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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Individual Variability and Predictive Biomarkers of Human Sensitivity to Ionizing Radiation

High-linear energy transfer (LET) ionizing radiation is a major health hazard for astronauts who will be exposed to galactic cosmic rays during upcoming lunar and Mars missions. Predicting and mitigating personalized health risks from space radiation exposure requires understanding the factors underlying individual radiation sensitivity, which currently remain unknown. We started to address this challenge by identifying the genomic and demographic associations with sensitivity to low and high-LET ionizing radiation in the largest human sample cohort to date and by evaluating baseline DNA damage as a potential predictor of radiation responses. We have collected and irradiated peripheral blood mononuclear cells (PBMCs) from 768 healthy donors of matched ethnicity, 50/50 male/female, 18-70 years old. We have analyzed the responses of 576 donor PBMCs to irradiation with simulated galactic cosmic ray components: 350MeV/n 28Si, 350MeV/n 40Ar and 600MeV/n 56Fe, at 1.1 and 3 particles/100μm2 fluences, as well as 0.1Gy and 1Gy doses of gamma rays, at 4 and 24 hours post irradiation. We quantified post-irradiation DNA repair based on radiation-induced 53BP1+ foci formation, together with oxidative stress and cell death using a flow cytometry-based assay. We also assessed baseline levels of DNA repair in PBMCs at the time of sample collection prior to irradiation experiments. We observed a wide variability of subject- and LET-dependent radiation responses, with radiation-induced DNA repair foci increasing with LET, though oxidative stress being notably reduced by high-LET irradiation, potentially due to a switch between hydrogen peroxide and oxygen radical-based mechanisms. We also identified a relationship between few DNA repair foci at baseline and increased DNA repair after irradiation, accompanied by an alteration in immunoregulatory cytokine secretion, which might be adapted as biomarkers to predict ionizing radiation sensitivity. Among demographic variables, baseline foci levels were positively associated with age and latent cytomegalovirus infection. Finally, we have performed low-throughput whole genome sequencing of all samples and are currently in process of identifying the genes and pathways associated with low and high-LET ionizing radiation sensitivity in humans.

Space radiation, human spaceflight, DNA damage↗
DOE OSTI2017

Statistical Framework for Planning a Component Shelf Life Program.

This document outlines a statistical framework for establishing a shelf-life program for components whose performance is measured by the value of a continuous variable such as voltage or function time. The approach applies to both single measurement devices and repeated measurement devices, although additional process control charts may be useful in the case of repeated measurements. The approach is to choose a sample size that protects the margin associated with a particular variable over the life of the component. Deviations from expected performance of the measured variable are detected prior to the complete loss of margin. This ensures the reliability of the component over its lifetime.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Metaproteomics Identifies the Protein Machinery Involved in Metal and Radionuclide Reduction in Subsurface Microbiomes and Elucidates Mechanisms and U(VI) Reduction Immobilization

The overall goal for this funded project was to develop and exploit environmental metaproteomics tools to identify biomarkers for monitoring microbial activity affecting U speciation at U-contaminated sites, correlate metaproteomics profiles with geochemical parameters and U(VI) reduction activity (or lack thereof), elucidate mechanisms contributing to U(VI) reduction, and provide remediation project managers with additional information to make science-based site management decisions for achieving cleanup goals more efficiently. Although significant progress has been made in elucidating the microbiology contribution to metal and radionuclide reduction, the cellular components, pathway(s), and mechanisms involved in U trans-formation remain poorly understood. Recent advances in (meta)proteomics technology enable detailed studies of complex samples, including environmental samples, which differ between sites and even show considerable variability within the same site (e.g., the Oak Ridge IFRC site). Additionally, site-specific geochemical conditions affect microbial activity and function, suggesting generalized assessment and interpretations may not suffice. This research effort integrated current understanding of the microbiology and biochemistry of U(VI) reduction and capitalize on advances in proteomics technology made over the past few years. Field-related analyses used Oak Ridge IFRC field ground water samples from locations where slow-release substrate biostimulation has been implemented to accelerate in situ U(VI) reduction rates. Our overarching hypothesis was that the metabolic signature in environmental samples, as deciphered by the metaproteome measurements, would show a relationship with U(VI) reduction activity. Since metaproteomic and metagenomic characterizations were computationally challenging and time-consuming, we used a tiered approach that combines database mining, controlled laboratory studies, U(VI) reduction activity measurements, phylogenetic analyses, and gene expression studies to support the metaproteomics characterizations. Growth experiments of target microorganisms (Anaeromyxobacter, Shewanella, Geobacter) revealed tremendous respiratory versatility, as evidenced by the ability to utilize a range of electron donors (e.g. acetate, hydrogen, pyruvate, lactate, succinate, formate) and electron acceptors (e.g. nitrate, fumarate, halogenated phenols, ferric iron, nitrous oxide, etc.). In particular, the dissimilatory metabolic reduction of metals, including radionuclides, by target microorganisms spurred interest for in situ bioremediation of contaminated soils and sediments. Distinct c-type cytochrome expression patterns were observed in target microorganisms grown with the different electron acceptors. For each target microorganism, the core proteome covered almost all metabolic pathways represented by their corresponding pan-proteomes. Unique proteins were detected for each target microorganism, and their expression and possible functionalities were linked to specific growth conditions through proteomics measurements. Optimization of the proteomic tools included in-depth comprehensive metagenomic and metaproteomic analyses on a limited number of samples. The optimized metaproteomic analyses were then applied to Oak Ridge IFRC field samples from the slow-release substrate biostimulation. Metaproteomic analysis and pathway mapping results demonstrated the distinct effects of metal and non-metal growth conditions on the proteome expression. With these metaproteomic tools, we identified which previously hypothetical metabolic pathways were active during the analyzed time points of the slow release substrate biostimulation. Thus, we demonstrated the utility of these tools for site assessment, efficient implementation of bioremediation and long-term monitoring. This research of detailed protein analysis linked with metal reduction activity did (1) show that c-type cytochrome isoforms, previously associated with radionuclide reduction activity, are suitable biomarkers, (2) identify new biomarker targets for site assessment and bioremediation monitoring, and (3) provide new information about specific proteins and mechanisms involved in U(VI) reduction and immobilization. This expanded metagenomic and metaproteomic toolbox contributed to implementing science-driven site management with broad benefits to the DOE mission.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Auroral radio emission from late L and T dwarfs: A new constraint on dynamo theory in the substellar regime

We have observed six late L and T dwarfs with the Karl G. Jansky Very Large Array (VLA) to investigate the presence of highly circularly polarized radio emission, associated with large-scale auroral currents. Previous surveys encompassing ∼60 L6 or later targets have yielded only one detection. Our sample includes the previously detected T6.5 dwarf 2MASS 10475385+2124234, as well as five new targets selected for the presence of Hα emission and/or optical infrared photometric variability, which are possible manifestations of auroral activity. We detect 2MASS 10475385+2124234, as well as four of the five targets in our biased sample, including the strong IR-variable source SIMP J01365662+0933473 and bright Hα emitter 2MASS 12373919+6526148, reinforcing the possibility that activity at these disparate wavelengths is related. The radio emission frequency corresponds to a precise determination of the lower-bound magnetic field strength near the surface of each dwarf, and this new sample provides robust constraints on dynamo theory in the low-mass brown dwarf regime. Magnetic fields ≳2.5 kG are confirmed for five of six targets. Our results provide tentative evidence that the dynamo operating in this mass regime may be inconsistent with predicted values from a recently proposed model. Further observations at higher radio frequencies are essential for verifying this assertion.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

Degree of Ice Particle Surface Roughness Inferred from Polarimetric Observations

The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multidirectional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed that provides a more noise-resilient roughness estimate than the conventional best-fit approach. The improvements include the introduction of a quantitative roughness parameter based on empirical orthogonal function analysis and proper treatment of polarization due to atmospheric scattering above clouds. A global 1-month data sample supports the use of a severely roughened ice habit to simulate the polarized reflectivity associated with ice clouds over ocean. The density distribution of the roughness parameter inferred from the global 1- month data sample and further analyses of a few case studies demonstrate the significant variability of ice cloud single-scattering properties. However, the present theoretical results do not agree with observations in the tropics. In the extra-tropics, the roughness parameter is inferred but 74% of the sample is out of the expected parameter range. Potential improvements are discussed to enhance the depiction of the natural variability on a global scale.

surface↗
DOE OSTI2017

Substantial soil organic carbon retention along floodplains of mountain streams

Small, snowmelt–dominated mountain streams have the potential to store substantial organic carbon in floodplain sediment because of high inputs of particulate organic matter, relatively lower temperatures compared with lowland regions, and potential for increased moisture conditions. This work (i) quantifies mean soil organic carbon (OC) content along 24 study reaches in the Colorado Rocky Mountains using 660 soil samples, (ii) identifies potential controls of OC content based on soil properties and spatial position with respect to the channel, and (iii) and examines soil properties and OC across various floodplain geomorphic features in the study area. Stepwise multiple linear regression (adjusted r 2 = 0.48, p < 0.001) indicates that percentage of silt and clay, sample depth, percent sand, distance from the channel, and relative elevation from the channel are significant predictors of OC content in the study area. Principle component analysis indicates limited separation between geomorphic floodplain features based on predictors of OC content. A lack of significant differences among floodplain features suggests that the systematic random sampling employed in this study can capture the variability of OC across floodplains in the study area. Mean floodplain OC (6.3 ± 0.3%) is more variable but on average greater than values in uplands (1.5 ± 0.08% to 2.2 ± 0.14%) of the Colorado Front Range and higher than published values from floodplains in other regions, particularly those of larger rivers.

58 GEOSCIENCES↗
DOE OSTI2018

On the morphological behavior of ABC miktoarm stars containing poly(cis 1,4-isoprene), poly(styrene), and poly(2-vinylpyridine)

Fundamental understanding of microphase separation in ABC miktoarm copolymers is vital to access a plethora of nonconventional morphologies. Miktoarm stars based on poly(cis 1,4–isoprene) (I), poly(styrene) (S), and poly(2–vinylpyridine) (V) are model systems, which allow systematic studies of the effects of composition, chemical microstructure, and temperature on the thermodynamics of microphase separation. Eleven ISV–x (I:S:V = 1:1:x, v:v:v) miktoarm copolymers were synthesized by anionic polymerization affording well-defined copolymers with a variable V arm. Equilibrium bulk morphologies of all samples, as evidenced by small–angle X–ray scattering, transmission electron microscopy (TEM), and self–consistent field theory, showed a systematic transition from lamellae (x ≈ 0–0.2) to [8.8.4] tiling (x ≈ 0.6–0.9) to cylinders in undulating lamellae (x ≈ 2–4) and, finally, to hexagonally packed core–shell cylinders (x ≈ 5–8). Chemical microstructure of the I arm [poly(cis 1,4–isoprene)] versus poly(3,4–isoprene) is shown to play important role in affecting morphological behavior. Furthermore, to reconcile differences between ISV–x star morphologies reported in the literature and those reported herein, even for the same composition, effects of the microstructure of I arm on the Flory–Huggins parameter between I and V arms were taken into account in a qualitative manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comparison of greenhouse gas emission estimates from six hydropower reservoirs using modeling versus field surveys

As with most aquatic ecosystems, reservoirs play an important role in the global carbon (C) cycle and emit greenhouse gases (GHG) as carbon dioxide (CO 2 ) and methane (CH 4 ). However, GHG emissions from reservoirs are poorly quantified, especially in temperate systems, resulting in high uncertainty. We compared reservoir C emission estimates and uncertainty of diffusive, ebullitive, and degassing pathways in six hydropower reservoirs in the southeastern United States among four data sources: two field-based surveys and two models (including the GHG Reservoir “G-res” Tool). We found that CH 4 diffusion was most similar across data sources (modeled minus observed, bias = - 21 g CO 2-eq m -2 y -1 ) and had low relative uncertainty (coefficient of variation, CV = 0.98). On the other hand, CO 2 diffusion was least consistent across data sources (bias = - 518 g CO 2-eq m -2 y -1 ). Both field surveys indicated strong negative CO 2 diffusion (i.e., CO 2 uptake) at all reservoirs, while G-res estimated positive CO 2 diffusion. By extension, total C emissions showed similar discrepancies, leading to high uncertainty in upscaling and interpreting reservoir source-sink dynamics. Finally, CH 4 ebullition had the highest relative uncertainty (CV = 2.77) due to high variability across sites. We discuss limitations of field surveys and these models, including temperature-based annualization methods, varying definitions of ebullition zones, low sampling resolution, and lack of dynamism. Future field efforts focused on capturing variability in CO 2 diffusion and CH 4 ebullition will be especially valuable in reducing uncertainty and improving models to advance our understanding reservoir GHG emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Resilient information and inference networks under mixed-trust sensing

With ubiquitous digitization, sensing, and computational intelligence deployed in increasingly more and broader domains, including critical infrastructure, potentially misleading and destabilizing effects of multimodal anomalies and adversarial behavior are growing in importance. Here, we develop randomized and reinforcement learning-based strategies for strategically recruiting and utilizing deployed (and, thus, vulnerable and potentially faulty and/or compromised) nodes from information and inference networks, while defending against adversaries that attempt to misguide assessments of inferred variables. Recognizing that, besides communication and other costs, sampling from any observable node can either provide true data or dangerously expose our inference to misinformation (without being easily distinguishable what actually happens), the proposed strategies proceed by progressively recruiting nodes and cautiously scaling their information contribution based on assumed, or, in our reinforcement learning approach, intelligently weighed trustworthiness, with the learning approach also considering network-wide, threat-inclusive risk/value tradeoffs. While avoiding the hardware, communication, analytical and computational burden of explicit redundancy, the proposed defensive schemes enable on-the-fly assessments of underlying processes, and system-wide situational awareness with demonstrable resilience against adversarial activities.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

DeePCG: Constructing coarse-grained models via deep neural networks

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application, we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

Chemistry↗
DOE OSTI2024

Optical conductivity of overdoped cuprates from ab initio out-of-plane impurity potentials

Dopant impurity potentials determined by ab initio supercell density functional theory calculations are used to calculate the optical conductivity of overdoped La 2-x ⁢Sr x CuO 4 (LSCO) and Tl 2 ⁢Ba 2⁢ CuO 6+δ (Tl-2201) in the superconducting and normal states. Vertex corrections are included to account for the effect of forward scattering on two-particle properties. This approach was previously shown to provide good, semiquantitative agreement with measurements of superfluid density in LSCO. Here, in this study, we compare calculations of conductivity with measurements of terahertz conductivity on LSCO using identical impurity, band, and correlation parameters and find similarly good correspondence with experiment. In the process, we delineate the impact of the different disorder mechanisms on single-particle and transport relaxation processes. In particular, we reveal the critical role of apical oxygen vacancies in transport scattering and show that transport relaxation rates in LSCO are significantly reduced when apical oxygen vacancies are annealed out. These considerations are shown to be crucial for understanding the variability of experimental results for overdoped LSCO in samples of nominally identical doping but different types. Finally, we give predictions for Tl-2201 terahertz conductivity experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Moleculo Long-Read Sequencing Facilitates Assembly and Genomic Binning from Complex Soil Metagenomes

ABSTRACT Soil metagenomics has been touted as the “grand challenge” for metagenomics, as the high microbial diversity and spatial heterogeneity of soils make them unamenable to current assembly platforms. Here, we aimed to improve soil metagenomic sequence assembly by applying the Moleculo synthetic long-read sequencing technology. In total, we obtained 267 Gbp of raw sequence data from a native prairie soil; these data included 109.7 Gbp of short-read data (~100 bp) from the Joint Genome Institute (JGI), an additional 87.7 Gbp of rapid-mode read data (~250 bp), plus 69.6 Gbp (>1.5 kbp) from Moleculo sequencing. The Moleculo data alone yielded over 5,600 reads of >10 kbp in length, and over 95% of the unassembled reads mapped to contigs of >1.5 kbp. Hybrid assembly of all data resulted in more than 10,000 contigs over 10 kbp in length. We mapped three replicate metatranscriptomes derived from the same parent soil to the Moleculo subassembly and found that 95% of the predicted genes, based on their assignments to Enzyme Commission (EC) numbers, were expressed. The Moleculo subassembly also enabled binning of >100 microbial genome bins. We obtained via direct binning the first complete genome, that of “ Candidatus Pseudomonas sp. strain JKJ-1” from a native soil metagenome. By mapping metatranscriptome sequence reads back to the bins, we found that several bins corresponding to low-relative-abundance Acidobacteria were highly transcriptionally active, whereas bins corresponding to high-relative-abundance Verrucomicrobia were not. These results demonstrate that Moleculo sequencing provides a significant advance for resolving complex soil microbial communities. IMPORTANCE Soil microorganisms carry out key processes for life on our planet, including cycling of carbon and other nutrients and supporting growth of plants. However, there is poor molecular-level understanding of their functional roles in ecosystem stability and responses to environmental perturbations. This knowledge gap is largely due to the difficulty in culturing the majority of soil microbes. Thus, use of culture-independent approaches, such as metagenomics, promises the direct assessment of the functional potential of soil microbiomes. Soil is, however, a challenge for metagenomic assembly due to its high microbial diversity and variable evenness, resulting in low coverage and uneven sampling of microbial genomes. Despite increasingly large soil metagenome data volumes (>200 Gbp), the majority of the data do not assemble. Here, we used the cutting-edge approach of synthetic long-read sequencing technology (Moleculo) to assemble soil metagenome sequence data into long contigs and used the assemblies for binning of genomes. Author Video : An author video summary of this article is available.

De Novo assembly↗
DOE OSTI2024

Subsurface microbial community structure shifts along the geological features of the Central American Volcanic Arc

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features (e.g., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.

Science & Technology - Other Topics↗
DOE OSTI2022

Technical Guidance on Use of the Linseis Vertical Dilatometer L75

Linseis vertical Dilatometer L75 thermal expansion equipment is a complete system which can be used for measuring change of the absolute length of a sample, determination of the thermal expansion coefficient (CTE), shrinkage, and sintering. The 238 Pu Supply Program is interested in re-establishing the capability to determine CTE and measuring the change of the absolute length of various 237 Np Al/cermet samples during heating. The change in length of the measured sample is recorded using a highly sensitive Linear Variable Differential Transformer (LVDT). The dilatometer is user-friendly and simple to operate which will allow technicians to be trained easily on its use. This equipment was designed to be used specifically for ceramics. It has a highly vacuum tight push rod dilatometer, a high-resolution inductive transducer using innovative and comprehensive temperature controls which provide excellent performance as a measurement system yielding high accuracy, reproducibility, and long-term stability.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Application of Principal Component Analysis to Electrochemical Reprocessing PM and NMAC

In this report, data from an electrorefiner (ER) for nuclear fuel reprocessing is evaluated for process monitoring (PM) conclusions. This data comes from tests performed at the Idaho National Laboratory in 2022. Multivariate approaches utilizing methods of Principal Component Analysis (PCA) is applied. This is based off established work in process monitoring for fault detection in industrial facilities. This report will discuss the background, methods, and results of the application and some of the conclusions and applications that can be drawn from them. PCA is applied to two different electrorefiner (ER) operations that occurred at Idaho National Laboratory between August and October 2022. The first operation occurred with little incident while the second had several noted faults in the equipment in operational logs. The data from the first run was used to train the data for “normal” operations and applied to both sets of data to determine when operations were in an “off-normal” condition and identify where the fault occurs through PCA. PCA was able to identify off-normal events and identify the cause for off-normal operations. These identified off-normal events matched with the events and their causes in the operational logs. However, small amounts of variance in the data led to false detection of “off-normal” events. Thus, careful selection of training data and a-posteriori conclusions based off operator assessments will both be required for application of PCA to PM applications. This work demonstrated that multivariate approaches and latent variables are applicable to pyroprocessing PM applications and can be further expanded in future work as quality variables such as salt concentration from sensors and sampling become available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Report on technical development in ANL for innovative creep testing and measurement

This report summarizes a research effort undertaken by Argonne National Laboratory to accelerate the development of creep data package for ASME code qualification. Progress on the fabrication of a 3-sample load train and the application of a direct current potential drop (DCPD) technique to measure large creep strains are reported. Specifically: (1) The 3-sample load train, with three sets of Linear Variable Differential Transformers (LVDT), has been fabricated and assembled. (2) A DCPD method for creep-strain measurement up to 50% has been demonstrated. The DCPD data matches very well to LVDT results, suggesting its potential application in future creep testing. The technical developments contribute towards shortening the time needed to generate a creep data package for an ASME code case of the laser powder bed fusion 316H stainless steel, an effort under the US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗