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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Disc dichotomy signature in the vertical distribution of [Mg/Fe] and the delayed gas infall scenario

Context. Analysis of the Apache Point Observatory Galactic Evolution Experiment project (APOGEE) data suggests the existence of a clear distinction between two sequences of disc stars in the [α/Fe] versus [Fe/H] abundance ratio space, known as the high- and low-α sequence, respectively. This dichotomy also emerges from an analysis of the vertical distribution of the [α/Fe] abundance ratio. Aims. We aim to test whether the revised two-infall chemical evolution models designed to reproduce the low- and high-α sequences in the [α/Fe] versus [Fe/H] ratios in the solar neighbourhood are also capable of predicting the disc bimodality observed in the vertical distribution of [Mg/Fe] in APOGEE DR16 data. Methods. Along with the chemical composition of the simple stellar populations born at different Galactic times predicted by our reference chemical evolution models in the solar vicinity, we provide their maximum vertical height above the Galactic plane |zmax| computed assuming the relation between the vertical action and stellar age in APOGEE thin-disc stars. Result. The vertical distribution of the [Mg/Fe] abundance ratio predicted by the reference chemical evolution models is in agreement with that observed when combining the APOGEE DR16 data (chemical abundances) with the astroNN catalogue (stellar ages, orbital parameters) for stars younger than 8 Gyr (only low-α sequence stars). Including the high-α disc component, the dichotomy in the vertical [Mg/Fe] abundance distribution is reproduced considering the observational cut in the Galactic height of |z|< 2 kpc. However, our model predicts an overly flat (almost constant) growth of the maximum vertical height |zmax| quantity as a function of [Mg/Fe] for high-α objects in contrast with the median values from APOGEE data. Possible explanations for such a tension are that: (i) the APOGEE sample with |z|< 2 kpc is more likely than ours to be contaminated by halo stars, causing the median values to be kinematically hotter, and (ii) external perturbations – such as minor mergers – that the Milky Way experienced in the past could have heated up the disc, and the heating of the orbits cannot be modeled by only scattering processes. Assuming a disc dissection based on chemistry for APOGEE-DR16 stars (|z|< 2 kpc), the observed |zmax| distributions for high-α and low-α sequences are in good agreement with our model predictions if we consider the errors in the vertical action estimates in the calculation. Moreover, a better agreement between predicted and observed stellar distributions at different Galactic vertical heights is achieved if asteroseismic ages are included as a constraint in the best-fit model calculations. Conclusions. The signature of a delayed gas infall episode, which gives rise to a hiatus in the star formation history of the Galaxy, are imprinted both in the [Mg/Fe] versus [Fe/H] relation and in vertical distribution of [Mg/Fe] abundances in the solar vicinity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Tuscaloosa Marine Shale: Seal or Source? Petrophysical Comparative Study of Wells in SE Louisiana and SW Mississippi

The Tuscaloosa Marine Shale (TMS) is a versatile Late Cretaceous shale formation present in central and SE Louisiana and SW Mississippi, which drew attention because of the various roles played within the Tuscaloosa Group. In this paper, it is debated whether the Tuscaloosa Marine Shale can act as a source, reservoir, or seal all throughout the shale play or only in certain areas. Well log and core data from Adams County, Mississippi, are compared to data from East Feliciana Parish in Louisiana. Conclusions were drawn based on the results of well log analysis, X-ray Diffraction (XRD), porosity–permeability measurements, programmed pyrolysis, and fracture analysis. It was shown that the Tuscaloosa Marine Shale interval in SE Louisiana consists of important amounts of calcite, exhibits multiple natural fractures, has porosity values as high as 9.3%, and shows a TOC content of up to 2.8 wt%. On the other hand, samples from a well at the Cranfield field, MS, are characterized by considerably lower TOC values of around 0.88 wt%, porosities between 0.33% and 4%, and no serious fracturing. The formation demonstrates better reservoir and source potential in SE Louisiana and reliable CO2 sealing capacity in SW Mississippi. The analysis presented in this paper represents a holistic approach to the characterization of shale formations, is applicable to other plays around the world, and can be used as an integral part of CO2 sequestration or hydraulic fracturing programs.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Current and future federal and state sampling guidance for per- and polyfluoroalkyl substances in environmental matrices

Per- and polyfluoroalkyl substances (PFAS) are a class of emerging contaminants composed of an estimated 5000 to 10,000 human-made, fluorinated, organic chemicals. Due to the complexity of PFAS, the need for multiple environmental matrix considerations and the absence of a promulgated federal standard for environmental sampling and analysis, U.S. states have begun developing health-based regulatory and/or guidance values for a limited number of PFAS in environmental matrices. As there is a growing body of science to inform PFAS sampling guidance standard development, it is important to understand which U.S. states are implementing sampling guidelines and how they plan to handle emerging PFAS. This critical review discusses the current and impending federal and state sampling guidelines for PFAS in environmental matrices, the data gaps surrounding PFAS sampling guidance in U.S. states, and the future impacts of impending guidance documents and regulations. Ten federal guidance documents are available for PFAS sampling guidance and analysis. The maximum number of PFAS covered in these guidance documents is 25 analytes spanning across 8 unique media. While the EPA has developed several different sampling and analytical guidelines for PFAS, there is no formal regulation of PFAS or requirements of states to enforce these guidelines. Consequently, only 31 states have informally adopted sampling guidelines, while the other 19 states have no guidance documentation in place for PFAS. The introduction of new PFAS sampling guidelines by the EPA, as well as updated analytical guidelines that target more PFAS or total organofluoride, is expected to continuously shift the landscape of federal and state guidance for PFAS sampling moving forward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis

Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. Finally, we demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Insights from a workplace SARS-CoV-2 specimen collection program, with genomes placed into global sequence phylogeny

In 2020, the Department of Energy established the National Virtual Biotechnology Laboratory (NVBL) to address key challenges associated with COVID-19. As part of that effort, Pacific Northwest National Laboratory (PNNL) established a capability to collect and analyze specimens from employees who self-reported symptoms consistent with the disease. During the spring and fall of 2021, 688 specimens were screened for SARS-CoV-2, with 64 (9.3%) testing positive using reverse-transcriptase quantitative PCR (RT-qPCR). Of these, 36 samples were released for research. All 36 positive samples released for research were sequenced and genotyped. Here, the relationship between patient age and viral load as measured by Ct values was measured and determined to be only weakly significant. Consensus sequences for each sample were placed into a global phylogeny and transmission dynamics were investigated, revealing that the closest relative for many samples was from outside of Washington state, indicating mixing of viral pools within geographic regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Development of Ti 3 SiC 2 MAX phase tubular structures for solar receiver applications

Solar receiver tubes are key components of concentrating solar-thermal power (CSP) systems that harvest solar energy. For better efficiency, the Gen3 CSP receivers, which collect heat into a heat transfer fluid, require a temperature exceeding 700 °C during operation and need to perform under extreme conditions of high temperature and high thermal stress. Operators are seeking CSP designs using new high-temperature structural materials with high thermal conductivity and high creep resistance to achieve a design life of 30 years and thus help recover the plant capital cost sooner. MAX phase materials, which consist of an early transition metal element, an A-group element, and carbon or nitrogen, are expected to exhibit high creep resistance as well as high fracture toughness. Here, in this paper, we describe fabricating both (1) dense Ti 3 SiC 2 MAX phase disks and (2) short-length tubes using field-assisted sintering technology (FAST). First, the disk samples that we fabricated are fully dense and contain ≈90 % Ti 3 SiC 2 MAX phase materials and ≈10 % TiC phase materials. We determined a flexure strength of 519 ± 32 MPa by conducting a four-point bending test at room temperature with rectangular bar samples of ≈100 % density. The thermal conductivity of the Ti 3 SiC 2 MAX phase samples, measured by light flashing analysis, decreases linearly from a value of 41 W . m -1 . K -1 at room temperature to a value of 36 W . m -1 . K -1 at 650 °C. A solar reflectance measurement of the Ti 3 SiC 2 MAX phase revealed that, temperature increases from 400 to 1400 °C, thermal emittance increases from 0.39 to 0.49, while selectivity decreases from 1.8 to 1.4, respectively. Whereas the surface oxidized MAX phase samples after 100 h exposure to air at 1000 °C exhibit that of SiC. Next, we discuss fabrication of the crack-free Ti 3 SiC 2 MAX phase tubular structures accomplished by using FAST processing in graphite bedding. A Ti 3 SiC 2 MAX phase content of > 95 % with traceable ≈3% remaining TiC phase and ≈15 % porosity were demonstrated after high-temperature annealing. An average fracture strength of ≈250 MPa was determined with Ti 3 SiC 2 MAX phase tubes of ≈85 % density by diametral compression testing at room temperature. Our work demonstrated that using FAST processing to produce Ti 3 SiC 2 MAX phase tubular structures for CSP receiver applications is a viable approach.

14 SOLAR ENERGY↗
DOE OSTI2026

Aluminum bonded SmCo5 magnets with enhanced mechanical strength

Despite their versatility and easy manufacturability, polymer-bonded magnets find limited application due to their limited mechanical properties and temperature resistance. In this study, we developed mechanically robust, aluminum-bonded SmCo5 magnets by utilizing a low-temperature, shear-assisted friction consolidation technique. Bonded magnets with varying magnetic phase content were fabricated at processing temperature of 500°C, and their microstructures, magnetic, mechanical, and electrical properties were systematically investigated. The resulting bonded-magnets showed effective consolidation with nanocrystalline microstructures. Magnetic properties were found to be tunable through variations in binder phase content, with coercivity reaching values up to 15.3 kOe (1217.5 kA/m) and maximum magnetic energy product up to 3.6 MGOe (28.7 kJ/m3), which are comparable to other bonded magnets. Electrical resistivities were lower than the polymer-bonded magnets, but comparable to the sintered ones. We observed an average flexural strength of 223 MPa for 60 vol.% SmCo5/Al sample, which is nearly two times higher than the reported values for sintered and bonded Sm-Co magnets. Our research demonstrated an effective approach to fabricate metal-bonded permanent magnets with superior magnetic and mechanical properties potentially applicable for high temperature applications.

Poudel Chhetri, Tej Bahadur↗
DOE OSTI2024

Pennsylvania Department of Environmental Protection (PA DEP) 26r Detailed Produced Water Compositions (version 1.0)

A database of geochemical compositions of aqueous species in produced water reported to the PA DEP. Samples were collected between mid-2012 to early-2020. Data from publicly-available PA DEP 26r reports were scraped from pdf files and cumulated into tabular spreadsheet format for >1000 produced water streams from Marcellus wells in Pennsylvania. In addition to providing the original values, the NETL NEWTS team has reformatted the dataset to allow sample streams to be easily copied into OLI Studio and Geochemist WorkBench (GWB) software for modeling the geochemistry and the recovery of critical minerals, such as lithium, from these produced water streams. In addition, a version of the dataset has been included with predictions for some missing values in the original dataset using machine learning techniques within CoDaRT software, a public ML software developed by the Nation Energy Technology Laboratory. We have made the Input into CoDaRT and one example output from CoDaRT available in this dataset.

Aqueous Chemistry↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Determination of proton PDF uncertainties with Markov chain Monte Carlo

We present an analysis of parton distribution functions (PDFs) of the proton using Markov chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes’ theorem and, thus, provides a means to directly sample the underlying probability distribution—in this case, the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. In our analysis we include a broad set of deep inelastic scattering data from HERA, BCDMS and NMC experiments along with the Drell-Yan, 𝑊 and 𝑍 boson data from LHC and Tevatron experiments, which combined with theoretical calculations at next-to-next-to-leading order in QCD allow for realistic determination of PDFs. The main focus of this analysis is to explore alternative methods for PDF uncertainty estimation that are more firmly grounded in statistical principles. We show that the flexibility of the Bayes framework, allowing one, e.g., to account for non-Gaussianity or inconsistencies of datasets, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. We also demonstrate that MCMC allows one to determine the Δ⁢𝜒 2 value corresponding to a given confidence level in the sample, which can, in turn, be used as a statistically well-founded tolerance criterion used in the Hessian method, thus addressing one of its main long-standing drawbacks.

Risse, Peter Clemens [Universität Münster (Germany↗
DOE OSTI2021

Combining Spectroscopic Ellipsometry with Transmission Spectroscopy to Derive Accurate Optical Constants for Organic Materials

A method for deriving the optical constants of organic powdered materials in the mid-infrared spectral range is introduced using variable angle spectroscopic ellipsometry and transmission spectroscopy. The approach uses pressed pellets of the powder and is applied to organic solids, which have both strong and weak infrared absorption features. Many powders have significant voids and do not press into smooth, homogenous pellets. To account for pellet non-idealities and to accurately measure both n and k, three different forms of pellets were pressed and measured: A pure analyte pellet, a mixed analyte/KBr pellet with a large analyte percentage, and a KBr transmission pellet with only a small analyte percentage. Using all three pellets in a multi-sample analysis involving both ellipsometric and transmission data, the complex refractive index (n/k) values can be derived for many organic compounds. This method is illustrated to calculate the optical constants for anhydrous lactose from 6000-400 cm-1. The transmission measurements improve the spectral fitting of weak absorption features, and the multi-sample analysis enables a better determination of the significant void space that is present in the pure pellet, leading to lower values for both n and k if not properly accounted for in the multi-oscillator model used to fit the ellipsometric data.

Optical constants, refractive index, extinction co↗
DOE OSTI2023

Comparison of quantum advantage experiments using random circuit sampling

Random circuit sampling, the task of sampling bit strings from a random unitary operator, has been implemented to demonstrate quantum advantage on the Sycamore quantum processor with 53 qubits and on the Zuchongzhi quantum processor with 56 and 61 qubits. Recently, it was claimed that classical computers using tensor network simulation could catch on to current noisy quantum processors for random circuit sampling. While the linear cross-entropy benchmark fidelity was used to certify all these claims, it may not capture statistical properties of outputs in detail. Here, we compare the bit strings sampled from classical computers using tensor network simulation by Pan et al. [F. Pan, K. Chen, and P. Zhang, Phys. Rev. Lett. 129, 090502 (2022)] and by Kalachev et al. [G. Kalachev, P. Panteleev, P. Zhou, and M.-H. Yung, arXiv:2112.15083] with the bit strings from the Sycamore quantum processor. It is shown that all of Kalachev et al.'s samples passed the NIST random number tests. The heat maps of bit strings show that Pan et al.'s and Kalachev et al.'s samples are quite different from the Sycamore or Zuchongzhi samples. The analysis with the Marchenko-Pastur distribution and the Wasssertein distances demonstrates that Kalachev et al.'s samples are statistically closer to the Sycamore samples than Pan et al.'s while the three datasets have similar values for the linear cross-entropy fidelity. In conclusion, our finding implies that further study is needed to certify or beat the claims of quantum advantage using random circuit sampling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

D$e^+e^-$ffusion: Capturing the Beam-Beam Physics of $e^+e^-$ Collisions with Diffusion Models

Beam-induced backgrounds at high-luminosity $e^+e^-$ colliders, such as the FCC-ee, are dominated by incoherent pair creation (IPC), and require computationally expensive simulations with dedicated Monte Carlo (MC) event generators. Reliable detector and machine-detector interface studies necessitate event samples that are several orders of magnitude larger than what is practically attainable with existing MC. To alleviate this bottleneck, we present D$e^+e^-$ffusion, a denoising diffusion probabilistic model that operates as a permutation-equivariant, set-valued surrogate for fast IPC simulation. Trained on a small GuineaPig++ sample, D$e^+e^-$ffusion faithfully reproduces the marginal and joint kinematic, angular, and positional distributions of all three IPC production processes. In addition, we assess the fidelity at the detector level by propagating both Geant4 and D$e^+e^-$ffusion events through a Geant4 simulation of the CLD vertex detector and by training a transformer-based two-sample classifier; the classifier achieves an area under the ROC curve of $0.553 \pm 0.016$. The trained model generates events nearly four orders of magnitude faster than Geant4, paving the way for a fast-simulation surrogate for FCC-ee design studies.

Chahine, Antonio [Imperial Coll., London]↗
DOE OSTI2022

Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps

Many-to-one maps are ubiquitous in machine learning, from the image recognition model that assigns a multitude of distinct images to the concept of ``cat'' to the time series forecasting model which assigns a range of distinct time-series to a single scalar regression value. While the primary use of such models is naturally to associate correct output to each input, in many problems it is also useful to be able to explore, understand, and sample from a model's fibers, which are the set of input values x such that f(x) = y, for fixed y in the output space. In this paper we show that popular generative architectures are ill-suited to such tasks. Motivated by this we introduce a novel generative architecture, a Bundle Network, based on the concept of a fiber bundle from (differential) topology. BundleNets exploit the idea of a local trivialization wherein a space can be locally decomposed into a product space that cleanly encodes the many-to-one nature of the map. By enforcing this decomposition in BundleNets and by utilizing state-of-the-art invertible components, investigating a network's fibers becomes natural.

Courts, Nicolas C.↗
DOE OSTI2022

Coupling of nitric acid digestion and anion-exchange resin separation for the determination of methylmercury isotopic composition within organisms

Isotope ratios of methylmercury (MeHg) within organisms can be used to identify sources of MeHg that have accumulated in food webs, but these isotopic compositions are masked in organisms at lower trophic levels by the presence of inorganic mercury (iHg). To facilitate measurement of MeHg isotope ratios in organisms, we developed a method of extracting and isolating MeHg from fsh and aquatic invertebrates for compound-specifc isotopic analysis involving nitric acid digestion, batch anion-exchange resin separation, and pre-concentration by purge and trap. Recovery of MeHg was quantifed after each step in the procedure, and the average cumulative recovery of MeHg was 93.4±2.9% (1 SD, n=28) for biological reference materials and natural biota samples and 96.9±1.8% (1 SD, n=5) for aqueous MeHgCl standards. The amount of iHg impurities was also quantifed after each step, and the average MeHg purity was 97.8±4.3% (1 SD, n=28) across all reference materials and natural biota samples after the fnal separation step. Measured MeHg isotopic compositions of reference materials agreed with literature values obtained using other MeHg separation techniques, and MeHg isotope ratios of aqueous standards, reference materials, and natural biota samples were reproducible. On average, the reproducibility associated with reference material process replicates (2 SD) was 0.10‰ for δ 202 MeHg and 0.04‰ for Δ 199 MeHg. This new method provides a streamlined, reliable technique that utilizes a single sample aliquot for MeHg concentration and isotopic analysis. Furthermore, this promotes a tight coupling between MeHg concentration, %MeHg, and Hg isotopic composition, which may be especially benefcial for studying complex food webs with multiple isotopically distinct sources of iHg and/or MeHg.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Predicting failure parameters of semiconductor devices subjected to stress conditions

A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.

Ahmed, Moinuddin↗
DOE OSTI2020

A data-driven approach to sampling matrix selection for compressive sensing

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.

compressive sensing, hyperspectral imaging, Walsh-↗
DOE OSTI2025

Speciation mapping of the oxidation layer on aged uranium dioxide using scanning transmission x-ray spectromicroscopy

In this study, UO 2 was aged in humid air and prepared as a thin section using a focused ion beam (FIB) instrument. The specimen was measured using synchrotron radiation spectromicroscopy techniques at the Beamline 11.0.2 STXM end station of the Advanced Light Source (ALS). Non-negative matrix factorization (NMF) methods were used to identify and map three component x-ray absorption near-edge structure (XANES) spectra in the oxygen K-edge data, revealing a surface layer of U 4 O 9 with a thickness of 206 ± 21 nm, and the bulk of the sample remaining as UO 2 . Uranium N 4,5 -edge XANES spectromicroscopy supports these results. The diffusion-controlled parabolic rate constant for UO 2 oxidation to U 4 O 9 was calculated from the observed layer thickness and compared to literature values. Complementary transmission electron microscopy (TEM) was used to image the sample and identify the phases present in various regions, confirming the STXM results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗