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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Tantalum alloy–based resonators for quantum information systems

Utilizing tantalum (Ta) in superconducting circuits has led to significant improvements, such as high qubit lifetime (T 1 ) and quality factors in both qubits and resonators, suggesting that material optimization plays an important role in the development of superconducting circuits. Thus we here explore superconducting gap engineering in Ta-based devices as a powerful strategy for expanding the range of suitable host materials. By alloying 20 atomic percent (at.%) hafnium into Ta thin films, we achieve a superconducting transition temperature (T c ) of 6.09 K as observed in direct current (DC) transport measurements, reflecting an increase in the superconducting gap. We systematically vary deposition conditions to control film orientation and transport properties of Ta-Hf alloy thin films. We then confirm the enhancement in T c via microwave measurements at millikelvin temperatures. We verify the ~40 increase in T c relative to bare Ta devices, while the loss contributions from two-level systems and quasi-particles remain unchanged in the low temperature regime. These findings emphasize the promise of material engineering in superconducting circuits and point to many potential material candidates for further exploration.

Ta-Hf alloy thin films↗
DOE OSTI2025

Optimization of scintillated imaging detectors for MeV X-ray radiography

Radiographic imaging at X-ray energies in the MeV range poses a complex problem. Detection of high energy X-rays depends heavily on the ability of the detection material, in this work the scintillator, to attenuate and ultimately stop the X-ray. Attenuation is more readily achieved by increasing material thickness, however thick scintillators are plagued with relatively high internal scattering, leading to an overall decrease in spatial resolution and image quality.

42 ENGINEERING↗
DOE OSTI2024

Synthesizing realistic sand assemblies with denoising diffusion in latent space

Abstract The shapes and morphological features of grains in sand assemblies have far‐reaching implications in many engineering applications, such as geotechnical engineering, computer animations, petroleum engineering, and concentrated solar power. Yet, our understanding of the influence of grain geometries on macroscopic response is often only qualitative, due to the limited availability of high‐quality 3D grain geometry data. In this paper, we introduce a denoising diffusion algorithm that uses a set of point clouds collected from the surface of individual sand grains to generate grains in the latent space. By employing a point cloud autoencoder, the three‐dimensional point cloud structures of sand grains are first encoded into a lower‐dimensional latent space. A generative denoising diffusion probabilistic model is trained to produce synthetic sand that maximizes the log‐likelihood of the generated samples belonging to the original data distribution measured by a Kullback‐Leibler divergence. Numerical experiments suggest that the proposed method is capable of generating realistic grains with morphology, shapes and sizes consistent with the training data inferred from an F50 sand database. We then use a rigid contact dynamic simulator to pour the synthetic sand in a confined volume to form granular assemblies in a static equilibrium state with targeted distribution properties. To ensure third‐party validation, 50,000 synthetic sand grains and the 1542 real synchrotron microcomputed tomography (SMT) scans of the F50 sand, as well as the granular assemblies composed of synthetic sand grains are made available in an open‐source repository.

Vlassis, Nikolaos N.↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2024

Simultaneous access to high normalized density, current, pressure, and confinement in strongly-shaped diverted negative triangularity plasmas

Strongly-shaped diverted negative triangularity (NT) plasmas in the DIII-D tokamak demonstrate simultaneous access to high normalized density, current, pressure, and confinement. NT plasmas are shown to exist across an expansive parameter space compatible with high fusion power production, revealing surprisingly good core stability properties that compare favorably to conventional positive triangularity plasmas in DIII-D. Non-dimensionalizing the key parameters, expanded operating spaces featuring edge safety factors below 3, normalized betas above 3, Greenwald density fractions above 1, and high-confinement mode (H-mode) confinement qualities above 1 are observed, even simultaneously, and all with a robustly stable edge free from deleterious edge-localized mode instabilities. Scaling of the confinement time with engineering parameters reveals at least a linear dependence on plasma current although with significant power degradation, both in excess of expected H-mode scalings. These results increase confidence that NT plasmas are a viable approach to realize fusion power and open directions for future detailed study.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Dynamic Modeling and Simulation of a Subcritical Coal-Fired Power Plant under Load-Following Conditions

Dynamic models for power plants that capture realistic general process trends and effects of manipulated variables are needed to improve load-following, while minimizing carbon footprint. In this work, a dynamic modeling approach and simulation results for subcritical coal-fired power plant components are presented. These encompass simulation of the dynamics in the fireside, including the effects of fuel, air combustion, and the dynamics of the entire waterside and power generation sections. This model development enables the simulation and analysis of the important short and long-time scale dynamics of components such as heaters, evaporative loop, and power generation units. Furthermore, additional variables in the power generation section are introduced to improve model accuracy, extending the prediction capability of subcritical power plant models and opening new opportunities for research in operator training, optimization, and advanced model-based controller design that are based on these models. The change in process gain for different ramp rates associated with disturbance signals that affect process variables is also explored and a correlation developed. This provides opportunities to study disturbance rejection control implementation and adaptation for scenarios with such variations in ramp rates. The prediction capabilities of selected components are compared to data available in literature, with the obtained root mean squared error ranges that reflect the model performance and quality of predictions.

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

NA-22 Quarterly Report: IST for Extended Deterrence (Q4FY24)

Department of Energy (DOE) scientists have long enjoyed technical collaboration with Japanese colleagues – valuing their technical and scientific prowess in materials science and engineering, large-scale, scientific computing, and many other areas. In particular, NNSA’s laboratories are attractive partners for this effort because they have a heritage of performing high quality, basic research; they work with an awareness of the dual use nature of emerging science and technology; and, importantly, they work with deep national security sensibilities through engagement with NNSA and other national security mission responsibilities. Other DOE laboratories with significant national security bona fides are also significant collaborators. In FY23 a Trilateral Leaders Summit was convened at Camp David. The DOE was given responsibility for “Trilateral National Laboratories Cooperation: The United States, Japan, and the ROK will drive new trilateral cooperation between the U.S. Department of Energy’s National Laboratories and counterpart laboratories—supported by a budget of at least $6 million—to advance knowledge, strengthen scientific collaboration, and spearhead innovation in support of the three countries’ shared interests. Scientists and innovators from the three countries will advance collaborative projects on priority critical and emerging technology areas; potential areas of cooperation include advanced computing, artificial intelligence, materials research, and climate and earthquake modeling among other technology areas.”

36 MATERIALS SCIENCE↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗
DOE OSTI2025

Anomalous superconductivity in twisted MoTe 2 nanojunctions

Introducing superconductivity in topological materials can lead to innovative electronic phases and device functionalities. Here, we present a unique strategy for quantum engineering of superconducting junctions in moiré materials through direct, on-chip, and fully encapsulated 2D crystal growth. We achieve robust and designable superconductivity in Pd-metalized twisted bilayer molybdenum ditelluride (MoTe 2 ) and observe anomalous superconducting effects in high-quality junctions across ~20 moiré cells. Unexpectedly, the junction develops enhanced, instead of weakened, superconducting behaviors, exhibiting fluctuations to a higher critical magnetic field compared to its adjacent Pd 7 MoTe 2 superconductor. In addition, the critical current further exhibits a notable V-shaped minimum at zero magnetic field. These features are unexpected in conventional Josephson junctions and absent in junctions of natural bilayer MoTe 2 created using the same approach. We discuss implications of these observations, including the possible formation of mixed even- and odd-parity superconductivity at the moiré junctions. Our results also demonstrate a pathway to engineer and investigate superconductivity in fractional Chern insulators.

Science & Technology - Other Topics↗
DOE OSTI2025

Drying of strawberries with airborne ultrasound and other integrated dehydration mechanisms

This article presents a comprehensive investigation into the drying of strawberries using multiple dehydration methods with an emphasis on energy efficiency and sustainability. Initially, an airborne ultrasonic transducer with a frequency of 21 kHz was employed in batch operations to examine the effects of controlling parameters, including applied power, distance from the transducer plate, sample thickness, and sample holder type. The Energy Ratio, defined as the ratio of thermal energy required to evaporate moisture to ultrasonic energy, was observed to reach up to 2.2, particularly during the initial stages of drying. Subsequently, the Smart Dryer integrated airborne ultrasound (US) dehydration, slot jet reattachment (SJR) nozzle convective drying, infrared (IR) drying, and their combinations, enabling a systematic exploration of various drying conditions on the drying time and quality of strawberries. The integration of airborne US with SJR nozzles and IR drying demonstrates a promising approach for optimizing drying processes. This method not only preserves the quality of dried strawberries but also improves the energy factor to 0.88, reducing drying time by 89% compared with other conditions. Key quality attributes of the dried strawberries, such as color and water activity, were evaluated to understand the influence of each drying method. The findings highlight the potential of these integrated drying techniques as sustainable solutions for efficient and high-quality strawberry dehydration.

42 ENGINEERING↗
DOE OSTI2024

Modification of CO2/H2O Selectivity of Polymer Through Graphene Coating for Carbon Capture Materials

A harmful issue that needs attention and solution is the rising carbon dioxide (CO2) in our atmosphere. Carbon dioxide in our atmosphere is at an all time high and has continuously increased since the industrial revolution. It has increased tremendously going from 315 parts per million (ppm) in the 1960s up to 419.3 ppm in 2023 as shown in Figure 1. Moreover, CO2 emissions have increased from 11 billion tons/year in the 1960s to 38.6 billion tons/year in 2023. The increase in CO2 found in our atmosphere has a number of detrimental effects such as increase in global temperatures and an increase in the ocean’s acidity. Human activities are greatly involved in the cause of CO2 emissions. At Lawrence Livermore National Lab (LLNL) the Microencapsulated CO2 sorbents (MECS) division has been doing research and investigating formulations for their microcapsules. MECS are core-shell microcapsules consisted of a highly permeable polymer shell and a fluid (sodium carbonate solution) that reacts and absorbs carbon dioxide. An example of the microcapsules are shown in Figure 2. Equation 1 shows the chemical reaction of the fluid (sodium carbonate) contained in the polymer shell that acts as the carbon dioxide sorbent and becomes sodium bicarbonate. The LLNL MECS team is in the process of scaling up their microcapsules for potential applications in “carbon capture from flue gas streams generated by fossil fuel combustion in industrial plants and operations, carbon capture in breweries and soft drink manufacture, carbon capture directly from indoor air to improve its quality”. The microcapsule’s possibility for commercial applications was discovered in 2017.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Closure models for the feedback of energetic particles on plasma turbulence

Energetic particles interact with the plasma surrounding them, resonating with certain types of plasma waves to stabilize them while destabilizing others, and changing the character of the background turbulence in ways that have not been fully quantified or understood. Interaction with the turbulent background plasma is key to the acceleration of many types of energetic particles including high-energy cosmic rays, solar energetic particles, and pick-up ions. The acceleration of particles is a process that would ideally be described by a kinetic model, a type of model that follows a probability distribution function (PDF) for all particles in 7-dimensional (x, y, z, v x , v y , v z , t) space. Because of the high dimensionality of a kinetic model, simulations that solve kinetic equations use the largest computational resources currently available, and are yet unable to simulate a realistic number of particles, reach the large scales necessary for astrophysical problems, and use high-precision numerical methods. Two available alternatives to kinetic plasma models have been explored for this problem, with limited success. One is a multi-fluid model produced by a cumulant discarding closure, which evolves coupled equations for the velocity, magnetic field, and internal energy for both the background plasma and the fluid of energetic particles. However, simulations that solve multi-fluid magnetohydrodynamic (MHD) equations are able to include the interaction with energetic particles only in crude ways, typically as an add-on pressure term. The second alternative is to use a hybrid method to couple a fluid description of the background plasma to a kinetic model or a Fokker–Planck model for the energetic particles. These methods are hampered by the physical modeling of the coupling. In this work, we develop a new model, which follows the PDF for all particles; this can be viewed as a step toward physical realism above a multi-fluid MHD model, while also being more computationally efficient than a kinetic model. The equations we develop model both the background plasma and the energetic particles self-consistently. Over the last decade, similar PDF methods have been developed to a high level of sophistication to model reactive flows and turbulent combustion for engineering applications. For treatment of the feedback of the energetic particles on a background plasma, a PDF closure approach should evaluate the mean characteristics, including the density, with better statistical quality than will particle-sampling procedures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Molten Salt Electrodeposition: Review

Molten salt electrodeposition is the process of producing impressively dense deposits of refractory metals using the electrolysis of molten salts. However, predicting which electrochemical parameters and setup will best control different kinds of deposition (density, homogeneity, etc.) is an ongoing challenge, due to our limited understanding of the properties and mechanisms that drive molten salt electrodeposition. Because these advancements have been made rapidly and in different arenas, it is worth taking the time to stop and assess the progress of the field as a whole. These advancements have increasing relevance for the energy sector, the development of space materials and engineering applications. In this review, we assess four critical facets of this field: (1) how the current understanding of process variables enhances the electrodeposition of various molten salts and the quality of the resulting product; (2) how the electrochemical setup and the process parameters (e.g., cell reactions) are known to impact the electrodeposition of different metal coatings and refractory-metal coatings; (3) the benefits and drawbacks of non-aqueous molten salt electrodeposition, and (4) promising future avenues of research. The aim of this work is to enhance our understanding of the many procedures and variables that have been developed to date. The expectation is that this review will act as a stimulant, motivating scientists to delve further into the investigation of refractory-metal alloys by utilizing molten salt electrodeposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

3D mesh regularization within an ALE code using a weighted line sweeping method

The Lagrangian formalism is widely used to simulate hydrodynamic responses in complex engineering applications, particularly those involving strong shock waves. However, as the mesh moves with the fluid, it can become highly distorted, requiring a regularization step. This involves constructing a new grid and remapping conservative quantities onto it to restore mesh quality. This work introduces a regularization method for block-structured meshes within a 3D ALE (Arbitrary Lagrangian-Eulerian) code. The proposed approach prevents mesh tangling while preserving the anisotropic features of the initial Lagrangian mesh. This regularization technique incorporates aspect ratio-based weights to control mesh smoothing. Unlike uniform rezoning techniques, this weighted approach maintains proximity to the Lagrangian mesh while improving mesh quality. Here, the method effectively handles concave geometries by mitigating the grid attraction phenomenon, which typically leads to mesh concentration along concave edges. Numerical experiments demonstrate its efficiency in regularizing severely deformed meshes, and its integration within the ALE framework is validated on challenging hydrodynamic test cases, including the triple point problem.

42 ENGINEERING↗
DOE OSTI2024

Epitaxial Growth of Ga2O3: A Review

Beta-phase gallium oxide (β-Ga2O3) is a cutting-edge ultrawide bandgap (UWBG) semiconductor, featuring a bandgap energy of around 4.8 eV and a highly critical electric field strength of about 8 MV/cm. These properties make it highly suitable for next-generation power electronics and deep ultraviolet optoelectronics. Key advantages of β-Ga2O3 include the availability of large-size single-crystal bulk native substrates produced from melt and the precise control of n-type doping during both bulk growth and thin-film epitaxy. A comprehensive understanding of the fundamental growth processes, control parameters, and underlying mechanisms is essential to enable scalable manufacturing of high-performance epitaxial structures. This review highlights recent advancements in the epitaxial growth of β-Ga2O3 through various techniques, including Molecular Beam Epitaxy (MBE), Metal-Organic Chemical Vapor Deposition (MOCVD), Hydride Vapor Phase Epitaxy (HVPE), Mist Chemical Vapor Deposition (Mist CVD), Pulsed Laser Deposition (PLD), and Low-Pressure Chemical Vapor Deposition (LPCVD). This review concentrates on the progress of Ga2O3 growth in achieving high growth rates, low defect densities, excellent crystalline quality, and high carrier mobilities through different approaches. It aims to advance the development of device-grade epitaxial Ga2O3 thin films and serves as a crucial resource for researchers and engineers focused on UWBG semiconductors and the future of power electronics.

Chemistry↗
DOE OSTI2025

Understanding Model Inadequacy in TRISO Nuclear Fuel Fission Products Release Models: Empirical and Mechanistic Approaches

The increasing use of tristructural isotropic (TRISO) particle fuel in both advanced and existing reactors necessitates a thorough evaluation of uncertainties and shortcomings in TRISO fission product release models. These inadequacies arise from the simplifications made in computational models compared to experimental data. Utilizing the BISON fuel performance code and experimental data from the Advanced Gas Reactor (AGR) program provides a unique chance to rigorously assess these inadequacies within a Bayesian uncertainty quantification (UQ) framework. This study contrasts the standard Bayesian framework with the Kennedy-O'Hagan (KOH) framework, which explicitly accounts for modeling inadequacies, in the context of UQ for TRISO silver release models. It examines both the traditional Arrhenius equation and a more advanced lower-length-scale (LLS)-informed model that incorporates microstructure information. The inverse UQ process applied to AGR-2 and AGR-3/4 datasets identified modeling inadequacy as the primary source of uncertainty, with experimental noise also being significant, while model parameter uncertainty was minimal. Both the Arrhenius and LLS-informed models showed similar levels of modeling inadequacy. For forward predictive UQ using the AGR-1 dataset, the KOH framework enhanced the accuracy and quality of quantified uncertainties by approximately 30% and 40%, respectively, compared to the standard Bayesian framework. This improvement was observed for both the Arrhenius and LLS-informed models. At the engineering scale, both models performed similarly, but the LLS-informed model outperformed the Arrhenius equation at the mesoscale. These findings underscore the importance of explicitly considering modeling inadequacy in the UQ process and highlight the need for ongoing refinement of physics-based models to address these shortcomings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗