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NASA NTRS2022

Remote Diagnosis and Operational Response to an In-Flight Failure of the Drill Feed Mechanism Onboard the Mars Science Laboratory Rover

On November 30th, 2016 (Sol 1536 of the mission), the Mars Science Laboratory (MSL) flight operations team commanded the Curiosity rover to drill into a Martian rock to acquire the mission’s 16th rock powder sample. The drilling operation was halted when the drill feed mechanism unexpectedly stalled shortly after disengaging its magnetically-actuated friction brake at the start of a long move to extend the drill bit into contact with the target rock. The drilling campaign was put on hold and later abandoned as diagnostic experimentation revealed a tendency for the actuator to transition in and out of a high-drag state in which the mechanism could not actuate. In the subsequent months, the team conducted a variety of in-flight and ground-based investigations to determine root cause and identify mitigation strategies for continued operation. Mechanism reliability continued to degrade and by June 2017, it became clear that the mechanism could not be relied upon to support drilling operations. The team succeeded in fully extending the feed, enabling the development of new sampling techniques that would not rely on the feed mechanism. The first feed-extended drilling sample was successfully collected on Sol 2057, 521 sols after the initial feed stall. This paper discusses the anomaly investigation process, failure analysis, and feed mechanism operation mitigation strategies. It concludes with key lessons learned about mechanism design, mission architecture, and operational best practices.

Lin, Megan Richardson↗
NASA NTRSDate not supplied

STARscan: Spatial Targeting and Alignment Rig for Scanning

The Spatial Targeting and Alignment Rig for Scanning (STARScan) is a 3D photogrammetry system developed at NASA Ames Research Center to address bottlenecks in pre/post-test scanning of arcjet test articles. It reduces scan time from 15 minutes with handheld laser scanners to under 2 minutes, while maintaining high accuracy (±0.2-0.5 mm). STARScan integrates an array of cameras, a 3D-printed rack, turntable, and LED light panels, all controlled via a user-friendly graphical user interface (GUI). The system offers tools for scan visualization, mesh analysis, and data export, automating tasks such as alignment of pre/post-test scans, material recession measurements, surface roughness assessment, and curvature analysis. By integrating scanning, imaging, and post-processing into one application, STARScan significantly improves efficiency in scanning and analyzing arcjet test samples.

Ablation↗
NASA NTRS1981

NASA and Deere to study effects of low-g on iron processing

A technical exchange agreement to study the effects of processing iron alloys in microgravity is described. Alloy solidification experiments are to be performed in the low-g facilities at Marshall Space Center. Deere is to prepare and evaluate the samples, and perform thermal characterization studies of the furnace used for melting and solidifying the samples. Experiment planning and analysis are to be performed jointly and data shared between the two parties. The technical exchange concept, developed by NASA to involve the private sector in low gravity research programs, is described. Other low gravity processing fields in which industry is invited to participate are listed.

Source record↗
NASA NTRS2004

Microbial Characterization and Comparison of Isolates During the Mir and ISS Missions

Spacecraft represent a semi-closed ecosystem that provides a unique model of microbial interaction with other microbes, potential hosts, and their environment. Environmental samples from the Mir Space Station (1995-1998) and the International Space Station (ISS) (2000-Present) were collected and processed to provide insight into the characterization of microbial diversity aboard spacecraft over time and assess any potential health risks to the crew. All microbiota were isolated using standard media-based methodologies. Isolates from Mir and ISS were processed using various methods of analysis, including VITEK biochemical analysis, 16s ribosomal identification, and fingerprinting using rep-PCR analysis. Over the first 41 months of habitation, the diversity of the microbiota from air and surface samples aboard ISS increased from an initial six to 53 different bacterial species. During the same period, fungal diversity increased from 2 to 24 species. Based upon rep-PCR analysis, the majority of isolates were unique suggesting the need for increased sampling frequency and a more thorough analysis of samples to properly characterize the ISS microbiota. This limited fungal and bacterial data from environmental samples acquired during monitoring currently do not indicate a microbial hazard to ISS or any trends suggesting potential health risks.

Fontenot, Sondra L.↗
NASA NTRSDate not supplied

Determining Surface Exposure Ages of Regolith Grains in Lunar Core Sample 73002 By Investigating Solar Particle Irradiation Damage

Space weathering causes the surface soils of airless bodies like the Moon to be micro-structurally and chemically altered by exposure to micrometeoroid bombardment and solar wind irradiation. These alterations accumulate in surface grains with continued residence in the uppermost millimeters of regolith. Low energy solar wind ions (largely H+ and He+) produce a layer of radiation damage as they become implanted in grain surfaces. Also present are solar energetic particle (SEP) tracks, which are nanoscale lineations within grain interiors formed by heavy, high-energy ions (mainly Fe group nuclei) penetrating up to millimeters below the surface. Recent work has determined that the width of solar wind-damaged rims on anorthite and olivine, and their respective SEP track densities are correlated with each other and with grain surface exposure age. Core sample 73002, recently released under the Apollo Next Generation Sample Analysis (ANGSA) Program, has provided an opportunity to study material collected from the light mantle formation in the Taurus-Littrow Valley during Apollo 17. The light mantle is thought to have been deposited vial and slide originating from the neighboring South Massif. Spectral analysis of bulk core soils found a plagioclase rich composition, confirming a highlands origin of the formation. Spectral profiles and ferromagnetic resonance (FMR) measurements of bulk soils sampled at cm-intervals from 73002 indicate that regolith up to~9.5 cm in depth have maturity indices consistent with longer durations of surface processing than samples deeper in the core. Thus, this upper ~9 cm layer likely represents the maximum extent of an in-situ reworking zone which may have persisted over 5 to 17million years. Here we present an analysis of grain exposure ages within the in-situ reworking zone of 73002derived from SEP track density measurements via transmission electron microscopy.

J A McFadden↗
NASA NTRS2015

Curation and Analysis of Samples from Comet Wild-2 Returned by NASA's Stardust Mission

The NASA Stardust mission returned the first direct samples of a cometary coma from comet 81P/Wild‐2 in 2006. Intact capture of samples encountered at 6 km/s was enabled by the use of aerogel, an ultralow dense silica polymer. Approximately 1000 particles were captured, with micron and submicron materials distributed along ~mm scale length tracks. This sample collection method and the fine scale of the samples posed new challenges to the curation and cosmochemistry communities. Sample curation involved extensive, detailed photo‐documentation and delicate micro‐surgery to remove particles without loss from the aerogel tracks. This work had to be performed in highly clean facility to minimize the potential of contamination. JSC Curation provided samples ranging from entire tracks to micrometer‐sized particles to external investigators. From the analysis perspective, distinguishing cometary materials from aerogel and identifying the potential alteration from the capture process were essential. Here, transmission electron microscopy (TEM) proved to be the key technique that would make this possible. Based on TEM work by ourselves and others, a variety of surprising findings were reported, such as the observation of high temperature phases resembling those found in meteorites, rarely intact presolar grains and scarce organic grains and submicrometer silicates. An important lesson from this experience is that curation and analysis teams must work closely together to understand the requirements and challenges of each task. The Stardust Mission also has laid important foundation to future sample returns including OSIRIS‐REx and Hayabusa II and future cometary nucleus sample return missions.

Nakamura-Messenger, Keiko↗
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↗
NASA NTRS1984

Analysis of glow discharges for understanding the process of film formation

The physical and chemical processes which occur during the formation of different types of films in a variety of glow discharge plasmas are discussed. Emphasis is placed on plasma diagnostic experiments using spectroscopic methods, probe analysis, mass spectrometric sampling and magnetic resonance techniques which are well suited to investigate the neutral and ionized gas phase species as well as some aspects of plasma surface interactions. The results on metallic, semi-conducting and insulating films are reviewed in conjunction with proposed models and the problem encountered under film deposition conditions. It is concluded that the understanding of film deposition process requires additional experimental information on plasma surface interactions of free radicals and the synergetic effects where photon, electron and ion bombardment change the reactivity of the incident radical with the surface.

Venugopalan, M.↗
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 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 NTRS1975

The effects of correlation on goodness of fit

Autocorrelated normal random variates were generated via computer and the effects of various levels of correlation on goodness of fit problems were studied. The results are useful in determining the distribution or estimating the parameters of populations that correlate observations such as wind speeds and temperature. The model used to generate the autocorrelated data is an autoregressive process of order 1. The Kolmogorov-Smirnov and chi-square statistics are used in the analysis. It was observed in the simulation that high positive correlations tend to shift the sample mean away from the population mean and negative correlations tend to shift the sample mean towards the population mean. In many cases, it was observed that positive and negative correlations tend to decrease the standard deviation. However, since this did not occur in all cases, no definite conclusion can be made regarding the standard deviation. Since the autoregressive process is a linear transformation, it is not surprising that normality was preserved. However, a possible extension of this problem could be to generate non-normal data and observe how the distribution is affected by correlation. Another extension might utilize another model such as autoregressive of order k or a moving average process of order k.

Kitchens, L. J.↗
NASA NTRS2015

WetLab-2: Providing Quantitative PCR Capabilities on ISS

The objective of NASA Ames Research Centers WetLab-2 Project is to place on the ISS a system capable of conducting gene expression analysis via quantitative real-time PCR (qRT-PCR) of biological specimens sampled or cultured on orbit. The WetLab-2 system is capable of processing sample types ranging from microbial cultures to animal tissues dissected on-orbit. The project has developed a RNA preparation module that can lyse cells and extract RNA of sufficient quality and quantity for use as templates in qRT-PCR reactions. Our protocol has the advantage that it uses non-toxic chemicals, alcohols or other organics. The resulting RNA is transferred into a pipette and then dispensed into reaction tubes that contain all lyophilized reagents needed to perform qRT-PCR reactions. These reaction tubes are mounted on rotors to centrifuge the liquid to the reaction window of the tube using a cordless drill. System operations require simple and limited crew actions including syringe pushes, valve turns and pipette dispenses. The resulting process takes less than 30 min to have tubes ready for loading into the qRT-PCR unit.The project has selected a Commercial-Off-The-Shelf (COTS) qRT-PCR unit, the Cepheid SmartCycler, that will fly in its COTS configuration. The SmartCycler has a number of advantages including modular design (16 independent PCR modules), low power consumption, rapid thermal ramp times and four-color detection. The ability to detect up to four fluorescent channels will enable multiplex assays that can be used to normalize for RNA concentration and integrity, and to study multiple genes of interest in each module. The WetLab-2 system will have the capability to downlink data from the ISS to the ground after a completed run and to uplink new programs. The ability to conduct qRT-PCR on-orbit eliminates the confounding effects on gene expression of reentry stresses and shock acting on live cells and organisms or the concern of RNA degradation of fixed samples. The system can be used to validate terrestrial analyses of samples returned from ISS by providing on-orbit gene expression benchmarking prior to sample return. The ability to get on-orbit data will provide investigators with the opportunity to adjust experimental parameters in real time for subsequent trials, without the need for sample return and re-flight to sample multigenerational changes. The system can also be used for analysis of air, surface, water, and clinical samples to monitor environmental contaminants and crew health. The verification flight of the instrument is scheduled to launch on SpaceX-7 in June 2015. The WetLab-2 Project is supported by NASAs ISS Program at JSC, Code OZ.

ISS tools↗
NASA NTRS1990

Fluid Phase Separation (FPS) experiment for flight on a space shuttle Get Away Special (GAS) canister

The separation of fluid phases in microgravity environments is of importance to environmental control and life support systems (ECLSS) and materials processing in space. A successful fluid phase separation experiment will demonstrate a proof of concept for the separation technique and add to the knowledge base of material behavior. The phase separation experiment will contain a premixed fluid which will be exposed to a microgravity environment. After the phase separation of the compound has occurred, small samples of each of the species will be taken for analysis on the Earth. By correlating the time of separation and the temperature history of the fluid, it will be possible to characterize the process. The experiment has been integrated into space available on a manifested Get Away Special (GAS) experiment, CONCAP 2, part of the Consortium for Materials Complex Autonomous Payload (CAP) Program, scheduled for STS-42. The design and the production of a fluid phase separation experiment for rapid implementation at low cost is presented.

Peters, Bruce↗
NASA NTRS2014

NASA Bioculture System: From Experiment Definition to Flight Payload

Starting in 2015, the NASA Bioculture System will be available to the science community to conduct cell biology and microbiology experiments on ISS. The Bioculture System carries ten environmentally independent Cassettes, which house the experiments. The closed loop fluids flow path subsystem in each Cassette provides a perfusion-based method for maintain specimen cultures in a shear-free environment by using a biochamber based on porous hollow fiber bioreactor technology. Each Cassette contains an incubator and separate insulated refrigerator compartment for storage of media, samples, nutrients and additives. The hardware is capable of fully automated or manual specimen culturing and processing, including in-flight experiment initiation, sampling and fixation, up to BSL-2 specimen culturing, and the ability to up to 10 independent cultures in parallel for statistical analysis. The incubation and culturing of specimens in the Bioculture System is a departure from standard laboratory culturing methods. Therefore, it is critical that the PI has an understanding the pre-flight test required for successfully using the Bioculture System to conduct an on-orbit experiment. Overall, the PI will conduct a series of ground tests to define flight experiment and on-orbit implementation requirements, verify biocompatibility, and determine base bioreactor conditions. The ground test processes for the utilization of the Bioculture System, from experiment selection to flight, will be reviewed. Also, pre-flight test schedules and use of COTS ground test equipment (CellMax and FiberCell systems) and the Bioculture System will be discussed.

Bioculture System↗
NASA NTRS2006

Optical Spectroscopy of Stardust Samples

The Stardust spacecraft collected dust samples of the Kuiper belt comet 81P Wild-2 in aerogel and returned them to Earth January 15, 2006. Preliminary examination (PE) of the collected dust includes teams focused on mineralogy, chemical composition, isotopic measurements, organic analysis, cratering and spectroscopic properties. The main PE science goals are to provide an initial characterization of the returned samples with an emphasis on the capture process and its effects on the samples, a comparison of Stardust samples to other meteoritic materials, and the abundance of presolar materials in the Stardust samples. The science objectives of the Spectroscopy team are to obtain spectroscopic data on Stardust particles through infrared (IR), UV/Vis and Raman measurements of particles in aerogel, extracted particles, keystones, and microtome thin sections. These data will be used to answer fundamental science questions about the nature of the samples, but will also serve as preliminary mineralogical data to guide follow-on measurements that will be performed in the other preliminary examination teams. The IR characteristics of Stardust particles are measured to determine: 1) the nature of the indigenous 3.4 micron organic feature, is it detected and can it be differentiated/deconvolved from the contaminated aerogel? How does it compare to features observed in interplanetary dust particles (IDPs) and to astronomical measurements of comets and interstellar dust? 2) the shape and fine structure within the 10 micron silicate feature. Overlap with the strong Si-O stretching vibration from the aerogel complicates this analysis, but we hope to determine if the feature is dominated by amorphous silicates such as those observed in IDPs and comets and whether or not crystalline silicates (e.g. olivine, pyroxene, clays) are present, 3) the presence of secondary (alteration) phases. Deep Impact results suggest that IR observations of Stardust particles should be evaluated for the presence of hydrated materials (water bands at 3 and 6 microns) and carbonates (6.8 microns and other resonances) and 4) the detection of crystalline features in the far-IR (20-100 microns) region where crystalline silicates and other minerals have strong bands that can be used both for phase analysis and phase chemistry. It has been demonstrated that these far-IR measurements can be obtained in situ on particles in aerogel keystones.

Keller, Lindsay P.↗
NASA NTRS2016

Low Temperature Regolith Bricks for In-Situ Structural Material

Current technology for producing in-situ structural materials on future missions to Mars or the moon relies heavily on energy-intensive sintering processes to produce solid bricks from regolith. This process requires heating the material up to temperatures in excess of 1000 C and results in solid regolith pieces with compressive strengths in the range of 14000 to 28000 psi, but are heavily dependent on the porosity of the final material and are brittle. This method is currently preferred over a low temperature cementation process to prevent consumption of precious water and other non-renewable materials. A high strength structural material with low energy requirements is still needed for future colonization of other planets. To fulfill these requirements, a nano-functionalization process has been developed to produce structural bricks from regolith simulant and shows promising mechanical strength results. Functionalization of granular silicate particles into alkoxides using a simple low temperature chemical process produces a high surface area zeolite particles that are held together via inter-particle oxygen bonding. Addition of water in the resulting zeolite particles produces a sol-gel reaction called "inorganic polymerization" which gives a strong solid material after a curing process at 60 C. The aqueous solution by-product of the reaction is currently being investigated for its reusability; an essential component of any ISRU technology. For this study, two batches of regolith bricks are synthesized from JSC-1A; the first batch from fresh solvents and chemicals, the second batch made from the water solution by-product of the first batch. This is done to determine the feasibility of recycling necessary components of the synthesis process, mainly water. Characterization including BET surface area, SEM, and EDS has been done on the regolith bricks as well as the constituent particles,. The specific surface area of 17.53 sq m/g (average) of the granular regolith material was obtained from nitrogen adsorption isotherm measurement. The size, shape and textures of regolith from SEM shows that the particles are 25-50 micrometers in size and mostly irregular in shape (Figure 1a). The elemental composition of regolith was identified from EDS analysis showed the presence of Si, Al, Fe, Na, Mg, Ca, Ti, O and C (see figure 1b). Each set of cylindrical brick samples were prepared by low energy process, and cured for 21 and 28 days, respectively to compare their compressive strength. Figure 1c, and d shows the JSC-1A brick and the compressive strength measurements. The results from the 21 day cured bricks (2 bricks) have been done and yielded an aver-age strength of 3050 psi, considerably higher than Portland cement mortars (Type IV and V). This promising technology provides the benefits of construction material similar to concrete, with a low complexity, low energy synthesis process and the likelihood of complete reusability of precious resources. Compressive strength using this method can be improved by increasing the surface area of the particles, using bi-modal particle size distribution, and adding certain additives to increase inter-particle forces.

ISRU↗
NASA NTRS1976

Satellite data for surface-mine inventory

To determine the feasibility of satellite data for surface-mine inventory, particularly as it applies to coal, a case study was conducted in Maryland. A band-ratio method was developed to measure disturbed surface areas, and it proved to be extendible both temporally and geographically. This method was used to measure area changes in the region over three time periods from September 1972 through July 1974 and to map the entire two-county area for 1973. For mines ranging between 31 and 244 acres (12 to 98 hectares) the measurement accuracy of total affected acreage was determined to be 92%. Mines of 120 acres (50 hectares) and larger were measured with greater accuracy, some within one percent of the actual area. The ability to identify, classify, and measure strip-mine surfaces in a two-county area (1,541 square kilometers - 595 square miles) of western Maryland was demonstrated through the use of computer processing. On the basis of these results the use of LANDSAT satellite data and multilevel sampling of aircraft and field verification inspections, multispectral analysis of digital data is shown to be an effective, rapid, and accurate means of monitoring the surface mining cycle.

Anderson, A. T.↗
NASA NTRS1976

Geochemistry

The most effective strategy for the geochemical study of a planet is to proceed systematically with the mineralogical and chemical characterization of its materials in accordance with geologically determined priorities. It is insufficient merely to analyze chemically the surface rocks. To appreciate the meaning of a chemical analysis, some assessment must be made of the geologic history of the sample - what its source and mode of origin are and what processes have operated upon the sample to cause chemical fractionation. Determination of mineralogy, texture, lithology, and other properties of the rock that might be relevant to origin is, therefore, necessary.

Carr, M. H.↗