. R. Bowler, T. Miyazaki, J. Phys. Soc. Jpn., 91, 091011 (2022). [2] A. Nakata, D. R. Bowler, T. Miyazaki, Phys. Chem. Chem. Phys. 17, 31427 (2015). [3] http://www.order-n.org/ [4] A. Nakata, J. S. Baker, S. Y. Mujahed, J. T. L. Poulton, S. Arapan, J. Lin, Z. Raza, S. Yadav, L. Truflandier, T. Miyazaki, D. R. Bowler, J. Chem. Phys., 152, 164112 (2020). Figure 1

Developing the Metrology for Atomic Level Spatial Distribution and Local Chemistry Analysis of in-Situ Dopants By Atom Probe Tomography

Increasing the in-situ active doping level is a primary method for reducing source-drain resistance1. The impact of this approach is dependent on the dopant atoms spatial distribution and their local chemical environment. To measure and optimize these parameters is a characterization challenge although atom probe tomography (APT) has the potential to capture the required data. Likewise, extracting the relevant information and its interpretation still requires development. In this presentation, we show how a 3D APT dataset can be analyzed using a statistical algorithm to characterize the spatial (in)homogeneity of all the elements present. Moreover, the influence of the dopant atoms on their local chemical environment is also revealed.For demonstration purposes, a 43 nm thick in-situ highly boron-doped (~4 at.%) epitaxially grown SiGe layer with an intended site fraction of 0.7:0.3 (Si:Ge) was utilized. A custom Python code, developed upon a similar algorithm to that proposed by Stephenson et al. 2, alongside a significance test to evaluate the element spatial inhomogeneity within our dataset was employed. The code utilized a fixed number (500) of nearest neighbor atoms surrounding each dopant element, thereby establishing the local composition pertaining to the dopant element. A two-sample Kolmogorov-Smirnov test3 was conducted to validate the spatial (in)homogeneity. This involved comparing the resulting composition frequency of the experimental dataset with a randomized version of the same dataset featuring shuffled chemical identities. A significance threshold of α ≤ 0.01 was applied to reject the null hypothesis for a random distribution of elements throughout the dataset.The empirical cumulative distribution functions (CDF) for the B-B, along with the B-Si and B-Ge site fractions are presented in Fig. 1. Statistical analysis of the data supports all three elements being spatially inhomogeneous i.e., p value < 0.01. The B-B distribution also shows a notable deviation towards higher concentration relative to the randomized version and indicated ~10% of the B was aggregating. Sub-dividing the B-B composition distribution (Fig. 2a) into three regions, with the division points being where the two composition distributions (measured and randomized data) intersected, enabled further insight about the localized matrix behavior (Fig. 2b, c). The Si site fraction was found to monotonically shift from a lower to higher level with increasing B composition, while the Ge exhibited the opposite trend. This implies that the B incorporation modifies the matrix at a localized scale and leads to an enhanced Si site fraction around the B. This finding is consistent with a recent DFT simulation for B doped SiGe4. The 3D-APT metrology presented here enhances our ability to study the spatial distribution of dopants on an atomic scale and could be used, for example, to refine the epitaxial growth or link it to the performance of devices. J.-L. Everaert et al., in 2017 Symposium on VLSI Technology, p. T214–T215, IEEE, Kyoto, Japan (2017).L. T. Stephenson et al., MethodsX, 1, 12–18 (2014).J. N. Miller, J. C. Miller, and R. D. Miller, Statistics and chemometrics for analytical chemistry, Seventh edition., Pearson Education Limited, Harlow, United Kingdom, (2018).G. Rengo et al., J. Phys. Chem. C (submitted) Figure 1

Prediction of Protein Secondary Structures Based on Substructural Descriptors of Molecular Fragments.

The accurate prediction of secondary structures of proteins (SSPs) is a critical challenge in molecular biology and structural bioinformatics. Despite recent advancements, this task remains complex and demands further exploration. This study presents a novel approach to SSP prediction using atom-centric substructural multilevel neighborhoods of atoms (MNA) descriptors for protein molecular fragments. A dataset comprising over 335,000 SSPs, annotated by the Dictionary of Secondary Structure in Proteins (DSSP) software from 37,000 proteins, was constructed from Protein Data Bank (PDB) records with a resolution of 2 Å or better. Protein fragments were converted into structural formulae using the RDKit Python package and stored in SD files using the MOL V3000 format. Classification sequence-structure-property relationships (SSPR) models were developed with varying levels of MNA descriptors and a Bayesian algorithm implemented in MultiPASS software. The average prediction accuracy (AUC) for eight SSP types, calculated via leave-one-out cross-validation, was 0.902. For independent test sets (ASTRAL and CB513 datasets), the best SSPR models achieved AUC, Q3, and Q8 values of 0.860, 77.32%, 70.92% and 0.889, 78.78%, 74.74%, respectively. Based on the created models, a freely available web application MNA-PSS-Pred was developed.

Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures

We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We apply four different models, including physically relevant observables, and assess numerical constants by studying 32 ferro- and ferrimagnets. With the best-performing model, the Curie temperature can be predicted with a mean absolute error of approximately 126 K. As predictive factors, the models consider either the energy differences between the magnetic ground state and a magnetically disordered paramagnetic state, or the average constraining fields acting on magnetic moments in a disordered local moments calculation. Additionally, the energy differences are refined by incorporating the magnetic entropy of the paramagnetic state and the number of nearest magnetic neighbors of the magnetic atoms. The most advanced model is found to extend well into Fe1−xCox alloys, indicating the potential efficacy of utilizing our model in designing materials with tailored Curie temperatures by altering alloy compositions. This examination can illuminate the factors influencing magnetic transition temperatures in magnetic materials and provide insights into how they can be employed to make quantitative predictions of Curie temperatures. Our approach is not restricted to specific crystal structures or chemical compositions. It offers a more cost-effective alternative, in terms of human time and need for hands-on oversight, to other density functional theory methods for predicting the Curie temperature. As a result, it provides a practical strategy for conducting high-throughput screening for new technologically applicable magnetic materials. Alternatively, it can complement ML-based screening of magnetic materials by integrating physical principles into such approaches, thereby enhancing their prediction accuracy. Published by the American Physical Society 2024

Intracellular Redox Environment Determines Cancer-normal Cell Selectivity of Selenium Nanoclusters.

Elucidating the chemical structure and intracellular action mechanisms is still the critical limit for the clinical translation of nanomedicines. Intracellular redox environments originating from cell metabolism are key factors affecting internalized drug efficacy. Herein, we engineer Se-Se/Se-S bond to assemble selenium (Se) nanoclusters (SeClus) with intracellular redox environment-driven selective structure. Chemical structure analysis reveals that, the bonding of sulfur atom in intermediates to the two neighboring or interposition Se atoms in Se rings is the key internal driving force for SeClus formation. This nanocluster can be predominantly transformed to selenocysteine to facilitate selenoproteins synthesis in normal cells, while metabolize to cytotoxic SeO3 2- based on the oxidative intracellular redox environment of cancer cells. Resultantly, SeClus exhibit significant cell proliferation inhibition ability to cancer cells and impressive safety to normal cells. Taken together, this study not only clarifies the chemical nature of the atom engineering of SeClus, but also elucidates its intracellular redox environment-oriented anticancer mechanism.

Intracellular Redox Environment Determines Cancer‐normal Cell Selectivity of Selenium Nanoclusters

AbstractElucidating the chemical structure and intracellular action mechanisms is still the critical limit for the clinical translation of nanomedicines. Intracellular redox environments originating from cell metabolism are key factors affecting internalized drug efficacy. Herein, we engineer Se−Se/Se−S bond to assemble selenium (Se) nanoclusters (SeClus) with intracellular redox environment‐driven selective structure. Chemical structure analysis reveals that, the bonding of sulfur atom in intermediates to the two neighboring or interposition Se atoms in Se rings is the key internal driving force for SeClus formation. This nanocluster can be predominantly transformed to selenocysteine to facilitate selenoproteins synthesis in normal cells, while metabolize to cytotoxic SeO32− based on the oxidative intracellular redox environment of cancer cells. Resultantly, SeClus exhibit significant cell proliferation inhibition ability to cancer cells and impressive safety to normal cells. Taken together, this study not only clarifies the chemical nature of the atom engineering of SeClus, but also elucidates its intracellular redox environment‐oriented anticancer mechanism.

Machine-Learning-Assisted Screening of Nanocluster Electrocatalysts: Mapping and Reshaping the Activity Volcano for the Oxygen Reduction Reaction.

In computational heterogeneous catalysis, Sabatier's principle-based activity volcano plots provide an intuitive guide to catalyst design but impose a fundamental constraint on the maximum achievable catalytic performance. Recently, subnano clusters have emerged as an exciting platform, offering high noble metal utilization and superior performance for various reactions compared to extended surfaces, reflecting a complex structure-activity relationship in the non-scalable regime. However, understanding their non-monotonic catalytic activity, attributed to the large configurational space and their fluxional identity, poses a formidable challenge. Here, we present a machine learning (ML) framework that captures the non-monotonic trends in oxygen reduction reaction (ORR) activity at the subnanometer scale, attributed to their dynamic fluxional characteristics. We demonstrate a size-dependent shifting and reshaping of the ORR activity volcano, with Au replacing Pt at the peak. Leveraging only upon the non-ab initio geometric and electronic properties, our trained ML model accurately captures the site-specific adsorption energies of intermediates at the subnanometer regime. To account for the inconsistent trend in activity, we analyzed the correlation between electronic and geometric properties. Our findings reveal that the d-filling and coupling matrix of the neighboring metal atom significantly influences the intermediate adsorption on the local chemical environment compared to the d-band center. Following this analysis, we utilized ML to map the catalyst distribution in the activity volcano and identified the five best sub-nano electrocatalysts, demonstrating overpotential values lower than or comparable to the Pt(111) surface for the ORR. This study provides intuitive guidelines for the rational designing of highly efficient electrocatalysts for fuel cell applications while modifying the activity volcano plots for electrocatalysts at the subnanometer regime.

Interatomic Coulombic electron capture beyond the virtual photon approximation.

Via the interatomic Coulombic electron capture (ICEC) process, an electron can be captured by an atom or a molecule, while the binding and excess energy is transferred, via a long-range Coulomb interaction, to a neighboring atom or molecule. The transferred energy can be used to ionize or electronically excite the neighboring species. When the two species are asymptotically far apart, an analytical formula for the ICEC cross sections can be derived. The latter can then be estimated using only the energies and the photoionization cross sections of each species. In this work, we develop an analytical model that allows us to predict the ICEC cross sections when the size of the involved species is comparable to the distance between the two entities. Using abinitio R-matrix results for various systems, we show that the new model reduces the error of the asymptotic formula by two orders of magnitude on average while only using parameters that can be taken from the properties of each species.

First-principle study origin of ferromagnetism in non-magnetic perovskite BaSnO3 doped with 2p-X (X=C, N)

In this study, our goal is to analyze the origin of magnetization in non magnetic cubic structure perovskite BaSnO3 doped with 2p-X (X=C, N) using the full potential linearized augmented plane wave (FP-LAPW) method based on density functional theory (DFT). For the exchange and correlation potential we have applied the generalized gradient approximation (GGA) and the GGA plus-modified Becke-Johnson potential (mBJ-GGA). The results show that BaSnO3 doped with C then with N and finally with C and N exhibit half-metallic ferromagnetism behavior with the integer magnetic moment of 1, 2 and 3 μB per cell respectively. The origin of the ferromagnetism that occurs within these compounds is mainly caused by the p-p hybridization between 2p-impurities and its neighboring oxygen atoms. These results allowed to conclude that doped perovskite could provide a new type of materials, called half-metallic for future spintronic devices.

PerCNet: Periodic complete representation for crystal graphs

Crystal molecules are considered as graph structures in different representation methods. A reasonable crystal representation method should capture the local and global information. However, existing methods only consider the local information of crystal molecules by modeling the bond distance and bond angle of first-order neighbors of atoms, which leads to the issue that different crystals will have the same representation. To solve this many-to-one issue, we consider the global information by further considering dihedral angles. We propose a periodic complete representation of graph modeling and a calculation algorithm for infinite extended crystal materials. A theoretical proof for the representation that satisfies the periodic completeness is provided. Based on the proposed representation, we then propose a network for predicting crystal material properties, PerCNet, with a specially designed message-passing mechanism. To our best known, we are the first work that ensures the representation corresponds one-to-one with the crystal material based on graph modeling. Extensive experiments are conducted on two large-scale real-world material benchmark datasets. The PerCNet achieves the best performance among baseline methods in terms of MAE. Our code is available at https://github.com/JiaoHuang111/PerCNet.

Exact average many-body interatomic interaction model for random alloys

Understanding the physical origin of mechanisms in random alloys that lead to the formation of microstructures requires an understanding of their average behavior and, equally important, the role of local fluctuations around the average. Material properties of random alloys can be computed using direct simulations on random configurations. However, some properties are very difficult to compute, for others it is not even fully understood how to compute them using random sampling, in particular, interaction energies between multiple defects. To that end, we develop an atomistic model that does the averaging on the level of interatomic potentials. With such an average interatomic interaction model the problem of averaging via random sampling is bypassed since the problem of computing material properties on random configurations reduces to the problem of computing material properties on single crystals, the average alloy, which can be done using standard techniques.To be predictive, we develop our average model on the class of linear machine-learning interatomic potentials (MLIPs). To that end, using tools from higher-order statistics, we derive an analytic expansion of average many-body per-atom energies of linear MLIPs in terms of average tensor products of the feature vectors of the underlying machine-learning model that scales linearly with the size of an atomic neighborhood. In order to avoid forming higher-order tensors composed of products of feature vectors, we develop an implementation using equivariant tensor network (ETN) potentials, a class of linear MLIPs, that contracts the feature vectors to small-sized tensors, and then takes the average. We validate the average ETN potential by demonstrating the convergence of direct Monte Carlo simulations to the exact value for properties of the NbMoTaW medium-entropy alloy. Simulating average properties of dislocations has thus far only been possible with average embedded atom method potentials that, however, predict artificial polarized cores. Here, we show that average ETN potentials predict the compact screw dislocation core structure, in agreement with density functional theory. Hence, we anticipate that our model will become useful for understanding mechanistic origins of material properties and for developing predictive models of mechanical properties of random alloys.

OH Regulator of Amorphous CrOx on Defect-Rich Ultrafine Pd Nanowires Boosts Electrocatalytic Ethanol Oxidation.

Reasonable construction of high activity and selectivity electrocatalysts is crucial to achieve efficient ethanol oxidation reaction (EOR). However, the oxidation of ethanol tends to produce CO species that poison the active centers of the EOR electrocatalysts. Herein, a unique amorphous CrOx-protected defect-rich ultrafine Pd nanowires (CrOx-Pd NWs) is developed. On the one hand, the CrOx layer can act as a protective layer to maintain the structure of the nanowire. On the other hand, it can play the role of OH regulator to optimize the adsorption energy barrier of intermediate species in Pd nanowire, thereby enhancing the ability of the catalyst to resist CO poisoning. The CrOx-Pd NWs exhibit excellent EOR performance with 3.64 times higher mass activity and 50mV lower CO electro-oxidation potential than commercial Pd black. The results show that the CrOx layer promotes the dissociation of H2O into OHads, while the CrOx transfers electrons to neighboring Pd atoms optimizing the electronic configuration of Pd, thus selectively oxidizing ethanol to acetate and preventing the formation of toxic *CO. This work provides an effective strategy for the synthesis of nanowire materials with oxide/metal interfaces and offers new ideas for the design of catalysts that can efficiently drive EOR.

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments

The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. 19F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT). However, the screening of compounds and discovery of feasible drug candidates is limited due to its computational cost. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated 19F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional environments, representing their neighboring atoms within a radius of n Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. Among the models, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 3.31 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while reducing computational time from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments.

Characterizing the surface compositions of supported bimetallic PtSn clusters: Effects of cluster-support interactions and surface adsorbates

PtSn bimetallic clusters on TiO2(110) and highly oriented pyrolytic graphite (HOPG) surfaces have been characterized by scanning tunneling microscopy, low energy ion scattering (LEIS), X-ray photoelectron spectroscopy, and temperature programmed desorption (TPD); density functional theory (DFT) calculations have also been performed to better understand adsorption of CO and D2 on the PtSn surfaces. On TiO2 at coverages of 2 ML of Pt and 2 ML of Sn, exclusively bimetallic clusters are formed for both orders of deposition because clusters of the first metal completely cover the surface such that all atoms of the second metal are incorporated into the existing clusters. In contrast, on HOPG, the high mobility and weak cluster-support interactions on HOPG result in much larger 2 ML monometallic clusters (∼30 Å high) that do not completely cover the surface, and deposition of the second metal produces larger clusters as well as smaller ones. Despite the difference in cluster morphologies for the different orders of deposition and supports, the LEIS experiments demonstrate that in all cases, the PtSn clusters are rich in Sn at the surface, as expected based on the lower surface free energy for Sn compared to Pt. Furthermore, the +0.2 eV shift in the Sn(3d5/2) binding energy observed on all surfaces in the presence of Pt is consistent with PtSn alloy formation. Deposition of 2 ML of Sn on TiO2 produces two-dimensional clusters with oxidation of Sn and reduction of titania at the cluster-support interface, but addition of Pt to the Sn clusters causes Sn to diffuse away from this interface, leaving Sn in the metallic state. TPD experiments on 2 ML Pt/TiO2 with increasing coverages of Sn show that the number of adsorption sites for D2 sharply decreases to nearly zero at 0.5 ML, while CO adsorption decreases to zero only at much higher Sn coverages of 2 ML. DFT studies for Sn modified Pt surfaces and bulk structures demonstrate that for CO adsorption at low Sn coverages (≤0.25 ML), the strong Pt-CO interactions induce diffusion of Pt to the cluster surface and the formation of a bulk Pt3Sn alloy, whereas D2 adsorption does not lead to interactions with the Pt surface that are strong enough to induce alloy formation. A single Sn adatom prevents D2 adsorption on four neighboring Pt atoms via site-blocking and the donation of electron density to Pt.