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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Longitudinal Changes in Immune Activation Serum Biomarkers Prior to Diagnosis and Risk of B-cell NHL Subtypes

To examine the contribution of B-cell activation molecules to B-cell follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL), a prospective study was conducted using pre-diagnosis serial serum samples from the US Department of Defense Serum Repository. Each case (n = 142 FL, n = 211 DLBCL) was matched to two controls on age, gender, race, military branch, and blood collection dates. Immune activation molecules (IL1β, IL2, IL4, IL5, IL6, IL10, IL12, CXCL13, IL8, TNFα, IFNγ, GM-CSF, VEGF, sCD30, IgE) were quantified using ELISA or multiplex immunometric (Luminex) assay. Longitudinal data were analyzed using linear mixed modeling. As serial specimens were collected over several years before diagnosis, we evaluated the temporal dynamics of these markers. Increased serum levels of sCD30, CXCL13, and to a lesser extent IL10, were associated with both FL and DLBCL in cases compared with controls, with a median follow-up of 5.5 years from the earliest specimen collection to diagnosis date. Significant increasing sCD30 and CXCL13 trajectories for FL and DLBCL subtypes were noted starting at the earliest time points and with IL10 levels increasing significantly at time points closer to diagnosis. In conclusion, these results suggest that sCD30, CXCL13, and IL10 may contribute to the etiology of FL and DLBCL and are potential biomarkers for these non–Hodgkin lymphoma subtypes. The increasing trajectories of the B-cell activation molecules, sCD30, CXCL13, and to a lesser extent IL10, may indicate early disease-induced effects or reflect the chronic stimulation of B-cells that promotes the development of FL and DLBCL subtypes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Application of Manufacturing Quality Management Principles to PV System Installations

To help SETO/DOE achieve its goals, the IBTS team proposed a project addressing system reliability by improving installation standards and quality management. The proposed approach was designed to help achieve measurable reductions in installation defect density and improvements in the performance of PV systems by optimizing design and installation of residential and commercial PV systems. This approach addressed the soft costs associated with installations and quality management. The project demonstrated improved system reliability and reduced PV system installation costs. The software developed improved operations, decreased risk, and increased the overall value of PV systems across their lifecycle. The project used several data collection methods, including extensive industry surveys, face-to-face high-level interviews at industry conferences, stakeholder teleconferences, and in-depth interviews conducted by IBTS staff. Results from the research found the industry needs a uniform assessment method for national providers to be more efficient; the software should support both code officials and installers; most industry stakeholders would find value in a centralized software system that allows them to collect, report, and review information on in-process and completed solar installations; and mobile solutions that bridge existing knowledge gaps with inspectors and integrate with existing methodologies (such as permitting software) are of great value. The software solution developed is web-based, allowing for national access, and is built on a Google Firebase platform that can handle significant users and data. It can be used onsite or remotely, allowing for code compliance to continue despite ongoing pandemic related delays or shutdowns for local economies. The information provided by the software tool allows users to uniformly assess a system for compliance and use that aggregated data to identify training topics or create internal process designed to improving issues and reducing occurrence. This solution has multiple benefits in managing quality at time of use and promoting an increase in future safety and quality through education. Perhaps most importantly, this software increases public safety by ensuring compliance of installed systems and allows for local AHJs to remotely engage specialized and qualified solar specific expertise for oversite of the installation in their jurisdictions. Data analysis provides the quality feedback loop identifying the root cause of failure and drives installation practices to improve through training and education, resulting in systems with higher performance, greater reliability, and reduced operations and maintenance costs. With the successful completion of this project, the industry can expect reduced soft costs and increased performance and safety and will ultimately benefit from longer performing systems that cost less to operate.

14 SOLAR ENERGY↗
DOE OSTI2024

Adaptable SEC‐SAXS data collection for higher quality structure analysis in solution

Abstract The two major challenges in synchrotron size‐exclusion chromatography coupled in‐line with small‐angle x‐ray scattering (SEC‐SAXS) experiments are the overlapping peaks in the elution profile and the fouling of radiation‐damaged materials on the walls of the sample cell. In recent years, many post‐experimental analyses techniques have been developed and applied to extract scattering profiles from these problematic SEC‐SAXS data. Here, we present three modes of data collection at the BioSAXS Beamline 4–2 of the Stanford Synchrotron Radiation Lightsource (SSRL BL4‐2). The first mode, the High‐Resolution mode, enables SEC‐SAXS data collection with excellent sample separation and virtually no additional peak broadening from the UHPLC UV detector to the x‐ray position by taking advantage of the low system dispersion of the UHPLC. The small bed volume of the analytical SEC column minimizes sample dilution in the column and facilitates data collection at higher sample concentrations with excellent sample economy equal to or even less than that of the conventional equilibrium SAXS method. Radiation damage problems during SEC‐SAXS data collection are evaded by additional cleaning of the sample cell after buffer data collection and avoidance of unnecessary exposures through the use of the x‐ray shutter control options, allowing sample data collection with a clean sample cell. Therefore, accurate background subtraction can be performed at a level equivalent to the conventional equilibrium SAXS method without requiring baseline correction, thereby leading to more reliable downstream structural analysis and quicker access to new science. The two other data collection modes, the High‐Throughput mode and the Co‐Flow mode, add agility to the planning and execution of experiments to efficiently achieve the user's scientific objectives at the SSRL BL4‐2.

Matsui, Tsutomu↗
DOE OSTI2024

Search for Fast Magnetic Monopoles with NOvA Far Detector

The NOvA experiment at Fermilab consists of two functionally identical liquid scintillator detectors called near detector and far detector to study neutrino oscillations using GeV-scale neutrinos from the Fermilab NuMI beam. Due to its location close to the earth’s surface, surface area of over 4,000 $(m^{2})$, and little overburden, the NOvA far detector is sensitive to an extensive range of magnetic monopole masses and velocities. With the help of the far detector, we are looking for signals of relic monopoles in the cosmic rays flux that might have been produced in the early universe. We have developed the data-driven trigger(DDT), a robust trigger algorithm optimized for continuously searching the magnetic monopole-like patterns in the live data. Due to the surface proximity of the far detector, the major challenge for this analysis at the offline level is the rejection of cosmic ray background in the collected data. In this talk, I will present the status of the search for fast-moving magnetic monopoles using the data collected by the NOvA far detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

An Analysis of FPGA LUT Bias and Entropy for Physical Unclonable Functions

Process variations within Field Programmable Gate Arrays (FPGAs) provide a rich source of entropy and are therefore well-suited for the implementation of Physical Unclonable Functions (PUFs). However, careful considerations must be given to the design of the PUF architecture as a means of avoiding undesirable localized bias effects that adversely impact randomness, an important statistical quality characteristic of a PUF. Here in this paper, we investigate a ring-oscillator (RO) PUF that leverages localized entropy from individual look-up table (LUT) primitives. A novel RO construction is presented that enables the individual paths through the LUT primitive to be measured and isolated at high precision, and an analysis is presented that demonstrates significant levels of localized design bias. The analysis demonstrates that delay-based PUFs that utilize LUTs as a source of entropy should avoid using FPGA primitives that are localized to specific regions of the FPGA, and instead, a more robust PUF architecture can be constructed by distributing path delay components over a wider region of the FPGA fabric. Compact RO PUF architectures that utilize multiple configurations within a small group of LUTs are particularly susceptible to these types of design-level bias effects. The analysis is carried out on data collected from a set of identically designed, hard macro instantiations of the RO implemented on 30 copies of a Zynq 7010 SoC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Tailoring microstructures with mild magnetic-field processing: A case study of CuNiFe alloys

Combined experimental and computational investigations of the CuNiFe spinodal system confirm that application of a mild magnetic field during thermal treatment alters elemental redistribution and the resulting microstructure, relative to that obtained from zero-field annealing. Spinodal decomposition of a Cu 40 Ni 42 Fe 18 alloy was initiated during thermal treatment at 773 K, conducted either under zero field or modest (60 mT) magnetic f ield conditions for up to 200 h. Periodic (~10 nm) chemical modulations into Cu-rich and NiFe-rich regions were observed under both conditions, with the amplitude and wavelength of the segregated regions increasing with treatment time. However, magnetic field annealing resulted in a more than twofold increase in the amplitude of elemental modulations relative to zero-field conditions – consistent with enhanced diffusional f luxes during spinodal decomposition – while the modulation wavelength remained largely unaffected. These microstructural differences are reflected in various extrinsic magnetic properties. In parallel, first-principles DFT calculations indicate that long-range ferromagnetic order, as induced by an applied magnetic field, substantially alters the strength and nature of atomic interactions, enhancing the thermodynamic instability of the CuNiFe solid solution. Collectively, these results suggest that incorporating a mild (millitesla-level) magnetic field – distinct from the strong (tesla-level) fields commonly used in prior studies – during thermal processing has the potential to deliver enhanced control of microstructures for targeted engineering outcomes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Maternal undernutrition modulates hepatic MicroRNAs expression in the early life of offspring

Emerging studies revealed that a poor intrauterine environment elicited by maternal nutrient restriction (MNR) is associated with an increased risk of metabolic diseases in adulthood. Previous research has shown that microRNAs (miRNAs) exert pivotal roles in modulating molecular pathways involved in disease pathogenesis and progression. In this respect, we herein examined miRNA profiles in samples of liver from offspring whose mothers were fed either with a 50% food-restricted diet or standard laboratory chow during pregnancy. Our findings enumerated that miR-181a, involved in lipid metabolism, was found to be downregulated in the liver of MNR offspring at 1 day of age when compared to that of control offspring. We also noted that overexpression of miR-181a reduced the lipid droplets after treatment with oleic acid for 48 h, which suppressed the expressions levels of SIRT1, FOXO1, KLF6 and PPARγ in BRL-3A cells, while the opposite results were observed with decreased expression of miR-181a. Furthermore, the luciferase reporter assay confirmed the direct interactions between miR-181a with KLF6 and SIRT1. In adults, the MNR offspring elucidated increased TG content, decreased expression of miR-181a, and increased expressions levels of SIRT1, FOXO1, KLF6, and PPARγ in liver tissues. Collectively, these findings provided novel evidence that MNR could regulate miRNAs expression, which might be related to lipid metabolism in MNR offspring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Orthologs of human circulating miRNAs associated with hepatocellular carcinoma are elevated in mouse plasma months before tumour detection

Research examining the potential for circulating miRNA to serve as markers for preneoplastic lesions or early-stage hepatocellular carcinoma (HCC) is hindered by the difficulties of obtaining samples from asymptomatic individuals. As a surrogate for human samples, we identified hub miRNAs in gene co-expression networks using HCC-bearing C3H mice. We confirmed 38 hub miRNAs as associated with HCC in F2 hybrid mice derived from radiogenic HCC susceptible and resistant founders. When compared to a panel of 12 circulating miRNAs associated with human HCC, two had no mouse ortholog and 7 of the remaining 10 miRNAs overlapped with the 38 mouse HCC hub miRNAs. Using small RNA sequencing data generated from serially collected plasma samples in F2 mice, we examined the temporal levels of these 7 circulating miRNAs and found that the levels of 4 human circulating markers, miR-122-5p, miR-100-5p, miR-34a-5p and miR-365-3p increased linearly as the time approaching HCC detection neared, suggesting a correlation of miRNA levels with oncogenic progression. Estimation of change points in the kinetics of the 4 circulating miRNAs suggested the changes started 17.5 to 6.8 months prior to HCC detection. These data establish these 4 circulating miRNAs as potential sentinels for preneoplastic lesions or early-stage HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Laser field control of subradiant states of a system of several atoms in a configuration close to a regular polygon

Some collective excitations of an ensemble of closely spaced (two-level) atoms can be characterised by extremely low rates of radiation decay, which is of considerable interest for storing quantum information. Such (dark) states are realised, for example, in planar geometry when atoms are located at the nodes of a regular polygon. However, the decay rate of such excitations increases markedly when the geometry deviates from ideal. Using the example of small deformation of a square in a rhombus, we show that an increase in the decay rate of the dark state due to symmetry breaking can be compensated for by adjusting the transition frequency of some atoms exposed to external laser radiation. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Cold-cap structure in a slurry-fed electric melter

We report as glass batch is charged into an electric melter, a cold cap forms on the glass melt surface. Heat transfer to the cold cap from the molten glass below and the melter atmosphere above determines the melting rate. A mathematical model of the cold cap and the experimental kinetic data of the feed-to-glass conversion that were collected for several simulated low-activity and high-level waste melter feeds allowed us to develop relationships between the internal structure of the cold cap, its properties, its thickness, and the internal heat transfer. This contribution shows the distribution of major crystalline phases and the cumulative evolution of gases within the cold cap. It also examines the temperature, conversion degree, and heating rate the melter feed is experiencing during the passage through the cold cap and their effects on the cold-cap bottom temperature and morphology, which are important for the computational fluid dynamics simulations of melters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Melanoma Brain Metastasis CTC Signature and CTC:B-cell Clusters Associate with Secondary Liver Metastasis: A Melanoma Brain–Liver Metastasis Axis

Melanoma brain metastasis is linked to dismal prognosis and low overall survival and is detected in up to 80% of patients at autopsy. Circulating tumor cells (CTC) are the smallest functional units of cancer and precursors of fatal metastasis. We previously used an unbiased multilevel approach to discover a unique ribosomal protein large/small subunit (RPL/RPS) CTC gene signature associated with melanoma brain metastasis. In this study, we hypothesized that CTC-driven melanoma brain metastasis secondary metastasis (“metastasis of metastasis” per clinical scenarios) has targeted organ specificity for the liver. We injected parallel cohorts of immunodeficient and newly developed humanized NBSGW (huNBSGW) mice with cells from CTC-derived melanoma brain metastasis to identify secondary metastatic patterns. We found the presence of a melanoma brain–liver metastasis axis in huNBSGW mice. Furthermore, RNA sequencing analysis of tissues showed a significant upregulation of the RPL/RPS CTC gene signature linked to metastatic spread to the liver. Additional RNA sequencing of CTCs from huNBSGW blood revealed extensive CTC clustering with human B cells in these mice. CTC:B-cell clusters were also upregulated in the blood of patients with primary melanoma and maintained either in CTC-driven melanoma brain metastasis or melanoma brain metastasis CTC–derived cells promoting liver metastasis. CTC-generated tumor tissues were interrogated at single-cell gene and protein expression levels (10x Genomics Xenium and HALO spatial biology platforms, respectively). Collectively, our findings suggest that heterotypic CTC:B-cell interactions can be critical at multiple stages of metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

KCMP Minnesota Tall Tower Carbon Dioxide Data 2007-2019

This data set contains carbon dioxide concentration values and supporting metadata information measured at a tall tower (KCMP radio station, 244 meters) site near St. Paul, Minnesota, USA. The data package includes a text file containing hourly continuous carbon dioxide concentration measurements from 2007 to 2019, including raw, de-spiked, and gap filled data. Carbon dioxide concentrations were measured at approximately 100 meters using tunable diode laser spectroscopy. The instrumentation was maintained at the base of the tall tower in a temperature-controlled building. This data was collected to track and improve the understanding of carbon dioxide levels over a twelve year period for the region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stem hydraulic conductivity and vulnerability to cavitation for 26 tree species in Panama

Stem hydraulic conductivity and vulnerability to cavitation were measured for 26 tree species located in Panama. The data were generated to better understand the ecology of the focal tree species. Complementary NGEE-Tropics datasets for these species include sap flow, leaf-level gas exchange, and leaf water potential. Stem samples were collected from distal branches of canopy trees, brought to the Smithsonian Tropical Research Institute laboratory in Gamboa, Panama, and allowed to dry to various water potentials before measurements. For each species, a Weibull function was fit to the 90% quantile of the relationship between stem area specific hydraulic conductivity (Ks) and stem water potential. From these functions, maximum Ks and vulnerability parameters were derived. The data files in the package include raw data, derived parameters, and the R script used for analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Metatransciptomic Analysis Data for Interactive effects of depth and differential irrigation on soil microbiome composition and functioning

RNA was collected from soil at different depths and after three different levels of irrigation T1 100% of normal field irrigation, T4: 18.75% or normal irrigation and T5: unirrigated controls. Total RNA was isolated using the Zymo Quick-RNA fecal/soil microbe miniprep (catalog no. R2040), incorporating the DNase I treatment using Zymo’s DNase I kit (catalog no. E1010). To increase the yield of RNA, we modified the manufacturer’s instructions by first doubling the amount of soil per extraction (from 0.25 g to 0.5 g) and by performing extractions in triplicate before pooling separate extractions together. Certain soil samples (largely those from deeper soil layers) had low yield (< 100 ng per extraction) so additional rounds of extraction were performed to obtain sufficient RNA. RNA concentration was assessed using a Qubit RNA HS assay kit (Thermo Fisher) and RNA quality was determined using an Agilent 2100 BioAnalyzer (Agilent; Santa Clara, CA). The resultant RNA samples were then sequenced by GENEWIZ using Illumina technology (GENEWIZ; South Plainfield, NJ). Sequences were then aligned to a soil metagenome previously obtained from the same site using the Burrows-Wheeler aligner (BWA). SAM files were then converted to raw counts using HTSeq.

Soil microbiome, metatranscriptomics↗
DOE OSTI2023

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Assessing Microbial Communities Related to Mercury Transformations in Contaminated Streambank Soils

In nature, the bioaccumulative potent neurotoxin methylmercury (MeHg) is produced from inorganic mercury (Hg) predominantly by anaerobic microorganisms. Hg-contaminated soils are a potential source of MeHg due to microbial activity. We examine streambank soils collected from the contaminated East Fork Poplar Creek (EFPC) in Tennessee, USA, where seasonal variations in MeHg levels have been observed throughout the year, suggesting active microbial Hg methylation. In this study, we characterized the microbial community in contaminated bank soil samples collected from two locations over a period of one year and compared the results to soil samples from an uncontaminated reference site with similar geochemistry (n = 12). Microbial community composition and diversity were assessed by 16S rRNA gene amplicon sequencing. Furthermore, to isolate potential methylators from soils, enrichment cultures were prepared using selective media. A set of three clade-specific primers targeting the gene hgcA were used to detect Hg methylators among the δ-Proteobacteria in EFPC bank soils across all seasons. Two families among the δ-Proteobacteria that have been previously associated with Hg methylation, Geobacteraceae and Syntrophobacteraceae, were found to be predominant with relative abundances of 0.13% and 4.0%, respectively. However, in soil enrichment cultures, Firmicutes were predominant among families associated with Hg methylation. Specifically, Clostridiaceae and Peptococcaceae and their genera Clostridium and Desulfosporosinus were among the ten most abundant genera with relative abundances of 2.6% and 1.7%, respectively. These results offer insights into the role of microbial communities on Hg transformation processes in contaminated bank soils in EFPC. Identifying the biogeochemical drivers of MeHg production is critical for future remediation efforts.

54 ENVIRONMENTAL SCIENCES↗