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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SR19021 Tritium Aging of Regenerated LANA.75 (Final Project Report)

The Savannah River Tritium Enterprise (SRTE) has used the metal hydride LaNi 4.25 Al 0.75 (LANA.75) in the Tritium Facilities for over two decades. LANA.75 beds store significant quantities of tritium but have a limited service life due to the radiolytic decay of tritium to He-3 within the metal matrix. It has been shown that heating tritium-aged LANA.75 under vacuum can reverse tritium aging effects, eliminating the heel of trapped hydrogen, and restoring the reversible capacity. Additional investigation is needed to ensure there are no unexpected changes to the hydride before this restoration technique is employed in full scale beds in the Tritium Facilities. This project was to be comprised of three distinct scopes: obtain tritium aging data on the sample regenerated in 2018, regenerate a tritium-aged sample at 600 °C, and perform thermal stability testing on a non-tritiated sample. Isotherms were collected on the previously regenerated sample after approximately two years of tritium aging. Isotherms were collected at 80, 100, and 120 °C. As expected, there was a decrease in the plateau pressure, an increase in plateau slope, and a portion of the “heel” of tritium trapped in the metal had been reestablished. Unexpectedly, it appeared that the plateau had shortened at the higher tritium to metal ratios as well. This is typically seen in older samples. The second scope, to regenerate a second tritium-aged LANA.75 sample, was not completed. A Task Technical and Quality Assurance Plan was written and approved, a high temperature test cell was fabricated, several pre-job briefs were held, and the hydride sample was passivated with air. Despite these successes, the hydride sample was not recovered from the legacy test cell. The third scope was to perform thermal stability testing on a “cold” LANA.75 sample. A non-tritiated sample of LANA.75 was held at 750 °C under vacuum for 200 hours to simulate exposure to multiple regeneration evolutions. Hydride isotherm performance, chemical composition, crystallinity, particle size, and morphology are compared between the pre- and post-regeneration samples. No significant changes were observed in composition, crystallinity, or particle size. Comparison of before and after isotherms showed that performance improved rather than deteriorated during the evolution. Scanning Electron Microscopy (SEM) analysis showed small growths on the particle surface after exposure to regeneration conditions. Additional testing will be required to determine the cause of these growths.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Optimizing multichannel luminaire control using a color efficient matrix

The described embodiments relate to multichannel luminaires (102, 212, 404) that employ a color coefficient matrix (CCM) that can be used to adjust an operation of the multichannel luminaires according to their contribution of light to an area (112, 402). The CCM can be generated based on images captured within the area and/or a software simulation that approximates the contribution of light from the multichannel luminaires to the area. Once the CCM has been generated, the CCM can be used to compensate control signals to each multichannel luminaire in order to provide more accurate color rendering and uniform light distribution. Furthermore, feedback from embedded sensors (206) in the multichannel luminaires and/or sensors (116, 208, 210, 418) in the area can provide further basis for compensating control signals according to the qualities of the natural and artificial light entering the area, and the effects of different surfaces (128) on the light.

42 ENGINEERING↗
DOE OSTI2021

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles

The message passing neural network (MPNN) framework is a promising tool for modeling atomic properties but is, until recently, incompatible with directional properties, such as Cartesian tensors. We propose a modified Cartesian MPNN (CMPNN) suitable for predicting atom-centered multipoles, an essential component of ab initio force fields. The efficacy of this model is demonstrated on a newly developed dataset consisting of 46 623 chemical structures and corresponding high-quality atomic multipoles, which was deposited into the publicly available Molecular Sciences Software Institute QCArchive server. We show that the CMPNN accurately predicts atom-centered charges, dipoles, and quadrupoles and that errors in the predicted atomic multipoles have a negligible effect on multipole–multipole electrostatic energies. The CMPNN is accurate enough to model conformational dependencies of a molecule’s electronic structure. This opens up the possibility of recomputing atomic multipoles on the fly throughout a simulation in which they might exhibit strong conformational dependence.

Glick, Zachary L. (ORCID:0000000309002849)↗
DOE OSTI2020

Electron-electron interactions in low-dimensional Si:P delta layers

Scientists have long studied the physics of highly disordered conducting systems, seeking to understand the multitude of quantum phenomena that govern how electrons move through material systems. Recently, research into silicon-based quantum computing has made disordered conducting systems, such as Si:P monolayers embedded in isotopically pure Si, technically relevant. Consequently, applying and advancing the theoretical frameworks developed to describe electron behavior in disordered systems is a necessary objective in this field of research. This study investigates key components of dopant-based Si quantum computing devices: embedded regions of highly doped delta layers (δ layers). We examine the transport behavior and the electron-electron interaction (EEI) physics in embedded Si:P δ layers by means of self-consistent magnetotransport measurements. Parameters associated with the electronic transport offer a meaningful quantitative characterization of δ-layer quality and dopant diffusion. In addition, by examining EEI behaviors in a set of samples with embedded Si:P δ layers produced with different PH3 exposure procedures prior to Si encapsulation, we show how details of material synthesis affect the dimensionality of charge carrier interactions in embedded Si:P δ layers. Furthermore, the relationship between δ-layer confinement and EEI screening lengths is established here. This understanding will help validate important models used for device simulation and design and lead to improvements in the control of electrostatic gating of and tunneling transport through Si:P single atom transistors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. Here, in this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Reflective binary amplitude grating for soft x-ray shearing and Hartmann wavefront sensing

Here, we demonstrate a reflective wavefront sensor grating suitable for the characterization of high-quality x-ray beamlines and optical systems with high power densities. Operating at glancing incidence angles, the optical element is deeply etched with a two-level pattern of shearing interferometry gratings and Hartmann wavefront sensor grids. Transverse features block unwanted light, enabling binary amplitude in reflection with high pattern contrast. We present surface characterization and soft x-ray reflectometry of a prototype grating array to demonstrate function prior to wavefront measurement applications. A simulation of device performance is shown.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Summertime ozone pollution in China affected by stratospheric quasi-biennial oscillation

In recent years, the near-surface ozone (O 3 ) level has been rising fast in China, with increasing damage to human health and ecosystems. In this study, the impact of stratospheric quasi-biennial oscillation (QBO) on interannual variations in summertime tropospheric O 3 over China is investigated based on GEOS-Chem model simulations and satellite retrievals. QBO has a significant positive correlation with near-surface O 3 concentrations over central China (92.5–112.5° E, 26–38° N) when the sea surface temperature (SST) over the eastern tropical Pacific is warmer than normal, with a correlation coefficient of 0.53, but QBO has no significant effect on O 3 under the cold SST anomaly. Compared to the easterly phase of QBO, the near-surface O 3 concentrations have an increase of up to 3 ppb (5% relative to the average) over central China during its westerly phase under the warm SST anomaly. O 3 also increases above the surface and up to the upper troposphere, with a maximum increase of 2–3 ppb (3%–5%) in 850–500 hPa over central China when comparing westerly phase to easterly phase. Process-based analysis and sensitivity simulations suggest that the O 3 increase over central China is mainly attributed to the anomalous downward transport of O 3 during the westerly phase of QBO when a warm SST anomaly occurs in the eastern tropical Pacific, while the local chemical reactions and horizontal transport processes partly offset the O 3 increase. This work suggests a potentially important role of QBO and the related vertical transport process in affecting near-surface O 3 air quality, with an indication for O 3 pollution prediction and prevention.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Technology Strategy Assessment: Findings from Storage Innovations 2030 Supercapacitors

Electrochemical capacitors, which are commercially called supercapacitors or ultracapacitors, are a family of energy storage devices with remarkably high specific power compared with other electrochemical storage devices. Supercapacitors do not require a solid dielectric layer between the two electrodes, instead they store energy by accumulating electric charge on porous electrodes filled with an electrolyte solution and separated by an insulating porous membrane. Supercapacitors offer large specific capacitance and high power output. They can be charged and discharged very quickly, offer excellent cycle life and long operational life, and operate over a broad temperature range. The major drawbacks of supercapacitors are low energy density and a high self-discharge rate. For example, a supercapacitor passively discharges from 100% to 50% in a month compared with only 5% for a lithium-ion battery [1]. The high capital cost and low energy density of supercapacitors make the unit cost of energy stored ($/kWh) more expensive than alternatives such as batteries. Their attributes make them attractive for uses in which frequent small charges/discharges are required (e.g., ensuring power quality or providing frequency regulation). Their attributes and cost make them less attractive for long-duration energy storage, which favors technologies with low self-discharge that cost less per unit of energy stored.

25 ENERGY STORAGE↗
DOE OSTI2024

Accommodating a hexagonal ζ- phase Mn 2 N film on a cubic MgO (001) substrate

The thin films of c -plane dominated hexagonal ζ- phase Mn 2 N were successfully grown on a cubic MgO (001) substrate directly using plasma-assisted molecular beam epitaxy. The surface was comprehensively studied through experimental and theoretical approaches. Reflection high energy electron diffraction revealed two pseudo-cubic domains along [100] MgO and [110] MgO , and one hexagonal domain, which is 30° apart from the [100] MgO direction. Scanning tunneling microscopy was used to image and resolve the high-quality surface, revealing a distorted hexagonal surface structure. Furthermore, atomic resolution of the hexagonal domain with a 2 × 2 reconstructed surface is presented. For c-plane surface, theoretical investigations were carried out using first principles studies to determine the surface formation energy for various reconstructed surfaces. The theoretical study shows that the nitrogen terminated 2 × 2 structure with a manganese adatom on the surface as the most stable reconstruction that reproduces the experimental results. A corresponding simulated scanning tunneling microscopy model is also presented, providing strong support for the experimentally observed in-plane lattice structures. Furthermore, the manganese: nitrogen stoichiometry within the bulk and surface is in good agreement with the expected ratio of 2:1.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

A Chemist’s Guide to Solid-State Qubits: Diamond Color Centers as Embedded Vacancy-Centered Molecules

Diamond point defects or color centers are at the crux of solid-state quantum technology applications, from computing to sensing. Modeling molecular qubits has advanced by leaps and bounds mainly due to the advanced quantum mechanical (QM) tools in the arsenal of quantum chemists for modeling (isolated) molecular systems, which should be easily translatable for analogous systems in the solid state. Here, we simulate a series of diamond defects: a carbon vacancy (V C ) and second row substitutions that are adjacent to a V C or D C V C , with D = B, N, and O, to evaluate their magnetic and optical properties. We applied molecular orbital principles, an embedded cluster method, and a multiconfigurational QM theory to computationally characterize the above-mentioned defects for a multitude of spin, charge, and excited states. Our computational approach delivered high-quality QM insights that compare well with experiments. Furthermore, with this framework, we confirm the spin-triplets B C V C – and O C V C 0 to be analogous to N C V C – , with calculated bright transitions that respectively correspond to UV (3.80 eV) and blue (2.65 eV) light absorption and emission lifetimes (τ) of 2.3 and 5.6 ns, which are higher in energy and more short-lived (and thus brighter) than that for N C V C – : green (2.26 eV), τ = 9.5 ns.

Martirez, John Mark P. [Princeton Plasma Physics L↗
DOE OSTI2023

Quantifying the Contributions of Aerosol- and Snow-Produced ClNO 2 through Observations and 1D Modeling

Nitryl chloride (ClNO 2 ) is a radical reservoir that forms and accumulates in the nocturnal atmospheric boundary layer influenced by combustion emissions and chloride (e.g., sea salt and road salt). Upon sunrise, ClNO 2 rapidly photolyzes to generate highly reactive chlorine radicals (Cl • ) that affect the air quality by generating secondary air pollutants. Recent studies have shown road salt aerosols and saline snowpack to be sources of ClNO 2 in the wintertime urban environment; however, the quantitative contributions of each chloride source are not known. In this study, we examine the vertically resolved contributions of aerosol particles and saline snowpack as sources of ClNO 2 by using an observationally constrained snow–atmosphere coupled one-dimensional model applied to wintertime Kalamazoo, Michigan, U.S. Model simulations show that ClNO 2 emitted from urban snowpack can be vertically transported throughout the entire atmospheric boundary layer and can be a significant source of ClNO 2 , contributing up to ~60% of the ClNO 2 budget near the surface. Here, modeled snowpack ClNO 2 emission rates were 6 (±7) times higher than the observationally derived emission rates, suggesting that not all snow chloride is available for reaction. ClNO 2 production from both aerosol particles and snow emissions are required to best simulate the observed surface-level ClNO 2 . Using the traditional bulk parameterization for ClNO 2 produced from particles significantly overestimated ClNO 2 due to the assumption of having equivalent dinitrogen pentoxide (N 2 O 5 ) uptake and chloride availability for the entire particle population. In comparison, the chemically resolved surface area-based parameterization slightly underestimated the observations and had uncertainties deriving from ClNO 2 production from residential wood burning particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2024

Estimating internal moisture generation rates in recently constructed, occupied homes in the southeastern United States

Internal moisture generation (IMG), or moisture generated by building occupants via activities such as respiration, cooking, bathing, and cleaning, is a critical input required for design, analysis, and simulation of building enclosure and heating, ventilation, and air conditioning (HVAC) systems. Based on previously published values, ASHRAE Standard 160-2021 provides guidance for estimating IMG rates for moisture control design analysis, which is based on occupied home datasets collected in the 1980s and 1990s. Residential energy use simulation software also utilizes estimates for IMG for energy rating, energy analysis, and code compliance calculations, as specified in ANSI/RESNET/ICC Standard 301. Data quantifying IMG rates in newer homes is useful in determining the continued relevance of current design and simulation guidance, and whether the guidance represents conditions found in new housing stock. ASHRAE Research Project 1844, conducted by the Florida Solar Energy Center (FSEC), a research institute of the University of Central Florida, estimated IMG rates in newer occupied homes built since 2013 in the southeastern US using a moisture balance model approach. Furthermore, the project obtained occupied home data in cooperation with a US Department of Energy Building America research project to characterize indoor air quality (IAQ) in newer US homes, along with presence, functionality, and occupant use of control measures. Full-scale laboratory homes operating with known IMG rates were used to validate a moisture balance model and quantify the accuracy of estimates obtained when the model is applied to data from occupied homes with unknown IMG rates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

First Simultaneous Acceleration of Multiple Charge States of Heavy Ion Beams in a Large-Scale Superconducting Linear Accelerator

Experimental studies of the simultaneous acceleration of three-charge-state 129 Xe 49+,50+,51+ beam from 17 to 180 MeV/nucleon in a superconducting linear accelerator are presented. The beam parameters for each individual- and multiple-charge-state beam were measured and compared with the particle tracking simulations. Detailed measurements were performed to characterize the multiple-charge-state beam’s recombination after a second-order achromat and isopath 180° bending system. As a result of the recombination of three charge states in the six-dimensional phase space, the xenon beam intensity was increased by 2.5-fold compared to the single-charge-state beam. Lastly, the results presented in the Letter fully validate the possibility to produce and utilize high-quality multiple-charge-state heavy-ion beams in a large-scale superconducting linac to increase the available beam power on an isotope production target.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data.

Hopwood, Michael W. (ORCID:0000000161901767)↗
DOE OSTI2024

Life Cycle Inventory Availability: Status and Prospects for Leveraging New Technologies

The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.

blockchain platforms↗
DOE OSTI2019

An ultrathin ionomer interphase for high efficiency lithium anode in carbonate based electrolyte

High coulombic efficiency and dendrite suppression in carbonate electrolytes remain challenges to the development of high-energy lithium ion batteries containing lithium metal anodes. Here we demonstrate an ultrathin (≤100 nm) lithium-ion ionomer membrane consisting of lithium-exchanged sulfonated polyether ether ketone embedded with polyhedral oligosilsesquioxane as a coating layer on copper or lithium for achieving efficient and stable lithium plating-stripping cycles in a carbonate-based electrolyte. Operando analyses and theoretical simulation reveal the remarkable ability of the ionomer coating to enable electric field homogenization over a considerably large lithium-plating surface. The membrane coating, serving as an artificial solid-electrolyte interphase filter in minimizing parasitic reactions at the electrolyte-electrode interface, enables dendrite-free lithium plating on copper with outstanding coulombic efficiencies at room and elevated (50 °C) temperatures. The membrane coated copper demonstrates itself as a promising current collector for manufacturing high-quality pre-plated lithium thin-film anode.

25 ENERGY STORAGE↗
DOE OSTI2021

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Abstract Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Engineering↗