Surface energies of elemental crystals

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.

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.

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.

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.

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.

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.

Preferential growth and electron trap synergistically promoting photoreduction CO2 of Tm ion doping bismuth titanate nanosheets

In this study, we prepared two-dimensional Bi4Ti3O12 nanosheets doped with rare earth ions. The experimental results show that Bi4-xTmxTi3O12 exhibits the highest reduction performance among various rare earth doped Bi4Ti3O12 materials, with a CO yield of 7.25 μmol g-1h-1. Furthermore, a delayed reaction in Bi3.97Tm0.03Ti3O12 is observed upon a cessation of light irradiation. Theoretical calculations reveal that the introduction of Tm ion not only reduces the surface energy of (001) plane and make it preferential growth in Bi4Ti3O12, but also brings the intervening energy level of Tm ion (4f and 4d mixed orbital), which is closer to the conduction band of Bi4Ti3O12 and facilitates charge carrier accumulation in trap states. The electrons retained in the shallow traps promote the hysteresis reaction following a cessation of illumination. This work provides further insights into elucidating precise reduction reaction mechanisms underlying rare earth dopant on photocatalysts. This research provides enhanced insights into unraveling the precise reduction reaction mechanisms influenced by rare earth dopants in photocatalysts.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Unveiling the destabilization of sp3 and sp2 bonds in transition metal-modified borohydrides to improve reversible dehydrogenation and rehydrogenation

Borohydrides offer promise as potential carriers for hydrogen storage due to their high hydrogen concentration. However, the strong chemical bonding within borohydrides poses challenges for efficient hydrogen release during usage and restricts the re-hydrogenation process when attempting to regenerate the material. These high thermodynamic and kinetic barriers present obstacles in achieving reversible de-hydrogenation and re-hydrogenation of borohydrides, impeding their practical application in hydrogen storage systems. Employing density functional theory calculations, we conduct a comprehensive investigation into the influence of transition metals on both the BH4 cluster, a fundamental building block of borohydrides, and pure boron, which is formed as the end product following hydrogen release. Our research reveals correlations among the d-band center, work function, and surface energy of 3d and 4d transition metals. These correlations are directly linked to the weakening of bonding within the BH4 cluster when adsorbed on catalyst surfaces. On the other hand, we also explore how various intrinsic properties of transition metals influence the formation of boron vacancies and the hydrogen bonding process. By establishing a comprehensive correlation between the weakening of sp3 hybridization in the BH4 cluster and the sp2 hybridization in boron, we facilitate the identification and screening of optimal candidates capable of achieving reversible de-hydrogenation and re-hydrogenation in borohydrides.

Atom-by-atom optimizing the surface termination of Fe-Pt intermetallic catalysts for alkaline hydrogen evolution reaction

Controlling the atomic arrangement of elemental atoms in intermetallic catalysts to govern their surface and subsurface properties is a crucial but challenging endeavor in electrocatalytic reactions. In hydrogen evolution reaction (HER), adjusting the d-band center of the conventional noble-metallic Pt by introducing Fe enables the optimization of catalytic performance. However, a notable gap exists in research on the effective transition from disordered Fe/Pt alloys to highly ordered intermetallic compounds (IMCs) such as FePt3 in the alkaline HER, hampering their broader application. In this study, a series of catalysts FePt3-xH (x = 5, 6, 7, 8 and 9) supported on carbon nanotubes (CNTs) were synthesized via a simple impregnation method, along with a range of heat treatment processes, including annealing in a reductive atmosphere, to regulate the order degree of the arrangement of Fe/Pt atoms within the FePt3 catalyst. By using advanced microscopy and spectroscopy techniques, we systematically explored the impact of the order degree of FePt3 in the HER. The as-prepared FePt3-8H exhibited notable HER catalytic activity with low overpotentials (η = 37 mV in 1.0 mol L-1 KOH) at j = 10 mA cm-2. The surface of the L12 FePt3-8H catalyst was demonstrated to be Pt-rich. The Pt on the surface was not easily oxidized due to the unique Fe/Pt coordination, resulting in significant enhancement of HER performance.

Efficient ammonia synthesis from the air using tandem non-thermal plasma and electrocatalysis at ambient conditions

While electrochemical N2 reduction presents a sustainable approach to NH3 synthesis, addressing the emission- and energy-intensive limitations of the Haber-Bosch process, it grapples with challenges in N2 activation and competing with pronounced hydrogen evolution reaction. Here we present a tandem air-NOx-NOx--NH3 system that combines non-thermal plasma-enabled N2 oxidation with Ni(OH)x/Cu-catalyzed electrochemical NOx- reduction. It delivers a high NH3 yield rate of 3 mmol h-1 cm-2 and a corresponding Faradaic efficiency of 92% at -0.25 V versus reversible hydrogen electrode in batch experiments, outperforming previously reported ones. Furthermore, in a flow mode concurrently operating the non-thermal plasma and the NOx- electrolyzer, a stable NH3 yield rate of approximately 1.25 mmol h-1 cm-2 is sustained over 100 h using pure air as the intake. Mechanistic studies indicate that amorphous Ni(OH)x on Cu interacts with hydrated K+ in the double layer through noncovalent interactions and accelerates the activation of water, enriching adsorbed hydrogen species that can readily react with N-containing intermediates. In situ spectroscopies and density functional theory (DFT) results reveal that NOx- adsorption and their hydrogenation process are optimized over the Ni(OH)x/Cu surface. This work provides new insights into electricity-driven distributed NH3 production using natural air at ambient conditions.

Thermodynamics, Adhesion, and Wetting at Li/Cu(-Oxide) Interfaces: Relevance for Anode-Free Lithium-Metal Batteries

A rechargeable battery that employs a Li metal anode requires that Li be plated in a uniform fashion during charging. In "anode-free" configurations, this plating will occur on the surface of the Cu current collector (CC) during the initial cycle and in any subsequent cycle where the capacity of the cell is fully accessed. Experimental measurements have shown that the plating of Li on Cu can be inhomogeneous, which can lower the efficiency of plating and foster the formation of Li dendrites. The present study employs a combination of first-principles calculations and sessile drop experiments to characterize the thermodynamics and adhesive (i.e., wetting) properties of interfaces involving Li and other phases present on or near the CC. Interfaces between Li and Cu, Cu2O, and Li2O are considered. The calculations predict that both Cu and Cu2O surfaces are lithiophilic. However, sessile drop measurements reveal that Li wetting occurs readily only on pristine Cu. This apparent discrepancy is explained by the occurrence of a spontaneous conversion reaction, 2 Li + Cu2O → Li2O + 2 Cu, that generates Li2O as one of its products. Calculations and sessile drop measurements show that Li does not wet (newly formed) Li2O. Hence, Li that is deposited on a Cu CC where surface oxide species are present will encounter a compositionally heterogeneous substrate comprising lithiophillic (Cu) and lithiophobic (Li2O) regions. These initial heterogeneities have the potential to influence the longer-term behavior of the anode under cycling. In sum, the present study provides insights into the early stage processes associated with Li plating in anode-free batteries and describes mechanisms that contribute to inefficiencies in their operation.

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.

First-principles study for quasi-static growth model in FeAl intermetallic based on Wulff cluster model

In order to investigate the structure of FeAl mesoscopic crystals segregating in liquid state alloys, we have determined their equilibrium structures (Wulff shape) based on the Wulff cluster model. For non-stoichiometric surface terminations, the chemical environment is taken into account through the chemical potential of the constituents. In this case, different cluster shapes change as a function of the chemical environment. In order to model the growth process in more detail, we propose a quasi-static growth model based on the sequential addition of (sub-)monolayers in the most favorable surface directions. Thus, a sequence of different Wulff shapes results in the growth process, as illustrated for the FeAl intermetallic compound. This model is proved preliminarily by calculating the concentration trend of Al/Fe atoms on both Al-terminated and Fe-terminated surfaces, and by simulating the most stable layer adsorbed on these two surfaces. This model might be helpful in analyzing the growth processes including nucleation barriers during nucleation processes theoretically.

Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials

The properties of lithium metal are key parameters in the design of lithium-ion and lithium-metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties, and ab initio calculations are too costly. In this work, we train a machine learning interaction potential on density functional theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants, and various surface properties inaccessible using DFT. We establish that there exists a weak Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.

The sensitive aspects of modelling polymer-ceramic composite solid-state electrolytes using molecular dynamics simulations

Solid-state composite electrolytes have arisen as one of the most promising materials classes for next-generation Li-ion battery technology. These composites mix ceramic and solid-polymer ion conductors with the aim of combining the advantages of each material. The ion-transport mechanisms within such materials, however, remain elusive. This knowledge gap can to a large part be attributed to difficulties in studying processes at the ceramic-polymer interface, which are expected to play a major role in the overall ion transport through the electrolyte. Computational efforts have the potential of providing significant insight into these processes. One of the main challenges to overcome is then to understand how a sufficiently robust model can be constructed in order to provide reliable results. To this end, a series of molecular dynamics simulations are here carried out with a variation of certain structural (surface termination and polymer length) and pair potential (van der Waals parameters and partial charges) models of the Li7La3Zr2O12 (LLZO) poly(ethylene oxide) (PEO) system, in order to test how sensitive the outcome is to each variation. The study shows that the static and dynamic properties of Li-ion are significantly affected by van der Waals parameters as well as the surface terminations, while the thickness of the interfacial region - where the structure-dynamic properties are different as compared to the bulk-like regime - is the same irrespective of the simulation setup.

Predicting Phase Stability at Interfaces

We present the RAFFLE methodology for structural prediction of the interface between two materials and demonstrate its effectiveness by applying it to MgO encapsulated by two layers of graphene. To address the challenge of interface structure prediction, our methodology combines physical insights derived from morphological features observed in related systems with an iterative machine learning technique. This employs physical-based methods, including void-filling and n-body distribution functions to predict interface structures. For the carbon-MgO encapsulated system, we have shown the rocksalt and hexagonal phases of MgO to be the two most energetically stable in the few-layer regime. We demonstrate that monolayer rocksalt is heavily stabilized by interfacing with graphene, becoming more energetically favorable than the graphenelike monolayer hexagonal MgO. The RAFFLE methodology provides valuable insights into interface behavior, and a route to finding new materials at interfaces.

Fundamental Understanding of Hydrogen Evolution Reaction on Zinc Anode Surface: A First-Principles Study

Hydrogen evolution reaction (HER) has become a key factor affecting the cycling stability of aqueous Zn-ion batteries, while the corresponding fundamental issues involving HER are still unclear. Herein, the reaction mechanisms of HER on various crystalline surfaces have been investigated by first-principle calculations based on density functional theory. It is found that the Volmer step is the rate-limiting step of HER on the Zn (002) and (100) surfaces, while, the reaction rates of HER on the Zn (101), (102) and (103) surfaces are determined by the Tafel step. Moreover, the correlation between HER activity and the generalized coordination number ([Formula: see text]) of Zn at the surfaces has been revealed. The relatively weaker HER activity on Zn (002) surface can be attributed to the higher [Formula: see text] of surface Zn atom. The atomically uneven Zn (002) surface shows significantly higher HER activity than the flat Zn (002) surface as the [Formula: see text] of the surface Zn atom is lowered. The [Formula: see text] of surface Zn atom is proposed as a key descriptor of HER activity. Tuning the [Formula: see text] of surface Zn atom would be a vital strategy to inhibit HER on the Zn anode surface based on the presented theoretical studies. Furthermore, this work provides a theoretical basis for the in-depth understanding of HER on the Zn surface.

Tailoring the facet distribution on copper with chloride

Electrocatalytic reactions are sensitive to the catalyst surface structure. Therefore, finding methods to determine active surface sites with different geometry is essential to address the structure-electrocatalytic performance relationships. In this work, we propose a simple methodology to tune and quantify the surface structure on copper catalysts. We tailor the distribution and ratio of facets on copper by electrochemically oxidizing and reducing the surface in chloride-rich aqueous solutions. We then address the formation of new facets with voltammetric lead (Pb) underpotential deposition (UPD). We first record the voltammetric lead UPD on different single facets, which have intense peaks at different potential values. We use this data to decouple each facet peak-contribution in the lead (Pb) UPD curves of the tailored and multifaceted copper surfaces and determine the geometry of the active sites. We combine experiments with density functional theory (DFT) calculations to assess the ligand effect of chloride anions on the copper facet distribution during the surface oxidation/electrodeposition treatment. Our experiments and Wulff constructions suggest that chloride preferentially adsorbs on the (310) facet, reducing the number of (111) sites and inducing the growth of (310) or n(100) × (110) domains. Our work provides a tool to correlate active sites with copper geometries, which is needed to assess the structure-performance relationships in electrocatalysis. We also demonstrate an easy method for selectively tailoring the facet distribution of copper, which is essential to design a well-defined nanostructured catalyst.

Gold Nanoparticles for CO2 Electroreduction: An Optimum Defined by Size and Shape

Understanding the size-dependent behavior of nanoparticles is crucial for optimizing catalytic performance. We investigate the differences in selectivity of size-selected gold nanoparticles for CO2 electroreduction with sizes ranging from 1.5 to 6.5 nm. Our findings reveal an optimal size of approximately 3 nm that maximizes selectivity toward CO, exhibiting up to 60% Faradaic efficiency at low potentials. High-resolution transmission electron microscopy reveals different shapes for the particles and suggests that multiply twinned nanoparticles are favorable for CO2 reduction to CO. Our analysis shows that twin boundaries pin 8-fold coordinated surface sites and in turn suggests that a variation of size and shape to optimize the abundance of 8-fold coordinated sites is a viable path for optimizing the CO2 electrocatalytic reduction to CO. This work contributes to the advancement of nanocatalyst design for achieving tunable selectivity for CO2 conversion into valuable products.

Confined Layer Slip Process in Nanolaminated Ag and Two Ag/Cu Nanolaminates

The exceptional strength of nanolaminates is attributed to the influence of their fine stratification on the movement of dislocations. Through atomistic simulations, the impact of interfacial structure on the dynamics of an edge dislocation, which is compelled to move within a nanoscale layer of a nanolaminate, is examined for three different nanolaminates. In this study, we model confined layer slip in three structures: nanolaminated Ag and two types of Ag/Cu nanolaminates. We find that the glide motion is jerky in the presence of incoherent interfaces characterized by distinct arrays of misfit dislocations. In addition, the glide planes exhibit varying levels of resistance to dislocation motion, where planes with intersection lines that coincide with misfit dislocation lines experience greater resistance than planes without such intersection lines.

Selective formation of metastable polymorphs in solid-state synthesis

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO4, we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable targeted materials synthesis across diverse chemistries through selective polymorph nucleation.

Modulation of Carbon Nanotube Electronic Structure by Grain Boundary Defects in RbI on Au(111)

The electronic properties of single-walled carbon nanotubes (SWCNTs) are known to be highly sensitive to environmental effects. Here, we use scanning tunneling microscopy and spectroscopy to investigate the electronic properties of SWCNTs deposited on RbI monolayer films grown on Au(111). We find that grain boundary defects in RbI monolayers cause the appearance of spatially confined localized states in the SWCNTs. Our density functional theory calculations show that grain boundary defects in RbI/Au(111) produce a stabilizing electrostatic potential caused by reduced coordination of iodine atoms at the RbI grain boundary. The presented results may offer insights into the performance of devices involving transport through SWCNTs subjected to external electrostatic disorder.

Oxygen-regulated spontaneous solid electrolyte interphase enabling ultra-stable solid-state Na metal batteries

Solid-state sodium metal batteries utilizing inorganic solid electrolytes (SEs) hold immense potentials such as intrinsical safety, high energy density, and environmental sustainability. However, the interfacial inhomogeneity/instability at the anode-SE interface usually triggers the penetration of sodium dendrites into the electrolyte, leading to short circuit and battery failure. Herein, confronting with the original nonuniform and high-resistance solid electrolyte interphase (SEI) at the Na-Na3Zr2Si2PO12 interface, an oxygen-regulated SEI innovative approach is proposed to enhance the cycling stability of anode-SEs interface, through a spontaneous reaction between the metallic sodium (containing trace amounts of oxygen) and the Na3Zr2Si2PO12 SE. The oxygen-regulated spontaneous SEI is thin, uniform, and kinetically stable to facilitate homogenous interfacial Na+ transportation. Benefitting from the optimized SEI, the assembled symmetric cell exhibits an ultra-stable sodium plating/stripping cycle for over 6600 h under a practical capacity of 3 mAh cm-2. Quasi-solid-state batteries with Na3V2(PO4)3 cathode deliver excellent cyclability over 500 cycles at a rate of 0.5 C (1 C = 117 mA cm-2) with a high capacity retention of 95.4%. This oxygen-regulated SEI strategy may offer a potential avenue for the future development of high-energy-density solid-state metal batteries.

Mean inner potential of elemental crystals from density-functional theory calculations: Efficient computation and trends

The mean inner potential (V0) of crystals plays an important role in electron microscopy. In a few cases, it has been measured experimentally, using mostly electron holography; however, it is not uncommon to find reports that disagree by a few volts regarding the mean inner potential of the same material. Different levels of theory have also been used to estimate its value, often by building the crystal as a superposition of isolated atoms or ions-an independent-atom approximation that does not take bonding into account. In a few cases, density-functional theory (DFT) calculations were done to capture such bonding, frequently using computer-intensive all-electron approaches. In this article, we describe in detail a faster implementation based on postprocessing files produced by a DFT code that relies on the projector-augmented wave method. We deployed this approach to compute values of V0 for 44 elemental solids, and we provide the first quantum-mechanical calculation of the mean inner potential beyond the independent-atom approximation for many of them. We also report instances in which different surface terminations for the same material led to differences in V0 of more than 3 V, highlighting the dependence of the mean inner potential on the boundary conditions of the sample. Finally, by comparing our values of V0 with other material properties, we show that it correlates mostly linearly with the mass density, that it can be used to compute a good approximation to the orbital diamagnetic contribution to the magnetic susceptibility, and that it provides a simple route to compute atomic scattering amplitudes for forward scattering of electrons.

Discovering polyelemental nanostructures with redistributed plasmonic modes through combinatorial synthesis

Coupling plasmonic and functional materials provides a promising way to generate multifunctional structures. However, finding plasmonic nanomaterials and elucidating the roles of various geometric and dielectric configurations are tedious. This work describes a combinatorial approach to rapidly exploring and identifying plasmonic heteronanomaterials. Symmetry-broken noble/non-noble metal particle heterojunctions (~100 nanometers) were synthesized on multiwindow silicon chips with silicon nitride membranes. The metal types and the interface locations were controlled to establish a nanoparticle library, where the particle morphology and scattering color can be rapidly screened. By correlating structural data with near- and far-field single-particle spectroscopy data, we found that certain low-energy plasmonic modes could be supported across the heterointerface, while others are localized. Furthermore, we found a series of triangular heteronanoplates stabilized by epitaxial Moiré superlattices, which show strong plasmonic responses despite largely comprising a lossy metal (~70 atomic %). These architectures can become the basis for multifunctional and cost-effective plasmonic devices.

Support-facet-dependent morphology of small Pt particles on ceria

Direct atomic scale information on how the structure of supported nanoparticles is affected by the metal-support interaction is rare. Using scanning transmission electron microscopy, we provide direct evidence of a facet-dependent support interaction for Pt nanoparticles on CeO2, governing the dimensionality of small platinum particles. Our findings indicate that particles consisting of less than ∼130 atoms prefer a 3D shape on CeO2(111) facets, while 2D raft structures are favored on CeO2(100) facets. Measurements of stationary particles on both surface facets are supplemented by time resolved measurements following a single particle with atomic resolution as it migrates from CeO2(111) to CeO2(100), undergoing a dimensionality change from 3D to 2D. The intricate transformation mechanism reveals how the 3D particle disassembles and completely wets a neighboring CeO2(100) facet. Density functional theory calculations confirm the structure-trend and reveal the thermodynamic driving force for the migration of small particles. Knowledge of the presented metal-support interactions is crucial to establish structure-function relationships in a range of applications based on supported nanostructures.

2D Non-van der Waals Nanoplatelets of Hematene and Magnetene: Nonlinear Optical Response and Optical Limiting Performance from UV to NIR

Recently, the preparation of some hematene and magnetene ultrathin non van der Waals (non-vdW) 2D nanoplatelets was reported starting from hematite and magnetite natural iron ores. The present work reports on the determination and evaluation of the nonlinear optical response and the optical limiting (OL) action of these 2D nanoplatelets dispersed in water under ns laser excitation. The obtained results show that both hematene and magnetene exhibit strong nonlinear absorption and refraction, comparable and even larger than those of other van der Waals (vdW) 2D counterpart materials. In addition, due to their strong nonlinear absorption, both hematene and magnetene show exceptional OL performance from the UV to visible, attaining very low values of optical limiting onset (OLon ), comparable and even lower than that of vdW 2D nanomaterials, such as graphene, graphene oxide, other transition metal dichalcogenides like MoS2 , WS2 and MoSe2 , black phosphorous and antimonene. Moreover, hematene was found to exhibit more efficient OL action than magnetene for all the excitation wavelengths studied, attributed to more efficient ligand to metal charge transfer. The present findings open new possibilities for the potential use of these non-vdW 2D materials in photonics and optoelectronics, e. g., as optical limiters and optical switchers.

Preparation of Azoxystrobin-Zinc Metal-Organic Framework/Biomass Charcoal Composite Materials and Application in the Prevention and Control of Gray Mold in Tomato

In order to reduce the use of fungicide and ensure food safety, it is necessary to develop fungicide with low toxicity and high efficiency to reduce residues. Azoxystrobin (AZOX), which is derived from mushrooms, is an excellent choice. However, conventional AZOX release is difficult to regulate. In this paper, a pH-responsive fungicide delivery system for the preparation of AZOX by impregnation method was reported. The Zinc metal-organic framework/Biomass charcoal (ZIF-8/BC) support was first prepared, and subsequently, the AZOX-ZIF-8/BC nano fungicide was prepared by adsorption of AZOX onto ZIF-8/BC by dipping. Gray mold, caused by Botrytis cinerea, is one of the most important crop diseases worldwide. AZOX-ZIF-8/BC could respond to oxalic acid produced by Botrytis cinerea to release loaded AZOX. When pH = 4.8, it was 48.42% faster than when pH = 8.2. The loading of AZOX on ZIF-8/BC was 19.83%. In vitro and pot experiments showed that AZOX-ZIF-8/BC had significant fungicidal activity, and 300 mg/L concentration of AZOX-ZIF-8-BC could be considered as a safe and effective control of Botrytis cinerea. The above results indicated that the prepared AZOX-ZIF-8/BC not only exhibited good drug efficacy but also demonstrated pH-responsive fungicide release.

ColabFit exchange: Open-access datasets for data-driven interatomic potentials

Data-driven interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale simulations. Despite this, apart from a few notable exceptions, there is a distinct lack of well-organized, public datasets in common formats available for use with IP development. This deficiency precludes the research community from implementing widespread benchmarking, which is essential for gaining insight into model performance and transferability, and also limits the development of more general, or even universal, IPs. To address this issue, we introduce the ColabFit Exchange, the first database providing open access to a large collection of systematically organized datasets from multiple domains that is especially designed for IP development. The ColabFit Exchange is publicly available at https://colabfit.org, providing a web-based interface for exploring, downloading, and contributing datasets. Composed of data collected from the literature or provided by community researchers, the ColabFit Exchange currently (September 2023) consists of 139 datasets spanning nearly 70 000 unique chemistries, and is intended to continuously grow. In addition to outlining the software framework used for constructing and accessing the ColabFit Exchange, we also provide analyses of the data, quantifying the diversity of the database and proposing metrics for assessing the relative diversity of multiple datasets. Finally, we demonstrate an end-to-end IP development pipeline, utilizing datasets from the ColabFit Exchange, fitting tools from the KLIFF software package, and validation tests provided by the OpenKIM framework.

Controlled Growth of Vertically Aligned Nanocomposites through a Au Seeding-Assisted Method

Heteroepitaxial metal-oxide vertically aligned nanocomposites (VAN) have piqued significant interest due to their remarkable vertical interfacial coupling effects, strong structural and property anisotropy, and potential applications in magnetoelectrics, photocatalysts, and optical metamaterials. VANs present a unique pillar-in-matrix structure with uniform but rather random pillar distributions. Achieving a well-controlled pillar growth remains a major challenge in this field. Here, we use BaTiO3 (BTO)-Au as a model VAN system to demonstrate the effects of Au seedings on achieving such pillar-growth control with enhanced ordering and morphology tuning. The Au seedings are introduced using an anodic aluminum oxide (AAO) template through pulsed laser deposition (PLD). TEM characterization reveals that the Au seedings result in straighter and more evenly distributed Au pillars in the BTO matrix compared to those without seeding, with the diameter of the Au seedings increasing with the number of pulses. Additionally, spectroscopic ellipsometry demonstrates distinct permittivity dispersion for all samples. This demonstration lays a foundation for future controlled and selective growth of VAN systems for on-chip integration.

Hierarchical Structuring of Black Silicon Wafers by Ion-Flow-Stimulated Roughening Transition: Fundamentals and Applications for Photovoltaics

Ion-flow-stimulated roughening transition is a phenomenon that may prove useful in the hierarchical structuring of nanostructures. In this work, we have investigated theoretically and experimentally the surface texturing of single-crystal and multi-crystalline silicon wafers irradiated using ion-beam flows. In contrast to previous studies, ions had relatively low energies, whereas flow densities were high enough to induce a quasi-liquid state in the upper silicon layers. The resulting surface modifications reduced the wafer light reflectance to values characteristic of black silicon, widely used in solar energetics. Features of nanostructures on different faces of silicon single crystals were studied numerically based on the mesoscopic Monte Carlo model. We established that the formation of nano-pyramids, ridges, and twisting dune-like structures is due to the stimulated roughening transition effect. The aforementioned variety of modified surface morphologies arises due to the fact that the effects of stimulated surface diffusion of atoms and re-deposition of free atoms on the wafer surface from the near-surface region are manifested to different degrees on different Si faces. It is these two factors that determine the selection of the allowable "trajectories" (evolution paths) of the thermodynamic system along which its Helmholtz free energy, F, decreases, concomitant with an increase in the surface area of the wafer and the corresponding changes in its internal energy, U (dU>0), and entropy, S (dS>0), so that dF=dU - TdS<0, where T is the absolute temperature. The basic theoretical concepts developed were confirmed in experimental studies, the results of which showed that our method could produce, abundantly, black silicon wafers in an environmentally friendly manner compared to traditional chemical etching.

A Flexible Theory for Catalysis: Learning Alkaline Oxygen Reduction on Complex Solid Solutions within the Ag-Pd-Pt-Ru Composition Space

Compositionally complex materials such as high-entropy alloys and oxides have the potential to be efficient platforms for catalyst discovery because of the vast chemical space spanned by these novel materials. Identifying the composition of the most active catalyst materials, however, requires unraveling the descriptor-activity relationship, as experimentally screening the multitude of possible element ratios quickly becomes a daunting task. In this work, we show that inferred adsorption energy distributions of *OH and *O on complex solid solution surfaces within the space spanned by the system Ag-Pd-Pt-Ru are coupled to the experimentally observed electrocatalytic performance for the oxygen reduction reaction. In total, the catalytic activity of 1582 alloy compositions is predicted with a cross-validated mean absolute error of 0.042 mA/cm2 by applying a theory-derived model with only two adjustable parameters. Trends in the discrepancies between predicted electrochemical performance values of the model and the measured values on thin film surfaces subsequently provide insight into the alloys' surface compositions during reaction conditions. Bridging this gap between computationally modeled and experimentally observed catalytic activities, not only reveals insight into the underlying theory of catalysis but also takes a step closer to realizing exploration and exploitation of high-entropy materials.

Tendency Regulation of Competing Reactions Toward Highly Reversible Tin Anode for Aqueous Alkaline Batteries

Investigating dendrite-free stripping/plating anodes is highly significant for advancing the practical application of aqueous alkaline batteries. Sn has been identified as a promising candidate for anode material, but its deposition/dissolution efficiency is hindered by the strong electrostatic repulsion between Sn(OH)3 - and the substrate. Herein, this work constructs a nondense copper layer which serves as stannophile and hydrogen evolution inhibitor to adjust the tendency of competing reactions on Sn foil surface, thus achieving a highly reversible Sn anode. The interactions between the deposited Sn and the substrates are also strengthened to prevent shedding. Notably, the ratio of Sn redox reaction is significantly boosted from ≈20% to ≈100%, which results in outstanding cycling stability over 560 h at 10 mA cm-2 . A Sn//Ni(OH)2 battery device is also demonstrated with capacities from 0.94 to 22.4 mA h cm-2 and maximum stability of 1800 cycles.

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

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

Hydrothermal Synthesis of Te Nanosheets: Growth Mechanism and Electrical Property

Hydrothermal synthesis is a highly efficient way to yield multiform Te nanosheets. However, the growth mechanisms and property discrepancies between different types of Te nanosheets are still unclear. In this paper, we perform an investigation on this issue by monitoring the hydrothermally synthesized Te nanosheets at different growth stages with transmission electron microscopy and electrical tests. Three main types of Te nanosheets and their variants are revealed including trapezoidal and "V"-shaped configurations. It is found that the different types of Te nanosheets dominate at different reaction stages, indicating a sequential growth scenario. Surfactants and surface energy co-determine the growth kinetics, while the crystallographic attachments lead to specifically included angles of 74° and 41° in the "V"-shaped Te nanosheets. The fractions of the three main types of Te nanosheets as a function of reaction time are statistically tracked, and their crystalline structures, interfaces, and preferential growth orientations are uncovered. Moreover, the electrical properties of the Te nanosheets are tested, and the results show an interface-related feature. These findings provide some new insights into the synthesis and property of low-dimensional Te functional materials.