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At least 289 records · Page 16

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NASA NTRS2000

Numerous Unpaired Meteorites Exposed on a Deflating Playa Lake at Lucerne Valley, California

Out of 16 well-characterized 1 to 37 g meteorite specimens recovered from Lucerne Dry Lake (an approximately 3 7 km playa in the southern Mojave Desert of California), there are 9 separate ordinary chondrite finds. The ratio of independent meteorites to total number of specimens (~0.6) is among the highest in the world. This is due to lack of initial deep burial of the small meteorites, significant deflation of the lake exposing falls of individual stones (or small numbers of paired meteorites), and the absence of a large meteorite shower in the region. Playas appear to be excellent candidates for high-yield meteorite-collecting areas.

Rubin, Alan E.↗

NASA NTRS2013

Further Analysis of Materials Exposed on MISSE-6 and-7B

Materials samples were exposed to the low Earth orbit (LEO) environment as part of the MISSE- 6 and -7B flight experiments. Optical properties, thickness/mass loss, surface elemental analysis, visual and microscopic analysis for surface change are some of the techniques employed in this investigation. Effects of molecular contamination are discussed. Where possible, the MISSE-6 and -7B results are compared to analyses from other LEO experiments.

Finckenor, Miria↗

NASA NTRS2014

Gene Expression Profiling of Lung Tissue of Rats Exposed to Lunar Dust Particles

The purpose of the study is to analyze the dynamics of global gene expression changes in the lung tissue of rats exposed to lunar dust particles. Multiple pathways and transcription factors were identified using the Ingenuity Pathway Analysis tool, showing the potential networks of these signaling regulations involved in lunar dust‐induced prolonged proflammatory response and toxicity. The data presented in this study, for the first time, explores the molecular mechanisms of lunar dust induced toxicity. This work contributes not only to the risk assessment for future space exploration, but also to the understanding of the dust‐induced toxicity to humans on earth.

Zhang, Ye↗

NASA NTRS2014

Variations in Cathodoluminescent Intensity of Spacecraft Materials Exposed to Energetic Electron Bombardment

Many contemporary spacecraft materials exhibit cathodoluminescence when exposed to electron flux from the space plasma environment. A quantitative, physics-based model has been developed to predict the intensity of the glow as a function of incident electron current density and energy, temperature, and intrinsic material properties. We present a comparative study of the absolute spectral radiance for several types of dielectric and composite materials based on this model which spans three orders of magnitude. Variations in intensity are contrasted for different electron environments, different sizes of samples and sample sets, different testing and analysis methods, and data acquired at different test facilities. Together, these results allow us to estimate the accuracy and precision to which laboratory studies may be able to determine the response of spacecraft materials in the actual space environment. It also provides guidance as to the distribution of emissions that may be expected for sets of similar flight hardware under similar environmental conditions.

plasma environment↗

NASA NTRS2014

Variations in Cathodoluminescent Intensity of Spacecraft Materials Exposed to Energetic Electron Bombardment

orders of magnitude↗

NASA NTRS2009

The Effect of Electrolyte Additives upon the Lithium Kinetics of Li-Ion Cells Containing MCMB and LiNi(x)Co(1-x)O2 Electrodes and Exposed to High Temperatures

With the intent of improving the performance of lithium-ion cells at high temperatures, we have investigated the use of a number of electrolyte additives in experimental MCMB- Li(x)Ni(y)Co(1-y)O2 cells, which were exposed to temperatures as high as 80 C. In the present work, we have evaluated the use of a number of additives, namely vinylene carbonate (VC), dimethyl acetamide (DMAc), and mono-fluoroethylene carbonate (FEC), in an electrolyte solution anticipated to perform well at warm temperature (i.e., 1.0M LiPF6 in EC+EMC (50:50 v/v %). In addition, we have explored the use of novel electrolyte additives, namely lithium oxalate and lithium tetraborate. In addition to determining the capacity and power losses at various temperatures sustained as a result of high temperature cycling (cycling performed at 60 and 80 C), the three-electrode MCMB-Li(x)Ni(y)Co(1-y)O2 cells (lithium reference) enabled us to study the impact of high temperature storage upon the solid electrolyte interphase (SEI) film characteristics on carbon anodes (MCMB-based materials), metal oxide cathodes, and the subsequent impact upon electrode kinetics.

High Temperture Resilience↗

NASA NTRS2016

Pharmaceuticals Exposed to the Space Environment: Problems and Prospects

The NASA Human Research Program (HRP) Health Countermeasures Element maintains ongoing efforts to inform detailed risks, gaps, and further questions associated with the use of pharmaceuticals in space. Most recently, the Pharmacology Risk Report, released in 2010, illustrates the problems associated with maintaining pharmaceutical efficacy. Since the report, one key publication includes evaluation of pharmaceutical products stored on the International Space Station (ISS). This study shows that selected pharmaceuticals on ISS have a shorter shelf-life in space than corresponding terrestrial controls. The HRP Human Research Roadmap for planetary exploration identifies the risk of ineffective or toxic medications due to long-term storage during missions to Mars. The roadmap also identifies the need to understand and predict how pharmaceuticals will behave when exposed to radiation for long durations. Terrestrial studies of returned samples offer a start for predictive modeling. This paper shows that pharmaceuticals returned to Earth for post-flight analyses are amenable to a Weibull distribution analysis in order to support probabilistic risk assessment modeling. The paper also considers the prospect of passive payloads of key pharmaceuticals on sample return missions outside of Earth's magnetic field to gather additional statistics. Ongoing work in radiation chemistry suggests possible mitigation strategies where future work could be done at cryogenic temperatures to explore methods for preserving the strength of pharmaceuticals in the space radiation environment, perhaps one day leading to an architecture where pharmaceuticals are cached on the Martian surface and preserved cryogenically.

pharmacology↗

NASA NTRS2018

Effects of Mitochondrial-Targeted Human Catalase in Skeletal Tissue of Mice Exposed to Simulated Spaceflight

During prolonged spaceflight, astronauts are exposed to both microgravity and space radiation and are at risk forincreased skeletal fragility due to bone loss, Evidence from rodent experiments has established that bothmicrogravity and ionizing radiation can cause bone loss due to increasd of bone-resorbing osteoclasts and decreasedin bone-forming osteoblasts, although the underlying molecular mechanisms for these changes are not fullyunderstood. We hypothesized that excess reactive oxidative species (ROS) produced by conditions that simulatedspaceflight alters the tight balance between osteoclast and osteoblast activities, leading to accelerated skeletalremodeling and culminating in loss of mineralized tissue. To begin to explore this hypothesis, we used the mCATmouse model [1]; these transgenic mice over-express the human catalase gene targeted to mitochondria, which arethe major organelle responsible for cellular production of free radicals. Catalase is an anti-oxidant that catalyzes theconversion of the reactive species, hydrogen peroxide (H202), into water and oxygen. This animal model wasselected as it displays extended lifespan, reduced cardiovascular disease and reduced central nervous systemradiosensitivity, consistent with elevated anti-oxidant activity conferred by the transgene. We reasoned that miceoverexpressing catalase the mitochondria of osteoblast and osteoclast lineage cells would be protected from the boneloss caused by simulated spaceflight.

CATALASE IN SKELETAL TISSUE↗

NASA NTRS2019

Effects of Surface Treatments on Stainless Steel 316 Exposed to Potable Water Containing Silver Disinfectant

Silver has been selected as the forward disinfectant candidate for potable water systems in future space exploration missions. To develop a reliable antibacterial system that requires minimal maintenance, it is necessary to address relevant challenges to preclude problems for future missions. One such challenge is silver depletion in potable water systems. When in contact with various materials, silver ions can be easily reduced to silver metal or form insoluble compounds. The same chemical properties that make ionic silver a powerful antimicrobial agent also result in its quick inactivation or depletion in various environments. Different metal surface treatments, such as thermal oxidation and electropolishing, have been investigated for their effectiveness in reducing silver disinfectant depletion in potable water. However, their effects on the metal surface microstructure and chemical resistance have not often been included in the studies. This paper reports the effects of surface treatments on stainless steel 316 (SS316) exposed to potable water containing silver ion as a disinfectant. Early experimental results showed that thermal oxidation, when compared with electropolishing, resulted in a thicker oxide layer but compromised the corrosion resistance of SS316.

Li, Wenyan↗

NASA NTRS2019

Changing Climate, Changing Data: Exposing Climate Data to New Users Through GeoPlatform.gov’s Resilience Community

Over 700 climate related datasets were curated by subject matter experts into 9 thematic areas as a part of the Climate Data Initiative (CDI). NASA was tasked with maintaining the collection’s data inventory and supporting web pages at data.gov/climate. Today, the Data Curation for Discovery (DCD) team at MSFC continues to support the CDI collection. In order to expose the collection to a new and growing user community, the DCD team has partnered with GeoPlatform.gov to develop the Resilience community. The Resilience community serves as an interactive, topically-focused web portal that further promotes and shares CDI web content, datasets, services, maps, and other tools relevant to global resilience and change. This poster focuses on the team’s efforts to leverage GeoPlatform’s semantic applications to link CDI objects within the platform to improve discoverability. This poster also provides insights as to how this effort may serve as an example for building and expanding future Geoplatform.gov communities..

Sisco, Adam↗

NASA NTRS2018

Operational Testing of 4H-SiC JFET ICs for 60 Days Directly Exposed to Venus Surface Atmospheric Conditions

Prolonged Venus surface missions (lasting months instead of hours) have proven infeasible to date in the absence of a complete suite of electronics able to function for such durations without protection from the planet’s extreme conditions of ~460 °C, ~9.3 MPa (~ 92 Earth atmospheres) chemically reactive environment. Here we report testing data from a successful two-month (60-day) operational demonstration of two 175-transistor 4H-SiC junction field effect transistor (JFET) semiconductor integrated circuits (ICs) directly exposed (no cooling and no protective chip packaging) to a high-fidelity physical and chemical reproduction of Venus surface atmospheric conditions in a test chamber. These results extend the longest reported duration of electronics operation in Venus surface atmospheric conditions almost 3-fold and were accomplished using prototype SiC JFET chips of more than 7-fold increased complexity. The demonstrated advancement marks a significant step towards realization of electronics with sufficient complexity and durability for implementing robotic landers capable of returning months of scientific data from the surface of Venus.

Integrated Circuit↗

NASA NTRSDate not supplied

Thermal Analysis of an In-Space Heat Shield Exposed to a Rocket Plume During Stage Separation

Thermal analyses were conducted to evaluate the thermal response of various individual materials and multi-layer configurations for an in-space heat shield exposed to a rocket plume during stage separation. Frequently used, readily available thermal protection materials consisting of cork, carbon cloth phenolic (CCP), and silica cloth phenolic (SCP) were selected for an initial screening analysis. SCP was chosen as the primary material for further evaluation using an updated thermal environment calculated using computational fluid dynamics (CFD) results. A multi-layer configuration consisting of SCP providing erosion resistance and a Nomex honeycomb providing thermal protection was then evaluated as a solution for more mass-efficient performance. Pyrolysis depth, bondline temperature, back-side temperature, and a thermal factor of safety were used to define the necessary material thickness profile. Adhesives were included in the analyses with considerations for maximum temperature and application method. One dimensional thermal analyses at several stations along the heat shield surface were performed for the initial screening and follow-on analyses. The methodology used standard processes and computer programs applicable to solid rocket motor internal insulation and nozzle thermal analysis such as Chemical Equilibrium and Applications (CEA), Aerotherm Chemical Equilibrium (ACE), Momentum/Energy Integral Technique (MEIT), and Insulation Thermal Response and Ablation Code (ITRAC). The baseline SCP/Nomex honeycomb multi-layer configuration thermal performance predictions satisfied the objectives with an acceptable mass estimate for the design maturity. The EA9673 film adhesive and SCP resin were the best performing bonding agents considered for the SCP/Nomex honeycomb and Nomex honeycomb/structural substrate interfaces. The baseline mass estimate was lowest when the resin was the bonding agent due to the higher maximum service temperature. These thermal analyses evaluated typical internal and external nozzle materials using a calculated thermal environment and heat loads that were lower than a typical rocket motor internal environment but greater than standard external aeroheating.

Andrew T Hiatt↗

NASA NTRS2021

The Search for Lunar Mantle Rocks Exposed on the Surface of the Moon

The lunar surface is ancient and well-preserved, recording Solar System history and planetary evolution processes. Ancient basin-scale impacts excavated lunar mantle rocks, which are still expected to be present on the surface. Sampling these rocks would provide insight into fundamental planetary processes, including differentiation and magmatic evolution. There is contention among lunar scientists as to what lithologies make up the upper lunar mantle, and where they may have been exposed on the surface. We review dynamical models of lunar differentiation in the context of recent experiments and spacecraft data, assessing candidate lithologies, their distribution, and implications for lunar evolution.

Geochemistry↗

NASA NTRS2021

Classifications and Requirements for Testing Systems and Hardware to be Exposed to Dust in Planetary Environments

The purpose of this NASA Technical Standard is to establish minimum requirements and provide effective guidance regarding methodologies and best practices for testing systems and hardware to be exposed to dust in dust laden and generating environments. The intent is to facilitate consistency and efficiency in testing space systems, subsystems, or components with operations and missions in dusty environments.

Kristen Kathleen John↗

NASA NTRSDate not supplied

Root Growth Response of Arabidopsis Thaliana Seeds Exposed to Simulated Solar Particle Event Radiation and Microgravity

As NASA manned missions are planned to go past earth’s orbit, the effects of microgravity and radiation on plants will be essential to know as plants will be a part of the crew’s diet. APPROACH: Imbibed Arabidopsis thaliana seeds were exposed to simulated solar particle event (SPE) radiation, experiencing up to 80 cGy of radiation. After the radiation exposure, they were grown under simulated microgravity using a Random Positioning Machine. Images of the plants were taken after six to seven days of growth and the root length, top angle, tip angle were analyzed.

Karen Perkins↗

NASA NTRSDate not supplied

AMMPER: Agent-based Model for Microbial Populations Exposed to Radiation

Exposure of microbial populations to galactic cosmic radiation (GCR) in the deep-space radiation environment may lead to intracellular damage that compromises the ability of cells to repair and replicate. While simulation programs of deep-space radiation do exist, they focus primarily on single-cell damage, rather than population-wide effects. In this work, a new application and graphical user interface, Agent-Based Model for Microbial Populations Exposed to Radiation (AMMPER) is presented, which simulates the effects of proton-based GCR on Saccharomyces cerevisiae population growth. AMMPER consists of a 50x50x50 µm simulation space, analogous to an aqueous culture medium with non-limiting nutrient and pH buffering, in a microwell plate or microfluidic culture card. This model utilizes Relativistic Ion Tracks (RITRACKS) to create detailed track structures of the radiation traversals. AMMPER then calculates the radiation dose present at each cell, and subsequently determines the damage (chromosomal aberrations, oxidative stress, etc.) and resulting loss of cell viability from both primary and secondary radiative effects. Through implementing cell replication, repair, damage, and death, the effect of radiation exposure on the population growth can be determined. With AMMPER, long-duration effects of the deep space environment on entire populations can be determined and used to assess the feasibility of sustaining life in space.

Amrita Singh↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗