Search NASA⌕ Search

SEARCH · Search NASA

Results for “Sample selection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27

Node Distortions in UiO-66 Inform Negative Thermal Expansion Mechanisms: Kinetic Effects, Frustration, and Lattice Hysteresis

In metal–organic frameworks (MOFs) the interplay between the dynamics of individual components and how these are constrained by the extended lattice can yield unusual emergent phenomena. For the archetypal Zr-MOF, UiO-66, we explore the cooperative dynamics of a Zr-node transformation that gives rise to negative thermal expansion (NTE). Here, using in situ synchrotron X-ray scattering, with powder diffraction and pair distribution function (PDF) analyses, we identify lattice hysteresis and a thermal ramp-rate-dependence of the thermal expansion. Specifically, kinetic trapping of distorted node states formed at high temperature, leads to broad variability in the apparent thermal expansion which ranges from large positive to large negative thermal expansion with coefficients of thermal expansion (CTE) from +45 to –80 × 10 –6 K –1 . Time-resolved relaxation studies at selected temperatures suggest that when equilibrated UiO-66 is intrinsically NTE, with a CTE of –35 × 10 –6 K –1 . Kinetic trapping of the node-distorted state following high temperature activation has broad implications for characterization and applications of these Zr-MOFs; the nonequilibrium node state depends on the thermal history of the sample with quench vs slow cooling likely to impact gas binding, pore volume, and accessible catalytic sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Search for New Physics via EFT in collisions containing a top quark and a boosted Z or Higgs boson

We present a generator-level sensitivity study of physics beyond the Standard Model within the framework of Effective Field Theory (EFT). The targeted signal processes are single top quark production in association with a Lorentz-boosted Higgs or Z boson (tHq and tZq) in proton-proton collisions at a center-of-mass energy of 13.6 TeV. Event selection requires one electron or muon, a high-transverse-momentum, large-radius (AK8) jet reconstructing hadronic H → b̄b or Z → b̄b decays, at least two distinct, non-overlapping small-radius (AK4) jets with one originating from a b quark, and a missing transverse energy greater than 30 GeV. Signal and background samples are generated via Monte Carlo simulation using MadGraph with MadJax to perform EFT matrix-element calculations. Deviations from the Standard Model are parametrized through dimension-six EFT operators that probe modified top quark interactions. This generator-level study identifies kinematic observables with enhanced sensitivity to EFT operators and motivates future reconstruction-level analyses of tHq and tZq channels.

Meshramkar, Daniel [Baylor U., Waco]↗

Search for New Physics via EFT in collisions containing a top quark and a boosted Z or Higgs boson

We present a generator-level sensitivity study of physics beyond the Standard Model within the framework of Effective Field Theory (EFT). The targeted signal processes are single top quark production in association with a Lorentz-boosted Higgs or Z boson (tHq and tZq) in proton-proton collisions at a center-of-mass energy of 13.6 TeV. Event selection requires one electron or muon, a high-transverse-momentum, large-radius (AK8) jet reconstructing hadronic H → b̄b or Z → b̄b decays, at least two distinct, non-overlapping small-radius (AK4) jets with one originating from a b quark, and a missing transverse energy greater than 30 GeV. Signal and background samples are generated via Monte Carlo simulation using MadGraph with MadJax to perform EFT matrix-element calculations. Deviations from the Standard Model are parametrized through dimension-six EFT operators that probe modified top quark interactions. This generator-level study identifies kinematic observables with enhanced sensitivity to EFT operators and motivates future reconstruction-level analyses of tHq and tZq channels.

Meshramkar, Daniel [Baylor U., Waco]↗

An iterative method to deblend AGN-Host contributions for Integral Field spectroscopic observations

ABSTRACT We present a new iterative deblending method to separate the host galaxy (HG) and their Active Galactic Nuclei (AGNs) emission with the use of Integral Field spectroscopic (IFS) data. The method decomposes the resolved HG emission from the unresolved AGN emission by modelling the two-dimensional surface brightness (SB) profile of the point-spread function (PSF) and the two-dimensional SB HG continuum simultaneously per each monochromatic slide. Our method does not require any prior information about the observed SB profile or a detailed fitting of the PSF, making it ideal for the automatic analysis of large galaxy samples. In this work, we test the quality of our method, its advantages, and its disadvantages. We test our method by using a set of IFS mock data cubes to quantify the reliability of our deblending process and further compare our method with the qdblend3d analysis tool. Furthermore, we applied our method to three data cubes selected from the MaNGA survey according to the dominance of either its HG or its AGN. We show that our deblending method is capable of disengaging the bright, non-resolved AGN emission from the HG continuum and its narrow emission lines. However, the decoupling depends on how well the IFS spatially resolves the PSF, and on the relative flux intensity of the HG-AGN. Therefore, the method is ideal for disentangling the bright-flux contribution from AGN-dominated spectra.

Ibarra-Medel, H. (ORCID:0000000297906313)↗

Intrinsic Kinetics of Polyethylene Terephthalate Pyrolysis via Micropyrolysis and Multivariate Chromatographic Analysis

This study provides an in-depth investigation of the primary decomposition of polyethylene terephthalate (PET) via pyrolysis, employing an experimental-analytic workflow that integrates design of experiments (DoE), micropyrolysis coupled with comprehensive two-dimensional gas chromatography (GC×GC), and multivariate data analysis to verify intrinsic kinetic conditions and elucidate evolving product distributions for mapping key reaction pathways. Peaks that could not be identified using commercial spectral libraries were assigned using Mass Frontier simulations, enabling the identification of divinyl terephthalate, ethyl vinyl terephthalate, and 2-(benzoyloxy)ethyl vinyl terephthalate. A polar×polar (non-orthogonal) column set tailored for the detection of carboxylic acids enhanced the quantification of benzoic acid, 4-vinylbenzoic acid, 4-ethylbenzoic acid, and methylbenzoic acid by up to 6-fold relative to an orthogonal column combination (non-polar×mid-polar). Moreover, pyrolysis variables were systematically evaluated using a Box- Behnken design (BBD), encompassing pyrolysis temperature (500−600 °C), sample weight (50−150 μg), and carrier gas flow rate (100−300 mL min −1 ). Among these, pyrolysis temperature was the only statistically significant factor influencing product yields, ranging from 58.78 to 84.26 wt %. In contrast, neither the sample weight nor the carrier gas flow rate had a significant effect on product yields within the evaluated experimental space. At 600 °C, the major pyrolysis products were benzoic acid (up to 20.20 ± 1.46 wt %) and CO 2 (up to 21.28 ± 1.46 wt %), which can be produced through decarboxylation reactions. These findings underscore the critical importance of selecting appropriate analytical columns for the accurate quantification of heteroatomcontaining products such as carboxylic acids, which may otherwise be underestimated or undetected due to their reactivity with the stationary phase of non-polar and mid-polar columns, as well as other GC components. They also highlight the importance of selecting pyrolysis conditions for investigating the primary decomposition of PET under an isothermal kinetically limited regime.

aromatic compounds↗

A large interlaboratory electron diffraction study of monolayer graphene

Standardisation of data collection and analysis is essential to enable commercialisation of 2D materials in a wide range of technologies. Selected area electron diffraction (SAED) in the transmission electron microscope (TEM) is one of the key methods for distinguishing monolayer from bilayer and few-layer graphene by comparing the 1st and 2nd order diffraction spot intensities. Yet there are many factors that can affect the reliability of data collection and interpretation, causing the measurement of monolayer samples to deviate from the literature boundary condition of $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1 for monolayer graphene (1LG). Here we present the results of a large interlaboratory SAED comparison study, where 15 international laboratories measured and analysed nominally identical samples of chemical vapour deposited graphene. Large variations were observed in the measured ratios of diffraction spot intensities, with the largest variance associated with poor quality SAED data resulting from inadequate specimen handling and storage. To inform the reliable determination of monolayer thickness from SAED patterns we provide a description of best practice for specimen handling, TEM operation, data collection and analysis. This work was undertaken within VAMAS Technical Working Area 41: Graphene and related 2D materials—Project 9, the results of which have been directly incorporated into ISO/TS 21356–2 for the characterisation of graphene sheets. We find that when this methodology is followed, 1LG can be distinguished from bilayer or thicker material with high confidence where analysis of a single SAED pattern gives $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1.2, even in the absence of precise specimen tilting.

2D materials↗

Cleaned 5-Minute Resolution Air Quality and Meteorological Data from Nine TCEQ CAMS Sites in Houston, Texas (Nov 2021 – Oct 2022)

These data encompass 5-minute air monitoring and meteorological observations collected in the greater Houston, Texas metropolitan region, at nine (9) Continuous Ambient Monitoring Stations (CAMS) operated by the Texas Commission on Environmental Quality (TCEQ) between November 1, 2021 and October 31, 2022. The CAMS sites (CAMS 1, 8, 35, 45, 148, 403, 405, 410, and 1052) were chosen because their instrumentation includes measurements of PM2.5. These sites also provide continuous multi-parameter air-quality and meteorological measurements. Particulate matter (PM2.5, PM10) was sampled along with several trace gases, including ozone (O3), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and carbon monoxide (CO). The data set also contains standard surface meteorological parameters (temperature, humidity, pressure, wind speed, and wind direction). Several sites also include AutoGC-based measurements of volatile organic compounds (VOCs). Air monitoring instruments deployed at the selected sites comprise the following systems: BAM-1020 or TEOM (PM2.5), Thermo Scientific TEI 49i (O3), TEI 42i (NOx), and AutoGCs (VOCs). This data set is similar to the data included within the houairq5mX1.00 datastream, except for a few additional quality control steps. A systematic data cleaning and verification process was performed on the data set to ensure its quality and preparation for analysis. Removal of non-numeric status flags (e.g., [LIM], [QAS], [SPZ], [CAL], [PMA], [AQI], [SPN], [MAL]) was accomplished by employing rule-based string parsing to extract valid numerical values. Missing entries were set to -9999; however, invalid or anomalous values (e.g., 99999) were retained as originally reported by the TCEQ to preserve data provenance. The time sequence was verified for completeness, removal of duplicates, and uniformity at 5-minute intervals. Column labeling was standardized, and corresponding values were assessed for physical plausibility. All timestamps in the data set were reported in Coordinated Universal Time (UTC) as provided by the TCEQ. Further, the latitude and longitude coordinates were added for each CAMS site. A subset of the data (June 1–September 30, 2022) has been used in the following publication: Subba et al. 2025. “Implications of sea breeze circulations on boundary layer aerosols in the southern coastal Texas region.” EGUsphere 2025: 1–49, https://doi.org/10.5194/egusphere-2025-2659.

latitude↗

Altering translation allows E. coli to overcome chemically stabilized G-quadruplexes

Genomic DNA from each sample was prepared using the Wizard Genomic DNA Purification Kit (Promega) and after, DNA was quantified using the QuantiFluor ONE dsDNA System (Promega). Genomic DNA underwent shearing to ~200 bp fragments via sonication and the gDNA fragments were prepared for sequencing using the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB). Bead-based size selection was used to select ~200 bp fragments and the fragments then underwent a splinkerette PCR using a Tn5-enriching forward primer and custom reverse primers for multiplexing. A final bead-based size selection was used to select for the correct length DNA. DNA was sequenced at the University of Michigan Advanced Genomics Core using Illumina sequencing with a custom read primer reading the last 10 nt of the transposon. PhiX174 DNA spike was added to the run to ensure sufficient sequence diversity on the flow cell. Then, a custom index read primer and standard Illumina primer were used to sequence the index reads and PhiX174, respectively.

Keck, James L.↗

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗

Post-composing ontology terms for efficient phenotyping in plant breeding

Abstract Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

Mathematical & Computational Biology↗

Characterization of Most Promising Sequestration Formations in the Rocky Mountain Region

The project Characterization of Most Promising Sequestration Formations in the Rocky Mountain Region is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The Rocky Mountain Carbon Capture and Storage (RMCCS) project investigated multiple geologic formations and characterized a local site on the Colorado Plateau for future CCS opportunities. The RMCCS project focused on the Cretaceous Dakota, Jurassic Entrada, and Pennsylvanian Weber Sandstones, the three largest regional formations. All formations in this project are potential CO2 storage resources for future power plants, natural gas processing plants, cement plants, and oil shale development projects. The area adjacent to Craig, Colorado, (Sand Wash Basin) was the area selected for detailed geologic characterization on the RMCCS project. The basin was selected in part because the geology can be extrapolated to other sites on the Colorado Plateau. Field mapping and seismic surveys were conducted to identify and evaluate the basin's structural configuration. A 9,745-foot deep characterization well was drilled to collect 131 feet of core and a suite of geophysical well log data. Petrophysical tests on samples of core were used to calibrate geophysical log data, which can be used to obtain storage resource estimates and evaluate associated uncertainty as well as simulate the hydrologic behavior of injected CO2. A detailed analysis of the primary formations (Dakota, Entrada and Weber sandstones) yielded a more accurate CO2 storage resource assessment for these formations within the Colorado Plateau; RMCCS estimates indicate a total CO2 storage resource of more than 38,000 million metric tons. The characterization of the Sand Wash Basin (2-D seismic surveys, multiple well logs and lithological, petrophysical and geochemical analyses) allowed for a detailed 3-D model to be constructed. The model served as the framework for analyses ranging from CO2 storage resource, injectivity, and subsurface flow to uncertainty estimates to evaluation of risk.

2-D seismic↗

Reducing module soiling with scalable and robust photocatalytic coatings

The air-glass interface at the front of a photovoltaic (PV) module reflects approximately 4% of incident light, decreasing the potential power output of the module by the same amount. Today’s modules reduce this loss by adding a low-refractive-index (1.25-1.30) SiO2 coating to the sunward side of the module glass; this antireflection coating recovers approximately 3% of the 4% light that would otherwise be lost. While such antireflection coatings work very well on clean, new modules, they do not inhibit soiling—the accumulation of soilants such as dust, pollen, soot, or other foreign material—on the module glass, and soilants reflect and scatter incident light. An improved coating would serve provide not only an antireflection effect, but also an anti-soiling effect. The goal of this project was to develop such a coating and provide a path for it to be manufactured in the U.S. The project successfully designed and fabricated coatings that provided >3% transmittance gain compared to bare glass (matching the performance of commercial antireflection coatings) and displayed anti-soiling behavior in standard laboratory soiling effects. This was achieved by using a Swift Coat proprietary coating deposition technique, aerosol impact-driven assembly (AIDA), to control the porosity and thus refractive index of coatings of photocatalytic materials—such as TiO2—that would otherwise increase (instead of decrease) reflection. These combined antireflection/anti-soiling coatings passed PV industry standard module reliability tests as well as coating-specific abrasion tests, showing that they have the durability needed for decades in the field. Swift Coat scaled the AIDA hardware and deposition process to make mini-modules that were monitored for nearly two years during field tests administered by a third party, as well as demonstrated scaling to the widths of full-sized modules. The fielded mini-modules outperformed reference modules (with commercial antireflection coatings) in two locations, providing a 1% absolute average performance boost and larger increases during periods of heavier soiling. Swift Coat’s cost analysis indicated a coating manufacturing cost below the sales price of today’s antireflection coatings. More than five module manufacturers sampled and assessed the coatings, and three provided letters of support. The coating developed in this project increases the energy output of PV modules, thereby decreasing the cost per kilowatt-hour of solar energy generated. Cheaper solar electricity benefits the public by accelerating the transition to a stable, affordable, carbon-free energy economy. In addition, for select applications in which PV modules are highly visible—such as on residential rooftops—the coating provides an aesthetic benefit because it stays cleaner than today’s modules. Finally, Swift Coat and its prospective customers are U.S. companies, and successful commercialization of this technology will provide U.S. jobs and a secure solar supply chain.

14 SOLAR ENERGY↗

Investigating Material Properties of Subsurface Rock Formations Modified by Engineering Mineral Precipitation (Final Scientific and Technical Report)

Montana State University’s (MSU) Energy Research Institute (ERI), in collaboration with the Center for Biofilm Engineering (CBE) and the Department of Civil Engineering (CE), has conducted a long‐term research program aimed at developing a novel cementing agent to address wellbore integrity and reduce the unwanted upward migration of fluids and greenhouse gases from the subsurface. The primary technology developed through this research program is known as ureolysis‐induced calcite precipitation (UICP), which harnesses bio‐chemical processes to precipitate calcium carbonate (CaCO 3 ). The same general process can also be called microbially-induced calcium carbonate precipitation (MICP) when microbes provide the process-catalyzing urease enzyme. Both terms are used in this report. Results have conclusively demonstrated that, if properly controlled, UICP can successfully seal fractures, high permeability zones, and compromised cement in the vicinity of wellbores and in nearby caprock. This technology has been successfully deployed to mitigate annular leakage in two test wells and over sixty commercial wells with a 100% success rate. This success in downhole deployment generates consideration of other subsurface applications where UICP could provide benefit to the energy sector, such as shale property modification for unconventional oil and gas recovery. The focus of this research project was to investigate fundamental material and mechanical properties of select shale cores and analyze how these properties change due to engineered mineral precipitation with the intent to control these properties to achieve a range of engineering objectives. Ultimately, the project aim was to identify valuable new areas where application of UICP might contribute to national energy security and environmental protection. The research workplan coupled UICP treatment of core samples, nuclear magnetic resonance (NMR) characterization, and mechanical strength testing at MSU with advanced X‐Ray micro-computed tomography (μCT) imaging and numerical modeling performed by collaborators at two national laboratories, the National Energy Technology Laboratory (NETL) and Lawrence Berkeley National Laboratory (LBNL). Experimental results are useful to inform geo-mechanical models which could be applied to predict mineralized rock formation behavior at field scale. Our findings suggest that NMR and μCT methods to detect and quantify biomineral formation in shale fractures are complementary and consistent with each other. Either could be used to estimate the volume of new mineral formed by UICP in shale fractures. The use of surfactants and guar gum to enhance biomineral precipitation in shale fractures merits further research. UICP can, under some conditions, increase the tensile strength of sealed shale fractures beyond that of the intact shale. These findings demonstrate that continued research in this area may be valuable to understanding and improving shale resource recovery techniques.

58 GEOSCIENCES↗

Impact of Molten Gallium on the Microstructure and Corrosion Behavior of Aluminum and Uranium-Aluminum Alloys for Used Nuclear Fuel Reprocessing

Test reactors around the world utilize highly enriched uranium fuel to achieve high neutron fluxes for materials testing. Once spent, the remaining uranium is a valuable resource for subsequent fuel fabrication. However, some of these test reactor cores consist of curved plate-type fuel elements, fabricated using aluminum alloy 6061 (AA6061) cladding to encapsulate a uranium-aluminum alloy (UAlx) fuel matrix. These assemblies require non-standard reprocessing approaches for uranium recovery, as aluminum dissolves readily in acidic solutions, generating large volumes of waste and complicating downstream chemical separations. In this work, we investigate a novel chemical decladding strategy based on the interaction between AA6061/UAlx and molten gallium (Ga). Ga is known to induce severe degradation of aluminum metal through liquid metal embrittlement (LME), even at relatively low Ga concentrations. By penetrating the aluminum crystal lattice, Ga disrupts grain cohesion and facilitates fracture or dissolution of the aluminum matrix. Thermodynamic analysis of the Al–Ga binary phase diagram suggests that Ga may offer a viable pathway to selectively weaken or dissolve the AA6061 cladding, and potentially the aluminum component of the UAlx fuel matrix within. To this end, parametric experiments were performed at 50 °C and 100 °C across a range of Al–Ga atomic fractions. At lower Al fractions, the AA6061 was completely molten after 2 hours of exposure to the Ga metal. In contrast, samples with higher Al fractions (0.9 Al, 0.1 Ga) contained residual solids after 2 hours, which were characterized by microstructural examination using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). These Al-Ga compositions were also evaluated using FactSage thermodynamic modeling to further elucidate the relationship between phase diagram behavior and LME.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials

High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can accelerate the development of new materials while limiting its cost. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of materials and temperatures in dry air and air + 10 % H2O. A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame. Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset distribution on the performance of the developed ML models was evaluated. Potential strategies to improve the predictions and enhance extrapolative capability of the previously trained model were investigated.

Romedenne, Marie [ORNL] (ORCID:0000000317936561)↗

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [U. Chicago (main)] (ORCID:00090002189↗

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [Chicago U.] (ORCID:0009000218901379)↗