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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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76 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Selective depolymerization of polyolefin copolymer via high-entropy alloy catalyzed pyrolysis in a double-layered ultrafast heating reactor

Chemical recycling of polyolefin copolymers (PCPs) is essential for mitigating plastic pollution, yet achieving selective depolymerization into C 2 –C 3 monomers at high productivity remains challenging. Here, we introduce a double-layered ultrafast heating reactor (DUHR) that integrates Joule heating with a high-entropy alloy (HEA) catalyst composed of near-equimolar Co, Cu, Fe, Mn, and Ni. We achieve highly selective depolymerization of PCP into ethylene and propylene, yielding a 70% non-condensable fraction. Significant aromatic fractions form via radical-driven pathways—a behavior distinct from conventional externally heated pyrolyzers—highlighting DUHR's ability to generate reactive species and modulate reaction pathways. Furthermore, these findings demonstrate an effective strategy for chemical recycling of real-world plastic waste for polyolefin circularity and sustainable resource recovery.

Chemical recycling↗
DOE OSTI2026

Atomistic Mechanisms of Stress-Dependent Molten Salt Corrosion in NiCr Alloys

Ni-based structural alloys in molten salt environments often experience simultaneous mechanical loading and corrosive attack, yet the mechanisms governing stress-corrosion interactions remain unclear. Prior studies largely emphasize tensile stress, while the role of compressive stress has received limited attention. Here, reactive molecular dynamics simulations are used to investigate the coupled effects of applied strain and corrosion in Ni 0.75 Cr 0.25 exposed to molten FLiNaK at 800 °C. A Σ5(210) grain boundary model is subjected to tensile (+4%) to compressive (−4%) uniaxial strains, and corrosion behavior is evaluated through fluorine adsorption, charge redistribution, and grain boundary evolution. Tensile strain accelerates intergranular corrosion susceptibility by reducing local atomic packing through elastic dilation and increasing excess free volume at the grain boundary, which enhances atomic mobility and salt infiltration. In contrast, compressive strain can suppress corrosion by promoting the formation of a ridge-like surface layer along the grain boundary, limiting salt access to the underlying alloy. These results provide atomistic insight into how stress states influence grain boundary corrosion in molten salts.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Chemomechanics in alloy phase stability

We describe a first-principles statistical mechanics method to calculate the free energies of crystalline alloys that depend on temperature, composition, and strain. The approach relies on an extension of the alloy cluster expansion to include an explicit dependence on homogeneous strain in addition to site occupation variables that track the degree of chemical ordering. The method is applied to the Si-Ge binary alloy and is used to calculate free energies that describe phase stability under arbitrary epitaxial constraints. We find that while the incoherent phase diagram (in which coexisting phases are not affected by coherency constraints) hosts a miscibility gap, coherent phase equilibrium predicts ordering and negative enthalpies of mixing. Instead of chemical instability, the chemomechanical free energy exhibits instabilities along directions that couple the composition of the alloy with a volumetric strain order parameter. Furthermore, this has fundamental implications for phase field models of spinodal decomposition as it indicates the importance of gradient energy coefficients that couple gradients in composition with gradients in strain.

Materials Science↗
DOE OSTI2026

Unlocking superplasticity in medium and high-entropy alloys

Superplasticity, the capacity of materials to sustain extraordinary tensile elongations at elevated temperatures, underpins a range of advanced metal-forming technologies. Conventionally, it is achieved in fine-grained alloys where deformation is dominated by grain-boundary sliding, accommodated by diffusion and dislocation activity. The advent of medium- and high-entropy alloys (M/HEAs), with their high chemical complexity and unconventional phase stability, offers new pathways to superplastic behavior beyond traditional alloy systems. Although investigated only recently, several M/HEAs already exhibit elongations that rival or exceed those of classical superplastic materials, particularly when ultrafine or metastable microstructures are engineered. Here, we review progress in understanding superplastic deformation in M/HEAs, emphasizing the interplay among composition, initial microstructure, thermomechanical processing, and microstructural evolution during high-temperature deformation. We discuss approaches to generating the fine-grained structures necessary for grain-boundary sliding, including severe plastic deformation and tailored heat treatments. We further highlight dynamic phenomena such as phase transformations, evolving grain-boundary chemistry, and deformation-induced grain refinement that can enhance plasticity in these systems. These mechanisms often shift the balance of deformation processes, enabling large elongations even outside classical criteria. Finally, we outline key challenges for application, including cost, scalability, recyclability, and microstructural stability.

klenam, Desmond [University of the Witwatersrand, ↗
DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗
DOE OSTI2025

Divergent evolution of slip banding in CrCoNi alloys

Abstract Metallic materials under high stress often exhibit deformation localization, manifesting as slip banding. Over seven decades ago, Frank and Read introduced the well-known model of dislocation multiplication at a source, explaining slip band formation. Here, we reveal two distinct types of slip bands (confined and extended) in compressed CrCoNi alloys through multi-scale testing and modeling from microscopic to atomic scales. The confined slip band, characterized by a thin glide zone, arises from the conventional process of repetitive full dislocation emissions at Frank–Read source. Contrary to the classical model, the extended band stems from slip-induced deactivation of dislocation sources, followed by consequent generation of new sources on adjacent planes, leading to rapid band thickening. Our findings provide insights into atomic-scale collective dislocation motion and microscopic deformation instability in advanced structural materials.

Science & Technology - Other Topics↗
DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗
DOE OSTI2026

Combinatorial deposition of Au–Bi alloys via high-rate magnetron sputtering

Gold–bismuth (Au–Bi) alloy films are promising candidate materials for inertial confinement fusion (ICF) hohlraums due to their high laser-to-x-ray conversion efficiency, particularly compared with Au, Ta–Au, and Bi hohlraums. However, the fabrication of uniform and dense Au–Bi alloy films remains a challenge. Here, we use a combination of Monte-Carlo modeling and experiments to demonstrate that the microstructure and properties of Au–Bi alloy films can be greatly improved when direct-current magnetron sputtering with high deposition rates of ⩾5 μm h -1 is used. Resultant films are ~90% of their maximum theoretical densities and have low O content of <1 at.%. Films with Bi content above 40 at. % exhibit high electrical resistivity >100μΩ cm, making them suitable for both magnetized and non-magnetized ICF schemes.

Materials science↗
DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2026

Microstructural Engineering of Cu-Rich Nanoprecipitate formation in NiCoFeCrCu0.12 High-Entropy Alloy via Severe Plastic Deformation for Enhanced Irradiation Tolerance

This study demonstrates a defect-engineering approach for controlling Cu-rich precipitates in FeNiCrCoCu0.2 high-entropy alloys (Cu-HEAs), delivering a novel pathway for next-generation nuclear reactor materials with superior irradiation resistance. This work establishes that severe plastic deformation (SPD) processing via Shear Assisted Processing and Extrusion (ShAPE) and Friction Stir Layer Deposition (FSLD) creates dense dislocation networks and subgrain boundaries that fundamentally alter precipitation behavior under identical thermal treatments. Atom probe tomography (APT) indicates that SPD produces a metastable, atomically homogeneous solid solution that, upon moderate heat treatment (500°C/10 hour), develops remarkedly stronger Cu clustering than the as-cast counterpart. High-temperature exposure (800°C/100 h) produces near-pure Cu precipitates (~90 at% Cu) with significantly enhanced defect-sink efficacy in SPD-processed alloys: precipitate sizes of 50-60 nm and number densities of 2.7-3.8 × 10¹7 m?³, compared to 89 nm and 0.44 × 10¹7 m?³ in as-cast materials. Collectively, the findings establish defect-mediated precipitation control as a scalable, high-impact route to tailor sink density and distribution in HEAs, enabling microstructures optimized for irradiation tolerance and mechanical robustness in nuclear reactor environments.

Meher, Subhashish↗
DOE OSTI2025

Bi‐Continuous W‐Rich Refractory High Entropy Alloy‐Cu Composite: Toward Material Innovation of Nuclear Reactor Coolant System

Abstract Refractory high‐entropy alloys (RHEAs) are considered promising candidate materials for next‐generation nuclear reactors due to their superior mechanical strength, irradiation resistance, and thermal stability at high temperatures. However, the significant positive heat of mixing between refractory alloying elements and Cu, commonly used in cooling systems, poses challenges in forming composite structures. This study addresses the issue using a liquid metal dealloying (LMD) process. A precursor alloy (WTaVTi) with a directional dendrite‐interdendrite structure is fabricated and reacted with molten Cu at 1200 °C for 96 h. This approach produced a RHEA‐Cu composite with a stable interface between RHEA (W 31.5 Ta 30.9 V 21.4 Ti 14.3 ) and Cu, featuring a spontaneously formed W‐rich interlayer that enhances interfacial bonding. The composite showed excellent irradiation resistance, with 30% less swelling under α‐ion irradiation than pure W. It also exhibited low thermal conductivity at room temperature, but reached ≈120 W m −1 ·K −1 at ≈650 °C, surpassing pure W. This temperature‐dependent rise in κ, with a positive gradient of +0.075 W m −1 ·K − 2 , is attributed to decreasing diffuse mismatch at elevated temperatures. The large‐scale reaction and stable microstructure achieved through LMD process highlight its industrial potential. This work offers a strategy for developing high‐performance materials by combining RHEA's radiation resistance with Cu's thermal conductivity for extreme environments.

Chemistry↗
DOE OSTI2025

In‐Plane Anisotropy in van der Waals NiTeSe Ternary Alloy

Abstract The anisotropic properties of materials profoundly influence their electronic, magnetic, optical, and mechanical behaviors and are critical for a wide range of applications. In this study, the anisotropic characteristics of Ni‐based van der Waals materials, specifically NiTe 2 and its alloy NiTeSe, utilizing a combination of comprehensive scanning tunneling microscopy (STM), angle‐resolved photoemission spectroscopy (ARPES), and density functional theory (DFT) calculations, are explored. Unlike 1T‐NiTe 2 , which exhibits trigonal in‐plane symmetry, the substitution of Te with Se in NiTe 2 (resulting in the NiTeSe alloy) induces a pronounced in‐plane anisotropy. This anisotropy is clear in the STM topographs, which reveal a distinct linear order of charge distribution. Corroborating these observations, ARPES measurements and DFT calculations reveal an anisotropic Fermi surface centered at the point, which is notably elongated along the k y direction, leading to directional variations in in‐plane carrier velocities. Consequently, the Fermi velocity is highest along the k x direction where the linear charge distribution aligns in real space and is lowest along the k y direction. These findings offer valuable insights into the tunability of anisotropic properties in ternary transition metal dichalcogenide systems, highlighting their potential applications in the development of anisotropic electronic and optoelectronic devices.

Chemistry↗
DOE OSTI2026

Electronegativity-Guided Site Differentiation in High-Entropy Alloy for pH-universal Hydrogen Evolution Reactions

Enhancing the intrinsic activity of transition metal catalysts for the hydrogen evolution reaction (HER) remains a critical challenge in sustainable energy conversion. Herein, we report an electronegativity-guided site differentiation strategy in a single-phase CoNiCuMoW high-entropy alloy (HEA) via electrodeposition by incorporating high-electronegativity 4d/5d orbital transition metals (Mo, W) into the face-centered cubic (fcc) matrix (CoNiCu). The as-synthesized HEA demonstrates exceptional HER performance in all pH conditions, delivering an outstanding overpotential of 65 mV (alkaline), 28 mV (acidic), and 155 mV (neutral) at a current density of 100 mA cm−2, showing performance comparable to commercial Pt/C and has excellent long-term stability at high current density (1 A cm−2, 1000 h). X-Ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations reveal that the incorporation of Mo/W simultaneously alters the local coordination environment and induces element-dependent charge redistribution, accompanied by a system-level d-band center downshift, thereby optimizing the hydrogen binding strength across multimetallic sites. Meanwhile, oxophilic Mo/W sites lower the water dissociation energy barrier. These synergistic effects collectively enable efficient and durable pH-universal HER performance.

Wu, Yutong↗
DOE OSTI2026

Effect of Oxidizing Impurities on the Corrosion Behavior of Structural Alloys Exposed to MgCl 2 Molten Salt Vapor

The corrosion behavior of structural alloys SS304 and IN617 when exposed to vapors of MgCl 2 molten salt at 764 °C for 100 h in an argon atmosphere was investigated as a function of the level of oxidizing impurities (e.g., NaOH) present in the salt. Increasing the concentration of oxidizing impurities in the salt caused an increase in corrosion in both structural alloys, as measured by Cr depletion layers. A poorly adhered oxide scale layer containing Mg, Al, and Cr was observed on the surface of the metals, and local attack was observed as well. The Ni-based IN617 showed lower Cr depletion and less scale formation than the Fe-based SS304. These results demonstrate that oxidizing impurities in the molten salt will impact the vapor phase corrosion for structural members exposed to the molten salt headspace. This finding is highly relevant for understanding the effects of the vapor phase in molten salt applications, including molten salt thermal energy storage, molten salt nuclear reactors, and molten salt electrochemistry.

25 ENERGY STORAGE↗
DOE OSTI2026

Atomic faulting drives exceptional toughness in low thermal expansion chromium alloys

Endowing functional properties with mechanical responses in traditional metals has been a frontier topic, akin to transforming base metal into gold. Chromium and its alloys, with their functional deficiencies and limited ductility, serve as typical examples. Herein, we report a Cr96Fe4Ge1.3B1 alloy that unifies low thermal expansion (LTE, αl = 1.79 × 10-6 K-1, 200 − 315 K) with exceptional toughness (240.2 J·cm-3). The enhancement in mechanical responses is primarily attributed to layered Cr2B intermetallic precipitates, which ameliorate interfacial cohesion and simultaneously refine the grain structure. The weakened interlayer interactions within the Cr-B layers facilitate the nucleation and movement of numerous tiny stacking faults in precipitates, efficiently alleviating strain energy and resulting in marked work-hardening ability. Additionally, antiferromagnetic fluctuations in the BCC matrix contribute to the unique LTE behavior. This paves the way for the design of high-performance alloys featuring layered-symmetry precipitates.

Yu, Chengyi [University of Science and Technology ↗
DOE OSTI2025

Electronic structure prediction of medium and high entropy alloys across composition space

We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

materials science↗
DOE OSTI2025

Grain boundary segregation and chemical ordering in CoCrFeMnNi multi-principal element alloy

Owing to their far-from-dilute compositions, multi-principal element alloys (MPEAs) can exhibit unique combinations of engineering properties. As nearly all MPEAs are polycrystalline aggregates, it is necessary to understand the interactions of various elemental species with grain boundaries (GBs). This is of particular importance in extreme environments, such as radiation and elevated temperatures, where such interactions have implications on the properties of MPEAs. Herein, we employ atomistic simulations to generate a series of [001] asymmetric tilt GBs in a model CoCrFeMnNi MPEA and quantify solute interactions and segregation to these boundaries. We employ the Warren-Cowley order parameters to investigate the interplay between GB segregation and chemical short-range order (SRO). At temperatures above 800 K, simulation results reveal the segregation of Cr and Mn to CoCrFeMnNi GBs and show weak dependence of boundary solute excess on GB geometry, at least for the boundaries explored in this work. At temperatures in the range of 673–800 K, formation of domains rich in Cr is observed at GBs in agreement with experimental observations. Quantitative analysis shows that solute excess of various alloying elements decreases rapidly with the increase in temperature in the range of 1000–1200 K. Furthermore, we show that GB regions exhibit SRO characteristics that are distinct from the bulk crystals, leading to spatial variations in SRO. In broad terms, our study highlights the need to account for GB interactions with alloying elements when designing advanced MPEAs with novel chemistries.

Wang, Yitao [Lehigh University, Bethlehem, PA (Uni↗