Transition-metal alloyed nanoparticles with core−shell features (shell enrichment by one of the metals) are becoming ubiquitous, from (electro-)catalysis to biomedical applications, due to their size ...control, performance, biocompatibility, and cost. We investigate 132 binary-alloyed nanoparticle systems (groups 8 to 11 in the Periodic Table) using density functional theory (DFT) and systematically explore their segregation energies to determine core−shell preferences. We find that core−shell preferences are generally described by two independent factors: (1) cohesive energy (related to vapor pressure) and (2) atomic size (quantified by the Wigner−Seitz radius), and the interplay between them. These independent factors are shown to provide general trends for the surface segregation preference for atoms in nanoparticles, as well as semi-infinite surfaces, and give a simple correlation (a “design map”) for the alloying and catalytic behavior. Finally, we provide a universal description of core−shell preference via tight-binding theory (band-energy differences) that (i) quantitatively reproduces the DFT segregation energies and (ii) confirms the electronic origins and correlations for core−shell behavior.
Order–disorder transformations hold an essential place in chemically complex high-entropy ferritic steels (HEFSs) due to their critical technological application. The chemical inhomogeneity arising ...from mixing of multi-principal elements of varying chemistry can drive property altering changes at the atomic scale, in particular short-range order. Using density-functional theory-based linear-response theory, we predict the effect of compositional tuning on the order–disorder transformation in ferritic steels—focusing on Cr–Ni–Al–Ti–Fe HEFSs. We show that Ti content in Cr–Ni–Al–Ti–Fe solid solutions can be tuned to modify short-range order that changes the order–disorder path from BCC-B2 (Ti atomic-fraction = 0) to BCC-B2-L2
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(Ti atomic-fraction > 0) consistent with existing experiments. Our study suggests that tuning degree of SRO through compositional variation can be used as an effective means to optimize phase selection in technologically useful alloys.
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Abstract
The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature ...and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.
How the crystal structures of ordered transition-metal phosphide catalysts affect the hydrogen-evolution reaction (HER) is investigated by measuring the anisotropic catalytic activities of selected ...crystallographic facets on large (mm-sized) single crystals of iron-phosphide (FeP) and monoclinic nickel-diphosphide (m-NiP2). We find that different crystallographic facets exhibit distinct HER activities, in contrast to a commonly held assumption of severe surface restructuring during catalytic activity. Moreover, density-functional-theory-based computational studies show that the observed facet activity correlates well with the H-binding energy to P atoms on specific surface terminations. Direction dependent catalytic properties of two different phosphides with different transition metals, crystal structures, and electronic properties (FeP is a metal, while m-NiP2 is a semiconductor) suggests that the anisotropy of catalytic properties is a common trend for HER phosphide catalysts. This realization opens an additional rational design for highly efficient HER phosphide catalysts, through the growth of nanocrystals with specific exposed facets. Furthermore, the agreement between theory and experimental trends indicates that screening using DFT methods can accelerate the identification of desirable facets, especially for ternary or multinary compounds. The large single-crystal nature of the phosphide electrodes with well-defined surfaces allows for determination of the catalytically important double-layer capacitance of a flat surface, Cdl = 39(2) μF cm−2 for FeP, useful for an accurate calculation of the turnover frequency (TOF). X-ray photoelectron spectroscopy (XPS) studies of the catalytic crystals that were used show the formation of a thin oxide/phosphate overlayer, presumably ex situ due to air-exposure. This layer is easily removed for FeP, revealing a surface of pristine metal phosphide.
For small Pt nanoparticles (NPs), catalytic activity is, as observed, adversely affected by size in the 1–3 nm range. We elucidate, via first-principles-based thermodynamics, the operation H* ...distribution and cyclic voltammetry (CV) during the hydrogen evolution reaction (HER) across the electrochemical potential, including the underpotential region (U ≤ 0) that is difficult to assess in experiment. We consider multiple adsorption sites on a 1 nm Pt NP model and show that the characteristic CV peaks from different H* species correspond well to experiment. We next quantify the activity contribution from each H* species to explain the adverse effect of size. From the resolved CV peaks at the standard hydrogen electrode potential (U = 0), we first deduce that the active species for the HER are the partially covered (100)-facet bridge sites and the (111)-facet hollow sites. Upon evaluation of the reaction barriers at operation H* distribution and microkinetic modeling of the exchange current, we find that the nearest-neighbor (100)-facet bridge site pairs have the lowest activation energy and contribute to ∼75% of the NP activity. Edge bridge sites (fully covered by H*) per se are not active; however, they react with neighboring (100)-facet H* to account for ∼18% of the activity, whereas (111)-facet hollow sites contribute little. Extrapolating the relative contributions to larger NPs in which the ratio of facet-to-edge sites increases, we show that the adverse size effect of Pt NP HER activity kicks in for sizes below 2 nm.
Multi-principal element alloys (MPEAs) continue to garner great interest due to their potentially remarkable mechanical properties, especially at elevated temperatures for key structural and energy ...applications. Despite extensive literature examining material properties of MPEAs at various compositions, much less is reported about the role of grain size on the mechanical properties. Here, we examine a representative nanocrystalline BCC refractory MPEA and identify a crossover from a Hall-Petch to inverse-Hall-Petch relation. While the considered MPEA predominantly assumes a single-phase BCC lattice, the presence of grain boundaries imparts amorphous distributions that increase with decreasing grain size (i.e., increasing grain boundary volume fraction). Using molecular dynamics simulations, we find that the average flow stress of the MPEA increases with decreasing average grain size, but below a critical grain size of 23.2 nm the average flow stress decreases. This change in the deformation behavior is driven by the transition from dislocation slip to grain-boundary slip as the predominant mechanism. The crossover to inverse-Hall-Petch regime is correlated to dislocation stacking at the grain boundary when dislocation density reaches a maximum.
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•Transition from Hall-Petch to inverse Hall-Petch phenomenon occurs when the grain size <23.2 nm for a nanocrystalline MPEA.•The transition is due to the deformation mechanism shifting from dislocation slip to grain boundary slip.•At the transition zone, the dislocation density is maximized at the grain boundaries.
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•A phylogeny inferred with 251 genes: target-enrichment, transcriptomes and genomes.•The tribe Cryphalini, and many other tribes are highly polyphyletic.•Fungus farming evolved 16 ...times, inbreeding 8 times, host plant generalism 11 times.
Bark and ambrosia beetles (Curculionidae, Scolytinae) display a conspicuous diversity of unusual genetic and ecological attributes and behaviors. Reconstructing the evolution of Scolytinae, particularly the large and ecologically significant tribe Cryphalini (pygmy borers), has long been problematic. These challenges have not adequately been addressed using morphological characters, and previous research has used only DNA sequence data from small numbers of genes.
Through a combination of anchored hybrid enrichment, low-coverage draft genomes, and transcriptomes, we addressed these challenges by amassing a large molecular phylogenetic dataset for bark and ambrosia beetles. The resulting DNA sequence data from 251 protein coding genes (114,276 bp of nucleotide sequence data) support inference of the first robust phylogeny of Scolytinae, with a special focus on the species rich tribe Cryphalini and its close relatives.
Key strategies, including inbreeding mating systems and fungus farming, evolved repeatedly across Scolytinae. We confirm 12 of 16 hypothesized origins of fungus farming, 6 of 8 origins of inbreeding polygyny and at least 11 independent origins of a super-generalist host range. These three innovations are statistically correlated, but their appearance within lineages was not necessarily simultaneous.
Additionally, the evolution of extreme host plant generalism often preceded, rather than succeeded, fungus farming. Of the high-diversity tribes of Scolytinae, only Xyleborini is monophyletic, Corthylini is paraphyletic and Cryphalini is highly polyphyletic. Cryphalini sensu stricto is part of a clade containing the genera Hypothenemus, Cryphalus and Trypophloeus, and the tribe Xyloterini. Stegomerus and Cryptocarenus (Cryphalini) are part of a clade otherwise containing all Corthylini. Several other genera, including Ernoporus and Scolytogenes (Cryphalini), make up a distantly related clade. Several of the genera of Cryphalini are also intermixed. For example, Cryphalus and Hypocryphalus are intermingled, as well as Ernoporicus, Ptilopodius and Scolytogenes.
Our data are consistent with widespread polyphyly and paraphyly across Scolytinae and within Cryphalini, and provides new insights into the evolution of inbreeding mating systems and fungus farming in the species rich and ecologically significant weevil subfamily Scolytinae.
We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid ...synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.
Reversible, diffusionless, first-order solid-solid phase transitions accompanied by caloric effects are critical for applications in the solid-state cooling and heat-pumping devices. Accelerated ...discovery of caloric materials requires reliable but faster estimators for predictions and high-throughput screening of system-specific dominant caloric contributions. We assess reliability of the computational methods that provide thermodynamic properties in relevant solid phases at or near a phase transition. We test the methods using the well-studied B2 FeRh alloy as a “fruit fly” in such a materials genome discovery, as it exhibits a metamagnetic transition which generates multicaloric (magneto-, elasto-, and baro-caloric) responses. For lattice entropy contributions, we find that the commonly-used linear-response and small-displacement phonon methods are invalid near instabilities that arise from the anharmonicity of atomic potentials, and we offer a more reliable and precise method for calculating lattice entropy at a fixed temperature. Then, we apply a set of reliable methods and estimators to the metamagnetic transition in FeRh (predicted 346±12 K, observed 353±1 K) and calculate the associated caloric properties, such as isothermal entropy and isentropic temperature changes.
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•Better caloric materials are needed for efficient cooling and heat-pump systems.•Reliability of the methods used for caloric assessment are of considerable import.•Some of the commonly used methods are inapplicable to first-order phase transitions.•Improved means to estimate caloric properties reliably are provided and tested.•Reliable methods are useful for materials-discovery searches using machine learning.
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