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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Extravehicular Activity Asteroid Exploration and Sample Collection Capability

One of the challenging primary objectives associated with NASA's Asteroid Redirect Crewed Mission (ARCM) is to demonstrate deep space Extravehicular Activity (EVA) and tools and to obtain asteroid samples to return to Earth for further study. Prior Shuttle and International Space Station (ISS) spacewalks have benefited from engineered EVA interfaces which have been designed and manufactured on Earth. Rigid structurally mounted handrails, and tools with customized interfaces and restraints optimize EVA performance. For ARCM, EVA complexity increases due to the uncertainty of the asteroid properties. The variability of rock size, shape and composition, as well as behavior of the asteroid capture mechanism will complicate EVA translation, tool restraint, and body stabilization. The unknown asteroid hardness and brittleness will complicate tool use. The rock surface will introduce added safety concerns for cut gloves and debris control. Feasible solutions to meet ARCM EVA objectives were identified using experience gained during Apollo, Shuttle, and ISS EVAs, terrestrial mountaineering practices, NASA Extreme Environment Mission Operations (NEEMO) 16 mission, and during Neutral Buoyancy Laboratory testing in the Modified Advanced Crew Escape Suit (MACES) suit. This paper will summarize the overall operational concepts for conducting EVAs for the ARCM mission including translation paths and body restraint methods, potential tools used to extract the samples, design implications for the Asteroid Redirect Vehicle (ARV) for EVA, and the results of early development testing of potential EVA tasks.

Sipila, Stephanie A.↗
NASA NTRS2014

Extravehicular Activity Asteroid Exploration and Sample Collection Capability

One of the challenging primary objectives associated with NASA's Asteroid Redirect Crewed Mission (ARCM) is to demonstrate deep space Extravehicular Activity (EVA) and tools and to obtain asteroid samples to return to Earth for further study. Prior Shuttle and International Space Station (ISS) spacewalks have benefited from engineered EVA interfaces which have been designed and manufactured on Earth. Rigid structurally mounted handrails, and tools with customized interfaces and restraints optimize EVA performance. For ARCM, EVA complexity increases due to the uncertainty of the asteroid properties. The variability of rock size, shape and composition, as well as behavior of the asteroid capture mechanism will complicate EVA translation, tool restraint, and body stabilization. The unknown asteroid hardness and brittleness will complicate tool use. The rock surface will introduce added safety concerns for cut gloves and debris control. Feasible solutions to meet ARCM EVA objectives were identified using experience gained during Apollo, Shuttle, and ISS EVAs, terrestrial mountaineering practices, NASA Extreme Environment Mission Operations (NEEMO) 16 mission, and during Neutral Buoyancy Laboratory testing in the Modified Advanced Crew Escape Suit (MACES) suit. This paper will summarize the overall operational concepts for conducting EVAs for the ARCM mission including translation paths and body restraint methods, potential tools used to extract the samples, design implications for the Asteroid Redirect Vehicle (ARV) for EVA, and the results of early development testing of potential EVA tasks.

Sipila, Stephanie A.↗
DOE OSTI2018

Preliminary Work Toward a Transuranic Activity Estimation Method for Rapid Discrimination of Anthropogenic from Transuranic Activity in Alpha Air Samples

Radon ( 222 Rn) and thoron ( 220 Rn) progeny (primarily bismuth and polonium) are known interferents when rapid evaluation of transuranic content on air filters is of interest. These complexities stem from the overlapping energies of the progeny alpha particles onto the transuranic region of interest (3–5.5 MeV) where naturally-occurring alpha emitters can overwhelm the spectra. Due to the immediacy of the alpha counting methods employed, coupled with the half-life of thoron progeny dominated by 212 Pb (t 1/2 =10.6 h), a conservative transuranic activity estimate with rigorous uncertainty is being sought. A successful transuranic activity estimation method will incorporate any thoron progeny present on the filter providing 95% confidence decision levels in which a filter may be evaluated for emergency response applications. Twenty-three pairs of samples of various duration having no transuranic content were taken over a 2-mo period. The resulting filters were counted in a time series before non-linear least squares decay curve fitting was applied to the decay profile. For the samples considered, a transuranic activity estima-tor decision level was determined at 0.2 Bq for the given geographic location and months analyzed. In conclusion, validation of this method for other seasonal and geographic regions could provide enhanced emergency response capability when the presence of transuranic activity is suspected.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative’s Workshop and Follow-On Activities

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Enhanced photocatalytic activity of surface disorder-engineered CaTiO3

Highlights: • Surface disorder of CaTiO{sub 3} nanocuboids was created by NaBH{sub 4} treatment. • Surface disorder-engineered CaTiO{sub 3} exhibits more efficient electron-hole separation. • Surface disorder-engineered CaTiO{sub 3} exhibits enhanced photocatalytic activity. • The photocatalytic mechanism was investigated and discussed. - Abstract: In this work, we attempted to create surface disorder on CaTiO{sub 3} nanocuboids by NaBH{sub 4} treatment with the aim of enhancing the photocatalytic activity. The samples were systematically investigated by XRD, SEM, TEM, BET, XPS, UV–vis DRS, PL, EIS and photocurrent response. The photocatalytic activity of the samples were evaluated by degrading RhB under irradiation of simulated sunlight, UV and visible light. It is demonstrated that NaBH{sub 4}-treated CaTiO{sub 3} samples exhibit an obviously enhanced photocatalytic activity. In particular, the sample treated at 0.1 M NaBH{sub 4} solution has a photocatalytic activity ca. 2.0 times higher than pristine CaTiO{sub 3}. This is ascribed to the fact that the created surface disorder facilitates the separation of photogenerated electron-hole pairs, and thus more photogenerated holes are able to participate in the photocatalytic reactions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2012

Synthesis, characterization and photocatalytic properties of lithium tantalate

Lithium tantalate (LiTaO{sub 3}) was successfully synthesized thorough an impregnation method of tantalum pentaoxide in the aqueous solution of lithium hydroxide. Next, the sample was divided into eight batches and calcinated in a temperature range of 450-800 Degree-Sign C. The materials produced were then characterized by X-ray diffraction, Raman spectroscopy, Diffuse Reflectance (DR) UV-vis spectroscopy, BET analysis, and high resolution transmission electron microscopy. Moreover, the samples were tested as photocatalysts in a reaction of hydrogen generation. Interestingly, it was found that the sample calcinated at 550 Degree-Sign C exhibits the highest photocatalytic activity. This sample is composed of 85% of LiTaO{sub 3} and 15% of Ta{sub 2}O{sub 5}. - Highlights: Black-Right-Pointing-Pointer LiTaO{sub 3} was synthesized thorough an impregnation of Ta{sub 2}O{sub 5} and LiOH and calcination at the temperature range of 450-800 Degree-Sign C. Black-Right-Pointing-Pointer Each calcinated sample was tested as photocatalyst in the reaction of hydrogen generation. Black-Right-Pointing-Pointer It was found that the key parameter determining their activity is the sample composition. Black-Right-Pointing-Pointer The sample exhibiting the highest activity is composed of LiTaO{sub 3} (85.1%) and unreacted Ta{sub 2}O{sub 5} (14.9%).

36 MATERIALS SCIENCE↗
NASA NTRS1989

The dependence of the soft X ray spectral slope with radio property, luminosity, and redshift, for a large sample of AGN from the Einstein IPC data base

The dependence of the soft X-ray spectral slope on radio, optical and X-ray properties, and on redshift are reported for a large sample of Active Galactic Nuclei (AGN). The sample includes 317 optically and radio-selected AGN from a preliminary version of the Einstein Imaging Proportional Counter (IPC) quasar and AGN data base. The main results are: the difference in X-ray slope between radio-loud and radio-quiet AGN were confirmed for an independent and much larger sample of sources; a difference in X-ray slope between flat and steep radio spectrum AGN is observed only in high luminosity sub-sample; in flat radio spectrum AGNs there is an indication for a dependence of the X-ray spectral index on X-ray luminosity redshift and alpha sub 0x.

Brunner, H.↗
DOE OSTI2021

Deep active learning for classifying cancer pathology reports

Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Less is more: Sampling chemical space with active learning

We present the development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble’s prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. Finally, this universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Prompt Gamma Activation Analysis (PGAA): Technique of choice for nondestructive bulk analysis of returned comet samples

Prompt gamma activation analysis (PGAA) is a well-developed analytical technique. The technique involves irradiation of samples in an external neutron beam from a nuclear reactor, with simultaneous counting of gamma rays produced in the sample by neutron capture. Capture of neutrons leads to excited nuclei which decay immediately with the emission of energetic gamma rays to the ground state. PGAA has several advantages over other techniques for the analysis of cometary materials: (1) It is nondestructive; (2) It can be used to determine abundances of a wide variety of elements, including most major and minor elements (Na, Mg, Al, Si, P, K, Ca, Ti, Cr, Mn, Fe, Co, Ni), volatiles (H, C, N, F, Cl, S), and some trace elements (those with high neutron capture cross sections, including B, Cd, Nd, Sm, and Gd); and (3) It is a true bulk analysis technique. Recent developments should improve the technique's sensitivity and accuracy considerably.

Lindstrom, David J.↗
NASA NTRS1974

Chemical studies of Apollo 16 and 17 samples

Instrumental neutron activation analyses were conducted to nine Apollo 16 samples and 23 Apollo 17 samples. Radiochemical neutron activation analyses were carried out in the case of six Apollo 16 boulder-2 rocks, five Apollo 17 soils, and one Apollo 16 soil. The elemental abundances obtained are presented in tables and the significance of the analytical results is discussed. Attention is also given to interelement correlations.

Laul, J. C.↗
DOE OSTI2013

Improved mesh based photon sampling techniques for neutron activation analysis

The design of fusion power systems requires analysis of neutron activation of large, complex volumes, and the resulting particles emitted from these volumes. Structured mesh-based discretization of these problems allows for improved modeling in these activation analysis problems. Finer discretization of these problems results in large computational costs, which drives the investigation of more efficient methods. Within an ad hoc subroutine of the Monte Carlo transport code MCNP, we implement sampling of voxels and photon energies for volumetric sources using the alias method. The alias method enables efficient sampling of a discrete probability distribution, and operates in 0(1) time, whereas the simpler direct discrete method requires 0(log(n)) time. By using the alias method, voxel sampling becomes a viable alternative to sampling space with the 0(1) approach of uniformly sampling the problem volume. Additionally, with voxel sampling it is straightforward to introduce biasing of volumetric sources, and we implement this biasing of voxels as an additional variance reduction technique that can be applied. We verify our implementation and compare the alias method, with and without biasing, to direct discrete sampling of voxels, and to uniform sampling. We study the behavior of source biasing in a second set of tests and find trends between improvements and source shape, material, and material density. Overall, however, the magnitude of improvements from source biasing appears to be limited. Future work will benefit from the implementation of efficient voxel sampling - particularly with conformal unstructured meshes where the uniform sampling approach cannot be applied. (authors)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

High-Throughput Characterization and Optimization of Polyamide Hydrolase Activity Using Open Port Sampling Interface Mass Spectrometry

Enzymatic biodegradation of polymers, such as polyamides (PA), has the potential to cost-effectively reduce plastic waste, but enhancements in degradation efficiency are needed. Engineering enzymes through directed evolution is one pathway toward identification of critical domains needed for improving activity. However, screening such enzymatic libraries (100s-to-1000s of samples) is time-consuming. Here we demonstrate the use of robotic autosampler (PAL) and immediate drop on demand technology (I.DOT) liquid handling systems coupled with open-port sampling interface-mass spectrometry (OPSI-MS) to screen for PA6 and PA66 hydrolysis by 6-aminohexanoate-oligomer endo-hydrolase (nylon hydrolase, NylC) in a high-throughput (8–20 s/sample) manner. The OPSI-MS technique required minimal sample preparation and was amenable to 96-well plate formats for automated processing. Enzymatic hydrolysis of PA characteristically produced soluble linear oligomer products that could be identified by OPSI-MS. Incubation temperatures and times were optimized for PA6 (65 °C, 24 h) and PA66 (75 °C, 24 h) over 108 experiments. In addition, the I.DOT/OPSI-MS quantified production of PA6 linear dimer (8.3 ± 1.6 μg/mL) and PA66 linear monomer (13.5 ± 1.5 μg/mL) by NylC with a lower limit of detection of 0.029 and 0.032 μg/mL, respectively. For PA6 and PA66, linear oligomer production corresponded to 0.096 ± 0.018% and 0.204 ± 0.028% conversion of dry pellet mass, respectively. The developed methodology is expected to be utilized to assess enzymatic hydrolysis of engineered enzyme libraries, comprising hundreds to thousands of individual samples.

47 OTHER INSTRUMENTATION↗
NASA NTRS1999

Deficiency of ''Thin'' Stellar Bars in Seyfert Host Galaxies

Using all available major samples of Seyfert galaxies and their corresponding control samples of closely matched non-active galaxies, we find that the bar ellipticities (or axial ratios) in Seyfert galaxies are systematically different from those in non-active galaxies. Overall, there is a deficiency of bars with large ellipticities (i.e., 'fat' or 'weak' bars) in Seyferts, compared to non-active galaxies. Accompanied with a large dispersion due to small number statistics, this effect is strictly speaking at the 2 sigma level. To obtain this result, the active galaxy samples of near-infrared surface photometry were matched to those of normal galaxies in type, host galaxy ellipticity, absolute magnitude, and, to some extent, in redshift. We discuss possible theoretical explanations of this phenomenon within the framework of galactic evolution, and, in particular, of radial gas redistribution in barred galaxies. Our conclusions provide further evidence that Seyfert hosts differ systematically from their non-active counterparts on scales of a few kpc.

Shlosman, Isaac↗
DOE OSTI2012

INVISIBLE ACTIVE GALACTIC NUCLEI. I. SAMPLE SELECTION AND OPTICAL/NEAR-IR SPECTRAL ENERGY DISTRIBUTIONS

In order to find more examples of the elusive high-redshift molecular absorbers, we have embarked on a systematic discovery program for highly obscured, radio-loud 'invisible active galactic nuclei' using the Very Large Array Faint Images of the Radio Sky at Twenty centimeters radio survey in conjunction with the Sloan Digital Sky Survey (SDSS) to identify 82 strong ({>=}300 mJy) radio sources positionally coincident with late-type, presumably gas-rich galaxies. In this first paper, the basic properties of this sample are described including the selection process and the analysis of the spectral energy distributions (SEDs) derived from the optical (SDSS) + near-IR (NIR) photometry obtained by us at the Apache Point Observatory 3.5 m. The NIR images confirm the late-type galaxy morphologies found by SDSS for these sources in all but a few (6 of 70) cases (12 previously well studied or misclassified sources were culled). Among 70 sources in the final sample, 33 show galaxy type SEDs, 17 have galaxy components to their SEDs, and 20 have quasar power-law continua. At least nine sources with galaxy SEDs have K-band flux densities too faint to be giant ellipticals if placed at their photometric redshifts. Photometric redshifts for this sample are analyzed and found to be too inaccurate for an efficient radio-frequency absorption line search; spectroscopic redshifts are required. A few new spectroscopic redshifts for these sources are presented here but more will be needed to make significant progress in this field. Subsequent papers will describe the radio continuum properties of the sample and the search for redshifted H I 21 cm absorption.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a database of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

User-Driven Sampling Strategies in Image Exploitation

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

97 MATHEMATICS AND COMPUTING↗