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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

New Clues to the Evolution of Dwarf Carbon Stars From Their Variability and X-Ray Emission

As main-sequence stars with C > O, dwarf carbon (dC) stars are never born alone but inherit carbon-enriched material from a former asymptotic giant branch (AGB) companion. In contrast to M dwarfs in post-mass-transfer binaries, C 2 and/or CN molecular bands allow dCs to be identified with modest-resolution optical spectroscopy, even after the AGB remnant has cooled beyond detectability. Accretion of substantial material from the AGB stars should spin up the dCs, potentially causing a rejuvenation of activity detectable in X-rays. Indeed, a few dozen dCs have recently been found to have photometric variability with periods under a day. However, most of those are likely post-common-envelope binaries, spin–orbit locked by tidal forces, rather than solely spun-up by accretion. Here, we study the X-ray properties of a sample of the five nearest-known dCs with Chandra. Two are detected in X-rays, the only two for which we also detected short-period photometric variability. We suggest that the coronal activity detected so far in dCs is attributable to rapid rotation due to tidal locking in short binary orbits after a common-envelope phase, late in the thermally pulsing (TP) phase of the former C-AGB primary (TP-AGB).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗
DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

The Fibrin slide assay for detecting urokinase activity in human fetal kidney cells

The Fibrin Slide Technique of Hau C. Kwaan and Tage Astrup is discussed. This relatively simple assay involves two steps: the formation of an artificial clot and then the addition of an enzyme (UKOKINASE) to dissolve the clot. The actual dissolving away of the clot is detected by the appearance of holes (lysis zones) in the stained clot. The procedure of Kwaan and Astrup is repeated, along with modifications and suggestions for improvements based on experience with the technique.

Sedor, K.↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗
DOE OSTI2017

Activation product analysis in a mixed sample containing both fission and neutron activation products

Activation analysis of gold (Au) is used to estimate neutron fluence resulting from a criticality event; however, such analyses are complicated by simultaneous production of other gamma-emitting fission products. Confidence in neutron fluence estimates can be increased by quantifying additional activation products such as platinum (Pt), tantalum (Ta), and tungsten (W). This work describes a radiochemical separation procedure for the determination of these activation products. Anion exchange chromatography is used to separate anionic forms of these metals in a nitric acid matrix; thiourea is used to isolate the Au and Pt fraction, followed by removal of the Ta fraction using hydrogen peroxide. W, which is not retained on the first anion exchange column, is transposed to an HCl/HF matrix to enhance retention on a second anion exchange column and finally eluted using HNO3/HF. Chemical separations result in a reduction in the minimum detectable activity by a factor of 287, 207, 141, and 471 for 182Ta, 187W, 197Pt, and 198Au respectively, with greater than 90% recovery for all elements. These results represent the highest recoveries and lowest minimum detectable activities for 182Ta, 187W, 197Pt, and 198Au from mixed fission-activation product samples to date, enabling considerable refinement in the measurement uncertainties for neutron fluences in highly complex sample matrices.

anion exchange↗
DOE OSTI2025

Secure State Estimation with Asynchronous Measurements for Coordinated Cyber Attack Detection in Active Distribution Systems

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data.

asynchronous measurements↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗
DOE OSTI2018

GEANT4 investigation of 40 K and 222 Rn interference effects on actinide detection sensitivities of CEMRC BEGe lung detectors

Internal dosimetry measurements of trans-uranic (TRU) radionuclides within the human body can be accomplished by γ-ray spectroscopy using high-purity germanium detectors. While measurements are typically conducted inside shielded chambers in order to minimize the background detector signal, the presence of radioactive 40 K within the body cannot be removed, which produces a high-energy γ ray at 1461 keV. The 40 K, in addition to airborne 222 Rn, contributes to the radiation field within the chamber that increases to the minimum detectable activity that can be achieved in practice. The objective of the present work is to estimate the contributions from 40 K internal to the human body and airborne 222 Rn to the background signal in a lung counter germanium detector array using GEANT4 simulations, with a focus on TRU isotope energy region (17–59 keV). The investigations consisted of three main simulations: 1. Simulating the γ-ray flux at the geometric center of the CEMRC counting chamber arising from a 25 nCi source of 40 K from (a) a point source, and (b) a source distributed throughout a BOMAB phantom. 2. Simulating the response of the CEMRC BEGe detectors arising from a 25 nCi source of 40 K from (a) a point source, and (b) a source distributed throughout a BOMAB phantom. 3. Simulating the response of the CEMRC BEGe detectors to the environmental background in the counting chamber, consisting of airborne 222 Rn (0–4 pCi/L) and 40 K (0–160 nCi) internal to the human body. The results are used to investigate the 40 K background contribution to a routine spectrum recorded by the lung detectors at the CEMRC Internal Dosimetry laboratory over a typical half hour counting interval used in the dosimetry of radiological workers. The minimum detectable activity was calculated for various 222 Rn and 40 K activities and compared to experimentally determined values. The contribution of cosmic rays was not included in this investigation.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Detecting phospholipase activity with the amphipathic lipid packing sensor motif of ArfGAP1

Highlights: • The amphipathic lipid packing sensor (ALPS) motif of ArfGAP1 binds membranes of high curvature. • The activity of phospholipase A2, C and D on membranes generate lipids with high spontaneous curvature. • This in-situ phospholipase activity induces ALPS binding to membranes with small curvature. • We propose ALPS not only as a curvature sensor but also as a tool to monitor phospholipase activity. The amphipathic lipid packing sensor (ALPS) motif of ArfGAP1 brings this GTPase activating protein to membranes of high curvature. Phospholipases are phospholipid-hydrolyzing enzymes that generate different lipid products that alter the lateral organization of membranes. Here, we evaluate by fluorescence microscopy how in-situ changes of membrane lipid composition driven by the activity of different phospholipases promotes the binding of ALPS. We show that the activity of phospholipase A2, phospholipase C and phospholipase D drastically enhances the binding of ALPS to the weakly-curved membrane of giant liposomes. Our results suggest that the enzymatic activity of phospholipases can modulate the ArfGAP1-mediated intracellular traffic and that amphiphilic peptides such as the ALPS motif can be used to study lipolytic activities at lipid membranes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession

We report permafrost underlies about one fifth of the global land area and affects ground stability, freshwater runoff, soil chemistry, and surface-atmosphere gas exchange. The depth of thawed ground overlying permafrost (active layer thickness) has broadly increased across the Arctic in recent decades, coincident with a period of increased streamflow, especially the lowest flows (baseflow). Mechanistic links between active layer thickness and baseflow have recently been explored using linear reservoir theory, but most watersheds behave as nonlinear reservoirs. We derive theoretical nonlinear relationships between long-term average saturated soil thickness $\bar{η}$(proxy for active layer thickness) and long-term average baseflow. When applied to 38 years of daily streamflow data for the Kuparuk River basin on the North Slope of Alaska, the theory predicts $\bar{η}$ increased 0.17 ± 0.22[2σ] cm a -1 between 1983 and 2020 (6.4 ± cm total). The rate of increase nearly doubled to 0.29 ± 0.31 cm a -1 between 1990 and 2020, during which time local field measurements from Circumpolar Active Layer Monitoring sites indicate the active layer increased 0.31 ± 0.22 cm a -1 . The predicted rate of increase more than doubled again between 2002 and 2020, outpacing a near doubling of observed active layer thickening, consistent with trends in terrestrial water storage inferred from Gravity Recovery and Climate Experiment satellite gravimetry and Modern-Era Retrospective Analysis for Research and Applications climate reanalysis. Overall, hydrologic change is accelerating in the Kuparuk River basin, and we provide a theoretical framework for estimating basin-scale changes in active layer water storage from streamflow measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optical activation and detection of charge transport between individual colour centres in diamond

Understanding the capture of charge carriers by colour centres in semiconductors is important for the development of novel forms of sensing and quantum information processing, but experiments typically involve ensemble measurements, often impacted by defect proximity. In this study, we show that confocal fluorescence microscopy and magnetic resonance can be used to induce and probe charge transport between individual nitrogen-vacancy centres in diamond at room temperature. In our experiments, a ‘source’ nitrogen vacancy undergoes optically driven cycles of ionization and recombination to produce a stream of photogenerated carriers, one of which is subsequently captured by a ‘target’ nitrogen vacancy several micrometres away. We use a spin-to-charge conversion scheme to encode the spin state of the source colour centre into the charge state of the target, which allows us to set an upper bound to carrier injection from other background defects. We attribute our observations to the action of unscreened Coulomb potentials producing giant carrier capture cross-sections, orders of magnitude greater than those measured in ensembles.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Detection and activation of HIV broadly neutralizing antibody precursor B cells using anti-idiotypes

Many tested vaccines fail to provide protection against disease despite the induction of antibodies that bind the pathogen of interest. In light of this, there is much interest in rationally designed subunit vaccines that direct the antibody response to protective epitopes. Here, we produced a panel of anti-idiotype antibodies able to specifically recognize the inferred germline version of the human immunodeficiency virus 1 (HIV-1) broadly neutralizing antibody b12 (iglb12). We determined the crystal structure of two anti-idiotypes in complex with iglb12 and used these anti-idiotypes to identify rare naive human B cells expressing B cell receptors with similarity to iglb12. Immunization with a multimerized version of this anti-idiotype induced the proliferation of transgenic murine B cells expressing the iglb12 heavy chain in vivo, despite the presence of deletion and anergy within this population. Together, our data indicate that anti-idiotypes are a valuable tool for the study and induction of potentially protective antibodies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Low Activity Tritium Detection in CCDs Using Deep Learning Techniques

Here, this study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual approach utilizing both measured CCD data and detailed Geant4 simulations. Our analysis compares classical techniques with advanced deep learning methods, including convolutional neural networks (CNNs), autoencoders trained exclusively on tritium data, and preliminary studies on boosted decision trees (BDTs). The CNN, trained on mixed signal/background datasets, demonstrates superior classification performance, while the autoencoder shows the potential of unsupervised, background-agnostic strategies when background characteristics are poorly defined. These results highlight the excellent sensitivity achievable thanks to the background rejection made possible by information-rich CCD data, paving the way for improved portable tritium monitoring.

Autoencoder↗
DOE OSTI2019

Tank 22 Supernate Sample Characterization for Select Radionuclides

The Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (SRR), through a Technical Assistance Request (G-TAR-H-00007) to provide additional sample characterization on unfiltered Tank 22 supernate identified as sample HTF-22-18-117, which was delivered to SRNL Shielded Cells on December 14, 2018. A summary of the average analytical results for this unfiltered Tank 22 supernate liquid follows. The density of the unfiltered Tank 22 supernate is 1.008 g/mL, 0.2%RSD. The Tank 22 supernate sample was analyzed for a total of 45 radionuclide species, with 15 of these radionuclides showing activity levels above minimum detectable activity of at least one replicate. Cesium-137 showed the highest activity at 2.90E+07 dpm/mL, 2.35 %RSD (1.30E-02 Ci/ L) with U-235 showing the lowest measurable activity at an average of 1.66E-01 dpm/mL, 6.26 %RSD (7.49E-11 Ci/L). The primary beta emitting radionuclides in the unfiltered Tank 22 supernate sample include Sr-90, Y-90 and Cs-137 at average activity concentrations of 1.10E-05, 1.10E-05 and 1.30E-02 Ci/L, respectively. The primary gamma emitting radionuclide is Ba-137 m at an average activity concentration of 1.23E-02 Ci/L (note that the activity concentration for Ba-137 m was calculated as 94.7% the Cs-137 activity concentration).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗