Surface energies of elemental crystals

Unraveling Electrode Surface Chemistry in Determining Interphase Stability and Deposition Homogeneity for Anode-Free Potassium Metal Batteries

Potassium metal batteries with an anode-less/-free configuration could realize competitive energy density, which requires exceptional potassium plating/stripping reversibility via guiding smooth potassium growth and building mechanically stable solid electrolyte interphase (SEI). Electrolyte engineering has been the most widely adopted strategy, but there is less understanding of the electrode effect. We demonstrate that the extent of electrolyte decomposition could also be regulated through electrode surface modification. Elevating the work function of an Al current collector by coating a thin layer of Ni-decorated carbon nanofiber could greatly suppress the copious solvent reduction, leading to the formation of inorganic-rich SEIs. Such SEIs possess a large elastic deformation energy to accommodate the volume change and a high ionic conductivity to boost the reaction kinetics. Moreover, the potassiophilic nickel species offer abundant active sites to induce homogeneous potassium deposition. Benefiting from the synergy of stable interphases and promoted nucleation, the modified Al enables a 4.4 V anode-free cell in a normal-concentration electrolyte without anode precycling.

Dual-functional triethyl phosphate additive enables stable Sn anodes in aqueous acidic batteries via interfacial adsorption and solvation engineering

The inactive "dead Sn" formation and the unfavorable hydrogen evolution reaction (HER) are the two issues limiting the lifespan and coulombic efficiency of Sn anodes in aqueous acidic batteries, particularly in high-areal-capacity cases. Herein, we propose the utilization of triethyl phosphate (TEP) as a bifunctional electrolyte additive to simultaneously modulate the interfacial properties and reshape the Sn2+ solvation structure. Systematic characterizations evidence that the presence of such dual-functional TEP additive can substantially prohibit hydrogen evolution and promote the "dead-Sn-free" Sn deposition. Accordingly, both Sn||Sn and Sn||Cu cells assembled in TEP-modified electrolyte present exceptional cycling performance, maintaining continuous operation for over 760 h at 5mAh cm-2 and 5 mA cm-2. When coupled with a MnO2 cathode, the existence of TEP additive endows Sn||MnO2 full batteries with a remarkable upgrade of rate capability and cycling stability. This study demonstrates that regulating interfacial adsorption and electrolyte solvation structure is a highly effective strategy to enhance the overall performance of Sn anodes.

MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures

Metal-organic frameworks are promising porous materials for applications like gas adsorption, separation, transportation, and photocatalysis, but their large-scale computational screening requires high-quality, computation-ready structural data. Existing databases often contain errors due to experimental limitations, including inaccurately determined hydrogen positions, atomic overlaps, and missing components. We introduce MOFChecker to address these issues, providing tools for duplicate detection, geometric and charge error checking, and structure correction. Some errors can be systematically corrected through atomic adjustments on structures in the database, including deleting duplicated structures and adding missing hydrogen atoms, counterions, and linkers. Evaluation of established MOF databases, like the CoRE2014 database, indicates that 38% of structures contain significant errors, highlighting the importance of MOFChecker in ensuring accurate structural data for subsequent density functional theory (DFT) optimizations and computational studies. This work aims to enhance the reliability of MOF databases for high-throughput screening and practical applications.

Critical Design Parameters of Tantalum-Based Comb Structures to Manipulate Mammalian Cell Morphology

Mammalian tissues and cells often orient naturally in specific patterns to function effectively. This cellular alignment is influenced by the chemical nature and topographic features of the extracellular matrix. In implants, a range of different materials have been used in vivo. Of those, tantalum and its alloys are promising materials, especially in orthopedic implant applications. Previous studies have demonstrated that nano- and micro-scale surface features, such as symmetric comb structures, can significantly affect cell behavior and alignment. However, patterning need not be restricted to symmetric geometries, and there remains a gap in knowledge regarding how cells respond to asymmetric comb structures, where the widths of the trenches and lines in the comb differ. This study aims to address this gap by examining how Vero cells (cells derived from an African green monkey) respond when applied to tantalum and tantalum/silicon oxide asymmetric comb structures having fixed trench widths of 1 μm and line widths ranging from 3 μm to 50 μm. We also look at the cell responses on inverted patterns, where the line widths were fixed at 1 μm while trench widths varied. The orientation and morphology of the adherent cells were analyzed using fluorescence confocal microscopy and scanning electron microscopy. Our results indicate that the widths of the trenches and lines are important design parameters influencing cell behavior on asymmetric comb structures. Furthermore, the ability to manipulate cell morphology using these structures decreased when parts of the tantalum lines were replaced with silicon oxide.

From flat to stepped: active learning frameworks for investigating local structure at copper-water interfaces

Understanding processes at solid-liquid interfaces at the atomic level is important for applications such as electrocatalysis. Here we explore the effects of different step densities on the structure of interfacial water at the copper-water interface. Utilizing spatially resolved uncertainties, we develop an active learning framework and train a machine-learning force field (MLFF) based on dispersion-corrected density functional theory data. Using molecular dynamics simulations, we investigate structural properties of water molecules in the contact layer, including density profiles, angular distributions, and 2D pair correlation functions. In accordance with previous studies, we observe the formation of two sublayers within the contact layer at the Cu(111) surface, whereas the structure on surfaces with a high step density is dominated by the undercoordinated ridge atoms. By systematically decreasing the step density, we identify the cross-over to when the behavior observed at the flat surface can be locally recovered. Using dimensionality reduction, we identify four distinct types of Cu environments at the interfaces, providing insights into analyzing less idealized surfaces with MLFFs.

Development of a Silver-Doped High-Entropy Nitride Coating: Bactericidal and Antiviral Evaluation for Biomedical Applications

AISI 420 martensitic stainless steel is used for the manufacture of surgical and dental instruments, among others, whose surfaces can be colonized by bacteria and/or viruses that negatively affect the health of patients. The use of binary and ternary nitride coatings doped with different metallic nanoparticles has contributed to reducing the problems of infection with bacteria. However, there are few reports and studies on the biocidal and virucidal effect of high-entropy nitride coatings doped with silver nanoparticles, which could be an important alternative for antibacterial applications, also considering other advantages such as their excellent mechanical and tribological properties. In this work, a high-entropy nitride of (TiTaZrNbN)Agx doped with silver particles (Ag) was synthesized on AISI 420 stainless steel substrates via the magnetron sputtering technique. An attempt was made to elucidate the relationship between the microstructure and surface properties of the coatings with their potential activity against the selected bacteria and viruses. The Ag content in the coatings varied between 15.4 and 26.8 atom % by increasing the power supplied to the silver target between 50 and 110 W. The bactericidal effect of the synthesized nitride compound was studied via inhibition and adhesion tests against the bacteria Pseudomonas aeruginosa and Staphylococcus aureus. Moreover, the SARS-CoV-2 virus was selected to determine its virucidal effect. The deposited coatings exhibited columnar growth, and both the metal nitride matrix and the silver particles presented a NaCl-type cubic structure with preferential growth in the (111) and (200) planes. All of the coatings had a columnar structure whose width, surface roughness, and grain size increased with increasing silver content. Furthermore, the coatings present a hydrophobic behavior (increasing contact angle with increasing silver content) and decreasing surface energy. All of the coated steel samples strongly inhibited P. aeruginosa bacteria, and only sample RN-50W, with the lowest silver content, presented low adhesion of this bacteria. None of the coatings inhibited the S. aureus bacteria, and all of the coatings highly colonized the S. aureus bacteria in the adhesion test. The coatings deposited with powers of 50 and 90 W supplied to the silver target presented an average virucidal potential of 50% against the SARS-CoV-2 virus.

Effect of Sn Content on Wettability and Interfacial Structure of Cu-Sn-Cr/Graphite Systems: Experimental and First-Principles Investigations

The co-addition of chromium (Cr) and tin (Sn) is known to enhance the wettability between copper (Cu) and graphite (Cgr), but the effect of Sn content remains poorly understood. This study aims to systematically investigate the influence of Sn content a (a = 0, 10, 20, 30, 40, 50, 80, 99 at. %) on the wettability, interfacial structure, surface/interface energy (σlv/σsl), and adhesion behavior of the Cu-aSn-1Cr/Cgr system at 1100 °C. The experimental results show that as the Sn content increases, the equilibrium contact angle (θe) of the metal droplet shows a non-monotonic trend; the thickness of the reaction product layer (RPL, consisting of Cr carbides (CrmCn)) gradually increases, accompanied by a decrease in the calculated adhesion work (Wadcal). A "sandwich" interface structure is observed, consisting of two interfaces: metal||CrmCn and CrmCn||Cgr. Sn content mainly affects the former. At metal||CrmCn, Sn exists in various forms (e.g., Cu-Sn solid solution, CuxSny compounds) in contact with CrmCn. To elucidate the wetting and bonding mechanisms of metal||CrmCn, simplified interfacial models are constructed and analyzed based on first-principles calculations of density functional theory (DFT). The trend of theoretically calculated results (σmetal and Wad) agrees with the experimental results (σlv and Wadcal). Further analysis of the partial density of state (PDOS) and charge density difference (CDD) reveals that charge distribution and bonding characteristics vary with Sn content, providing the microscopic insight into the nature of wettability and interfacial bonding strength.

Machine-learnt potential highlights melting and freezing of aluminum nanoparticles

We investigated the complete thermodynamic cycle of aluminum nanoparticles through classical molecular dynamics simulations, spanning a wide size range from 200 atoms to 11 000 atoms. The aluminum-aluminum interactions are modeled using a newly developed Bayesian Force Field (BFF) from the FLARE suite, a cutting-edge tool in our field. We discuss the database requirements to include melted nanodroplets to avoid unphysical behavior at the phase transition. Our study provides a comprehensive understanding of structural stability up to sizes as large as 3 × 105 atoms. The developed Al-BFF predicts an icosahedral stability range up to 2000 atoms, ∼2 nm, followed by a region of stability for decahedra, up to 25 000 atoms. Beyond this size, the expected structure favors face-centered cubic shapes. At a fixed heating/cooling rate of 100 K/ns, we consistently observe a hysteresis loop, where the melting temperatures are higher than those associated with solidification. The annealing of a liquid droplet further stabilizes icosahedral structures, extending their stability range to 5000 atoms. Using a hierarchical k-means clustering, we find no evidence of surface melting but observe some mild indication of surface freezing. In any event, the liquid droplet's surface shows local structural order at all sizes.

Atomic Structure of the Lithium-Lithium Oxide Interface from First Principles

While lithium-ion batteries (LIBs) have been largely commercialized as the rechargeable battery of choice, their liquid electrolyte limits the theoretical energy density of the battery and poses serious safety threats. Solid-state lithium batteries (SSLBs) use a solid electrolyte, which can provide much higher energy densities and better safety than LIBs. The adoption of SSLBs is held back by interactions that occur between the electrolyte and anode, such as high resistance to lithium (Li) ion flow and the growth of Li dendrites that lead to short circuits. This paper focuses on understanding the interface between oxide electrolytes and Li metal anodes with the goal of predicting the structure and properties dictated by the interface. By comparing interface energies for different orientations of Li and lithium oxide (Li2O), a primary component of the solid electrolyte interphase, the Li2O(110) surface was found to be the most energetically favorable. Furthermore, bonding between the metallic Li and the oxygen atoms on the Li2O(110) plane was observed to be more impactful on stability than the lattice strain. As a consequence, the lowest energy interface was obtained by introducing FCC Li between Li2O and BCC Li.

A Theoretical Study on the Structural Evolution of Ru-Zn Bimetallic Nanoparticles

Ru-Zn catalysts exhibit excellent catalytic performance for the selective hydrogenation of benzene to cyclohexene and has been utilized in industrial production. However, the structure-performance relationship between Ru-Zn catalysts and benzene hydrogenation remains lacking. In this work, we focused on the evolution of Ru-Zn nanoparticles with size and Ru/Zn ratio. The structures of Ru nanoparticles and Ru-Zn bimetallic nanoparticles with different sizes were determined by the minima-hopping global optimization method in combination with density functional theory and high-dimensional neural network potential. Furthermore, we propose the growth mechanism for Ru nanoparticles and evolution processes for Ru-Zn bimetallic nanoparticles. Additionally, we analyzed the structural stability, electronic properties, and adsorption properties of Zn atoms. This work provides valuable reference and guidance for future theoretical research and applications.

Machine learning assisted approximation of descriptors (CO and OH) binding energy on Cu-based bimetallic alloys

Data driven machine learning (ML) based methods have the potential to significantly reduce the computational as well as experimental cost for the rapid and high-throughput screening of catalyst materials using binding energy as a descriptor. In this study, a set of eight widely used ML models classified as linear, kernel and tree-based ensemble models were evaluated to predict the binding energy of catalytic descriptors (CO* and OH*) on (111)-terminated Cu3M alloy surfaces using the readily available metal properties in the periodic table as features. Among all the models tested, the extreme gradient boosting regressor (xGBR) model showed the best performance with the root mean square errors (RMSEs) of 0.091 eV and 0.196 eV for CO and OH binding energy predictions on (111)-terminated A3B alloy surfaces. Moreover, the xGBR model gave the highest R2 scores of 0.970 and 0.890 for CO and OH binding energies. The time taken by the ML predictions for 25 000 fits for each model was varied between 5 and 60 min on a 6 core and 8 GB RAM laptop, which was very negligible as compared to DFT calculations. Our ML model showed remarkable performance for accurately predicting the CO and OH binding energies on a (111)-terminated Cu3M alloy with a mean absolute error (MAE) of 0.02 to 0.03 eV compared to DFT calculated values. The ML predicted binding energies can be further used with an ab initio microkinetic model (MKM) to efficiently screen A3B-type bimetallic alloys for the formic acid decomposition reaction.

In situ Detection of the Molecule-Crowded Aqueous Electrode-Electrolyte Interface

Electrode-electrolyte interface plays a crucial role in determining the stability and behavior of electrochemical electrodes. Although X-ray photoelectron spectroscopy has been established as a powerful analytical technique for interface chemistry, the necessity for ultrahigh vacuum remains a significant obstacle to directly detecting dynamic interfacial evolution, particularly in aqueous environments. Here, we employ tender-energy ambient pressure X-ray photoelectron spectroscopy (AP-XPS) to bridge the gap between ultrahigh vacuum and near-atmospheric pressure, enabling an in-depth investigation of the molecule-crowded aqueous interface evolution in a Zn metal anode. The results demonstrate that the persistent presence of additive molecules effectively inhibits direct contact between reactive Zn and H2O, while also facilitating uniform Zn deposition. In situ optical microscopy observations and synchrotron radiation X-ray diffraction further verified the uniform and dense Zn deposition, attributed to lateral growth induced by the (002) crystal facet evolution. As proof of its effectiveness, batteries incorporating the Zn//Zn, Zn//Cu, and full cell with the additive demonstrate significantly improved stability and reversibility. This finding opens up new avenues for exploration of interfacial chemistry at the molecule level, offering insights into the design of highly stable metal anodes of aqueous ion batteries for practical applications.

Wetting Behavior of Zn Droplets on Fe Surfaces: Insights from Molecular Dynamics Simulations

The liquid metal embrittlement (LME) induced by Zn melts in advanced high strength steels has seriously hindered their wide application in various industries. Microscopically, wetting is the precursor for LME; it is therefore crucial to understand the wetting of Zn melts on Fe surfaces. Molecular dynamics simulations were conducted to investigate the wetting behavior of Zn droplets on Fe(001), Fe(110), and Fe(111) surfaces from both thermodynamics and dynamics aspects. The simulation results reveal that the surface energy of solid Fe is significantly greater than the surface tension of liquid Zn and the interfacial energy of Fe-Zn solid-liquid interface at the pertinent temperatures. Consequently, Zn droplets tend to completely envelop the Fe substrates as they spread toward the equilibrium state. Specifically, Fe(111) surfaces possess the highest surface energy, whereas Fe(110) surfaces have the lowest surface energy. Meanwhile, the solid-liquid interfacial energy is minimal for Fe(111)/Zn and maximal for Fe(110)/Zn. These differences contribute to the strongest spreading driving force for Zn droplets on Fe(111) surfaces and the weakest on Fe(110). During the initial spreading stage, Zn droplets form precursor films on all Fe surfaces. Nonetheless, on Fe(111), the dissolution reaction between the substrates and the droplets destabilizes the precursor films, ultimately resulting in complete wetting. Conversely, no dissolution is observed between Zn droplets and the Fe(001) or Fe(110) surface. As a result, the equilibrium Zn droplet consists of a prefreezing precursor film that grows epitaxially on the substrate and a main body of the droplet exhibiting a convex hull shape corresponding to pseudopartial wetting. These findings provide new insights into the wetting behavior of metal droplets on metal surfaces, particularly for understanding the liquid metal embrittlement induced by Zn melts in steels.

Rational design of optimal bimetallic and trimetallic nickel-based single-atom alloys for bio-oil upgrading to hydrogen

Designing highly active, cost-effective, stable, and coke-resistant catalysts is a hurdle in commercializing bio-oil steam reforming. Single-atom alloys (SAAs) are captivating atomic ensembles crosschecking affordability and activity, yet their stability is held questionable by trial-and-error synthesis practices. Herein, we employ descriptor-based density functional theory (DFT) calculations to elucidate the stability, activity, and regeneration of Ni-based SAA catalysts for acetic acid dehydrogenation. While 12 bimetallic candidates pass the cost/stability screening, they uncover varying dehydrogenation reactivity and selectivity, introduced by favoring different acetic acid adsorption modes on the SAA sites. We find that Pd-Ni catalyst provokes the utmost H2 activity, however, ab-initio molecular simulations at 873 K reveals the ability of Cu-Ni site to effectively desorb hydrogen compared to Pd-Ni and Ni, attributed to the narrowed surface charge depletion region. Notably, this Cu-Ni performance is coupled with enhancing C*-gasification and acetic acid dehydrogenation with respect to Ni. Building upon these findings, DFT-screening of trimetallic M1-M2-Ni co-dopants recognizes 6 novel modulated single-sites with high stability, balanced H*-adsorption, and anti-coking susceptibility. This work provides invaluable data to accelerate the discovery of affordable and efficient bimetallic and trimetallic SAA catalysts for bio-oil upgrading to green hydrogen.

2D edge-seeded heteroepitaxy of ultrathin high-κ dielectric CaNb2O6 for 2D field-effect transistors

The experimental realization of single-crystalline high-κ dielectrics beyond two-dimensional (2D) layered materials is highly desirable for nanoscale field-effect transistors (FETs). However, the scalable synthesis of 2D nonlayered high-κ insulators is currently limited by uncontrolled isotropic three-dimensional growth, hampering the achievement of simultaneous high dielectric constants and low trap densities for small film thicknesses. Herein, we show a 2D edge-seeded heteroepitaxial strategy to synthesize ultrathin nonlayered 2D CaNb2O6 nanosheets by chemical vapor deposition, exhibiting high-crystalline quality, thickness-independent dielectric constant (~ 16) and breakdown field strength up to ~ 12 MV cm-1. The MoS2/CaNb2O6 FETs exhibit an on/off ratio of over ~ 107, a subthreshold swing down to 61 mV/dec and a negligible hysteresis. This work suggests a universal 2D edge-seeded heteroepitaxy and slow kinetic strategy for the scalable growth of 2D nonlayered dielectric and demonstrates 2D CaNb2O6 nanosheets as promising dielectrics for facilitating 2D electronic applications.

Challenges and Opportunities for Rechargeable Aqueous Sn Metal Batteries

Rechargeable aqueous batteries based on metallic anodes hold tremendous potential of high energy density enabled by the combination of relatively low working potential and large capacity while retaining the intrinsic safety nature and economical value of aqueous systems; However, the realization of these promised advantages relies on the identification of an ideal metal anode chemistry with all these merits. In this review, the emerging Sn metal anode chemistry is examined as such an anode candidate in both acidic and alkaline media, where the inertness of Sn toward hydrogen evolution, flat low voltage profile, and low polarization make it a unique metal anode for aqueous batteries. From a panoramic viewpoint, the key challenges and detrimental issues of Sn metal batteries are discussed, including dead Sn formation, self-discharge, and electrolyte degradation, as well as strategies for mitigating these issues by constructing robust Sn anodes. New design approaches for more durable and reliable Sn metal batteries are also discussed, with the aim of fully realizing the potential of Sn anode chemistry.

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.

Sn(II)-Pyrophosphate Complex with Low Plating/Stripping Potential for Sn-I Flow Battery Applications

Exploring electrolyte formulations that can effectively reduce the plating/stripping potentials of metallic electrodes holds great significance in advancing the development of high-voltage redox flow batteries. In this study, we introduce a novel Sn-based chelated electrolyte, namely, Sn(P2O7)26-, by directly reacting the Sn2+ solution with an excess of P2O74- solution. Electrochemical tests prove that the incorporation of high-concentration P2O74- ligands could shift the plating/stripping potential to -0.67 V. Thus, the demonstrated Sn-I flow battery reveals an average cell voltage of nearly 1.2 V and maintains stable cycling over 250 cycles at a high current density of 80 mA cm-2, with an average energy efficiency of about 70%. Moreover, no dendrite formation formed during the Sn deposition on the carbon felt. This study offers broad prospects for the future development of high-voltage Sn-based flow batteries.

Nickel Dynamics Switches the Selectivity of CO2 Hydrogenation

The Reverse Water Gas-Shift reaction (CO2+H2↽ ⇀ ${ \mathbin{{\stackrel{\textstyle\rightharpoonup} { {\smash{\leftharpoondown}} } }} }$ CO+H2O) allows to balance syn-gas under industrial conditions. Nickel has been suggested as a potential catalyst but the temperature required is too high, more than 800 °C, limiting practical implementation but when lowering the temperature methanation occurs. Simulations via Density Functional Theory on well-defined surfaces have systematically failed to reproduce these experimental results. But under reaction conditions, Ni surfaces are not static and DFT models coupled to microkinetics show that low temperatures (high CO coverages) drive the generation of Ni adatoms that are the active sites for methanation. At higher temperatures, the adatom population decreases, and the selectivity towards CO increases. Thus the mechanism behind the selectivity switch is driven by the dynamics induced by reaction intermediates. Our work contributes to the inclusion of dynamic aspects of materials under reaction conditions in the understanding of complex catalytic behaviour.

Challenging the ideal strength limit in single-crystalline gold nanoflakes through phase engineering

Materials usually fracture before reaching their ideal strength limits. Meanwhile, materials with high strength generally have poor ductility, and vice versa. For example, gold with the conventional face-centered cubic (FCC) phase is highly ductile while the yield strength (~102 MPa) is significantly lower than its ideal theoretical limit. Here, through phase engineering, we show that defect-free single-crystalline gold nanoflakes with the hexagonal close-packed (HCP) phase can exhibit a strength of 6.0 GPa, which is beyond the ideal theoretical limit of the conventional FCC counterpart. The lattice structure is thickness-dependent and the FCC-HCP phase transformation happens in the range of 11-13 nm. Suspended-nanoindentations based on atomic force microscopy (AFM) show that the Young's modulus and tensile strength are also thickness-and phase- dependent. The maximum strength is reached in HCP nanoflakes thinner than 10 nm. First-principles and molecular dynamics (MD) calculations demonstrate that the mechanical properties arise from the unconventional HCP structure as well as the strong surface effect. Our study provides valuable insights into the fabrication of nanometals with extraordinary mechanical properties through phase engineering.

Large-Area Transfer of Nanometer-Thin C60 Films

Fullerenes, with well-defined molecular structures and high scalability, hold promise as fundamental building blocks for creating a variety of carbon materials. The fabrication and transfer of large-area films with precisely controlled thicknesses and morphologies on desired surfaces are crucial for designing and developing fullerene-based materials and devices. In this work, we present strategies for solid-state transferring C60 molecular nanometer-thin films, with dimensions of centimeters in lateral size and thicknesses controlled in the range of 1-20 nm, onto various substrates. Furthermore, we have successfully fabricated centimeter-wide graphene/C60/graphene heterostructures through layer-by-layer stacking of C60 and graphene films. This transfer methodology is versatile, allowing for the complete transfer of chemically modified C60 films, including oxygenated C60 films and C60Pdn organometallic polymer films. Additionally, direct solid-state transfer of C60 and C60Pdn films onto electrode surfaces has enabled their electrocatalytic performance for the hydrogen evolution reaction to be probed directly. This thin-film transfer strategy allows precise manipulation of large-area, ultrathin C60 films on various substrates, providing a platform for fullerene chemistry and the experimental synthesis of artificial carbon structures.

Structural and electronic features enabling delocalized charge-carriers in CuSbSe2

Inorganic semiconductors based on heavy pnictogen cations (Sb3+ and Bi3+) have gained significant attention as potential nontoxic and stable alternatives to lead-halide perovskites for solar cell applications. A limitation of these novel materials, which is being increasingly commonly found, is carrier localization, which substantially reduces mobilities and diffusion lengths. Herein, CuSbSe2 is investigated and discovered to have delocalized free carriers, as shown through optical pump terahertz probe spectroscopy and temperature-dependent mobility measurements. Using a combination of theory and experiment, the critical enabling factors are found to be: 1) having a layered structure, which allows distortions to the unit cell during the propagation of an acoustic wave to be relaxed in the interlayer gaps, with minimal changes in bond length, thus limiting deformation potentials; 2) favourable quasi-bonding interactions across the interlayer gap giving rise to higher electronic dimensionality; 3) Born effective charges not being anomalously high, which, combined with the small bandgap ( ≤ 1.2 eV), result in a low ionic contribution to the dielectric constant compared to the electronic contribution, thus reducing the strength of Fröhlich coupling. These insights can drive forward the rational discovery of perovskite-inspired materials that can avoid carrier localization.

Theoretical and Experimental Research on the Short-Range Structure in Gallium Melts Based on the Wulff Cluster Model

In this paper, the short-range ordering structures of Ga melts has been investigated using the Wulff cluster model (WCM). The structures with a Wulff shape outside and crystal symmetry inside have been derived as the equivalent system to describe the short-range-order (SRO) distribution of the Ga melts. It is observed that the simulated HTXRD patterns of the Ga WCM are in excellent agreement with the experimental data at various temperatures (523 K, 623 K, and 723 K). This agreement includes first and second peak positions, widths, and relative intensities of patterns, particularly at temperatures significantly above the melting point. A minor deviation in the second peak position has been observed at 523 K, attributed to the starting of the pre-nucleation stage. These findings demonstrate that the WCM can effectively describe the SRO structure in melt systems exhibiting a certain extent of covalency.

The Importance of Catalytic Effects in Hot-Electron-Driven Chemical Reactions

Hot electrons (HEs) represent out-of-equilibrium carriers that are capable of facilitating reactions which are inaccessible under conventional conditions. Despite the similarity of the HE process to catalysis, optimization strategies such as orbital alignment and adsorption kinetics have not received significant attention in enhancing the HE-driven reaction yield. Here, we investigate catalytic effects in HE-driven reactions using a compositional catalyst modification (CCM) approach. Through a top-down alloying process and systematic characterization, using electrochemical, photodegradation, and ultrafast spectroscopy, we are able to disentangle chemical effects from competing electronic phenomena. Correlation between reactant energetics and the HE reaction yield demonstrates the crucial role of orbital alignment in HE catalytic efficiency. Optimization of this parameter was found to enhance HE reaction efficiency 5-fold, paving the way for tailored design of HE-based catalysts for sustainable chemistry applications. Finally, our study unveils an emergent ordering effect in photocatalytic HE processes that imparts the catalyst with an unexpected polarization dependence.

Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors

In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e. grains, grain boundaries [GBs], and microstructures) and summarize their various aspects (experimental synthesis, artificial single GBs, multiscale experimental data acquisition via electron microscopy, formation process modeling, property description modeling, 3D reconstruction, and data-driven design methods). Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. As a proof of concept, a researcher- and data-driven process design methodology is applied to a polycrystalline iron-based superconductor to evaluate its bulk magnet properties. Finally, future challenges and prospects for data-driven material development and iron-based superconductors are discussed.

Studies on Morphological Evolution of Gravure-Printed ZnO Thin Films Induced by Low-Temperature Vapor Post-Treatment

In recent years, the morphology control of semiconductor nanomaterials has been attracting increasing attention toward maximizing their functional properties and reaching their end use in real-world devices. However, the development of easy and cost-effective methods for preparing large-scale patterned semiconductor structures on flexible temperature-sensitive substrates remains ever in demand. In this study, vapor post-treatment (VPT) is investigated as a potential, simple and low-cost post-preparative method to morphologically modify gravure-printed zinc oxide (ZnO) nanoparticulate thin films at low temperatures. Exposing nanoparticles (NPs) to acidic vapor solution, spontaneous restructuring pathways are observed as a consequence of NPs tending to reduce their high interfacial energy. Depending on the imposed environmental conditions during the treatment (e.g., temperature, vapor composition), various ZnO thin-film morphologies are produced, from dense to porous ones, as a result of the activation and interplay of different spontaneous interface elimination mechanisms, including dissolution-precipitation, grain boundary migration and grain rotation-coalescence. The influence of VPT on structural/optical properties has been examined via XRD, UV-visible and photoluminescence measurements. Controlling NP junctions and network nanoporosity, VPT appears as promising cost-effective, low-temperature and pressureless post-preparative platform for preparing supported ZnO NP-based films with improved connectivity and mechanical stability, favoring their practical use and integration in flexible devices.

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

General-purpose machine-learned potential for 16 elemental metals and their alloys

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys.

Ultrasensitive Acetone Gas Sensor Based on a K/Sn-Co3O4 Porous Microsphere for Noninvasive Diabetes Diagnosis

The detection of acetone in human exhaled breath is crucial for the noninvasive diagnosis of diabetes. However, the direct and reliable detection of acetone in exhaled breath with high humidity at the parts per billion level remains a great challenge. Here, an ultrasensitive acetone gas sensor based on a K/Sn-Co3O4 porous microsphere was reported. The sensor demonstrates a detection limit of up to 100 ppb, along with excellent repeatability and selectivity. Remarkably, without the removal of water vapor from exhaled breath, the sensor can accurately distinguish diabetic patients and healthy individuals according to the difference in acetone concentrations, demonstrating its great potential for diabetes diagnosis. The enhanced sensitivity of the sensor is attributed to the increased oxygen adsorption on the material surface due to K/Sn codoping and the stronger coadsorption of Sn-K atoms to acetone molecules. These findings shed light on the mechanisms underlying the sensor's improved performance.

Uncertainty of DFT Calculated Mechanical and Structural Properties of Solids due to Incompatibility of Pseudopotentials and Exchange-Correlation Functionals

The demand for pseudopotentials constructed for a given exchange-correlation (XC) functional far exceeds the supply, necessitating the use of those commonly available. The number of XC functionals currently available is in the hundreds, if not thousands, and the majority of pseudopotentials have been generated for LDA and PBE. The objective of this study is to identify the error in the determination of the mechanical and structural properties (lattice constant, cohesive energy, surface energy, elastic constants, and bulk modulus) of crystals calculated by DFT with such inconsistency. Additionally, this study aims to estimate the performance of popular XC functionals (LDA, PBE, PBEsol, and SCAN) for these calculations in a consistent manner.

Selenium Adsorption on the (111), (100), (110) and (211) surfaces of Face-Centered-Cubic Metals: Density Functional Calculations of the Potential Energy Surfaces

In this study, we expand the computational investigation of selenium, which has previously been limited to metals such as Cu, Fe, Pd, Au, and Pt. Utilizing density functional theory calculations, we explore the adsorption and diffusion of selenium at a low-coverage regime of 0.25 ML on a broader range of metal surfaces, including Ni, Cu, Rh, Pd, Ag, Ir, Pt, and Au. Our results reveal that selenium exhibits a distinct preference for three-fold or four-fold high-coordination sites on most studied surfaces. We further analyze the minimum energy diffusion pathways, demonstrating that the energy barrier for selenium's surface diffusion varies significantly based on the orientation and nature of the metal surfaces. Specifically, on (100) surfaces, selenium exhibits the highest diffusion energy, ranging from 0.60 eV in Au(100) to 1.12 eV in Pd(100). The diffusion behavior on (110) and (211) surfaces is also elaborated, emphasizing the unique trends observed compared to previously studied elements like sulfur. Importantly, this study is a new reference for future computational analyses, filling existing gaps by providing comprehensive data on selenium adsorption on various face-centered cubic metal surfaces not previously reported.

Self-Inhibition Phenomena in Cu3Pt Oxidation by CO2

This study investigates the oxidation behavior of Cu3Pt(100) in CO2 using a combination of ambient-pressure X-ray photoelectron spectroscopy, mass spectroscopy, and density functional theory modeling. Our in situ measurements reveal the simultaneous oxidation and reduction of Cu2O due to the opposing effects of atomic oxygen and CO generated from dissociative CO2 adsorption, leading to a dynamic equilibrium state of simultaneously occurring redox reactions. Complementary atomistic calculations elucidate the inhibitory effects of subsurface Pt enrichment and the counteracting roles of CO2 and CO in surface oxidation and reduction. These results provide mechanistic insights into the dissociative pathway of CO2 molecules and dynamic evolution of surface composition and reactivity of Cu-based alloy catalysts in CO2-rich environments, with broader implications for tuning gas-surface reactions by manipulating gas reactants or solid surface composition.

Radical-Induced Changes in Transition Metal Interfacial Magnetic Properties: A Blatter Derivative on Polycrystalline Cobalt

In this work, we study the interface obtained by depositing a monolayer of a Blatter radical derivative on polycrystalline cobalt. By examining the occupied and unoccupied states at the interface, using soft X-ray techniques, combined with electronic structure calculations, we could simultaneously determine the electronic structure of both the molecular and ferromagnetic sides of the interface, thus obtaining a full understanding of the interfacial magnetic properties. We found that the molecule is strongly hybridized with the surface. Changes in the core level spectra reflect the modification of the molecule and the cobalt electronic structures inducing a decrease in the magnetic moment of the cobalt atoms bonded to the molecules which, in turn, lose their radical character. Our method allowed us to screen, beforehand, organic/ferromagnetic interfaces given their potential applications in spintronics.

Electron microscopy evidence of gadolinium toxicity being mediated through cytoplasmic membrane dysregulation

Past functional toxicogenomic studies have indicated that genes relevant to membrane lipid synthesis are important for tolerance to the lanthanides. Moreover, previously reported imaging of patient's brains following administration of gadolinium-based contrast agents shows gadolinium lining the vessels of the brain. Taken together, these findings suggest the disruption of cytoplasmic membrane integrity as a mechanism by which lanthanides induce cytotoxicity. In the presented work we used scanning transmission electron microscopy and spatially resolved elemental spectroscopy to image the morphology and composition of gadolinium, europium, and samarium precipitates that formed on the outside of yeast cell membranes. In no sample did we find that the lanthanide contaminant had crossed the cell membrane, even in experiments using yeast mutants with disrupted genes for sphingolipid synthesis-the primary lipids found in yeast cytoplasmic membranes. Rather, we have evidence that lanthanides are co-located with phosphorus outside the yeast cells. These results lead us to hypothesize that the lanthanides scavenge or otherwise form complexes with phosphorus from the sphingophospholipid head groups in the cellular membrane, thereby compromising the structure or function of the membrane, and gaining the ability to disrupt membrane function without entering the cell.

Ultrafast Laser Manipulation of In-Lattice Plasmonic Nanoparticles

Plasmonic nanoparticles enable manipulation and enhancement of light fields at deep subwavelength scales, leading to structures and devices for diverse applications in optics. Despite hybrid plasmonic materials display remarkable optical properties due to interactions between components in nanoproximity, scalable production of plasmonic nanostructures within a single-crystalline matrix to achieve an ideal plasmon-crystal interface remains challenging. Here, a novel approach is presented to realize efficient manipulation of in-lattice plasmonic nanoparticles. Employing ultrafast-laser-driven plasmonic nanolithography, metallic nanoparticles with controllable morphology are precisely defined in the crystalline lattice of yttrium aluminum garnet (YAG) crystal. Through direct ion implantation, hybrid plasmonic material composed of nanoparticles embedded in a sub-surface amorphous YAG layer is created. Subsequently, femtosecond laser pulses guide formation and reshaping of plasmonic nanoparticles from the amorphous layer into the single-crystalline matrix along direction of light propagation, facilitated by a plasmon-mediated evolution of laser energy deposition. By tailoring resonance modes and optimizing the coupling between structured particle assemblies, a range of applications including polarization-dependent absorption and nonlinearity, controllable photoluminescence, and structural color generation is demonstrated. This research introduces a new approach for fabricating advanced optical materials featuring in-lattice plasmonic nanostructures, paving the way for the development of diverse functional photonic devices.

Characterization of Acid-Responsive-Release Matrine/ZIF-8@Sodium Alginate Microcapsules Prepared by Electrostatic Spray and Their Application in the Control of Soybean Cyst Nematode

Matrine (MT) is a kind of alkaloid extracted from Sophora and is a promising substitute for chemical nematicides and botanical pesticides. The present study utilized sodium alginate (SA), zeolite imidazole salt skeleton (ZIF), and MT as raw materials to prepare a pH-response-release nematicide through the electrostatic spray technique. Zinc metal-organic framework (ZIF-8) was initially synthesized, followed by the successful loading of MT. Subsequently, the electrostatic spray process was employed to encapsulate it in SA, resulting in the formation of MT/ZIF-8@SA microcapsules. The efficiency of encapsulation and drug loadings can reach 79.93 and 26.83%, respectively. Soybean cyst nematode (SCN) is one of the important pests that harm crops; acetic acid produced by plant roots and CO2 produced by root respiration causing a decrease in the pH of the surrounding environment, which is most attractive to the SCN when the pH is between 4.5 and 5.4. MT/ZIF-8@SA releases the loaded MT in response to acetic acid produced by roots and acidic oxides produced by root respiration. The rate of release was 37.67% higher at pH 5.25 compared with pH 8.60. The control efficiency can reach 89.08% under greenhouse conditions. The above results demonstrate that the prepared MT/ZIF-8@SA not only exhibited excellent efficacy but also demonstrated a pH-responsive release of the nematicide.

Hydrogen adsorption on fcc metal surfaces towards the rational design of electrode materials

The successful large-scale implementation of hydrogen as an energy vector requires high performance electrodes and catalysts made of abundant materials. Rational materials design strategies are the most efficient means of reaching this goal. Here we present a study on the adsorption of H-atoms onto fcc transition metal surfaces and propose descriptors for the rational design of electrodes and catalysts by means of correlations between fundamental properties of the materials and among other properties, their experimentally measured performance as hydrogen evolution electrodes (HEE). A large set of quantum mechanical modelling data at the DFT level was produced, covering the adsorption of H-atoms onto the most stable surfaces (100), (110) and (111) of: Ag, Au, Co, Cu, Ir, Ni, Pd, Pt and Rh. For each material and surface, a coverage dependent set of minimum energy structures was produced and chemical potentials for adsorption of H-atoms were obtained. Averaging procedures are here proposed to approach modelling to the experiments. Several correlations between the computed data and experimentally measured quantities are done to validate our methodology: surface plane dependent adsorption energies, chemical potentials and experimentally determined surface energies and work functions. We search for descriptors of catalytic activity by testing correlations between the DFT data obtained from our averaging procedures and experimental data on HEE performance. Our methodology allows us to obtain linear correlations between the adsorption energy of H-atoms and the exchange current density (i0) in a HEE, avoiding the volcano-like plots. We show that the chemical potential has limitations as a descriptor of i0 because it reaches an early plateau in terms of i0. Simple quantities obtained from database data such as the first stage electronegativity (χ) as devised by Mulliken has a strong linear correlation i0. With a quantity we denominate modified second-stage electronegativity (χ2m) we can reproduce the typical volcano plot in a correlation with i0. A theoretical and conceptual framework is presented. It shows that both χ and χ2m, that depend on the first ionization potential, second ionization potential and electron affinity of the elements can be used as descriptors in rational design of electrodes or of catalysts for hydrogen systems.

Predicting Yield Strength and Plastic Elongation in Body-Centered Cubic High-Entropy Alloys

We employ machine learning (ML) to predict the yield stress and plastic strain of body-centered cubic (BCC) high-entropy alloys (HEAs) in the compression test. Our machine learning model leverages currently available databases of BCC and BCC+B2 entropy alloys, using feature engineering to capture electronic factors, atomic ordering from mixing enthalpy, and the D parameter related to stacking fault energy. The model achieves low Root Mean Square Errors (RMSE). Utilizing Random Forest Regression (RFR) and Genetic Algorithms for feature selection, our model excels in both predictive accuracy and interpretability. Rigorous 10-fold cross-validation ensures robust generalization. Our discussion delves into feature importance, highlighting key predictors and their impact on mechanical properties. This work provides an important step toward designing high-performance structural high-entropy alloys, providing a powerful tool for predicting mechanical properties and identifying new alloys with superior strength and ductility.

Electrowinning for Room-Temperature Ironmaking: Mapping the Electrochemical Aqueous Iron Interface

A promising route toward room-temperature ironmaking is electrowinning, where iron ore dissolution is coupled with cation electrodeposition to grow pure iron. However, poor faradaic efficiencies against the hydrogen evolution reaction (HER) is a major bottleneck. To develop a mechanistic picture of this technology, we conduct a first-principles thermodynamic analysis of the Fe110 aqueous electrochemical interface. Constructing a surface Pourbaix diagram, we predict that the iron surface will always drive toward adsorbate coverage. We calculate theoretical overpotentials for terrace and step sites and predict that growth at the step sites are likely to dominate. Investigating the hydrogen surface phases, we model several hydrogen absorption mechanisms, all of which are predicted to be endothermic. Additionally, for HER we identify step sites as being more reactive than on the terrace and with competitive limiting potentials to iron plating. The results presented here further motivate electrolyte design toward HER suppression.

Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design

When Olga Kennard founded the Cambridge Crystallographic Data Centre in 1965, the Cambridge Structural Database was a pioneering attempt to collect scientific data in a standard format. Since then, it has evolved into an indispensable resource in contemporary molecular materials science, with over 1.25 million structures and comprehensive software tools for searching, visualizing and analyzing the data. In this perspective, we discuss the use of the CSD and CCDC tools to address the multiscale challenge of predictive materials design. We provide an overview of the core capabilities of the CSD and CCDC software and demonstrate their application to a range of materials design problems with recent case studies drawn from topical research areas, focusing in particular on the use of data mining and machine learning techniques. We also identify several challenges that can be addressed with existing capabilities or through new capabilities with varying levels of development effort.

Nonideality in Arrayed Carbon Nanotube Field Effect Transistors Revealed by High-Resolution Transmission Electron Microscopy

High density and high semiconducting-purity single-walled carbon nanotube array (A-CNT) have recently been demonstrated as promising candidates for high-performance nanoelectronics. Knowledge of the structures and arrangement of CNTs within the arrays and their interfaces to neighboring CNTs, metal contacts, and dielectrics, as the key components of an A-CNT field effect transistor (FET), is essential for device mechanistic understanding and further optimization, particularly considering that the current technologies for the fabrication of A-CNT wafers are mainly laboratory-level solution-based processes. Here, we conduct a systematic investigation into the microstructures of A-CNT FETs mainly via cross-sectional high-resolution transmission electron microscopy and tentatively establish a framework consisting of up to 11 parameters which can be used for structure-side quality evaluation of the A-CNT FETs. The parameter ensemble includes the diameter, length (or terminal), and density distribution of CNTs, radial deformation of CNTs, array alignment defects, surface crystallography facets of contact metal, thickness distribution of high-k dielectrics (HfO2), and the contact ratios for the CNT-CNT, CNT-metal, CNT-dielectric, and CNT-substrate interfaces. Enriched array alignment defects, i.e., bundle, stacking, misorientation, and voids, are observed with a total ratio sometimes up to ∼90% in pristine A-CNTs and even up to ∼95% after the device fabrication process. Thus, they are suggested as the prevalent performance-limiting factors for A-CNT FETs. Complex interfacial structures are observed at the CNT-CNT, CNT-metal contact, and CNT-high-k dielectric interfaces, making the local environment and the property of each component CNT involved in an A-CNT FET distinct from others in terms of the diameters, radial deformation, and interactions with the local surroundings (mainly through van der Waals interactions). The present study suggests further improvements on the fabrication technology of A-CNT wafers and devices and mechanistic investigations into the impacts of complex array alignment defects and interface structures on the electrical performance of A-CNT FETs as well.

Tunable Structured 2D Nanobiocatalysts: Synthesis, Catalytic Properties and New Horizons in Biomedical Applications

2D materials have offered essential contributions to boosting biocatalytic efficiency in diverse biomedical applications due to the intrinsic enzyme-mimetic activity and massive specific surface area for loading metal catalytic centers. Since the difficulty of high-quality synthesis, the varied structure, and the tough choice of efficient surface loading sites with catalytic properties, the artificial building of 2D nanobiocatalysts still faces great challenges. Here, in this review, a timely and comprehensive summarization of the latest progress and future trends in the design and biotherapeutic applications of 2D nanobiocatalysts is provided, which is essential for their development. First, an overview of the synthesis-structure-fundamentals and structure-property relationships of 2D nanobiocatalysts, both metal-free and metal-based is provided. After that, the effective design of the active sites of nanobiocatalysts is discussed. Then, the progress of their applied research in recent years, including biomedical analysis, biomedical therapeutics, pharmacokinetics, and toxicology is systematically highlighted. Finally, future research directions of 2D nanobiocatalysts are prospected. Overall, this review to provide cutting-edge and multidisciplinary guidance for accelerating future developments and biomedical applications of 2D nanobiocatalysts is expected.

From computational screening to the synthesis of a promising OER catalyst

The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co2.5Ga0.5O4, that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm-2 and a Tafel slope at 56.0 mV dec-1. Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts.

Tuning Vertical Electrodeposition for Dendrites-Free Zinc-Ion Batteries

Manipulating the crystallographic orientation of zinc deposition is recognized as an effective approach to address zinc dendrites and side reactions for aqueous zinc-ion batteries (ZIBs). We introduce 2-methylimidazole (Mlz) additive in zinc sulfate (ZSO) electrolyte to achieve vertical electrodeposition with preferential orientation of the (100) and (110) crystal planes. Significantly, the zinc anode exhibited long lifespan with 1500 h endurance at 1 mA cm-2 and an excellent 400 h capability at a depth of discharge (DOD) of 34% in Zn||Zn battery configurations, while in Zn||MnO2 battery assemblies, a capacity retention of 68.8% over 800 cycles is attained. Theoretical calculation reveals that the strong interactions between Mlz and (002) plane impeding its growth, while Zn atoms exhibit lower migration energy barrier and superior mobility on (100) and (110) crystal planes guaranteed the heightened mobility of zinc atoms on the (100) and (110) crystal planes, thus ensuring their superior ZIB performance than that with only ZSO electrolyte, which offers a route for designing next-generation high energy density ZIB devices.

Composition-dependent morphologies of CeO2 nanoparticles in the presence of Co-adsorbed H2O and CO2: a density functional theory study

Catalytic activity is affected by surface morphology, and specific surfaces display greater activity than others. A key challenge is to define synthetic strategies to enhance the expression of more active surfaces and to maintain their stability during the lifespan of the catalyst. In this work, we outline an ab initio approach, based on density functional theory, to predict surface composition and particle morphology as a function of environmental conditions, and we apply this to CeO2 nanoparticles in the presence of co-adsorbed H2O and CO2 as an industrially relevant test case. We find that dissociative adsorption of both molecules is generally the most favourable, and that the presence of H2O can stabilise co-adsorbed CO2. We show that changes in adsorption strength with temperature and adsorbate partial pressure lead to significant changes in surface stability, and in particular that co-adsorption of H2O and CO2 stabilizes the {100} and {110} surfaces over the {111} surface. Based on the changes in surface free energy induced by the adsorbed species, we predict that cuboidal nanoparticles are favoured in the presence of co-adsorbed H2O and CO2, suggesting that cuboidal particles should experience a lower thermodynamic driving force to reconstruct and thus be more stable as catalysts for processes involving these species.

Insights into Electrochemical CO2 Reduction on Metallic and Oxidized Tin Using Grand-Canonical DFT and In Situ ATR-SEIRA Spectroscopy

Electrochemical CO2 reduction (CO2R) to formate is an attractive carbon emissions mitigation strategy due to the existing market and attractive price for formic acid. Tin is an effective electrocatalyst for CO2R to formate, but the underlying reaction mechanism and whether the active phase of tin is metallic or oxidized during reduction is openly debated. In this report, we used grand-canonical density functional theory and attenuated total reflection surface-enhanced infrared absorption spectroscopy to identify differences in the vibrational signatures of surface species during CO2R on fully metallic and oxidized tin surfaces. Our results show that CO2R is feasible on both metallic and oxidized tin. We propose that the key difference between each surface termination is that CO2R catalyzed by metallic tin surfaces is limited by the electrochemical activation of CO2, whereas CO2R catalyzed by oxidized tin surfaces is limited by the slow reductive desorption of formate. While the exact degree of oxidation of tin surfaces during CO2R is unlikely to be either fully metallic or fully oxidized, this study highlights the limiting behavior of these two surfaces and lays out the key features of each that our results predict will promote rapid CO2R catalysis. Additionally, we highlight the power of integrating high-fidelity quantum mechanical modeling and spectroscopic measurements to elucidate intricate electrocatalytic reaction pathways.

Long-Range Atomic Order on Double-Stepped Al2O3(0001) Surfaces

The deterministic preparation of highly ordered single-crystalline surfaces is a key step for studying and utilizing the physical properties of various advanced materials. This paper presents the fast and straightforward preparation of vicinal Al2O3(0001) surfaces with micrometer-scale atomic order. Crisp electron-diffraction spots up to at least 20th order evidence atomic coherence on terraces with widths exceeding 1 μm. The unique combination of three properties of Al2O3(0001) underlie this remarkable coherence: its high-temperature stability; the differences in the ionic bonding systems of the surface as compared to the bulk; and the fact that the terraces are non-polar whereas the step edges have a polar character. The step edges are furthermore found to have alternating configurations, which drive a step-doubling transition. On double-stepped surfaces, the Al-rich( 31 × 31 ) R ± 9 $(\sqrt {31}\times \sqrt {31})\textrm {R}\pm 9$ ° surface reconstruction attains a singular in-plane orientation. These results set a benchmark for high-quality surface preparation and thus expand the scope for both fundamental studies on and the technological utilization of exciting material systems.

Crystal plane orientation-dependent surface atom diffusion in sub-10-nm Au nanocrystals

Surface atom diffusion is a ubiquitous phenomenon in nanostructured metals with ultrahigh surface-to-volume ratios. However, the fundamental atomic mechanism of surface atom diffusion remains elusive. Here, we report in situ atomic-scale observations of surface pressure-driven atom diffusion in gold nanocrystals at room temperature using high-resolution transmission electron microscopy with a high-speed detection camera. The topmost layer of atoms on (001) plane initially diffuse in a column-by-column manner. As diffusion proceeds, the remaining atomic columns collectively inject into adjacent underlayer, accompanied by nucleation of a surface dislocation. In comparison, atoms on (111) plane directly diffuse to the base without collective injection. Quantitative calculations indicate that these crystal plane orientation-dependent atom diffusion behaviors contribute to the larger diffusion coefficient of (111) plane compared to (001) plane in addition to the effect of diffusion activation energy. Our findings provide valuable insights into atomic mechanisms of diffusion-dominant morphology evolution of nanostructured metals and guide the design of nanostructured materials with enhanced structural stability.

Theoretical Investigation of the Adsorbate and Potential-Induced Stability of Cu Facets During Electrochemical CO2 and CO Reduction

The activity and product selectivity of electrocatalysts for reactions like the carbon dioxide reduction reaction (CO2RR) are intimately dependent on the catalyst's structure and composition. While engineering catalytic surfaces can improve performance, discovering the key sets of rational design principles remains challenging due to limitations in modeling catalyst stability under operating conditions. Herein, we perform first-principles density functional calculations adopting implicit solvation methods with potential control to study the influence of adsorbates and applied potential on the stability of different facets of model Cu electrocatalysts. Using coverage dependencies extracted from microkinetic models, we describe an approach for calculating potential and adsorbate-dependent contributions to surface energies under reaction conditions, where Wulff constructions are used to understand the morphological evolution of Cu electrocatalysts under CO2RR conditions. We identify that CO*, a key reaction intermediate, exhibits higher kinetically and thermodynamically accessible coverages on (100) relative to (111) facets, which can translate into an increased relative stabilization of the (100) facet during CO2RR. Our results support the known tendency for increased (111) faceting of Cu nanoparticles under more reducing conditions and that the relative increase in (100) faceting observed under CO2RR conditions is likely attributed to differences in CO* coverage between these facets.

Surface phase diagrams from nested sampling

Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite temperatures. The Bayesian-inference-based nested sampling (NS) algorithm allows for modeling phase equilibria at arbitrary temperatures by directly and efficiently calculating the partition function, whose relationship with free energy is well known. This work extends NS to calculate adsorbate phase diagrams, incorporating all relevant configurational contributions to the free energy. We apply NS to the adsorption of Lennard-Jones (LJ) gas particles on low-index and vicinal LJ solid surfaces and construct the canonical partition function from these recorded energies to calculate ensemble averages of thermodynamic properties, such as the constant-volume heat capacity and order parameters that characterize the structure of adsorbate phases. Key results include determining the nature of phase transitions of adsorbed LJ particles on flat and stepped LJ surfaces, which typically feature an enthalpy-driven condensation at higher temperatures and an entropy-driven reordering process at lower temperatures, and the effect of surface geometry on the presence of triple points in the phase diagrams. Overall, we demonstrate the ability and potential of NS for surface modeling.