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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Reactor and fuel cycle performance of light water reactor fuel with 235 U enrichments above 5%

Recent advances in nuclear fuel materials research, particularly on the topic of accident-tolerant fuels, have brought up potential opportunities for expanding the operating envelope of existing light water reactors. As many of the performance improvements offered by these technologies may be most fully realized by increasing fuel enrichment beyond the standard 5% limit, this paper examines the potential reactor performance and fuel cycle performance of low-enriched uranium oxide fueled light water reactors by generically considering pressurized water reactors with 235 U enrichment from 5 to 7%. Advanced cladding, including accident-tolerant cladding, has the potential to increase fuel burnup limits related to hydrogen in the cladding that coincide with those limits associated with end-of-life reactivity. Therefore, higher enrichment will be necessary in order to realize the higher fuel burnups. This work includes evaluation of the fuel cycle length, discharge burnup, reactivity coefficients, and fuel cycle performance, including radioactive waste and environmental impact metrics per unit energy generated. The analysis was performed using the evaluation metrics from the US Department of Energy Office of Nuclear Energy Fuel Cycle Evaluation and Screening Study. The reactor performance and safety analysis show that enrichments between 5 and 7% would have similar fuel temperature and moderator temperature coefficients. However, the soluble boron coefficient would decrease in magnitude, requiring more corrosive boric acid in the coolant or other methods of reactivity control during the fuel cycle. At these higher enrichments the maximum burnup at the rim of the fuel pellet would increase by almost a factor of two, which is expected to impact the formation of high-burnup structure in the fuel and the corresponding thermo-mechanical fuel properties. The fuel cycle performance assessment shows that increasing enrichment reduces the quantity of high-level waste disposed per unit energy generated, but it increases the natural resource requirements normalized to a gigawatt-electricity-per-year basis. Another impact is the slightly higher discharge burnup, resulting in somewhat different activity levels of the spent nuclear fuel and high-level waste radioactivity at 100 and 100,000 years after fuel discharge. The environmental impacts—including land use, water use, carbon emission, and radiological exposure—are of the same magnitude per unit energy generated. Finally, however, the impacts are distributed differently. Less than 5% enrichment has marginally more impact on the back-end of the fuel cycle, and greater than 5% enrichment has marginally more impact on the front-end of the fuel cycle. Ultimately, no neutronic or reactor safety hindrances to employing light water reactor fuel with enrichments greater than 5% are identified; given the achievable reactor performance benefits with advanced fuels, further practical exploration of increased enrichment fuel is recommended.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗
DOE OSTI2022

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders

The anomalies in the high voltage converter modulator (HVCM) remain a major down time for the spallation neutron source facility, that delivers the most intense neutron beam in the world for scientific materials research. In this work, we propose neural network architectures based on Recurrent AutoEncoders (RAE) to detect anomalies ahead of time in the power signals coming from the HVCM. Bi-directional gated recurrent unit, bi-directional long-short term memory (LSTM), and convolutional LSTM (ConvLSTM) are developed, trained, and tested using real experimental signals from the HVCM module. The results show a good performance of the proposed RAE models, achieving precision up to 91%, recall up to 88%, false omission rate as low as 20% (i.e. 80% of the anomalies were detected), and area under the ROC curve up to 0.9. The three RAE models provide very comparable performance, with LSTM showing slightly better performance than GRU and ConvLSTM. The RAE models are benchmarked against other anomaly detection methods, including isolation forest, support vector machine, local outlier factor, feedforward and convolutional autoencoders, and others; showing a better performance. Here, the results of this study demonstrate the promising potential of RAE in anomaly detection for real-world power systems, and for increasing the reliability of the HVCM modules in the spallation neutron source.

42 ENGINEERING↗
DOE OSTI2022

Radiation hard gallium oxide scintillators for high count rate radiation detection

Despite decades of materials research, the availability of appropriate crystalline radiation-hard scintillators with ultrafast decay times (~10ns) and high light yields (>2000 ph/MeV) are still limited. In this study, we demonstrate the potential of gallium oxide (β-Ga 2 O 3 ) based scintillators for high count rate applications. The low-cost rugged β-Ga 2 O 3 scintillators were grown using the optical float zone (FZ) technique. Several dopants and growth atmospheres were used to demonstrate the balance between ultrafast primary decay time and the light yield of the scintillators. Light yields as high as 6446 ± 716 ph/MeV were obtained with 18.3 ns decay time for 662 keV gamma rays. Decay times as fast as 12ns were obtained with a 3212 ± 357 ph/MeV light yield. An excellent 662 keV gamma energy resolution of 7% was obtained using the β-Ga 2 O 3 :Ce crystals. Even with these excellent scintillation properties, the gamma radiation hardness of β-Ga 2 O 3 crystals was better than the leading radiation-hard lead tungstate (PbWO 4 ) crystals. Twenty-six scintillators fabricated from the grown β-Ga 2 O 3 crystals were tested for radiation hardness with a 60 Co gamma irradiation rate of 14 krad/h with a total radiation dose of 2 Mrad. The scintillation properties of these crystals remained unaffected. To date, no other scintillator has demonstrated such high radiation resistance. Furthermore, these FZ-grown low-cost, non-hygroscopic, and high-density β-Ga 2 O 3 scintillators can replace the existing scintillators in many applications ranging from nuclear and high energy physics experiments to nuclear security.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Secondary-electron imaging of bulk crystalline specimens in an aberration corrected $\text{STEM}$

Atomic-scale electron microscopy traditionally probes thin specimens, with thickness below 100 nm, and its feasibility for bulk samples has not been documented. Here in this study, we explore the practicality of scanning transmission electron microscope (STEM) imaging with secondary electrons (SE), using a silicon-wedge specimen having a maximum thickness of 18 μm. We find that the atomic structure is present in the entire thickness range of the SE images although the background intensity increases moderately with thickness. The consistent intensity of secondary electron (SE) images at atomic positions and the modest increase in intensity in between (background) observed in silicon suggest a limited contribution from SEs generated by backscattered electrons, a conclusion supported by our multislice calculations. We conclude that achieving atomic resolution in SE imaging for bulk specimens is indeed attainable using aberration-corrected STEM and that an aberration-corrected scanning electron microscope (SEM) may have the capacity for atomic-level resolution, holding great promise for future strides in materials research.

25 ENERGY STORAGE↗
DOE OSTI2020

Hole Polaron Transport in Bismuth Vanadate BiVO 4 from Hybrid Density Functional Theory

In this paper, we address the chemical character and the mobility of hole polarons in BiVO 4 (BVO). Two distinct structures for hole polarons in BVO have been reported in the literature to date. “Standard” hybrid density functional theory (DFT) calculations have predicted hole polarons to be (BiO 8 ) dodecahedron-centered polarons, while Hubbard U-DFT (DFT + U) calculations have predicted single oxygen atom-centered polarons. Resolving these contrasting findings is critical to validate theory in materials research. We investigated the structure of hole polarons in BVO using hybrid DFT with varying fractions (α) of the exact exchange interaction that enters hybrid functionals. For values of the fraction α, from α = 0.25 to α = 0.45, we obtained both structures, the h + (BiO 8 ) hole structure and the h + (O) hole structure. For the smaller values of α, the h + (BiO 8 ) structure is lower in energy, while for the larger values of α, the h+(O) structure is more stable. The piece-wise linearity of DFT energies E(q) with respect to a point defect charge q is maintained satisfactorily in all cases, although the deviation is the smallest for hybrid DFT with a small exact exchange fraction. For α = 0.25, we determined the diffusivity of h + (BiO 8 ) holes from the Marcus/Holstein two-state model. The activation barrier for Bi-to-Bi hops (ΔG # ~76 meV) and mobility (μ ~0.029 cm2 V –1 s –1 ) are found to be in close agreement with the published THz experimental data (ΔG # ~90 meV and μ exp ~0.02 cm2 V –1 s –1 , respectively). In h + (BiO 8 ) structures, nearly 25% of the hole charge and unpaired spin reside on the Bi atom and the rest is divided among the eight O atoms. The hole is best described as a Bi-centered hole, rather than a Bi 6s hole. In contrast, in h + (O) structures, about 75% of the unpaired spin population resides on the O atom. These findings suggest that if one wishes to resolve this theoretical dichotomy, there remains a need for further experimental characterization of hole polarons in BVO with techniques that could identify the oxidation state of Bi and O atoms in operando conditions and help validate theoretical results of structure and transport.

14 SOLAR ENERGY↗
DOE OSTI2022

Membrane Strategies for Water Electrolysis

Hydrogen holds great promise as a clean energy resource to help the global carbon-free energy goal. Here, green hydrogen production from renewable energy-powered water electrolysis can decarbonize hard-to-abate industries and transport applications. Ion-exchange membranes are an essential component of membrane-based water electrolysis, enabling high hydrogen production efficiency through a zero-gap configuration. While perfluorosulfonic acids are the standard polymer electrolyte membrane material, research efforts for membrane alternatives have increased over the years to drive down the cost of electrolyzers and improve devices’ durability without sacrificing performance and efficiency. Here we present our perspectives on acidic, alkaline, and bipolar membranes for water electrolyzers and discuss future research directions to develop advanced membranes for green hydrogen production technology.

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

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2023

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Hard X-ray inverse Compton scattering at photon energy of 87.5 keV

Abstract Production of hard X-ray via inverse Compton scattering at photon energies below 100 keV range aimed at potential applications in medicine and material research is reported. Experiments have been performed at the Brookhaven National Laboratory, Accelerator Test Facility, employing the counter collision of a 70 MeV, 0.3 nC electron beam with a near infra-red Nd: YAG laser (1064 nm wavelength) pulse containing ~ 100 mJ in a single shot basis. The radiation distribution of the scattered photon beam is assessed to be sufficiently quasi monochromatic to produce clear contrast from the Au K - edge at 80.7 keV.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Bipolar membrane polarization behavior with systematically varied interfacial areas in the junction region

The palette of applications for bipolar membranes (BPMs) has expanded recently beyond electrodialysis as they are now being considered for fuel cell and electrolysis applications. Their deployment in emerging electrochemical technologies arises from the need to have a membrane separator that provides disparate pH environments and to prevent species crossover. Most materials research for BPMs has focused on water dissociation catalysts and less emphasis has been given to the design of the polycation–polyanion interface for improving BPM performance. Here, soft lithography fabricated a series of micropatterned BPMs with precise control over the interfacial area in the bipolar junction. Polarization experiments showed that a 2.28× increase in interfacial area led to a 250 mV reduction in the onset potential. Additionally, the same increase in interfacial area yielded marginal improvements in current density due to the junction region being under kinetics-diffusion control. A simple physics model based on the electric field of the junction region rationalized the reduction in the overpotential for water dissociation as a function of interfacial area. Finally, the soft lithography approach was also conducive for fabricating BPMs with different chemistries ranging from perfluorinated polymer backbones to alkaline stable poly(arylene) hydrocarbon polymers. These polymer chemistries are better suited for fuel cell and electrolysis applications. The BPM featuring the alkaline stable poly(terphenyl) anion exchange membrane had an onset potential of 0.84 V, which was near the thermodynamic limit, and was about 150 mV lower than a commercially available variant.

25 ENERGY STORAGE↗
DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2020

Interfacial properties in energy storage systems studied by soft x-ray absorption spectroscopy and resonant inelastic x-ray scattering

Interfacial behaviors and properties play critical roles in determining key practical parameters of electrochemical energy storage systems, such as lithium-ion and sodium-ion batteries. Soft x-ray spectroscopy features shallow penetration depth and demonstrates inherent surface sensitivity to characterize the interfacial behavior with elemental and chemical sensitivities. Here, we present a brief survey of modern synchrotron-based soft x-ray spectroscopy of the interface in electrochemical energy storage systems. The technical focus includes core-level spectroscopy of conventional x-ray absorption spectroscopy and resonant inelastic x-ray scattering (RIXS). We show that while conventional techniques remain powerful for probing the chemical species on the surface, today’s material research studies have triggered much more demanding chemical sensitivity that could only be offered by advanced techniques such as RIXS. Another direction in the field is the rapid development of various in situ/operando characterizations of complex electrochemical systems. Notably, the solid-state battery systems provide unique advantages for future studies of both the surface/interface and the bulk properties under operando conditions. We conclude with perspectives on the bright future of studying electrochemical systems through these advanced soft x-ray spectroscopic techniques.

25 ENERGY STORAGE↗
DOE OSTI2025

Femtosecond wavelength-tunable laser system using gain managed nonlinear amplifier

Optical parametric amplifiers require a seed energy at the spectral range of interest for amplification. In the case of pulsed laser systems, seed pulse characteristics such as spectral energy density and spectral uniformity influence laser system design and performance. In this letter, we present the design and modeling of a few micro-joules, wavelength-tunable, femtosecond parametric amplifier system. We employ a gain-managed nonlinear fiber amplifier output as the seed pulse. This seed pulse is relatively uniform across its bandwidth from 1010 to 1180 nm. Its spectral energy density averages 600 pJ/nm, 30 times higher than conventional counterparts. The overall amplification gain is 175, and the conversion efficiency is 17.5%. The inherent quasi-linear chirp of the seed pulse is utilized to alter the output wavelength. Our tunable femtosecond laser is an enabling technology for high-demand applications such as time-resolved spectroscopy, nonlinear and advanced material research, and biomedical imaging and microscopy modalities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Iron-based superconductors: Teenage, complex, challenging

Here, fifteen years after the surprising discovery of superconductivity in iron-based materials, researchers are beginning to impart some of their newfound wisdom on a slew of emerging superconductors that display similar traits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Role of Accelerated Burnup Irradiation Testing in Support of Accelerated Fuel Qualification

Accelerated fuel qualification has gained attention as a means to reduce the time needed to realize new nuclear fuel concepts and expand the operating windows of existing fuel forms. A key component of this approach is accelerated burnup irradiation testing. Although the concept of accelerated burnup has been familiar to the community for many decades, the specifics about how the increasing fission rate may be used as a qualification tool have not yet been elucidated. The present work provides a vision of how accelerated fission rate testing can enable accelerated fuel qualification. Technology readiness levels (TRLs) are reintroduced to demarcate the stages of traditional fuel qualification, and accelerated fuel qualification is presented in this context. The critical steps needed to achieve each TRL are reframed within the context of modern nuclear materials research and development, as revolutionary fuel concepts are more common than previous eras. The practical impacts of accelerated fuel qualification approaches as applied to contemporary fuel qualification efforts are illustrated. Examples are given to illustrate how accelerated burnup irradiations are being used currently and could be applied in the future to support qualification and licensure. Finally, outstanding challenges in the application of accelerated burnup methods to nuclear fuel qualification are summarized, with priority placed on understanding how fission rate impacts diffusion, microstructure evolution, and other critical mechanisms that dictate fuel performance.

Accelerated fuel qualification↗
DOE OSTI2021

When x-rays alter the course of your experiments

The continuing increase in the brilliance of synchrotron radiation beamlines allows for many new and exciting experiments that were impossible before the present generation of synchrotron radiation sources came on line. However, the exposure to such intense beams also tests the limits of what samples can endure. Whilst the effects of radiation induced damage in a static experiment often can easily be recognized by changes in the diffraction or spectroscopy curves, the influence of radiation on chemical or physical processes, where one expects curves to change, is less often recognized and can be misinterpreted as a 'real' result instead of as a 'radiation influenced result'. This is especially a concern in time-resolved materials science experiments using techniques as powder diffraction, small angle scattering and x-ray absorption spectroscopy. In this work, the effects of radiation (5–50 keV) on some time-resolved processes in different types of materials and in different physical states are discussed. We show that such effects are not limited to soft matter and biology but rather can be found across the whole spectrum of materials research, over a large range of radiation doses and is not limited to very high brilliance beamlines.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Emerging inorganic solar cell efficiency tables (version 2)

Abstract This paper presents the second version of the efficiency tables of materials considered as emerging inorganic absorbers for photovoltaic solar cell technologies. The materials collected in these tables are selected based on their progress in recent years, and their demonstrated potential as future photovoltaic absorbers. The first part of the paper consists of the guidelines for the inclusion of the different technologies in this paper, the verification means used by the authors, and recommendation for measurement best practices. The second part details the highest world-class certified solar cell efficiencies, and the highest non-certified cases (some independently confirmed). The third part highlights the new entries including the record efficiencies, as well as new materials included in this version of the tables. The final part is dedicated to review a specific aspect of materials research that the authors consider of high relevance for the scientific community. In this version of the efficiency tables, we are including an overview of the latest progress in quasi one-dimensional absorbers, such as antimony chalcogenides, for photovoltaic applications.

14 SOLAR ENERGY↗