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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Practical considerations for amino acid isotope analysis

Over the last few decades, isotopic analysis of amino acids at the compound- and position-specific levels has been rapidly advancing across diverse fields. As these techniques progress, evaluation of isotopic fractionation associated with sample workup is essential. This critical review of analytical methods through the lens of isotope geochemistry provides a benchmark for researchers across disciplines seeking to make compound- and position-specific amino acid isotope measurements. We focus on preparation, acid hydrolysis, clean-up, derivatization, separation, and C, H, N, and S isotope measurement. Despite substantial customizability across these steps, the following general recommendations should maximize recovery while minimizing isotopic fractionation. Samples should be freeze-dried and stored anoxically at ≤ –20 °C prior to conventional acid hydrolysis (6N HCl, 110 °C, 20–24 h, anoxic), which suffices for many residues. Both gas and liquid chromatographic (GC and LC, respectively) techniques are well-established and separate about 15 amino acids; LC bypasses the need for derivatization, while GC provides higher sensitivity. Furthermore, when derivatization is needed, n-acetyl and alkoxycarbonyl esters provide the most reproducible C isotope ratios. For compound-specific analyses, online GC–IRMS and LC–IRMS systems offer the easiest workflow, but EA–IRMS enables potential multi-element isotope analysis. Emerging techniques like high-resolution mass spectrometry are also promising for multi-element analysis and recover position-specific isotopic information. Looking forward to the next decade of innovation, isotope geochemists and ecologists can improve amino acid isotope analysis by focusing on streamlining multi-element analysis and standardizing calibration practices across laboratories.

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

MiniDAQ-3: Providing concurrent independent subdetector data-taking on CMS production DAQ resources

The data acquisition (DAQ) of the Compact Muon Solenoid (CMS) experiment at CERN, collects data for events accepted by the Level-1 Trigger from the different detector systems and assembles them in an event builder prior to making them available for further selection in the High Level Trigger, and finally storing the selected events for offline analysis. In addition to the central DAQ providing global acquisition functionality, several separate, so-called “MiniDAQ” setups allow operating independent data acquisition runs using an arbitrary subset of the CMS subdetectors. During Run 2 of the LHC, MiniDAQ setups were running their event builder and High Level Trigger applications on dedicated resources, separate from those used for the central DAQ. This cleanly separated MiniDAQ setups from the central DAQ system, but also meant limited throughput and a fixed number of possible MiniDAQ setups. In Run 3, MiniDAQ-3 setups share production resources with the new central DAQ system, allowing each setup to operate at the maximum Level-1 rate thanks to the reuse of the resources and network bandwidth. Configuration management tools had to be significantly extended to support the synchronization of the DAQ configurations needed for the various setups. We report on the new configuration management features and on the first year of operational experience with the new MiniDAQ-3 system.

Amoiridis, Vassileios↗
DOE OSTI2023

Mission Analysis for Marine Renewable Energy to Provide Power for Marine Carbon Dioxide Removal

The mission of this project was to provide a preliminary feasibility assessment of powering different marine carbon dioxide removal (mCDR), marine carbon capture, and marine carbon sequestration strategies with marine energy. In this report, carbon capture refers to methods that can separate or capture carbon dioxide (CO 2 ) from the air or ocean; carbon sequestration refers to methods that store CO 2 obtained by capture methods out of the atmosphere for long periods of time; and carbon dioxide removal (CDR) refers to methods that do both. The project found that mCDR powered by marine energy and offshore wind energy available in the United States could meet global CDR scales needed by 2040 and 2050 to limit warming to 1.5 degrees C by 2100. Note that this preliminary estimate assumes that it is possible to harvest all the marine and offshore wind resources available in the United States with existing technology options, and it does not account for the power needed for monitoring these methods, as these power needs are not yet well defined and require further research. Additionally, these CDR scales will still require emissions reductions.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Nonvolatile electrochemical memory at 600°C enabled by composition phase separation

Silicon-based microelectronics are limited to ~150°C and therefore not suitable for the extremely high temperatures in aerospace, energy, and space applications. While wide-band-gap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600°C. Through correlative scanning transmission electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.

42 ENGINEERING↗
DOE OSTI2021

Experience with Inner Reflector Plug exchange in SNS

The Inner Reflector Plug (IRP) is a central component of the Spallation Neutron Source target monolith, which houses the mercury target and four liter-sized neutron moderator units. It is exposed to high-level radiation fields during routine operation and builds up significant activity. The IRP needs to be replaced due to moderator neutron poison and decoupler burn-out, which is used for shaping neutron pulses. The first IRP exchange took place in March 2018. The old IRP was extracted from the target monolith, providing space for the new one. It was split into three segments, each of which was handled separately. The lowest section of the IRP is the largest segment in size and in activity and is temporarily stored on-site for cool down before conduction post irradiation examination. In support of planning the replacement activities, a wide range of activation and transport analyses were performed. This included calculating isotope inventories and the radiation fields for each segment as it is extracted in storage casks, and the radiation field from the empty IRP pit in the target monolith. While the replacement was taking place, measurements were performed and later on compared to the calculations. During these studies, it was discovered that a significant contributor to the radiation field from the lower IRP segment is from photo-neutrons. Photo-nuclear physics was added to the analyses and calculated results compared well with measured dose rates.

Popova, Irina I.↗
DOE OSTI2026

Low Activity Waste Glass Optimization with Property Models from Machine Learning, Part 2: Experimental Validation and Active Learning

The United States Department of Energy is responsible for managing legacy nuclear waste stored in underground tanks at the Hanford Site. To treat the waste, it is planned as the current baseline to separately vitrify low-activity waste (LAW) and high-level waste fractions. Previously, machine learning (ML) based glass property models (e.g., chemical durability, viscosity, electrical conductivity and SO3 solubility) were developed with prediction uncertainties. A waste glass optimization approach was then established to enable the capability of using these ML models in LAW glass formulation. In this study, the previous ML models were first experimentally validated, and the results were incorporated back into the database to update the ML models. The updated models and formulations showed increased waste loading while reducing the failure rate, demonstrating improved predictive accuracy, reduced uncertainties, and the effectiveness of active learning in guiding high-dimensional, nonlinear LAW glass design. This represents the first experimental validation of ML based LAW glass formulation, with practical benefits such as higher waste loading, shorter mission duration, and lower operational risk.

Lu, Xiaonan (ORCID:0000000179708148)↗
DOE OSTI2022

High-Assay Low Enriched Uranium Pyroprocessing and Electrometallurgical Treatment at the Fuel Conditioning Facility

Fuel Conditioning Facility (FCF) fulfills part of INL’s mission by reprocessing irradiated sodium-bonded fuel from the EBR-ll reactor and participates in research to further explore the fuel cycle for recovery of fuel from a variety of current reactors in use today. The fuel cycle of a nuclear reactor under the current non-proliferation act, leaves unused fuel in the spent fuel rods. The rods are safely stored until their fuel can be recovered. ? Fuel recovery from the EBR-II fuel pins has several steps to separate the uranium from the salts. The pyroprocessing technology involves high - temperatures, chemical and electrochemical methods for separating the unused uranium from the salts, fission products, and used fuel. The recovered uranium is then remixed and formed into ingots or Regulus with an enrichment of < 20% U-235. This final product is a source to fuel current nuclear reactors as well as research and development for future nuclear reactors

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Structural evidence for intermediates during O 2 formation in photosystem II

In natural photosynthesis, the light-driven splitting of water into electrons, protons and molecular oxygen forms the first step of the solar-to-chemical energy conversion process. The reaction takes place in photosystem II, where the Mn 4 CaO 5 cluster first stores four oxidizing equivalents, the S 0 to S 4 intermediate states in the Kok cycle, sequentially generated by photochemical charge separations in the reaction center and then catalyzes the O–O bond formation chemistry. Here, we report room temperature snapshots by serial femtosecond X-ray crystallography to provide structural insights into the final reaction step of Kok’s photosynthetic water oxidation cycle, the S 3 →[S 4 ]→S 0 transition where O 2 is formed and Kok’s water oxidation clock is reset. Our data reveal a complex sequence of events, which occur over micro- to milliseconds, comprising changes at the Mn 4 CaO 5 cluster, its ligands and water pathways as well as controlled proton release through the hydrogen-bonding network of the Cl1 channel. Importantly, the extra O atom O x , which was introduced as a bridging ligand between Ca and Mn1 during the S 2 →S 3 transition disappears or relocates in parallel with Y z reduction starting at approximately 700 μs after the third flash. The onset of O 2 evolution, as indicated by the shortening of the Mn1–Mn4 distance, occurs at around 1,200 μs, signifying the presence of a reduced intermediate, possibly a bound peroxide.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Oil Refinery

Efforts to identify the most-economic methods to decarbonize several sectors of the U.S. economy are underway. Industrial processes such as crude-oil refining rely heavily on energy-dense and easily stored and transported fossil fuels for powering their operations. Refineries use large amounts of energy, primarily derived from fossil sources to separate crude-oil components, break down heavier hydrocarbons into lighter compounds, remove impurities, reform hydrocarbon molecules, and generate steam and electricity for pumps and compressors and other various auxiliary systems. Crude-oil refining operations such as distillation, cracking, desulfurization, reforming, utilities systems and some offsite facilities collectively account for most of the energy consumption. Other operations such as hydrocracking or hydrotreating also require hydrogen for developing hydrogenation reactions which involve substantial heating to keep the reactors at high-temperature and pressure levels. All heat and energy demands are typically provided by natural gas (NG), oil, or other fuels, which makes refinery industry one of the most-difficult sectors to decarbonize. Nuclear power is a viable and energy-dense source of clean electricity, heat, and hydrogen to provide the large, sustainable energy supply that the refining industry demands. The U.S. Department of Energy’s (DOE’s) Integrated Energy Systems (IES) program is working to perform research and development, design, economic siting, and risk analysis. This state-of-the-art work will enable the first on-site demonstrations and commercial deployments of advanced small modular nuclear reactors (SMNRs) integrated with industries such as chemical production, refining, iron and steel making, and more. IES seeks to demonstrate the ability of advanced nuclear reactors to meet the heat and power demands of these industries while reducing carbon emissions in a sustainable and cost-competitive way. The primary objective of this research effort is to analyze industrial-scale SMNR integration intended to decarbonize refining facilities. The foreseen outcome is the provision of reliable, cost-competitive, and sustainable clean energy, alongside a reduction of carbon emissions. Specifically, the focus of this work lies on meeting the reference facilities’ heat and electricity demands with nuclear power while also supplying clean hydrogen via integrated high-temperature steam electrolysis (HTSE). This report presents a comprehensive technical and economic assessment of the integration of advanced nuclear reactors into a reference refinery, leveraging financial incentives from the Inflation Reduction Act (IRA). The evaluation aims to explore the potential economic benefits and challenges associated with incorporating advanced nuclear reactors into refinery operations, particularly in terms of energy efficiency, economic implications and environmental impact. By examining both the technical feasibility and economic viability, this analysis seeks to identify existing gaps and propose solutions for successful nuclear integration implementation. The findings are intended to provide valuable insights for stakeholders considering the adoption of advanced nuclear reactors in the refining sector. A refinery reference-plant was developed, using an open-source refinery model, Petroleum Refinery Lifecycle Inventory Model (PRELIM) and expert assessment, as a base case for comparison with various nuclear integration options. The capacity of 100 kbd/day (KBD) of heavy crude-oil feed was selected to represent a general coking-type refinery with deep conversion capabilities (incorporating heavy-oil upgrading with FCC, coking, and associated hydrotreating process units), using a heavy crude-oil feed, which represents about 70% of U.S. refineries configurations. A summary of all cases considered in this study is shown in Table 1.

13 HYDRO ENERGY↗
DOE OSTI2026

A high-voltage MR-ToF mass spectrometer and separator for the study of exotic isotopes at FRIB

The Facility for Rare Isotope Beams (FRIB) delivers a wide variety of rare isotopes as fast, stopped, or reaccelerated beams to enable forefront research in nuclear structure, astrophysics, and fundamental interactions. To expand the scientific potential of FRIB’s stopped and reaccelerated beam programs, we are designing a Multi-Reflection Time-of-Flight mass spectrometer and separator (MR-ToF MS). It will enable high-precision mass measurements of short-lived isotopes, improve beam diagnostics, and deliver isobarically and isomerically purified beams to downstream experimental stations. It is designed to store ions at a kinetic energy of 30 keV, significantly enhancing ion throughput while maintaining high mass resolving power. In conclusion, we present the scientific motivation, technical design, and simulations demonstrating the expected performance of the system, which has the potential to significantly enhance FRIB’s mass measurement, diagnostic, and mass separation capabilities.

Electrostatic ion beam trap↗
DOE OSTI2020

The JSNS 2 data acquisition system

The JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) experiment aims to search for neutrino oscillations over a 24 m short baseline at J-PARC. The JSNS 2 inner detector is filled with 17 tons of gadolinium(Gd)-loaded liquid scintillator (LS) with an additional 31 tons of unloaded LS in the intermediate γ-catcher and an optically separated outer veto volumes. A total of 120 10-inch photomultiplier tubes observe the scintillating optical photons and each analog waveform is stored with flash analog-to-digital converters. Here, we present details of the data acquisition, processing, and data quality monitoring system. We also present two different trigger logics which are developed for the beam and self-trigger.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2020

Testing the HM-5s and NaIGEM code

We tested the performance of the HM-5 units and the NaIGEM code to determine the uranium enrichment. The uranium materials consist of two groups, the NBS series and the UISO series. The NBS items are U 3 O 8 in aluminum containers with an inner diameter of 7.0 cm and a frontal surface thickness of 2.0 mm. The UISO items are U 3 O 8 stored in doubly encapsulated stainless steel containers with a total thickness of 0.82 mm. The diameter of the inner UISO container is 10.0 cm. Four separate tests were performed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

FY22 Development of Improved Grout Waste Forms for Supplemental Low Activity Waste Treatment

About 54 to 56 million gallons of radioactive mixed waste is currently stored in underground tanks at the United States Department of Energy’s (DOE’s) Hanford site in the State of Washington. This waste will be separated into low- and high activity waste fractions, which will then be vitrified respectively into Immobilized Low Activity Waste (ILAW) and Immobilized High Level Waste (IHLW) products for subsequent disposal. The ILAW product will be disposed of in an engineered facility at the Hanford site while the IHLW product is designed for acceptance into a national deep geological disposal facility for high level nuclear waste. Treatment of the tank waste will take place in the Hanford Tank Waste Treatment and Immobilization Plant (WTP), which is under construction. However, since the WTP Low Activity Waste (LAW) Vitrification Facility was not designed to process the entire inventory of Hanford LAW, up to half of the retrieved Hanford LAW will require supplemental immobilization. Immobilizing LAW in a cementitious waste form known as Cast Stone has been investigated as a possible candidate supplemental immobilization technology

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Evaluation of the lgnitability Hazards in Remediated Nitrate Salt Waste

The hazards associated with the characteristic of ignitability in remediated nitrate salt (RNS) waste containing nitric acid (HNO 3 ) and metal nitrate salts, currently stored at the Waste Control Specialists (WCS) facility near Andrews, Texas have been evaluated. For these waste drums there are two ignitability hazards that may apply, each separately distinguished from the other and uniquely correlated to one of two competing reaction mechanisms. One ignitability hazard, designated the autocatalytic thermal runway, is uniquely correlated to an acid/base reaction of hydrolysis of a hydrated metal nitrate salt. The other applicable ignitability hazard, designated the oxidizer property, is uniquely correlated to a redox disproportionation reaction involving an anhydrous metal nitrate salt. Which reaction mechanism pathway is accessible to a given metal nitrate, and therefore which ignitability hazard may apply, depends on the acidity of the metal ion and its corresponding capacity to maintain coordination with water.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Diverse simulations of time-resolved photoluminescence in thin-film solar cells: A SnO 2 /CdSe y Te 1-y case study

Time-resolved photoluminescence (TRPL) is widely used to measure carrier lifetime in thin-film solar cell absorbers. However, the injection dependence of data and frequent non-exponential decay shapes complicate the interpretation. Here, we develop a numerical model to simulate injection-dependent TRPL measurements in a SnO 2 /CdSe y Te 1-y case study solar cell structure, considering parameters of interest to researchers in industry and academia. Previous simulations have shown that, in low injection, excess electrons and holes injected by the laser pulse are rapidly separated in the electric field formed by the pn junction. As a result, at early times, the PL signal can decay faster than the Shockley-Read-Hall lifetime in the absorber bulk (t_bulk). Prior simulations have shown that the charge stored in the junction can slowly leak out to affect decays at late times. However, it has not been clear if and to what degree charge storage can affect the slopes extracted from TRPL decays - t_2 - commonly cited as the TRPL-measured lifetime. Here, we show that charge storage can, in some cases, result in t_2 values that substantially overestimate t_bulk. Previous simulations indicate that high injection conditions can screen the junction field and minimize charge separation. Here, we show that continued injection increases can drive down t_2 below t_bulk as radiative recombination becomes dominant. We catalog charge storage and radiative recombination impacts for a diverse set of material parameters and compare results to double-heterostructure models.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,S↗
DOE OSTI2020

Review of Mercury in the SRS Tank Waste Storage and Treatment Processes for Applicability to Hanford Waste Processing

The Savannah River Site (SRS) and Hanford produced large quantities of plutonium and other radioisotopes. The separations processes recovered plutonium and produced large quantities of High- Level Waste (HLW) that accumulated at each site in large, underground storage tanks. SRS has been retrieving the stored HLW, pretreating the waste for immobilization, and immobilizing the waste in glass or grout waste forms since the mid-1990s. Treatment of the waste at Hanford is expected to start in the early 2020s. Mercury is present in both the SRS and Hanford tank waste. SRS has recently completed an evaluation of mercury speciation throughout the tank waste treatment and immobilization flowsheet. This study was reviewed to determine what information from the study can be applied to the Hanford tank waste treatment program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗