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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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NASA NTRS2017

The NASA Ames Research Center Institutional Scientific Collection: History, Best Practices and Scientific Opportunities

The NASA Ames Life Sciences Institutional Scientific Collection (ISC), which is composed of the Ames Life Sciences Data Archive (ALSDA) and the Biospecimen Storage Facility (BSF), is managed by the Space Biosciences Division and has been operational since 1993. The ALSDA is responsible for archiving information and animal biospecimens collected from life science spaceflight experiments and matching ground control experiments. Both fixed and frozen spaceflight and ground tissues are stored in the BSF within the ISC. The ALSDA also manages a Biospecimen Sharing Program, performs curation and long-term storage operations, and makes biospecimens available to the scientific community for research purposes via the Life Science Data Archive public website (https:lsda.jsc.nasa.gov). As part of our best practices, a viability testing plan has been developed for the ISC, which will assess the quality of archived samples. We expect that results from the viability testing will catalyze sample use, enable broader science community interest, and improve operational efficiency of the ISC. The current viability test plan focuses on generating disposition recommendations and is based on using ribonucleic acid (RNA) integrity number (RIN) scores as a criteria for measurement of biospecimen viablity for downstream functional analysis. The plan includes (1) sorting and identification of candidate samples, (2) conducting a statiscally-based power analysis to generate representaive cohorts from the population of stored biospecimens, (3) completion of RIN analysis on select samples, and (4) development of disposition recommendations based on the RIN scores. Results of this work will also support NASA open science initiatives and guides development of the NASA Scientific Collections Directive (a policy on best practices for curation of biological collections). Our RIN-based methodology for characterizing the quality of tissues stored in the ISC since the 1980s also creates unique scientific opportunities for temporal assessment across historical missions. Support from the NASA Space Biology Program and the NASA Human Research Program is gratefully acknowledged.

ALSDA↗
NASA NTRS2005

Ultrasonic Micro-Blades for the Rapid Extraction of Impact Tracks from Aerogel

The science return of NASA's Stardust Mission with its valuable cargo of cometary debris hinges on the ability to efficiently extract particles from silica aerogel collectors. The current method for extracting cosmic dust impact tracks is a mature procedure involving sequential perforation of the aerogel with glass needles on computer controlled micromanipulators. This method is highly successful at removing well-defined aerogel fragments of reasonable optical clarity while causing minimal damage to the surrounding aerogel collector tile. Such a system will be adopted by the JSC Astromaterials Curation Facility in anticipation of Stardust s arrival in early 2006. In addition to Stardust, aerogel is a possible collector for future sample return missions and is used for capture of hypervelocity ejecta in high power laser experiments of interest to LLNL. Researchers will be eager to obtain Stardust samples for study as quickly as possible, and rapid extraction tools requiring little construction, training, or investment would be an attractive asset. To this end, we have experimented with micro-blades for the Stardust impact track extraction process. Our ultimate goal is a rapid extraction system in a clean electron beam environment, such as an SEM or dual-beam FIB, for in situ sample preparation, mounting and analysis.

Ishii, H. A.↗
DOE OSTI2026

Hyporheic zone, river, and groundwater metagenome resolved genomes and rpS3 genes in East River Watershed, Colorado USA Summer 2020, 2021

Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from water filter collected across 8 locations along the East River Watershed, CO, and 1 nearby groundwater well. The purpose was to look for connectivity and similarities across the network and to see the impact of the groundwater. As a part of Lawrence Berkeley National Laboratory (LBNL) Watershed Science Focus Area (SFA), we assessed community composition and strain similarities between the sites and we also compared it to previous metagenomic studies within the watershed looking at floodplain (Matheus Carnevali et al. 2021) and hillslope (Lavy et al. 2019) microbiomes. Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from filters across 8 locations during August 2020 and July 2021. This resulted in 32 samples. The groundwater sample was sequenced at UC Berkley's QB3. The other 31 samples were sequenced at University of Maryland. Metagenomes were assembled using four autobinners and the best bins were selected using dasTool. The genomes were dereplicated at 95% with dRep and the subset of winning genomes were manually curated based on visual inspection of taxonomic profile, GC content, coverage, and a set of 51 bacterial single copy genes (BSCG), and 38 archaeal signal copy genes (ASCG). The dataset includes a zip file of 311 genomes (HZ_River_SW_MAGS_Dereplicated_95.zip). The dataset additionally includes a zipped file of ribosomal protein small subunit 3 (rpS3) proteins from the hyporheic zone and river data (rpS3_Proteins_HZ_River.zip), a metadata file used to register associated samples with IGSNs (International Generic Sample Numbers) (samples.csv), a location metadata file (locations.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

DNA↗
DOE OSTI2024

Towards Diverse and Representative Global Pretraining Datasets for Remote Sensing Foundation Models

The design of a pretraining dataset is emerging as a critical component for the generality of foundation models. In the remote sensing realm, large volumes of imagery and benchmark datasets exist that can be leveraged to pretrain foundation models, however using this imagery in absence of a well-crafted sampling strategy is inefficient and has the potential to create biased and less generalizable models. Here, we provide a discussion and vision for the curation and assessment of pretraining datasets for remote sensing geospatial foundation models. We highlight the importance of geographic, temporal, and image acquisition diversity and review possible strategies to enable such diversity at global scale. In addition to these characteristics, support for various spatial-temporal pretext tasks within the dataset is also critical. Ultimately, our primary objective is to place emphasis on and draw attention to the data curation stage of the foundation model development pipeline. By doing so, we think it is possible to reduce biases of geospatial foundation models, as well as enable broader generalization to downstream remote sensing tasks and applications.

Arndt, Jacob↗
NASA NTRSDate not supplied

Enumeration and Fluorescence In Situ Hybridization of Microbial Bioburden on Cleanroom Surfaces

Introduction: Microorganisms are everywhere on Earth, even in the cleanest of places. Spacecraft assembly cleanrooms can harbor low levels of living and dead microbial cells (e.g., [1,2]), and cleanroom bioburden can also include organic molecules from industrial sources and in situ biomass. Life detection missions require careful attention to avoid contaminants that can be easily convoluted with analytical targets. We are evaluating epifluorescent microscopy and fluorescence in situ hybridization (FISH) as methods to complement organic contamination detection techniques. Epifluorescent cell counting offers an accurate and cost-effective way to quantify low levels of surface biomass. FISH could allow for the identification of residual organisms, and can be targeted to detect active populations of specific organisms such as bacteria known to resist cleaning procedures. This effort is part of a larger study that is concentrated on characterizing the surface and airborne molecular organic contamination background in Johnson Space Center (JSC) Astromaterials curation laboratories and Goddard Space Flight Center (GSFC) spacecraft assembly rooms, and understanding contaminants in the context of cleaning procedures and residual bioburden. Methods: Samples were collected by swabbing surfaces in ISO 5 and ISO 7 equivalent cleanrooms at JSC. Swabs for FISH were fixed in 4% paraformaldehyde (PFA) for 3 hours and then stored in 1:1 ethanol:PBS, while swabs for cell counting were stored in 4% PFA until analysis to avoid any cell loss during centrifugation that could impact quantification of very low biomass samples. Cell counting was performed with SYBR Gold as in [3], but adapted for very low biomass. FISH was performed as in [4], using DAPI as a counterstain for all DNA-containing cells. Negative controls included wells with no probe applied, to test for natural fluorescence, as well as the nonsense probe NONEUB (reverse complement of EUB338) to evaluate non-specific probe binding. Results and Discussion: Cleanroom surfaces had 102-103 cells cm-2. The extremely low biomass of these samples was challenging for enumeration, and required careful and routine use of “field” and laboratory blanks. FISH was performed with the general archaeal and bacterial probes ARCH915 and EUB338 (EUBMIX, [4]), probe GAMBET ([4]), and PSE227, which targets the genus Pseudomonas [5]). The latter two probes were selected because Pseudomonas spp. and other Gammaproteobacteria have not been isolated from cleanroom surfaces but do appear frequently in rRNA gene libraries from these surfaces. While some active bacteria were identified (Fig. 1c), most cells detectable by DAPI did not have a strong or any fluorescent signal (e.g., Fig. 1d), indicating that the vast majority of cells are dead or inactive. This suggests that cleaning protocols are effective at inactivating microbial contaminants, but that dead or inactive cells can remain on surfaces. Cells were often clumped in a weakly autofluorescent matrix, possibly biofilm material (Fig. 1c,d). We also observed other particulate material that was collected by the swabs, including apparent textile fibers (Fig. 1b). Our results are consistent with other studies that show that the bioburden present in clean rooms includes active, dormant, and dead cells. We will discuss how FISH and epifluorescent cell counting could be applied in planetary protection protocols, including the advantages and disadvantages of FISH and cell counting for routine use, as well as different possible applications for more specialized FISH procedures. References: [1] Moissl-Eichinger et al. (2015) Sci Rep, 5, 9156 [2] Hendrickson et al. (2021) Microbiome, 9, 238 [3] Jones et al. (2017) Appl Environ Microbiol, 83, e00909-17 [4] Jones et al. (2015) Appl Environ Microbiol, 81, 1242-1250. [5] Watt et al. (2006) Environ Microbiol, 8, 871-884

C J Huff↗
DOE OSTI2025

Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software

Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects.

Malviya Thakur, Addi [ORNL] (ORCID:000000022681999↗
NASA NTRS2012

The Mars Sample Return Lab(s) - Lessons from the Past and Implications for the Future

It has been widely understood for many years that an essential component of a Mars Sample Return mission is a Sample Receiving Facility (SRF). The purpose of such a facility would be to take delivery of the flight hardware that lands on Earth, open the spacecraft and extract the sample container and samples, and conduct an agreed upon test protocol, while ensuring strict containment and contamination control of the samples while in the SRF. Any samples that are found to be non-hazardous (or are rendered non-hazardous by sterilization) would then be transferred to long-term curation. Although the general concept of an SRF is relatively straightforward, there has been considerable discussion about implementation planning.

Allen, Carlton↗
NASA NTRS2023

Microbial Bioburden on NASA Cleanroom Surfaces: Cell Counting and Fluorescence in situ Hybridization

Spacecraft assembly cleanrooms can harbor a low number of microbial cells that could contaminate hardware designed for life detection missions (known as “bioburden”). We are evaluating epifluorescence microscopy and fluorescence in situ hybridization (FISH) as rapid, accurate, and cost-effective methods to complement other contamination detection techniques. This study is part of a larger effort to characterize background organic contamination and the efficacy of cleaning procedures in Johnson Space Center (JSC) Astromaterials Curation Laboratories and Goddard Space Flight Center (GSFC) spacecraft assembly rooms. The low level of biomass required careful use of blanks during each stage of sample collection and preparation. FISH was performed using general archaeal and bacterial probes, as well as some group-specific probes. While some active bacteria were identified, a large majority of them were dead or inactive, and thus were difficult to identify by standard FISH. Our results are consistent with other studies that show that the bioburden present in clean rooms includes active, dormant, and dead cells.

C J Huff↗
NASA NTRS2011

Allan Hills 76005 Polymict Eucrite Pairing Group: Curatorial and Scientific Update on a Jointly Curated Meteorite

Allan Hills 76005 (or 765) was collected by the joint US-Japan field search for meteorites in 1976-77. It was described in detail as "pale gray in color and consists of finely divided macrocrystalline pyroxene-rich matrix that contains abundant clastic fragments: (1) Clasts of white, plagioclase-rich rocks. (2) Medium-gray, partly devitrified, cryptocrystalline. (3) Monomineralic fragments and grains of pyroxene, plagioclases, oxide minerals, sulfides, and metal. In overall appearance it is very similar to some lunar breccias." Subsequent studies found a great diversity of basaltic clast textures and compositions, and therefore it is best classified as a polymict eucrite. Samples from the 1976-77, 77-78, and 78-79 field seasons (76, 77, and 78 prefixes) were split between US and Japan (NIPR). The US specimens are currently at NASA-JSC, Smithsonian Institution, or the Field Museum in Chicago. After this initial finding of ALH 76005, the next year s team recovered one additional mass ALH 77302, and then four additional masses were found during the third season ALH 78040 and ALH 78132, 78158 and 78165. The joint US-Japan collection effort ended after three years and the US began collecting in the Trans-Antarctic Mountains with the 1979-80 and subsequent field seasons. ALH 79017 and ALH 80102 were recovered in these first two years, and then in 1981-82 field season, 6 additional masses were recovered from the Allan Hills. Of course it took some time to establish pairing of all of these specimens, but altogether the samples comprise 4292.4 g of material. Here will be summarized the scientific findings as well as some curatorial details of how specimens have been subdivided and allocated for study. A detailed summary is also presented on the NASA-JSC curation webpage for the HED meteorite compendium.

Righter, K.↗
NASA NTRS2015

Adventitious Carbon on Primary Sample Containment Metal Surfaces

Future missions that return astromaterials with trace carbonaceous signatures will require strict protocols for reducing and controlling terrestrial carbon contamination. Adventitious carbon (AC) on primary sample containers and related hardware is an important source of that contamination. AC is a thin film layer or heterogeneously dispersed carbonaceous material that naturally accrues from the environment on the surface of atmospheric exposed metal parts. To test basic cleaning techniques for AC control, metal surfaces commonly used for flight hardware and curating astromaterials at JSC were cleaned using a basic cleaning protocol and characterized for AC residue. Two electropolished stainless steel 316L (SS- 316L) and two Al 6061 (Al-6061) test coupons (2.5 cm diameter by 0.3 cm thick) were subjected to precision cleaning in the JSC Genesis ISO class 4 cleanroom Precision Cleaning Laboratory. Afterwards, the samples were analyzed by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy.

Calaway, M. J.↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗
NASA NTRS2024

Cognitive Performance in ISS Astronauts on 6-Month Low Earth Orbit Missions

Introduction: Current and future astronauts will endure prolonged exposure to spaceflight hazards and environmental stressors that could compromise cognitive functioning, yet cognitive performance in current missions to the International Space Station remains critically under-characterized. We systematically assessed cognitive performance across 10 cognitive domains in astronauts on 6-month missions to the ISS. Methods: Twenty-five professional astronauts were administered the Cognition Battery as part of National Aeronautics and Space Administration (NASA) Human Research Program Standard Measures Cross-Cutting Project. Cognitive performance data were collected at five mission phases: pre-flight, early flight, late flight, early post-flight, and late post-flight. We calculated speed and accuracy scores, corrected for practice effects, and derived z-scores to represent deviations in cognitive performance across mission phases from the sample’s mean baseline (i.e., pre-flight) performance. Linear mixed models with random subject intercepts and pairwise comparisons examined the relationships between mission phase and cognitive performance. Results: Cognitive performance was generally stable over time with some differences observed across mission phases for specific subtests. There was slowed performance observed in early flight on tasks of processing speed, visual working memory, and sustained attention. We observed a decrease in risk-taking propensity during late flight and post-flight mission phases. Beyond examining group differences, we inspected scores that represented a significant shift from the sample’s mean baseline score, revealing that 11.8% of all flight and post-flight scores were at or below 1.5 standard deviations below the sample’s baseline mean. Finally, exploratory analyses yielded no clear pattern of associations between cognitive performance and either sleep or ratings of alertness. Conclusions: There was no evidence for a systematic decline in cognitive performance for astronauts on a 6-month missions to the ISS. Some differences were observed for specific subtests at specific mission phases, suggesting that processing speed, visual working memory, sustained attention, and risk-taking propensity may be the cognitive domains most susceptible to change in Low Earth Orbit for high performing, professional astronauts. We provide descriptive statistics of pre-flight cognitive performance from 25 astronauts, the largest published preliminary normative database of its kind to date, to help identify significant performance decrements in future samples.

Data curation↗
NASA NTRS2007

Sample Return Primer and Handbook

This three-part Sample Return Primer and Handbook provides a road map for conducting the terminal phase of a sample return mission. The main chapters describe element-by-element analyses and trade studies, as well as required operations plans, procedures, contingencies, interfaces, and corresponding documentation. Based on the experiences of the lead Stardust engineers, the topics include systems engineering (in particular range safety compliance), mission design and navigation, spacecraft hardware and entry, descent, and landing certification, flight and recovery operations, mission assurance and system safety, test and training, and the very important interactions with external support organizations (non-NASA tracking assets, landing site support, and science curation).

Stardust↗
NASA NTRSDate not supplied

Investigating Commercial Off-The-Shelf (COTS) Glovebox and Support Components Compared to Custom Curatorial Laboratories

There is a need envisioned to investigate the application of commercial off-the-shelf (COTS) systems as tools that could be used within commercial preliminary curation as the expected space economy is supported and begins to take flight. NASA is involved with supporting and developing the space economy [1] and therefore it’s feasible that at some point, a commercial space company would bring back materials and either store initially, or permanently, within COTS equipment as a stand-in for custom curation laboratories. While utilizing opportunities to explore this capability at NASA-Johnson Space Center (JSC) during the establishment of other laboratories [2] it was realized that cleanliness and/or other properties could be evaluated for these COTS systems during their installation in advanced research facilities that are not ISO-class rated clean rooms. Several aspects of a COTS-Curation system were explored including various gloves for curation manipulation within a negative pressure glovebox (Fig.1), Balazs organic and inorganic contaminant levels testing prior to glovebox ever being used, mode swapping comparison of recirculation vs. single pass, constant monitoring of oxygen (O2) and moisture (H2O) levels in various conditions, etc. To acquire inorganic and organic compound loads inside the glovebox, Balazs wafer testing and gas sampling were implemented. These are standardized analytical tests provided by Balazs™ NanoAnalysis, a division of Air Liquide USA. Deployment of 8-inch silicon wafer witness plates for 24-hours in an undisturbed environment capture the organic compound load and inorganic trace metal contents which can be obtained by Vapor Phase Decomposition Inductively Coupled Plasma Mass Spectrometry (VPD ICP-MS). Balazs gas sample analysis was also performed for better measurements of volatile organic compounds (VOC) in glovebox air analyzed by Thermal Desorption Gas Chromatography Mass Spectrometry (TD GC-MS). These analytical testings were carried out in a controlled ultra high pure (UHP) gaseous nitrogen (N2)-purged environment where oxygen and moisture contents were continuously monitored at certain temperature and pressure. The preliminary outcomes of these testings are promising. The COTS systems appear to maintain the steady-state controlled environment for days, if not weeks, with uninterrupted gaseous N2-supply which was operated from a standard medium pressure LN2 250L 230L dewar, exchanged as needed. The outgassing load can be maintained by selecting the glove materials that have the least outgassing and particle shedding performances. Further experiments will be considered to validate the preliminary findings. While this project is exploratory, it is not intended as an endorsement by NASA Curation for approved materials or usage for advanced curatorial activities. NASA does not endorse nor promote any one particular product or company. References: [1] McCubbin F. M. et al. (2019) Space Science Reviews 215:A48. [2] Lewis, E.K. et. al (2024) LPSC LV, Abstract #2457.

Curation↗
NASA NTRS1981

Antarctic meteorite newsletter. Volume 4: Number 1, February 1981: Antarctic meteorite descriptions, 1976, 1977, 1978, 1979

This issue of the Newsletter is essentially a catalog of all antarctic meteorites in the collections of the Johnson Space Center Curation Facility and the Smithsonian except for 288 pebbles now being classed. It includes listings of all previously distributed data sheets plus a number of new ones for 1979. Indexes of samples include meteorite name/number, classification, and weathering category. Separate indexes list type 3 and 4 chondrites, all irons, all achondrites, and all carbonaceous chondrites.

Stone, R.↗
DOE OSTI2024

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

Abstract A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide-pedestal (WP) quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, quiescent H (QH)-mode, or WP QH-mode were collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6×8 spatial configuration with a time window of 1024 µ s and recast the input to obtain spectral-like features via fast Fourier transform preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average F 1 score of 0.94, using only ∼1 ms snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗