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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

First Principles Evaluation of Phase Stability in the In-Sn Binary System

Abstract The In-Sn binary alloy system exhibits several unusual features that challenge crystallographic and thermodynamic expectations. We combine first principles total energy calculation with simple thermodynamic modeling to address two key points. First, we evaluate energies along the Bain path to interpret the discontinuous transition between the phases α-In (Pearson type tI2) and β-In 3 Sn (also Pearson type tI2) that are identical in symmetry. Second, we demonstrate that the solid solution phases β-In 3 Sn and γ-InSn 4 (Pearson type hP1) exist at high temperatures only, and they exhibit eutectoid decompositions at low temperatures.

Chemistry↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analytical gradient-based optimization of CALPHAD model parameters

The calibration of CALPHAD (CALculation of PHAse Diagrams) models involves the solution of a very challenging high-dimensional multiobjective optimization problem. Traditional approaches to parameter fitting predominantly rely on gradient-free methods, which while robust, are computationally inefficient and often scale poorly with model complexity. In this work, we introduce and demonstrate a generalizable framework for analytic gradient-based optimization of the parameters of the CALPHAD model enabled by the recently formalized Jansson derivative technique. This method allows for efficient evaluation of gradients of thermodynamic properties at equilibrium with respect to model parameters, even in the presence of arbitrarily complex internal degrees of freedom. Leveraging these semi-analytic gradients, we employ the conjugate gradient (CG) method to optimize thermodynamic model parameters for four binary alloy systems: Cu-Mg, Fe-Ni, Cr-Ni, and Cr-Fe. Across all systems, CG achieves comparable or superior optimality relative to Bayesian ensemble Markov Chain Monte Carlo (MCMC) with improvements in computational efficiency ranging from one to three orders of magnitude. Furthermore, our results establish a new paradigm for CALPHAD assessments in which high fidelity data-rich model calibration becomes tractable using deterministic gradient-informed algorithms.

CALPHAD↗
DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reconstructing parent microstructures in martensitic and pearlitic Ti-Cu

Evaluating the microstructural evolution of parent phases has long been a challenge in metals with a partial or complete solid state transformation. Most parent microstructure evolution has to be inferred from the product microstructure, with some information permanently lost. Parent microstructure reconstructions relying on orientation relationships and EBSD data are a useful tool to overcome this challenge, though much background knowledge is required to do so. Here, this work introduces a reconstruction process for a eutectoid composition Ti-Cu binary alloy to evaluate the high temperature β-Ti phase from martensitic and pearlitic product microstructures. Martensitic microstructures were accurately reconstructed, enabling elucidation of β-Ti grain size and texture, and the employed script included for scientific reference. Pearlitic microstructures did not reconstruct effectively with the current process. However, new insight into the interfacial crystallographic orientations of pearlitic Ti-Cu microstructures was gleaned via this analysis, and future investigations of interest are discussed accordingly. Ultimately, these findings demonstrate tools widely implemented on conventional titanium and ferrous alloys can also be applied to understand parent microstructures in Ti-Cu and other alloys of emerging interest. This is especially of use as new manufacturing processes for such materials require understanding how parent phase microstructures respond to new, novel material processing such as AM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Utilizing Site Disorder in the Development of New Energy-Relevant Semiconductors

We report controlling site disorder in ternary and multinary compounds enables tuning optical and electronic properties at fixed lattice constants and stoichiometries, moving beyond many of the challenges facing binary alloy systems. Here, we consider possible enhancements to energy-related applications through the integration of disorder-tunable materials in devices such as light-emitting diodes, photonics, photovoltaics, photocatalytic materials, batteries, and thermoelectrics. However, challenges remain in controlling and characterizing disorder. Focusing primarily on II–IV–V 2 materials, we identify three metrics for experimentally characterizing cation site disorder. Complementary to these experiments, we discuss simulation methods to understand disordered materials. Nonidealities, such as off-stoichiometry and oxygen incorporation, can occur while synthesizing metastable disordered materials. While nonidealities may seem undesirable, we describe how if harnessed they could provide another knob for tuning disorder and subsequently properties. To illustrate the effects of disorder on device-relevant properties, we provide case examples of disordered materials and their potential in device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inferring topological transitions in pattern-forming processes with self-supervised learning

Abstract The identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes.

Abram, Marcin↗
DOE OSTI2024

Computed entropy spectra for grain boundary segregation in polycrystals

Abstract Grain boundary solute segregation influences most bulk material properties, and understanding solute thermodynamics at grain boundaries is critical for engineering them. However, the vast grain boundary space in polycrystals is challenging to evaluate due to its size, especially for the intrinsically hard-to-compute segregation excess entropy. Here data science methods are used to generate a database of site-wise grain boundary segregation entropy spectra for 155 dilute binary alloys within the harmonic approximation. The spectral framework allows scale bridging between the calculated atomistic site-wise energy-entropy spectra and macroscopic segregation entropy estimates. The results affirm that macroscopic averaging is not sufficient: a spectral treatment of grain boundary segregation is needed to accurately model bulk temperature dependence of grain boundary solute segregation. The calculated spectral entropy database and thermodynamic framework can be applied for both understanding segregation experiments and alloy design exercises, paving the way to a finite-temperature grain boundary genome.

Chemistry↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phase-field model of freeze casting

Directional solidification of water-based solutions has emerged as a versatile technique for templating hierarchical porous materials. However, the underlying mechanisms of pattern formation remain incompletely understood. In this work, we present a detailed derivation and analysis of a quantitative phase-field model for simulating this nonequilibrium process. The phase-field model extends the thin-interface formulation of dilute binary alloy solidification with antitrapping to incorporate the highly anisotropic energetic and kinetic properties of the partially faceted ice-water interface. This interface is faceted in the basal plane normal to the ⟨0001⟩ directions and atomically rough in other directions within the basal plane. On the basal plane, the model reproduces a linear or nonlinear relationship between the interface growth rate and the kinetic undercooling that can be linked to experimental measurements. In both cases, spontaneous parity breaking of the solidification front is observed when the preferred growth direction is aligned with the temperature gradient. This phenomenon leads to the formation of partially faceted ice lamellae that drift laterally in one of the ⟨0001⟩ directions. Here, we demonstrate that the drifting velocity of the ice lamellae is controlled by the kinetics on the basal plane and converges as the thickness of the diffuse solid-liquid interface decreases. Furthermore, we examine the effect of the form of the kinetic anisotropy, which is chosen here such that the inverse of the kinetic coefficient varies linearly from a finite value in the ⟨0001⟩ directions to zero in all other directions within the basal plane, consistent with the assumption that the interface grows in local thermodynamic equilibrium in this plane. Our results indicate that the drifting velocity of ice lamellae is not affected by the slope of this linear relation, and the radius and undercooling at the tip of an ice lamella converge at relatively small slope values. Consequently, the phase-field simulations remain quantitative with computationally tractable choices of both the interface thickness and the slope assumed in the form of the kinetic anisotropy.

Materials science↗
DOE OSTI2025

Stability and Mobility of Disconnections in Solute Atmospheres: Insights from Interfacial Defect Diagrams

This Letter explores the stability of disconnections (step-dislocation defects) at grain boundaries in binary alloys. We introduce interfacial defect diagrams, derived from atomistic simulations and segregation theory, to predict the stability of disconnections in the temperature-solute concentration phase space and relate it to governing segregation mechanisms. These diagrams reveal multiple stability regimes influenced by solute-induced clustering and pinning effects impacting the thermal migration of disconnections and offering insights into their thermodynamics and kinetic properties.

Crystal structure↗
DOE OSTI2022

ORNL_AISD_NiNb

This dataset describes the nickel-niobium solid solution binary alloy, where the two constituent elements nickel (Ni) and niobium (Nb) are randomly placed on an underlying crystal lattice. This dataset for nickel-niobium (Ni-Nb) alloys available includes the formation energy and bulk modulus for each crystal structure. Each atomic sample has a disordered phase which is obtained starting from an initial regular crystal structure of type body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal compact packed (HCP). The geometry optimization ensures that all the alloy samples reached the equilibrium with negative formation energy. We perform geometry optimizations using the LAMMPS simulation package [1], a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. We utilized the embedded atom model (EAM) potential for Ni and Nb developed in a previous study [2]. The potential could describe behaviors of the liquid and solid phases of Ni-Nb alloy. The structural factors and angular distributions of three atoms are well-matched with X-ray and ab initio-based molecular dynamics data. We prepared the three different crystals with different initial lattice parameters (3.52 Ã… for FCC, 3.32 Ã… for BCC, and 3.5 Ã… for HCP). We performed energy minimization in two steps. Firstly, we minimized the structures with an isotropic unit cell to minimize the side effects from our arbitrary lattice parameters for all other compositions. Then, we applied geometry optimization with a triclinic (non-orthogonal) unit cell to fully minimize the stress components to calculate the elastic constants. In this procedure, we chose 10,000 as the maximum number of allowable steps aimed at obtaining fully relaxed atomic geometries. The dataset consists of three sets of crystal structures. The first set contains 46,086 irregular crystal structures, each of them with 54 atoms, obtained through optimization starting from a regular BCC crystal structure. The second set contains 24,543 irregular crystal structures, each of them with 32 atoms, obtained through optimization starting from a regular FCC crystal structure. The third set contains 39,303 irregular crystal structures, each of them with 48 atoms, obtained through optimization starting from a regular HCP crystal structure. The atomic configurations within each set span the possible compositional range. The three sets have been unified in a global dataset, which is extremely heterogeneous in terms of crystal structures, lattice volumes, and atomic configurations. Organization of files inside the dataset: the dataset contains three subdirectories called • BCC_opt • FCC_opt • HCP_opt based on the type of initial regular structure used to start the geometry optimization. Inside each of these folders, every atomic structure is identified by a string “A_B_Câ€, where A denotes the number of Nb in the system, B denotes index of structure with a given Nb number, and C denotes the total number of structures generated with a given Nb number. For each optimized crystal structure identified by the unique string of characters “A_B_Câ€, three files are provided: • A_B_C_opt.xyz: The optimized geometries in xyz format • A_B_C_opt.cfg: The optimized geometries in cfg format. It includes cell information and atomic energy, and forces calculated from LAMMPS. • A_B_C.elastic: Raw data of 21 elastic constants from LAMMPS output. • A_B_C.bulk: Calculated upper and lower bounds of bulk modulus and averaged one based on Voigt-Reuss-Hill approach from *.elastic. References: [1] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm., 271:108171, 2022. [2] Y Zhang, R Ashcraft, MI Mendelev, CZ Wang, and KF Kelton. Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy. The Journal of chemical physics, 145(20):204505, 2016.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling

The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.

Saidi, Wissam↗
DOE OSTI2026

Assessment of binary eutectic Ni alloys for high-temperature applications via laser remelting

It has been recently demonstrated that eutectic alloys processed by additive manufacturing have excellent high-temperature mechanical properties. We suggest that nickel-base eutectic alloys may enable new combinations of structural and functional properties. To this end, we investigate the processability, microstructure, and thermal stability of five, binary near-eutectic Ni-X (X = B, Ce, La, Y, and Zr) alloys processed via surface laser-remelting. The microstructure of all alloys contain a two-phase lamellar eutectic microstructure consisting of γ-Ni and intermetallic phases; this microstructure is significantly finer (100–200 nm lamellar spacing) in the laser-remelted alloys than in the cast substrate (0.5–1.0 µm lamellar spacing). The microhardness of the laser-remelted alloys (550–770 HV) is 35–50% higher than that of the cast alloys (370–570 HV) due to this finer eutectic spacing. An anomalous eutectic microstructure appears at the meltpool boundaries, containing globular and lamellar γ-Ni phases. The alloys contain a high volume fraction (>50 vol%) of intermetallic phase which forms a continuous network, causing brittleness. Following laser-remelting trials, the alloys showed a high density of solid-state cracks, except for the Ni-Zr alloy which processed well. During thermal exposure at 700 and 900°C for up to 500 h, the eutectic microstructure coarsens. Coarsening occurs heterogeneously and initiates at the meltpool boundaries. This process occurs more slowly in the Ni-Zr and Ni-Y alloys, and more rapidly in the remaining alloys, resulting in greater microhardness retention in the Ni-Zr and Ni-Y alloys following thermal exposure at 700°C. Thus, among the five alloys, the Ni-Zr system exhibits a good combination of high-temperature mechanical properties and processability. Here, we conclude with recommendations for future work on designing additively manufactured alloys based on these eutectic Ni systems.

Additive manufacturing↗
DOE OSTI2020

Quasi‐Binary Transition Metal Dichalcogenide Alloys: Thermodynamic Stability Prediction, Scalable Synthesis, and Application

Abstract Transition metal dichalcogenide (TMDCs) alloys could have a wide range of physical and chemical properties, ranging from charge density waves to superconductivity and electrochemical activities. While many exciting behaviors of unary TMDCs have been demonstrated, the vast compositional space of TMDC alloys has remained largely unexplored due to the lack of understanding regarding their stability when accommodating different cations or chalcogens in a single‐phase. Here, a theory‐guided synthesis approach is reported to achieve unexplored quasi‐binary TMDC alloys through computationally predicted stability maps. Equilibrium temperature–composition phase diagrams using first‐principles calculations are generated to identify the stability of 25 quasi‐binary TMDC alloys, including some involving non‐isovalent cations and are verified experimentally through the synthesis of a subset of 12 predicted alloys using a scalable chemical vapor transport method. It is demonstrated that the synthesized alloys can be exfoliated into 2D structures, and some of them exhibit: i) outstanding thermal stability tested up to 1230 K, ii) exceptionally high electrochemical activity for the CO 2 reduction reaction in a kinetically limited regime with near zero overpotential for CO formation, iii) excellent energy efficiency in a high rate Li–air battery, and iv) high break‐down current density for interconnect applications. This framework can be extended to accelerate the discovery of other TMDC alloys for various applications.

Hemmat, Zahra↗
DOE OSTI2020

Crystal structure and physical properties of Yb 2 In and Eu 2–x Yb x In alloys

While binary R E 2 In , where R E = rare earth , have been reported a few decades ago, recent investigations revealed intriguing new physical insights. For instance, the discovery of a nearly ideal first-order ferromagnetic transition in Eu 2 In calls for further exploration of structures and properties of R E 2 In , in particular for the least-documented R E = Eu and Yb cases. In this work, we investigate Eu 2 – x Yb x In pseudobinaries with nominal values of x = 0.25 , 0.5, 0.75, 1, 1.5, 2 by powder x-ray diffraction (including as function of temperature from 100 to 375 K for Yb 2 In ), magnetization (5–300 K), as well as electrical resistivity (5–300 K) and calorimetric (2–150 K) measurements for Yb 2 In . Compared to other RE , Yb or Eu always raise challenging questions linked to their valence states. From average atomic volume, Yb is anticipated to be divalent in Yb 2 In , at least between 100 and 375 K, which is in line with the absence of 4 f magnetism. In agreement with x-ray diffraction and magnetization data, the resistivity of Yb 2 In is rather featureless and typical of a metal. Establishing Yb 2 In as a nonmagnetic isostructural reference for Eu 2 In allows one to use its heat capacity to revisit that of the latter, and get experimental insights into the exceptional magnetocaloric effect of the compound with Eu. In particular, we show that a third of the total magnetic entropy ( S m ≈ 35.6 J mo l – 1 K – 1 at T = 100 K ) is concentrated in a 3 K temperature window around the T C of Eu 2 In . Starting from the ferromagnetic compound Eu 2 In [ T C = 55.2 ( 5 ) K ] , we show that Yb substitutions in Eu 2 – x Yb x In lead to a decrease in both the Curie temperature [ T C = 41 ( 2 ) and 32(2) K for x = 0.25 and 0.5] and magnetic saturation, while weakening the first-order character of the transition as x increases. A significant isothermal entropy change of 5.1 ( 4 ) J mo l – 1 K – 1 for Δ B = 2 T is found at 44 K in Eu 1.75 Yb 0.25 In , demonstrating that the giant magnetocaloric effect of Eu 2 In can be tuned to lower temperatures by Yb substitutions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Theoretical investigation of surface chemical reactivity of compositionally disordered multi-metallic alloys

Alloys are widely used in both academic and industrial research as heterogeneous catalysts for accelerating technologically important reactions ranging from hydrocarbon reforming, selective hydrogenation, to electrochemical reactions involving hydrogen and oxygen that are fundamental and indispensable to a sustainable future energy infrastructure. Most basic catalysis research has been limited to combinations of two elements that are usually ordered in some manner at the nanoscale because doing so allows observable results to be easily rationalized. It however leaves a huge material space consisting of multi metallic, compositionally disordered alloys unexplored. This research demonstrates a viable theoretical approach combining different types of first principles calculations (including Green’s function-based Korringa–Kohn–Rostoker coherent potential approximation (KKR–CPA) and planewave-based density functional theory) to enable the surface chemical reactivity of compositionally complex alloy surfaces to be investigated. We demonstrate the approach by investigating hydrogen adsorption and evolution on two types of compositionally disordered alloys: a quaternary CoCrFeNi high-entropy alloy, and binary Pt-3d base metal alloys.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Evaluation of high-temperature properties of binary, eutectic Ni-X alloys (X = B, Ca, Ce, La, Y, Zr)

We investigated the high-temperature behavior of binary eutectic nickel-base alloys with low-solubility elements, towards developing alloys with new combinations of structural and functional (e.g. thermal or corrosion) properties. Based on thermodynamic screening, we cast binary near-eutectic Ni-B, Ni-Ca, Ni-Ce, Ni-La, Ni-Y, and Ni-Zr alloys and characterized their microstructure and high-temperature properties. All alloys (except Ni-Ca) contain a fine eutectic microstructure, containing submicron alternating lamellae of γ-Ni and continuous intermetallic phases. The eutectic microstructure did not significantly coarsen at 700°C (except for Ni-B), but measurably coarsened in all alloys at 900°C, most quickly in Ni-B, followed by Ni-Ce, Ni-La, Ni-Y, and Ni-Zr. Coarsening in these alloys occurs by fault migration; diffusion within the intermetallic, and its interfacial energy, likely influence coarsening kinetics. At room-temperature, all alloys possess negligible ductility due to the continuous, brittle intermetallic. At 700 and 900°C, the Ni-Zr alloy showed the best combination of strength and ductility. Oxidation experiments at 900°C on Ni-Zr and Ni-Ce revealed heavy internal oxidation of the continuous intermetallic phase. Overall, the Ni-Zr alloy showed good high-temperature coarsening resistance and strength, though ductility and oxidation resistance require improvement for use in structural applications. Possible routes for improving alloy properties via additive manufacturing are discussed.

Ekaputra, Clement [ORNL] (ORCID:0000000307166479)↗