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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Core Design Optimization of the Westinghouse Lead Fast Reactor

Westinghouse is pursuing an advanced Nuclear Power Plant design based on Lead Fast Reactor (LFR) technology for global commercialization. To achieve an optimal combination of key attributes, such as safety, sustainability, and economic competitiveness, Westinghouse and ANL partnered in developing and applying a formalized core design optimization strategy. An LFR analysis workflow was developed to automate a suite of reactor physics, fuels performance, safety, and economics simulations on a selected LFR concept. The workflow streamlines analysis of a wide range of LFR designs with different dimensions and fuel types to assess their viability and economic performance, significantly reducing human processing time and risks of processing errors. The LFR optimization exercise was defined, resulting in selection of the design constraints (geometric, neutronics, thermo-mechanical, safety, thermal-hydraulics, and economics) and performance metrics researched (minimization of both the fuels LCOE and the first core inventory cost). A total of 14 varied design parameters were considered, including assembly dimensions, coolant temperature, and enrichment distribution throughout the core. The LFR analysis workflow was connected to DAKOTA for sensitivity and optimization analyses. Due to the extremely large size of the potential LFR optimization solution space relative to the computing time required to characterize one LFR solution, a multi-stage optimization approach was proposed to breakdown the problem into several stages with more reasonable sizes. This optimization approach enabled finding various viable core solutions with different cost tradeoffs that were considered by Westinghouse and justify selection of a smaller core with multi-batch 2-year cycle length.

Stauff, Nicolas E.↗
DOE OSTI2025

Sequencing and analysis of 131 SARS-CoV-2 isolates in previously sampled and unsampled regions of Jordan from 2020 to 2023

The Hashemite Kingdom of Jordan remains an understudied country for next generation sequencing analysis of SARS-CoV-2 genomes collected during the 2019 pandemic. Here we provide 131 additional reference genomes collected between 2020–2023 from SARS-CoV-2-positive patients across Jordan. Phylogenetic analysis supports existing pandemic narratives of changing clade dominance over time and adds genomes in novel Jordanian locations and timepoints to make Jordan SARS-CoV-2 databases more comprehensive. Samples from the less-sequenced cities of Ajloun, Jaresh, Karak, and Madaba identified previously unreported lineages while Amman, Irbid, and Zarqa have existing sequencing efforts bolstered. Despite many incomplete patient records and a relatively small sample size, we observe interesting symptom patterns that support existing global and Jordanian pandemic narratives. We note how in-country COVID-19 pandemic genomic studies showcase Jordan’s efforts to expand next generation sequencing capabilities, especially through the leveraging of EDGE COVID-19, a bioinformatics platform for performing rapid, batched analysis of SARS-CoV-2 sequencing that streamlines sample processing prepared from a network of hospital locations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Investigation into Na and Cs activity coefficients in high salt solutions to support Cs removal in Hanford tank waste

The treatment of Hanford tank waste is one of the most technically challenging environmental cleanup activities for the U.S. Department of Energy to date. To expedite the processing of liquid waste stored in underground tanks in southeastern Washington state, it is necessary to remove the significant dose contributor, 137 Cs. Toward this effort, ion exchange with crystalline silicotitanate (CST) has been employed as part of the Tank Side Cesium Removal system. The model used to predict Cs exchange onto CST was developed using activity coefficients calculated from the Bromley equation. A series of batch contact tests that varied in [Na] were conducted to look at the impact of Na concentration on Cs distribution. Experimental distribution ratios ( K d ) were compared to the distribution ratios predicted using three different activity coefficient models: (1) commercially available HSC software, (2) the Bromley equation, and (3) a simplified approach adapted from Marcos-Arroyo et al. Ultimately, the Bromley method underpredicted the effect of ionic strength on the Na activity coefficient ( γ Na+ ), HSC overestimated the impact of ionic strength on the expected performance due to the Cs activity coefficient ( γ Cs+ ), but the simplified approach predicted the experimental K d values quite well in a binary matrix. In conclusion, expansion of this approach in complex matrices is necessary for application to Hanford tank waste.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Uptake and speciation of trace metal inputs to Wetland Soils from Illinois and South Carolina and Stream Sediments from Tennessee

Metals occur in all ecosystems, although their concentrations vary depending on their natural geologic conditions and surrounding human activities. In addition to metals intrinsically present from the geology of a particular system, metals can enter environmental systems from a wide variety of natural and anthropogenic sources, such as sediment re-suspension, mining operations, industrial processes, agricultural activities, and atmospheric deposition. While metals can be toxic at high concentrations, some metals serve as essential micronutrients for biogeochemical processes. Metal transport and availability in engineered and natural water systems depend on processes of adsorption/desorption, oxidation/reduction, dissolution/precipitation, and ligand complexation. Insights into the speciation of metals and their bioavailability will also help advance understanding of the roles of metals in the biogeochemical cycling of nutrients. We conducted batch experiments under anoxic conditions on soils and sediments collected from three different natural aquatic systems to understand their response to influxes of dissolved Cu, Ni and Zn. While soils and sediments from all sites could strongly bind added trace metals, there were substantial differences in trace metal uptake trends between different sites, especially for Cu. There was no distinct correlation between trace metal uptake and the total organic matter, iron, and sulfur content present in the samples. X-ray absorption spectroscopy indicated that the speciation of the freshly added metals taken up by the solids differs substantially from the speciation of the metals originally present in unamended samples. Cu sulfides dominated speciation at low loadings (1 µmol/g), whereas complexation to thiol groups and formation of metallic Cu governed speciation at high loadings (10 µmol/g). For Ni and Zn, adsorption to mineral surfaces and organic matter governed their speciation in materials from most sites. This study suggests that the background speciation of metals in natural aquatic systems is a poor predictor of the speciation and lability of metals introduced to terrestrial aquatic systems from anthropogenic or natural processes. Our findings imply that geochemical processes controlling trace metal speciation may vary considerably with metal loading in different natural systems. Data are provided for marsh wetland soils at Argonne National Laboratory (Marsh 1 and Marsh 2), riparian wetland soils (Riparian 1 and Riparian 2) in the Tims Branch watershed at Savannah River National Laboratory, and stream bed sediments (Stream 1 and Stream 2) from East Fork Poplar Creek near Oak Ridge National Laboratory. Data package includes the results of trace metal uptake experiments conducted on the selected sites for determining their capacity to immobilize metals under different loadings. XANES spectra for Cu, Ni, ad Zn at different loadings are included in the package. The abundance of different trace metal species obtained using linear combination fitting in ATHENA are also included in the data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Low Temperature Sequential Melting and Anion Retention in Simplified Low Activity Waste

This study seeks to understand the low temperature reactions of the salt phase that occur during the vitrification of Hanford Low Activity Waste (LAW). Salts (such as nitrates, sulfates, carbonates, halides, etc.) play a key role in these low temperature reactions as they sequentially melt, decompose, and volatilize during batch-to-glass conversion. To further understand these complex processes, simplified LAW melts containing oxyanion salts (sodium salts of carbonate, sulfate, and/or nitrate) and early melting glass formers (boric acid) have been evaluated using thermal analysis, infrared absorption spectroscopy, and X-ray diffraction. Results from this study indicate that the volatilization behavior of particular salts is influenced by the presence or absence of other salts. Here, NaNO 3 volatilization is decreased by the presence of Na 2 SO 4 . The addition of either Na 2 SO 4 or NaNO 3 to the system may enhance the volatilization of Na 2 CO 3 . In all cases, Na 2 SO 4 was retained after melting and was often found to be in two different crystalline phases upon quenching.

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

A perspective on Bayesian methods applied to materials discovery and design

For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process-structure-property-performance (PSPP) chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian Optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. Here, the ultimate goal is to pave the way to autonomous materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Scaling the polymerization of polyaldehydes through continuous flow synthesis

Abstract A plug flow reactor was constructed to scale the synthesis of metastable, phthalaldehyde-based polymers to achieve production rates of 1–2 kg per day. The flow-induced mixing and in-line polymerization quench and precipitation sequences resulted in improved polymer purity and long-term stability compared to the same materials made from a conventional batch process. Cryogenic rheology was used to probe the complex fluid dynamics encountered during the polymerization of PPA homopolymers. It is envisioned that this continuous flow manufacturing approach could be extended to other low ceiling temperature or aldehyde monomer systems to help implement and support a plastic circular economy. Graphical abstract

Schwartz, Jared M.↗
DOE OSTI2021

Chromium and Vanadium Incorporation in Sulfate-Containing Sodium Aluminoborosilicate Glass

In the composition space for Hanford low-activity nuclear waste glass, the waste loading of some formulations is limited by poor incorporation of sulfate. In particular, certain elements identified in previous studies tend to raise or lower sulfate solubility in borosilicate glass. The aim of the current study is to understand how vanadium and chromium each affect a sulfate-containing sodium aluminoborosilicate glass structure. Glass transition temperature, mass density, visible absorption, and Raman scattering were measured to investigate changes in the glass structure. Both Cr6+ (CrO42-) and Cr3+ were found in the chromium-containing glasses while vanadium primarily existed in the 5+ oxidation state in the vanadium-containing glasses. Electron probe microanalysis determined elemental compositions to assess retention of components. Increasing Cr2O3 caused saturation of Cr concentration within the glass and formation of crystalline eskolaite (Cr2O3). On the other hand, all the targeted V was incorporated into the glass. A fraction of batched S was not incorporated in any glass, presumably due to volatilization during melting. Apparently, the details of incorporation of Cr, and to an extent V, are different depending on whether sulfur is added as in this study or in oversaturated conditions as described in the literature, where Cr partitions to a sulfate-containing salt phase.

Borosilicate glass, Waste Glass, vanadium chemistr↗
DOE OSTI2022

Process optimization of caustic scrubber and iodine-129 immobilization in sodalite-based waste forms

Caustic scrubbers are proposed to remove vaporized iodine from nuclear fuel reprocessing plants. Resulting caustic slurries of radioactive iodine ( 129 I) and other anions can be captured and immobilized into the cage structures of sodalite-type minerals. Here, mixed anion sodalites were hydrothermally synthesized from a simulated caustic scrubber solution containing hydroxide/water, carbonate, chlorine, bromine, iodine, nitrate, and nitrite ions with added kaolinite. Experiments were conducted with twenty-five different sets of process conditions. Resulting powders were characterized by quantitative X-ray diffraction (XRD) and multi-functional thermal analysis, including gravimetry, calorimetry, and mass spectrometry. Using statistical analysis, process variables were tailored to maximize conversion of kaolinite into cage silicates, immobilize caustic scrubber anions, and minimize amorphous content. Further, chemical analysis showed the incorporation of all the targeted ions into the solids. Detailed investigation of a batch characterized after washing (creating secondary waste) and one without washing but subsequent calcination is given. This work contributes to defining the constraints required for subsequent consolidation process steps of the waste form to avoid volatilization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Synthesis and characterization of polylactic acid microspheres via emulsion-based processing

Polylactic acid (PLA) microspheres are primarily used in drug delivery applications and rarely manufactured commercially. In encapsulation, the drug encapsulation and microsphere synthesis process happen simultaneously. Simultaneous synthesis necessitates conditions that ensure the drugs to be encapsulated remain viable in the synthesis process. Here, in the present work, solid pure PLA microspheres are prepared using a repeatable centrifugal mixing method to create a single emulsion system. A planetary thinky mixer is used to achieve centrifugal mixing. PLA acts as the dispersed phase of the emulsion, whereas Polyvinyl alcohol (emulsifier) dissolved in deionized water serves as the bulk phase. The emulsions are stirred for about 4 min at 2000 rpm. Microspheres prepared by mechanical mixing are compared with microspheres prepared by centrifugal mixing. The microspheres prepared by centrifugal mixing proved to be narrower and closer to a normal distribution with a mean size (D x 50) of 30 microns. The size distribution of planetary mixed microspheres did not change from one batch to another.

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

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

Here, we present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Plutonium Retention by Crystalline Silicotitanate under Hyperalkaline Conditions Relevant to Tank-Side Cesium-Removal at the Hanford Site

Crystalline silicotitanate (CST) is used in Hanford’s Tank-Side Cesium-Removal (TSCR) process to selectively remove Cs-137 from highly caustic, nitrate-rich tank supernatants. Recent testing with actual waste samples suggests that CST can also retain measurable plutonium (Pu), which could affect radiological classification and disposal pathways for spent CST. To quantify this behavior, Pu partitioning to CST was studied under Hanford-relevant conditions using batch-contact experiments in a representative simulant (2 M NaNO3, 0.7 M NaOH). Isotherm data were measured and distribution ratios calculated, with Cs+ uptake used as benchmark. Under low-carbonate conditions, Pu was retained strongly by CST in systems initially contacted with either PuO2 nanoparticles (Pu(IV)) or aqueous Pu(VI), with distribution ratios of ~2,200–3,700 mL/g, generally exceeding those for Cs+ (~400–1,000 mL/g). Increasing carbonate concentration strongly reduced PuO2 nanoparticle retention; at [Na2CO3] = 1 M, distribution ratios decreased by up to one order of magnitude to roughly 100–300 mL/g. Electron microscopy suggests that Pu retention involves a combination of mechanisms such as PuO2 NP aggregation induced by CST leachate components, and association with CST bead surfaces.

Neumann, J.↗
DOE OSTI2023

Geospatial and Information Substitution and Anonymization Tool (GISA)

The Geospatial and Information Substitution and Anonymization Tool (GISA) incorporates techniques for obfuscating identifiable information from point data or documents, while simultaneously maintaining chosen variables to enable future use and meaningful analysis. This approach promotes collaboration and data sharing while also reducing the risk of exposure to sensitive information. GISA can be used in a number of different ways, including the anonymization of point spatial data, batch replacement/removal of user-specified terms from file names and from within file content, and aid with the selection and redaction of images and terms based on recommendations using natural language processing. Version 1 of the tool, published here, has updated functionality and enhanced capabilities to the beta version published in 2023. Please see User Documentation for further information on capabilities, as well as a guide for how to download and use the tool. If there are any feedback you would like to provide for the tool, please reach out with your feedback to edxsupport@netl.doe.gov. Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The Geospatial and Information Substitution and Anonymization Tool (GISA) was developed jointly through the U.S. DOE Office of Fossil Energy and Carbon Management’s EDX4CCS Project, in part, from the Bipartisan Infrastructure Law.

Bipartisan Infrastructure Law↗
DOE OSTI2022

Simulation of Bunch Formation for the Mu2e Experiment

The Fermilab Recycler is an 8 GeV storage ring composed of permanent magnets that was crucial to the success of the Fermilab Tevatron Collider program. It is currently being used to slip-stack protons for the high energy neutrino program and to re-bunch protons for use in the Muon g-2 and Mu2e experiments. For the latter applications, the Recycler re-bunches each 1.6 µs "batch" from the Fermilab Booster into four 2.5 MHz bunches. For the Mu2e experiment, it is crucial that beam more than 125 ns from the nominal bunch center be suppressed by at least a factor of 1E-5. While bunch formation is currently in operation for the g-2 experiment, this out of time requirement has not been met, and the reason is not understood. This work presents a simulation of bunch formation in the Recycler, in an effort to understand the reason for this excessive out of time beam and to search for a way to reduce it.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Design Considerations and Operational Features of the Collimators for the Fermilab Main Injector and Recycler

The Fermilab Main Injector system delivers 700 kW of 120 GeV Proton beam for neutrino experiments. Since 2013 this has been achieved using slip stacking accumulation in the Recycler with up to 12 batches from the Fermilab Booster per Main Injector Ramp Cycle. To control activation from beam loss, collimation systems in the Booster to Recycler transfer line, in the Recycler and in the Main Injector are employed. Residual radiation measurements around the ring with detailed studies at the collimators are required to maintain adequate loss control. We will review design considerations, operational parameters and activation results for more than ten years of operation. Simulations with MARS15 are used to explore the activation rates and the isotopic composition of the resulting activation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Microbial potentiometric sensor technology for real-time detecting and monitoring of toxic metals in aquatic matrices

Considering that toxic metals can affect metabolic processes in microorganisms adversely, it can be hypothesized that these metals in water matrices would induce a decrease in metabolic activity of the biofilm microorganisms populating the surface of a sensing electrode, which could be registered as a change in the open-circuit potential (OCP) generated by the biofilm microorganisms. The goal of this study was to test this hypothesis and demonstrate the underlying principle that microbial potentiometric sensor (MPS) technology could be used for long-term and real-time monitoring and detection of rapid changes in metal concentrations in realistic aquatic environments. To address the goal, four objective were addressed: (1) a batch reactor with three graphite-based MPS electrodes was fabricated; (2) a set of single-ion solutions and one multiple ion solution were prepared reflecting realistic concentrations of metals found in electroplating wastewaters; (3) the responses of the MPS to the simultaneous presence of multiple toxic metal ions in a single solution were measured; and (4) the changes of the MPS signals to the presence of individual metal ion solutions were examined. While the hypothesis was validated, the study also revealed that the MPS was sufficiently sensitive to not only detect, but also quantify, toxic metal ion concentrations in aqueous solutions. The coefficients of determination, which were R 2 > 0.995, and responsiveness of <1 μmol/L for some toxic metal cations, strongly support the performance of MPS technology in the echelons of expensive analytical tools capable detecting and measuring trace elements.The magnitude of the MPS response was toxic metal specific. When the molar concertation normalizes the inhibition portion of the signal area, the assessed sensitivity order was: Se>Cd>Pb>Ag>Ni> Zn. The study provides valuable information for enforcement agents, environmental professionals, and wastewater treatment operators, so toxic metal pollution and its detrimental impacts can be prevented and mitigated.

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

Probing interspecies metabolic interactions within a synthetic binary microbiome using genome-scale modeling

Metabolic interactions within a microbial community play a key role in determining the structure, function, and composition of the community. However, due to the complexity and intractability of natural microbiomes, limited knowledge is available on interspecies interactions within a community. In this work, using a binary synthetic microbiome, a methanotroph-photoautotroph (M-P) coculture, as the model system, we examined different genome-scale metabolic modeling (GEM) approaches to gain a better understanding of the metabolic interactions within the coculture, how they contribute to the enhanced growth observed in the coculture, and how they evolve over time. Using batch growth data of the model M-P coculture, we compared three GEM approaches for microbial communities. Two of the methods are existing approaches: SteadyCom, a steady state GEM, and dynamic flux balance analysis (DFBA) Lab, a dynamic GEM. We also proposed an improved dynamic GEM approach, DynamiCom, for the M-P coculture. SteadyCom can predict the metabolic interactions within the coculture but not their dynamic evolutions; DFBA Lab can predict the dynamics of the coculture but cannot identify interspecies interactions. DynamiCom was able to identify the cross-fed metabolite within the coculture, as well as predict the evolution of the interspecies interactions over time. A new dynamic GEM approach, DynamiCom, was developed for a model M-P coculture. Constrained by the predictions from a validated kinetic model, DynamiCom consistently predicted the top metabolites being exchanged in the M-P coculture, as well as the establishment of the mutualistic N-exchange between the methanotroph and cyanobacteria. The interspecies interactions and their dynamic evolution predicted by DynamiCom are supported by ample evidence in the literature on methanotroph, cyanobacteria, and other cyanobacteria-heterotroph cocultures.

59 BASIC BIOLOGICAL SCIENCES↗