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Results for “compositionally complex alloys”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid grain refinement and compositional homogenization in a cast binary Cu50Ni alloy achieved by friction stir processing

Friction stir processing (FSP) has been increasingly adopted for joining and processing materials in automotive, aerospace and industrial construction. During FSP a dynamic competition between high-speed shear deformation and deformation-induced heating brings about a complex competition between multiple dynamic microstructural evolution mechanisms making it difficult to predict the microstructural evolution pathway. Hence, improved understanding of microstructural evolution mechanisms during FSP can be beneficial for continued growth in the adoption of FSP for demanding applications of future. Towards this goal, this study uses a model binary Cu – 50 at.% Ni alloy to clarify the effect of single and double pass FSP on the microstructural evolution of a coarse grained and compositionally heterogeneous cast microstructure. High energy synchrotron X-ray diffraction, electron backscatter diffraction, and nanoindentation are used to clarify the microstructural evolution due to FSP. The process of compositional homogenization of as-cast segregations is studied by energy dispersive spectroscopy and atom probe tomography. Our results show that a single fast FSP pass at 30 mm.s -1 produces a 100 µm deep layer of submicrometric and hall-petch hardened CuNi grains. The initial cast compositional heterogeneities in a micrometric scale is rapidly transformed to nano-sized domains, mainly confined at grain boundaries. Double pass FSP increases the penetration depth of the processed layer and leads to a 2.9 times grain growth relative to single pass FSP. Grain fragmentation, discontinuous dynamic recrystallization, grain growth, and twinning mechanisms are discussed. In conclusion, these results highlight the value of FSP for ultrafast grain refinement and compositional homogenization of cast alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2026

First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs

Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo 6+ , V 5+ ) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.

organic↗
NASA NTRS1973

Examination of oxide scales in the SEM using backscattered electron images

The complementary use of the scanning electron microscope in the backscattered electron mode with the more usual secondary electron mode results in a significant increase in the versatility of the instrument, since regions of different chemical composition can be readily detected, and their morphology examined. The use of this technique to examine complex oxide scales formed on heat-resistant alloys is described, and in particular the location of thoria particles in the scale formed on a Ni-20 wt pct Cr-2.3 wt pct ThO2 alloy, and the examination of the behavior of yttrium during the high-temperature oxidation of a Co-Cr-Al-Y alloy are discussed.

Price, C. W.↗
DOE OSTI2023

Surface Film Formation on Al-V Alloys with Far-From-Equilibrium Microstructure

Nanocrystalline supersaturated Al-V alloys produced by high-energy ball milling have been reported to exhibit enhanced corrosion resistance and mechanical properties compared to commercial Al alloys. Corrosion of passive alloys such as Al-V alloy relies on the characteristics of the surface film, which is studied using scanning/transmission electron microscopy and time-of-flight secondary ion mass spectrometry. The effect of microstructure and composition on the surface film has been investigated after different immersion periods (30 min, 2 h, and 1 day) in 0.1 M NaCl. The surface film was complex and composed of oxidized Al and V. The heterogeneous surface film was observed due to the presence of secondary phases and initiation of localized corrosion. The void formation was observed beneath the surface film that would potentially cause pitting corrosion. The generation of nano-sized voids was dependent on grain orientation. Compared to pure Al, the chloride penetration is suppressed in Al-V alloys. The effect of composition and microstructure on surface film formation and attendant corrosion behavior is discussed herein.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Heats of Segregation of BCC Metals Using Ab Initio and Quantum Approximate Methods

Many multicomponent alloys exhibit surface segregation, in which the composition at or near a surface may be substantially different from that of the bulk. A number of phenomenological explanations for this tendency have been suggested, involving, among other things, differences among the components' surface energies, molar volumes, and heats of solution. From a theoretical standpoint, the complexity of the problem has precluded a simple, unified explanation, thus preventing the development of computational tools that would enable the identification of the driving mechanisms for segregation. In that context, we investigate the problem of surface segregation in a variety of bcc metal alloys by computing dilute-limit heats of segregation using both the quantum-approximate energy method of Bozzolo, Ferrante and Smith (BFS), and all-electron density functional theory. In addition, the composition dependence of the heats of segregation is investigated using a BFS-based Monte Carlo procedure, and, for selected cases of interest, density functional calculations. Results are discussed in the context of a simple picture that describes segregation behavior as the result of a competition between size mismatch and alloying effects

Good, Brian↗
DOE OSTI2024

Lattice Parameter Evolution during the β-to-α and β-to-ω Transformations of Iron- and Aluminum-Modified Ti-11Cr(at.%)

β-titanium (β-Ti) alloys are useful in diverse industries because their mechanical properties can be tuned by transforming the metastable β phase into other metastable and stable phases. Relationships between lattice parameter and β-Ti alloy concentrations have been explored, but the lattice parameter evolution during β-phase transformations is not well understood. In this work, the β-Ti alloys, Ti-11Cr, Ti-11Cr-0.85Fe, Ti-11Cr-5.3Al, and Ti-11Cr-0.85Fe-5.3Al (all in at.%), underwent a 400 °C aging treatment for up to 12 h to induce the β-to-ω and β-to-α phase transformations. Phase identification and lattice parameters were measured in situ using high-temperature X-ray diffraction. Phase compositions were measured ex situ using atom probe tomography. During the phase transformations, Cr and Fe diffused from the ω and α phases into the β matrix, and the β-phase lattice parameter exhibited a corresponding decrease. The decrease in β-phase lattice parameter affected the α- and ω-phase lattice parameters. The α phase in the Fe-free alloys exhibited α-phase c/a ratios close to those of pure Ti. A larger β-phase composition change in Ti-11Cr resulted in larger ω-phase lattice parameter changes than that for Ti-11Cr-0.85Fe. This work illuminates the complex relationship between diffusion, composition, and structure for these diffusive/displacive transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of DED and LPBF Fe-based Alloys Process Application Envelopes based on Performance, Process Economics, Supply Chain Risks, and Reactor-specific Targeted Components

The U.S. Department of Energy (DOE), Office of Nuclear Energy (NE), Advanced Materials and Manufacturing Technologies (AMMT) program aims to develop extreme-environment materials solutions for use in the deployment of advanced nuclear reactors and the sustainment of the current fleet. To achieve this objective, a combination of experiment, a computational tool, and machine learning (ML) for the design of materials is adopted for the maturation of materials for nuclear technology. Through advanced manufacturing techniques such as laser powder bed fusion (LPBF) and laser powder direct energy deposition (LP-DED), components with complex geometries can be fabricated with reduced time and effort. Such advanced manufacturing methods can also provide the opportunity to improve materials performance through optimized microstructures and mechanical properties. However, existing engineering alloys are not always well suited for fabrication with additive manufacturing (AM), as their compositions have been tuned to optimize fabrication via conventional methods. Thus, similar alloys with modified compositions that are better suited for AM can be studied for improved performance. Over the past three years, the AMMT teams from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL) studied various known Fe-based alloys by evaluating their initial printability using LPBF, and an AMMT-developed down-selection and decision matrix reduced the number of alloys to be studied from six to three in fiscal year (FY) 2024. Additionally, in FY 2024, for parallel evaluation, these three alloys were studied using LPDED. While LPBF is better for small- to medium-sized components with high detail and internal features, LP-DED combines a material feed system to place the powder onto the exact spot where the laser will melt the material. This AM method can be easily scaled to extremely large components and provides high build rate speeds compared to those of conventional LPBF systems. Additionally, DED is a better choice for complex geometries and compositional gradients.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

High-throughput synthesis of high-entropy alloys via parallelized electric field assisted sintering

Materials discovery and design is an expensive and time-consuming process, though necessary to advance many engineering fields. In this work, a novel tooling design is utilized in conjunction with electric field assisted sintering (EFAS) to effectively create a new high-throughput synthesis technique: parallelized EFAS. Through this technique, a wide range of material compositions and geometries can be synthesized in parallel as isolated samples or as part of contiguous arrays. Multiple tooling designs are explored to examine both the flexibility and limitations of the technique. A series of increasing complex alloys is produced simultaneously using in situ alloying, beginning with pure Ni and adding equimolar constituents up to the septenary high-entropy alloy AlCoCrCuFeMnNi. Microstructural characterization reveals each sample is effectively fully dense and chemically homogenous while exhibiting phases in agreement with CALPHAD predictions. Scalability of parallelized EFAS is then experimentally demonstrated and the implications for materials discovery and automation are discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Additive manufacturing of amorphous metal soft magnetic composites

Soft magnet alloys are used as magnetic cores for electric motors, transformers, wind turbines and other power generation systems. Soft magnetic cores are expensive and time consuming to manufacture in the complex shapes required for next-generation devices using conventional press and sinter powder metallurgy. The objective of this effort is to additively manufacture high performance soft magnets, with reduced cost and reduced material waste and 10x lower energy (core) loss at the high operating frequencies of many electric machines. Laser powder bed fusion additive manufacturing is used as the fabrication method. Electrical steel and amorphous alloys atomized powders are used as a feedstock materials. Magnetic cores are printed in topology optimized structures such as the Hilbert curve because this has been shown to reduce energy losses by minimizing the eddy currents that circulate within the magnet at high frequencies. In our project, we succeed in printing FeSi 3.5wt% and FeSi 6.5wt% electrical steels, and iron-based soft magnetic amorphous alloys in the shape of Hilbert and Peano curve topology optimized structures. We found that the Peano curve has a higher cut-off frequency than the Hilbert curve, and that amorphous alloys have high cut-off frequencies and higher mechanical hardness than electrical steels. Processing conditions such as laser power, scan speed, and hatching pattern were optimized to achieve high density prints, and optimize magnetic performance. We find that the printing of amorphous alloy soft magnetic cores may be technoeconomically feasible for large scale applications such as transformers, for which supply chain issues and the labor costs of manual fabrication of magnetic cores is prohibitive in some cases.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Synthesis of monodisperse high entropy alloy nanocatalysts from core@shell nanoparticles

We report that high-entropy alloy (HEA) nanoparticles (NPs) hold great promise in electrocatalysis because of their nearly unlimited compositions, tailorable active sites, and high durability. However, the synthesis of these compositionally complex structures as monodisperse NPs remains a challenge by colloidal routes because the different rates of metal precursor reduction lead to phase separation. Here, we report the conversion of core@shell NPs into HEA NPs through annealing, with conservation of sample monodispersity. This potentially general route for high-quality HEA NPs was demonstrated by preparing PdCu@PtNiCo NPs via seed-mediated co-reduction, wherein Pt, Ni, and Co were co-deposited on PdCu seeds in solution. These multimetallic NPs were then converted to single-crystalline and single-phase PdCuPtNiCo NPs through annealing. On account of their small particle size, highly dispersed Pt/Pd content, and low elemental diffusivity, these HEA NPs were found to be a highly efficient and durable catalyst for the oxygen reduction reaction. They were also highly selective for the four-electron transfer pathway. We expect that this new synthetic strategy will facilitate the synthesis of new HEA NPs for catalysis and other applications.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Fabrication of tungsten wire reinforced nickel-base alloy composites

Fabrication methods for tungsten fiber reinforced nickel-base superalloy composites were investigated. Three matrix alloys in pre-alloyed powder or rolled sheet form were evaluated in terms of fabricability into composite monotape and multi-ply forms. The utility of monotapes for fabricating more complex shapes was demonstrated. Preliminary 1093C (2000F) stress rupture tests indicated that efficient utilization of fiber strength was achieved in composites fabricated by diffusion bonding processes. The fabrication of thermal fatigue specimens is also described.

Brentnall, W. D.↗
NASA NTRS1978

Trends in aerospace structures

Recent developments indicate that there may soon be a revolution in aerospace structures. Increases in allowable operational stress levels, utilization of high-strength, high-toughness materials, and new structural concepts will highlight this advancement. Improved titanium and aluminum alloys and high-modulus, high-strength advanced composites, with higher specific properties than aluminum and high-strength nickel alloys, are expected to be the principal materials. Significant advances in computer technology will cause major changes in the preliminary design cycle and permit solutions of otherwise too-complex interactive structural problems and thus the development of vehicles and components of higher performance. The energy crisis will have an impact on material costs and choices and will spur the development of more weight-efficient structures. There will also be significant spinoffs of aerospace structures technology, particularly in composites and design/analysis software.

Card, M. F.↗
DOE OSTI2023

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Resolving local ordering and structure in Mn x Ge 1- x Te alloys through thermodynamic ensembles of pair distribution functions

Characterizing local bonding environments in complex materials is essential for understanding and optimizing their properties. Equally as important is the ability to predict local motifs as a function of synthesis conditions, enhancing chemists’ ability to design properties into materials. In this study, we present an approach to leverage statistical mechanics to generate temperature- and energy-informed ensemble averaged pair distribution functions (PDFs). This method, which we have named Thermodynamic Ensemble Averages of PDFs for Ordering and Transformations (TEAPOT), utilizes density functional theory (DFT) to relax supercells while incorporating energetic penalties for local order, enabling accurate and computationally efficient analysis of local structure. We apply this method to the neutron PDF measurements of the pseudobinary MnTe–GeTe (MGT) alloy, demonstrating its capability to resolve complex local distortions and chemical ordering. Our results reveal detailed insights into phase transformations and local distortions driven by Mn substitution. For compositions that globally present as rock salt, our analysis reveals that Ge coordination geometry is heavily impacted by synthesis temperature. We propose that high temperature synthesis conditions promote a lowered Ge polyhedra distortion, promoting high charge carrier mobility due to the alignment of local and global structure. Incorporating statistical mechanics and computation into experimental analysis thus guides synthesis of tailored local structure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-throughput, combinatorial synthesis of multimetallic nanoclusters

Significance Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Spark plasma sintered, MoNbTi-based multi-principal element alloys with Cr, V, and Zr

MoNbTi, MoNbTiZr, CrMoNbTiZr, and MoNbTiVZr multi-principal element alloys (MPEAs) were fabricated via spark plasma sintering (SPS) and investigated for use in high-strength applications. The fabrication method by SPS and powder metallurgy differs from those presented in the prior literature, where most MPEAs are fabricated using arc melting (AM) methods. Here cryogenic milling was used to maximize potential defect sinks (grain boundaries) for radiation resistance and to increase ultimate tensile strength through the Hall-Petch effect. SPS was chosen for consolidation in order to maintain a fine-grained structure during densification. Each alloy was characterized using x-ray diffraction and scanning electron microscopy for phase identification and compositional homogeneity. The base ternary alloy MoNbTi presented a predominantly single BCC system with minor cubic phases. The introduction of additional alloying elements—Zr, V, and Cr—heightened the phase complexity and increased the fractions of a secondary BCC phase and an HCP phase from Zr. The addition of Cr induced a larger fraction of the Laves phase to form. The addition of V caused the precipitation of small Mo inclusions. Thermodynamic analysis was performed to understand the separation of phases in each alloy. Discrepancies among the phase predictions generated by thermodynamic models, phases previously presented in the literature, and the characterization results suggest that MPEA fabrication methods, especially solid-state methods, require significant investigation to ensure that alloys can remain stable throughout their anticipated service lifetimes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗