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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy

Traditional metrics measuring transportation and energy outcomes can be augmented to better represent impacts on people's lives and systems-level performance. In this context, this study introduces two novel metrics: road capacity (as number of people traveling and accessing services) and energy intensity (as energy use for people traveling and accessing services). Current national-level distributions of available data in the United States for factors contributing to the two new integrated metrics are used as context to evaluate potential outcomes. These factors include vehicle occupancy, mode share, fuel economy, and trip distance. Variations in input values provide insights on how these factors shape efficiencies in road capacity and energy intensity. Parametric sensitivity analysis indicates that the impact of each input depends upon the metric being evaluated. For the human-centered road capacity mobility metric, increasing vehicle occupancy has the largest effect – twice that of increasing mode share for bike, walk, and transit. For the energy intensity mobility metric, the effect of improving fuel economy is the largest. However, when the focus is on accessibility (instead of mobility), for both metrics the effect of lowering average trip distance is the largest. Additionally, a novel interactive tool to visualize the results for various parameter combinations makes the metrics practitioner ready. The findings suggest that the diffusion of new human-centric metrics that benchmark outcomes associated with road capacity and energy may be significant in motivating new sustainable transportation investments and efficient utilization of infrastructure, mobility assets, and services.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Challenge Problem 1: Preliminary Results of the Direct Numerical Simulation of Transient Flows

This report presents the first direct numerical simulations (DNS) of transient mixed convection in an idealized downcomer-like channel (Challenge Problem 1, Phase II). Using the GPU-accelerated NekRS solver, we modeled a sudden decay in driving pressure, mimicking loss-of-flow events, and tracked the resulting evolution of Reynolds number, boundary-layer structure, turbulence statistics, and heat-transfer metrics. Key findings include the systematic thickening and eventual asymmetry of velocity and thermal boundary layers under buoyant deceleration; minimal “memory” lag in Reynolds shear stress and TKE profiles when sampled at matching Re, yet clear shifts of peak locations toward the cooled wall; overshoots in transient eddy-viscosity and eddy-diffusivity (and corresponding sub-unity turbulent Prandtl numbers) on the cooled side; and a pronounced transient Nusselt-number enhancement driven by wall-temperature inertia and residual eddy mixing. These effects combined to offer a temporary cooling margin above steady-state predictions during reactor LOF transients. Future work will extend this work to a more complex “Case II” geometry (90° turn + lower plenum) and generate multi-Re/Pr datasets for data-driven turbulence closures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Unconventional Charge Transport in MgCr 2 O 4 and Implications for Battery Intercalation Hosts

Ion transport in solid-state cathode materials prescribes a fundamental limit to the rates batteries can operate; therefore, an accurate understanding of ion transport is a critical missing piece to enable new battery technologies, such as magnesium batteries. Based on our conventional understanding of lithium-ion materials, MgCr 2 O 4 is a promising magnesium-ion cathode material given its high capacity, high voltage against an Mg anode, and acceptable computed diffusion barriers. Electrochemical examinations of MgCr 2 O 4 , however, reveal significant energetic limitations. Motivated by these disparate observations; herein, we examine long-range ion transport by electrically polarizing dense pellets of MgCr 2 O 4 . Our conventional understanding of ion transport in battery cathode materials, e.g., Nernst-Einstein conduction, cannot explain the measured response since it neglects frictional interactions between mobile species and their nonideal free energies. In this work, we propose an extended theory that incorporates these interactions and reduces to the Nernst-Einstein conduction under dilute conditions. This theory describes the measured response, and we report the first study of long-range ion transport behavior in MgCr 2 O 4 . We conclusively show that the Mg chemical diffusivity is comparable to lithium-ion electrode materials, whereas the total conductivity is rate-limiting. Given these differences, energy storage in MgCr 2 O 4 is limited by particle-scale voltage drops, unlike lithium-ion particles that are limited by concentration gradients. Future materials design efforts should consider the interspecies interactions described in this extended theory, particularly with respect to multivalent-ion systems and their resultant effects on continuum transport properties.

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

Results of Oklo Diffusion Couple Experiments

Through the Gateway for Accelerated Innovation in Nuclear (GAIN) initiative, the U.S. Department of Energy funded a GAIN Nuclear Energy Voucher to perform out-of-pile experiments related to the fuel design for Oklo Inc., headquartered in Santa Clara, CA. GAIN’s goal is to accelerate the innovation and application of advanced nuclear technologies, and voucher recipients do not receive direct financial awards but are provided access to national laboratory capabilities at no cost. This report documents the results of diffusion couple experiments performed as part of the GAIN-funded project for Oklo. This work addresses the specific problem of filling the gap in understanding of the fuel-cladding chemical interaction (FCCI) that result from the past focus on a particular fuel design. These knowledge and mechanistic modeling gaps make it difficult to extrapolate from prior fuel performance data to advance an economically competitive fuel design. Specifically, the current state-of-the-art fuel performance modeling cannot separate the entangled effects of temperature ranges, burnups, materials, and geometries.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Combined Force-Frequency Sampling for Simulation of Systems Having Rugged Free Energy Landscapes.

An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.

Sevgen, Emre↗
DOE OSTI2020

Choosing the Best Modeling Platform for Radiological Risk Assessment Models - 20468

Radiological risk assessments, in the form of performance or safety assessments, are often required under regulations or guidance for remediation of contaminated land, decommissioning of contaminated buildings or structures, and radioactive waste disposal. These risk assessments are usually supported by fate and transport models that address decay and ingrowth of radionuclides, as well as their movement through engineered systems and the natural environment. These models are often projected thousands, or more, years into the future, largely because the radioactive species change through decay and ingrowth, and hence the magnitude of the radioactive effect changes with time. There are many computer codes that are available to address this type of modeling. They range from addressing specific pathways or processes such as infiltration of water, groundwater, surface water, air, biota, diffusion and advection of water and gases, to those that try to couple all processes together to evaluate the impact of fate and transport through the entire system to places in space and time to which access is assumed. These different types of codes are sometimes separated with the monikers process-level and systems-level codes, although it is often not clear that this separation does justice to the capabilities of the many codes that are available to evaluate fate and transport of radionuclides. The focus of this paper is the latter group of modeling codes. Several systems level modeling codes exist and are used. There are differences between these codes in terms of utility, flexibility, complexity and cost. The purpose of this paper is to compare a few of these codes in the context of work currently being performed by the International Atomic Energy Agency (IAEA) Modeling and Data for Radiological Impact Assessments (MODARIA) II Working Group 1 (WG1). The MODARIA II WG1's main focus is how stakeholder engaged decision analysis can, or should, be applied to radiological contamination problems so that better, longstanding, sustainable, solutions are reached. However, the WG1 also recognizes the potential impact of the modeling tools that are chosen to address radiological risk, which is often a primary objective of decision making for radiological problems. Other objectives might also be important, such as constraining costs, obtaining financing, minimizing impact on ecosystems, saving cultural resources, saving jobs, farmland, environmental justice, etc., in a full decision analysis for a given radiological contamination problem, but none of these other objectives have the same types of complex modeling needs as minimize radiological dose. Consequently, a further focus of WG1 is to evaluate the potential impacts on decision making of the choice of fate and transport, and risk assessment, modeling codes that are used to support decision making. The WG1 will produce a report at the end of 2020 that will focus on an approach to effective decision making and stakeholder engagement. The report will also consider the role that performance assessment modeling should play in the decision-making process, including the impact of the choice of modeling tools or computer codes on risk-informed decision making. Several sites around the World have been made available by Member States for these model comparisons, and several modeling tools have been considered. However, the focus of this paper is on two of the sites, one in Belgium and one in Ukraine, and on three of the tools: NORMALYSA (NORM And Legacy Site Assessment); GoldSim{sup C}, and AMBER{sup C}. The final report from this working group will also cover other modeling tools, including RESRAD, and PC-Cream{sup R}. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD

Here, the Deep Operator Network (DeepONet) is a neural network architecture used to approximate operators, including the solution operator of parametric PDEs. DeepONets have shown remarkable approximation ability. However, the performance of DeepONets deteriorates when the training data is polluted with noise, a scenario that occurs in practice. To handle noisy data, we propose a Bayesian DeepONet based on replica exchange Langevin diffusion (reLD). Replica exchange uses two particles. The first particle trains a DeepONet to exploit the loss landscape and make predictions. The other particle trains a different DeepONet to explore the loss landscape and escape local minima via swapping. Compared to DeepONets trained with state-of-the-art gradient-based algorithms (e.g., Adam), the proposed Bayesian DeepONet greatly improves the training convergence for noisy scenarios and accurately estimates the uncertainty. To further reduce the high computational cost of the reLD training of DeepONets, we propose (1) an accelerated training framework that exploits the DeepONet's architecture to reduce its computational cost up to 25% without compromising performance and (2) a transfer learning strategy that accelerates training DeepONets for PDEs with different parameter values. Finally, we illustrate the effectiveness of the proposed Bayesian DeepONet using four parametric PDE problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Diffusion and Solvation Dynamics of Ions in Water: Beyond the Brownian Approximation

The coupled dynamics of ions and water molecules in their first hydration shell impact a variety of processes including ion diffusion, selective ion transport in water-filled nanopores, and the kinetics of ion-pairing, ion adsorption, and metal-ligand binding reactions. In this work, we study these coupled dynamics for alkali metals (Li, Na, K, Rb, Cs), alkaline Earth metals (Mg, Ca, Sr, Ba), and chloride through the lens of their dependence on ion isotopic mass. Results are validated against previous measurements of the isotopic mass-dependence of ion diffusion coefficients in water and previous ab initio calculations of ion high-frequency dynamics in water. We find that the vibrational power spectra of ions in water consistently exhibit either two or three peaks, i.e., ions have several rattling frequencies within their solvations shells as previously reported for a subset of the species examined here. These frequencies have different sensititivies to isotopic mass that may serve as signatures of ion solvation processes (such as the tendency of ions to orient their first-shell water molecules) and that also may relate to Hofmeister-like effects including the relative affinity of different metals for ribonucleic acid (RNA).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimized In Situ Doping Strategy Stabling Single-Crystal Ultrahigh-Nickel Layered Cathode Materials

Single-crystal Ni-rich cathodes offer promising prospects in mitigating intergranular microcracks and side reaction issues commonly encountered in conventional polycrystalline cathodes. However, the utilization of micrometer-sized single-crystal particles has raised concerns about sluggish Li + diffusion kinetics and unfavorable structural degradation, particularly in high Ni content cathodes. Herein, we present an innovative in situ doping strategy to regulate the dominant growth of characteristic planes in the singlecrystal precursor, leading to enhanced mechanical properties and effectively tackling the challenges posed by ultrahigh-nickel layered cathodes. Compared with the traditional dry-doping method, our in situ doping approach possesses a more homogeneous and consistent modifying effect from the inside out, ensuring the uniform distribution of doping ions with large radius (Nb, Zr, W, etc). Further, this mitigates the generally unsatisfactory substitution effect, thereby minimizing undesirable coating layers induced by different solubilities during the calcination process. Additionally, the uniformly dispersed ions from this in situ doping are beneficial for alleviating the two-phase coexistence of H2/H3 and optimizing the Li + concentration gradient during cycling, thus inhibiting the formation of intragranular cracks and interfacial deterioration. Consequently, the in situ doped cathodes demonstrate exceptional cycle retention and rate performance under various harsh testing conditions. Our optimized in situ doping strategy not only expands the application prospects of elemental doping but also offers a promising research direction for developing high-energy-density single-crystal cathodes with extended lifetime.

25 ENERGY STORAGE↗
DOE OSTI2021

Time-dependent water vapor desorption isotherm model of hardened cement paste

Highlights: • Short-term and long-term desorption isotherms of hardened cement paste are reviewed. • Water content at RH depends on both thermodynamic and microstructural equilibria. • An extended GAB equation is proposed to fit the cavitation drop during desorption. • A dynamic time-dependent sorption isotherm model is shown and used for simulations. • Numerical results capture anomalous drying in the same RH as seen experimentally. This study aims to enhance moisture transport modeling by elucidating the so-called anomalous water diffusion in cementitious materials. Water desorption isotherms are measured from samples at various drying stages using techniques with different durations to quantify the impact of the drying duration on the water sorption ability. A single water sorption isotherm does not solely give a relationship between the water content and relative humidity but also represents the state of microstructure. The continuous evolution of a desorption isotherm due to drying-induced microstructural rearrangement is demonstrated. For numerical modeling, the microstructural alteration can be explicitly considered through a dynamic desorption isotherm model, which governs the local thermodynamic equilibrium at the capillary meniscus. This approach is implemented into a multiphase transport code, whose ability to predict drying is validated using literature data. Finally, the effect of prolonged drying on the colloidal nature of the calcium-silicate-hydrate gel is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Effective Fragment Molecular Orbital Method: Achieving High Scalability and Accuracy for Large Systems

The effective fragment molecular orbital (EFMO) method has been developed to predict the total energy of a very large molecular system accurately (with respect to the underlying quantum mechanical method) and efficiently by taking advantage of the locality of strong chemical interactions and employing a two-level hierarchical parallelism. The accuracy of the EFMO method is partly attributed to the accurate and robust intermolecular interaction prediction between distant fragments, in particular, the many-body polarization and dispersion effects, which require the generation of static and dynamic polarizability tensors by solving the coupled perturbed Hartree–Fock (CPHF) and time-dependent HF (TDHF) equations, respectively. Solving the CPHF and TDHF equations is the main EFMO computational bottleneck due to the inefficient (serial) and I/O-intensive implementation of the CPHF and TDHF solvers. In this work, the efficiency and scalability of the EFMO method are significantly improved with a new CPU memory-based implementation for solving the CPHF and TDHF equations that are parallelized by either message passing interface (MPI) or hybrid MPI/OpenMP. Here, the accuracy of the EFMO method is demonstrated for both covalently bonded systems and noncovalently bound molecular clusters by systematically examining the effects of basis sets and a key distance-related cutoff parameter, R cut . R cut determines whether a fragment pair (dimer) is treated by the chosen ab initio method or calculated using the effective fragment potential (EFP) method (separated dimers). Decreasing the value of Rcut increases the number of separated (EFP) dimers, thereby decreasing the computational effort. It is demonstrated that excellent accuracy (<1 kcal/mol error per fragment) can be achieved when using a sufficiently large basis set with diffuse functions coupled with a small R cut value. With the new parallel implementation, the total EFMO wall time is substantially reduced, especially with a high number of MPI ranks. Given a sufficient workload, nearly ideal strong scaling is achieved for the CPHF and TDHF parts of the calculation. For the first time, EFMO calculations with the inclusion of long-range polarization and dispersion interactions on a hydrated mesoporous silica nanoparticle with explicit water solvent molecules (more than 15k atoms) are achieved on a massively parallel supercomputer using nearly 1000 physical nodes. In addition, EFMO calculations on the carbinolamine formation step of an amine-catalyzed aldol reaction at the nanoscale with explicit solvent effects are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Tracer particle transport dynamics in the diffusive sandpile cellular automaton

The confinement properties of the diffusive running sandpile are characterized by tracking the motion of a population of marked grains of sand. It is found that, as the relative strength of the avalanching to the diffusive transport channel is varied, a point is reached at which the particle global confinement time and the probability density functions of the jump-sizes and waiting-times of the tracked grains experience a sudden change, thus revealing a dynamical transition, that is consistent with previous studies (Newman DE et al., Phys Rev Lett 2002;88(20):204304). Across this transition, the sandpile moves from a regime characterized by self-similarity and memory, where avalanches of all possible sizes dominate transport across the system, to another regime where transport is taken over by near system-size, quasi-periodic avalanches. Values for the fractional transport exponents that quantify effective transport across the sandpile prior to the transition are also obtained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Dual‐Functional High‐Entropy Polymer Exhibiting Giant Cross‐Energy Couplings at Low Fields

A key component of cooling devices is the transfer of entropy from the cold load to heat sink. An electrocaloric (EC) polymer capable of generating both large electrocaloric effect (ECE) and substantial electroactuation can enable EC cooling devices to pump heat without external mechanisms, resulting in compact designs and enhanced efficiency. However, achieving both high ECE and significant electroactuation remains challenging. Herein, it is demonstrated that poly(vinylidene fluoride‐trifluoroethylene‐chlorofluoroethylene‐double bond) [P(VDF‐TrFE‐CFE‐DB)] tetrapolymers can simultaneously generate high electrocaloric effects and electroactuations under low fields. These P(VDF‐TrFE‐CFE‐DB) tetrapolymers are synthesized through the dehydrochlorination of P(VDF‐TrFE‐CFE) terpolymer. By facile tuning the composition of the initial terpolymer to avoid pure relaxor state, tetrapolymers with optimal DB compositions are achieved, near the critical endpoint of normal ferroelectric phase with diffused phase transition. The nearly vanishing energy barriers between the nonpolar to polar phases result in a strong electrocaloric response and significant electroactuation. Specifically, the P(VDF‐TrFE‐CFE‐DB) tetrapolymer exhibits an EC entropy change Δ S of 100 J kg −1 K −1 under 100 MV m −1 : comparable to state‐of‐the‐art (SOA) EC polymers, while delivering nearly twice the electroactuation of the SOA EC polymers. This work presents a general strategy for developing EC materials that combine large electrocaloric effect and electroactuation at low electric fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unravelling Microstructure Selection in an Additively Manufactured Eutectic High‐Entropy Alloy

High-entropy alloys (HEAs) are promising candidates for advanced structural applications due to their excellent mechanical properties. Additive manufacturing (AM), with its rapid solidification conditions, enables the creation of unique nonequilibrium microstructures. To fully leverage the synergy between AM and HEAs, understanding how processing affects structure and properties is essential. Here, how solidification rate influences microstructure evolution and phase transformation pathway in laser additively manufactured AlCrFe2Ni2 eutectic HEAs is investigated. By increasing the laser scan speed and hence the solidification rate, distinct solidification modes evolving from coupled eutectic to anomalous eutectic and eventually to single-phase solidification are revealed. These transitions result in distinct microstructures and a wide range of mechanical properties. Thermodynamic modeling and molecular dynamics simulations reveal that low cooling rates allow for sufficient atomic diffusion and phase separation, facilitating coupled eutectic growth. In contrast, rapid cooling suppresses diffusion and destabilizes the solid–liquid interface, promoting anomalous or single-phase solidification. This integrated experimental and computational approach provides a multiscale understanding of solidification mechanisms in HEAs and underscores how kinetic effects can over-ride thermodynamic predictions under nonequilibrium conditions. Furthermore, these results demonstrate that AM can serve as a powerful tool to design HEAs with tailored microstructures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mechanism of ion pairing–induced co-ion penetration into the stern layer

In classical electric double layer (EDL) theory, co-ions—ions carrying the same charge as the electrode—are assumed to be excluded from the Stern layer because of electrostatic repulsion. However, ion pairing with adsorbed counterions may enable co-ions to access the EDL, a phenomenon that remains underexplored. Key questions therefore remain unresolved, including how deeply co-ions penetrate, how ion-specific properties govern them, and how ion pairing within the EDL differs from bulk solution. Here, in this study, we employ molecular dynamics simulations combined with well-tempered metadynamics to quantify co-ion penetration and ion-pairing thermodynamics at positively charged Mg-Al layered double hydroxide interfaces (σ = +20.6 μC/cm 2 ) in aqueous chloride electrolytes across 0.005–3 M range. We reveal a concentration-driven crossover: at dilute conditions, counterion–co-ion contact ion pairing (CIP) is thermodynamically unfavorable or only weakly favorable (ΔG CIP = +0.12 kcal/mol for Li + to −0.42 kcal/mol for K + ), leading to co-ion exclusion. As concentration increases (≥0.5 M), enhanced screening reduces surface repulsion and stabilizes CIP thermodynamics (ΔG CIP = −0.89 kcal/mol for Li + to −1.05 kcal/mol for K + at 3 M), enabling monovalent co-ions to penetrate the Stern layer following the hydration hierarchy K + > Na + > Li + , while divalent co-ions (Mg 2+ , Ca 2+ ) remain in the diffuse layer, forming only solvent-separated pairs. Comparison with bulk solution shows interfacial ion pairing is suppressed at low concentrations but converges to bulk-like thermodynamics at high concentrations, indicating surface effects vanish under strong screening. These results establish a quantitative framework linking ion-pairing thermodynamics to co-ion penetration and providing strategies to design electrochemical interfaces.

Co-ion penetration↗
DOE OSTI2023

A conservative Galerkin solver for the quasilinear diffusion model in magnetized plasmas

We propose a conservative Galerkin scheme for the quasilinear model in three-dimensional momentum space and three-dimensional spectral space, with cylindrical symmetry. We construct an unconditionally conservative weak form and use a discretization that preserves conservation properties independent of the wave emission probability. The discrete operators, combined with a consistent quadrature rule, preserve all the conservation laws rigorously. The proposed scheme is quite general: it works for both relativistic and non-relativistic systems, for both magnetized and unmagnetized plasmas, and even for problems with time-dependent dispersion relations. We represent the particle distribution by continuous basis functions and use discontinuous basis functions for the wave spectral energy density, which enables the application of a positivity-preserving technique. We adopt the marching simplex algorithm, designed initially for computer graphics, for numerical integration on the resonance manifold. Furthermore, the numerical examples with a bump-on-tail initial configuration show how the unstable waves produce strong momentum space diffusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Custom-designed heat treatment simultaneously resolves multiple challenges facing 3D-printed single-crystal superalloys

Single-crystal Ni-based superalloys are currently the material of choice for turbine blade applications, especially with the emerging additive manufacturing (AM) that facilitates the manufacture/repair of these single crystals. This promising AM route, however, comes with a dilemma: in the fusion and heat affected zones after e-beam or laser induced melting, one needs a solutionizing annealing to relieve the residual stresses and homogenize the chemical/microstructure. The super-solvus solutionizing temperature is usually adopted from the protocol for the cast superalloys, which almost always causes recrystallization and stray grain growth, resulting in a polycrystalline microstructure and degrading the high-temperature mechanical performance. Here, in this paper, we demonstrate a custom-designed post-printing heat treatment to replace the conventional super-solvus one. The recovery and relatively low temperature diminish the driving force for recrystallization and the movement of stray grain boundaries, without suffocating the chemical/microstructural homogenization thanks to the narrow dendrite width and short element segregation distance. The optimal duration of the heat treatment is proposed to achieve atomic-diffusion mediated chemical homogenization while limiting γ'-particle coarsening in the interdendritic regions. Our strategy makes it practically feasible to resolve several bottleneck problems with one processing/treatment, removing a seemingly formidable obstacle to effective additive manufacturing of superalloy single crystal products.

36 MATERIALS SCIENCE↗
DOE OSTI2020

In Situ Oxidation Studies of High-Entropy Alloy Nanoparticles

Although high-entropy alloys (HEAs) have shown tremendous potential for elevated temperature, anticorrosion, and catalysis applications, little is known on how HEA materials behave under complex service environments. Herein, we studied the high-temperature oxidation behavior of Fe 0.28 Co 0.21 Ni 0.20 Cu 0.08 Pt 0.23 HEA nanoparticles (NPs) in an atmospheric pressure dry air environment by in situ gas-cell transmission electron microscopy. It is found that the oxidation of HEA NPs is governed by Kirkendall effects with logarithmic oxidation rates rather than parabolic as predicted by Wagner's theory. Further, the HEA NPs are found to oxidize at a significantly slower rate compared to monometallic NPs. The outward diffusion of transition metals and formation of disordered oxide layer are observed in real time and confirmed through analytical energy dispersive spectroscopy, and electron energy loss spectroscopy characterizations. Localized ordered lattices are identified in the oxide, suggesting the formation of Fe 2 O 3 , CoO, NiO, and CuO crystallites in an overall disordered matrix. Hybrid Monte Carlo and molecular dynamics simulations based on first-principles energies and forces support these findings and show that the oxidation drives surface segregation of Fe, Co, Ni, and Cu, while Pt stays in the core region. The present work offers key insights into how HEA NPs behave under high-temperature oxidizing environment and sheds light on future design of highly stable alloys under complex service conditions.

36 MATERIALS SCIENCE↗