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Sample Return Container Opening Process Concept for Potential Mars Sample Return
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Sampling By the NASA Perserverance Rover for Mars Sample Return
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Mars Sample Return: Preparing for Biologically Sensitive Samples
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Compressive Sampling in Multivariate Polynomial Approximation with Corrupted Simulation Samples.
Abstract not provided.
Comparison of Accelerated Life Test Plans Based on Exact Small-Sample Methodology and Asymptotic Large-Sample Methodology.
Abstract not provided.
Comparison of Accelerated Life Test Plans Based on Exact Small-Sample Methodology and Asymptotic Large-Sample Methodology.
Abstract not provided.
A ?Universal? Bio-Sample Stabilization & Preservation Medium: Simple Low-Cost Environmental Sample Collection and Storage.
Abstract not provided.
NEW STANDARDS FOR SAMPLE PREPARATION: EFFECTIVE SAMPLE PREPARATION FOR METALS AND METALLOIDS USING AMMONIUM BIFLUORIDE
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Pre-Mission Input Requirements to Enable Successful Sample Collection by a Remote Field/EVA Team
This paper is intended to evaluate the sample collection process with respect to sample characterization and decision making. In some cases, it may be sufficient to know whether a given outcrop or hand sample is the same as or different from previous sampling localities or samples. In other cases, it may be important to have more in-depth characterization of the sample, such as basic composition, mineralogy, and petrology, in order to effectively identify the best sample. Contextual field observations, in situ/handheld analysis, and backroom evaluation may all play a role in understanding field lithologies and their importance for return. For example, whether a rock is a breccia or a clast-laden impact melt may be difficult based on a single sample, but becomes clear as exploration of a field site puts it into context. The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team is a new activity focused on a science and exploration field based research program aimed at generating strategic knowledge in preparation for the human and robotic exploration of the Moon, near-Earth asteroids (NEAs) and Phobos and Deimos. We used the FINESSE field excursion to the West Clearwater Lake Impact structure (WCIS) as an opportunity to test factors related to sampling decisions. In contract to other technology-driven NASA analog studies, The FINESSE WCIS activity is science-focused, and moreover, is sampling-focused, with the explicit intent to return the best samples for geochronology studies in the laboratory. This specific objective effectively reduces the number of variables in the goals of the field test and enables a more controlled investigation of the role of the crewmember in selecting samples. We formulated one hypothesis to test: that providing details regarding the analytical fate of the samples (e.g. geochronology, XRF/XRD, etc.) to the crew prior to their traverse will result in samples that are more likely to meet specific analytical objectives than samples collected in the absence of this premission information. We conducted three tests of this hypothesis. Our investigation was designed to document processes, tools and procedures for crew sampling of planetary targets. This is not meant to be a blind, controlled test of crew efficacy, but rather an effort to recognize the relevant variables that enter into sampling protocol and to develop recommendations for crew and backroom training in future endeavors. Methods: One of the primary FINESSE field deployment objectives was to collect impact melt rocks and impact melt-bearing breccias from a number of locations around the WCIS structure to enable high precision geochronology of the crater to be performed [1]. We conducted three tests at WCIS after two full days of team participation in field site activities, including using remote sensing data and geologic maps, hiking overland to become familiar with the terrain, and examining previously-collected samples from other islands. In addition, the team members shared their projects and techniques with the entire team. We chose our "crew members" as volunteers from the team, all of whom had had moderate training in geologic fieldwork and became familiar with the general field setting. The first two tests were short, focused tests of our hypothesis. Test A was to obtain hydrothermal vugs; Test B was to obtain impact melt and intrusive rock as well as the contact between the two to check for contact metamorphism and age differences. In both cases, the test director had prior knowledge of the site geology and had developed a study-specific objective for sampling prior to deployment. Prior to the field deployment, the crewmember was briefed on the sampling objective and the laboratory techniques that would be used on the samples. At the field sites (Fig. 2), the crewmember was given 30 minutes to survey a small section of outcrop (10-15 m) and acquire a suite of three samples. The crewmember talked through his process and the test director kept track of the timeline in verbal cues to the crewmember. At the conclusion, the team member conducting the scientific study appraised the samples and train of thought. Test C was a 90-minute EVA simulation using two crewmembers working out of line-of-sight in communication with a science backroom. The science objectives were determined by the science backroom team in advance using a Gigapan image of the outcrop (Fig. 1). The science team formulated hypotheses for the outcrop units and created sampling objectives for impact-melt lithologies; the science team turned these into a science plan, which they communicated to the crew in camp prior to crew deployment. As part of the science plan, the science team also discussed their sample needs in depth with the crewmembers, including laboratory methods, objectives, and samples sizes needed. During the deployment, the two crewmembers relayed real-time information to the science backroom by radio with no time delay. Both the crew and science team re-evaluated their hypotheses and science plans in real-time. Discussion: Upon evaluation, we found that the focused tests (Tests A and B) were successful in meeting their scientific objectives. The crewmember used their knowledge of how the samples were to be used in further study (technique, sample size, and scientific need) to focus on the sampling task. The crewmember was comfortable spending minimal time describing and mapping the outcrop. The crewmember used all available time to get a good sample. The larger test was unsuccessful in meeting the sampling objectives. When the crewmembers began describing the lithologies, it was quickly apparent that the lithologies were not as the backroom expected and had communicated to the crew. When the outcrop wasn't as expected, the crew members instinctively switched to field characterization mode, taking significant time to characterize and map the outcrop. One crew member admitted that he "kind of lost track" of the sampling strategy as he focused on the basic outcrop characterization. This is the logical first step in a field geology campaign, that a significant amount of time must be spent by the crew and backroom to understand the outcrop and its significance. Basic field characterization of an outcrop is a focused activity that takes significant time and training [2, 3]. Sampling of representational lithologies can be added to this activity for little cost [4]. However, we have shown that identification of unusual or specific samples for laboratory study also takes significant time and knowledge. We suggest that sampling of this type be considered a separate activity from field characterization, and that crewmembers be trained in sampling needs for different kinds of studies (representative lithologies vs. specialized samples) to acquire a mindset for sampling similar to field mapping. Sampling activities should be given a significant amount of specifically allocated time in scheduling EVA activities; and in the better case, that sampling be done as a second activity to a previously studied outcrop where both crew and backroom are comfortable with its context and characteristics. Our hypothesis posited that crewmember knowledge of how the samples would be used upon return would aid them in choosing relevant samples. Our testing bore this hypothesis out to some extent. We therefore recommend that crewmember training should include exposure to the laboratory techniques and analyses that will be used on the samples to foster this knowledge. There is also the potential for increasing crewmember contextual knowledge real-time in the field through the introduction of in situ geochemical technologies such as field portable XRF. The presence of field portable geochemical technology could enable the astronauts to interrogate the samples for K abundance real-time, ensuring they could collect valuable and dateable samples [5]. Though simulations such as these can teach us a fair bit about decision making processes and timeline building, one EVA participant noted that when he wasn't collecting "real" samples, he wasn't at his best. This effect suggests that higher-fidelity studies involving truly remote participants conducting actual scientific studies merit further attention to capture lessons for application to future crew situations.
Does Collection Time Bias the Ecology of Cleanroom Air Samples?
Microbial monitoring of astromaterials collections has taken on increased importance with the return of biologically sensitive samples from the asteroids Ryugu and Bennu and the initiation of the Mars Sample Return Program. Terrestrial bacteria and fungi can alter the mineralogy and organic composition of our collections causing irreversible contamination of pristine samples and increasing the risk of false positives for life detection measurements. NASA has conducted routine microbial monitoring of its existing collections since 20181. Initial monitoring focused on surface samples collected with foam swabs. Although, airborne microbiology is often decoupled from surface microbiology in the built environment2 culture-based air sampling techniques like impactors were not compliant with existing contamination control requirements. Bringing organic rich media, gelatin or liquids into curation cleanrooms presents an unacceptable risk to pristine samples. In 2022 NASA purchased a materials complaint air sampler and began collecting air samples from the cleanrooms in addition to surface samples3. The new instrument uses an electret filter to collect samples that are suitable for cultivating organisms or for direct DNA sequencing. Preliminary DNA sequencing results appeared to indicate that longer sampling times biased the microbial community in favor of hearty, spore-forming bacteria3. We present the results of a study comparing overnight sampling (17 hours) to short (1 hour) sampling of unoccupied curation cleanrooms. The results will help us optimize our monitoring protocols and develop a more detailed inventory of the ecology of astromaterials curation cleanrooms. Methods: We analyzed 72 paired air samples from six different cleanrooms including the meteorite processing lab (ISO 7 equivalent, 16 samples), the lunar lab (ISO 6 equivalent, 10 samples), the stardust lab (ISO 5 equivalent 14 samples), the OSIRIS-REx lab (ISO 5 equivalent, 12 samples), the Hayabusa2 lab (ISO 5 equivalent, 14 samples), and the Genesis lab (ISO 4 equivalent, 6 samples). All the samples were collected with an InnovaPrep Bobcat air sampler operating at a sampling rate of 200 L/min. The sampler operates for 5 minutes out of every 20 minute period. Half of the samples were collected by filtering 3,000L (15 min. of active sampling) of air across an electret filter for one hour. The rest of the samples were collected by filtering approximately 51,000 L air across the filter overnight (~17 hours, 255 min. of active sampling). Cells were eluted from the filter using 6-7 ml of pressurized 0.15% tween 20 in PBS (phosphate buffered saline). This liquid was used to cultivate bacteria according to previously published methods1,4,5 and for DNA extraction and next generation sequencing. DNA was extracted with a Qiagen MagAttract PowerMicrobiome kit6. To identify bacteria and archaea, the 16S rRNA gene was amplified using Earth Microbiome primers for the V4 region 7. The amplified DNA was sequenced on an Illumina MiSeq using a V3 reagent kit. The resulting sequences were processed using DADA2 and QIIME2 as implemented on the EDGE bioinformatics platform8–10. Results: Only two of the 72 samples had no amplifiable DNA. Amplified DNA concentrations ranged from 2.67 – 0.272 ng/µl. The median concentration of amplified DNA for the 1 hour samples was 0.770 ± 0.368 ng/µl. The median concentration of amplified DNA for the overnight samples was 0.877 ± 0.434 ng/µl. On average the overnight samples had slightly more sequences (58,960 vs. 59,456) and ASV’s (amplicon sequence variants) (60 vs 64.5) than the one hour samples, but these differences are not statistically significant. The most abundant ASV in every sample mapped to the genus Cupravidus. ASV’s mapping to the genuses Bacillus, Schlegelella, Thermus, and Staphylococcus were also common. Discussion and Future Work: Alpha diversity statistics like Shannon Entropy and Faith Phylogenetic Diversity are used to describe the diversity of organisms in a single sample. If a longer sampling time was biasing the data, we would expect to see a change in these diversity statistics vs. sample time. However, we did not observe this in our data. The median Shannon entropy was slightly higher for the overnight samples (3.773 vs 3.611) as was the Faith Phylogenetic Diversity (4.042 vs 3.596), but both values were within a standard deviation of each other for the two sampling times (Fig. 1). It is unlikely, that the longer sampling time is introducing bias into our data. We do observe a significant decrease in diversity when comparing the air samples by lab. The Genesis lab (ISO 4 equivalent) has a lower median number of ASV’s (45.5) than the other labs (62). Median values for Shannon Entropy (3.717 vs. 3.430) and Faith Phylogenetic Diversity (3.796 vs. 3.548) are also lower for Genesis, but those values are with one standard deviation of each other for the different sampling times. This is consistent with previous culture-based results suggesting that the environment in cleanrooms tends to select for a core group of organisms capable of surviving under dry, low nutrient, conditions. The presence of the ASV’s mapping to Cupravidus and Thermus in our sequencing blanks and controls suggests that several of the most common organisms in our samples represent contaminants from the reagents used to perform the DNA extractions and sequencing. Further work is needed to identify these contaminants, remove them from our data and recalculate the diversity statistics. This is a systematic error. Therefore, we do not expect removing the sequencing contaminants to change our conclusions. Longer air sample collection times appear to result in slightly higher diversity and do not bias the results towards “hardy” bacteria like spore-formers. Based on these preliminary results we conclude that sampling at least 3,000 liters of air is sufficient to capture the microbial diversity of cleanrooms, and that air samples can also be collected overnight without negatively impacting diversity. These results allow us to be flexible when designing microbial monitoring plans so that they do not interfere with routine lab activity. References: 1. Regberg, A. B. et al. 49th Lunar and Planetary Science Conference (2018). 2. The United States Pharmacopeial Convention. USP General Chapter <1116> (2013). 3. Regberg, A. B., et al. 54th Lunar and Planetary Science Conference (2023). 4. Regberg, A. B. et al. 53rd Lunar and Planetary Science Conference ( 2022). 5. Davis, R. E.,et al. 50th Lunar and Planetary Science Conference (2019). 6. Qiagen. MagAttract® PowerMicrobiome® DNA/RNA EP Kit Handbook. (2018). 7. Walters, W. et al. mSystems 1, (2015). 8. Callahan, B. J. et al. Nat. Methods 13, 581–583 (2016). 9. Hall, M. & Beiko, R. G. Microbiome Analysis: Methods and Protocols113–129 (Springer, 2018). 10. Philipson, C. et al. Bio-Protoc. 7, e2622 (2017).
Deployment of salt sample extraction system at an engineering-scale electrorefiner
The goal of the salt sampling program at Argonne is to develop and deploy automated molten salt sampling approaches for interfacing relevant unit operations with salt analysis to improve the timeliness of sampling-based accountancy measurements. Two technologies under development in support of this goal are a vacuum sampling loop module and a high-throughput pneumatic sample generator module. Compared to traditional point sampling approaches (i.e., dip probes), the vacuum sampling loop facilitates the collection of a larger cross-section of the bulk salt in order to collect more representative samples. The vacuum sampling approach also eliminates the risk of dross contamination of samples and avoids the use of moving parts in the salt. The pneumatic sample generator module is used to facilitate high-throughput sample analysis to improve the measurement precision of any given analytical technique by averaging out random sampling and measurement errors. In FY21, two methods for integrating these two modules were tested including direct fluidic coupling and coupling using a solid salt transfer mechanism. Solid salt transfer was ultimately selected over fluidic coupling, primarily to enable the transport of samples over longer distances to support automated at-line integration with high-precision techniques (such as microcalorimetry) that cannot withstand the conditions near an electrorefining process. To facilitate rapid solid salt coupling, new mechanisms were developed for rapidly charging and discharging salt sample tubes at the vacuum sampling loop and pneumatic sample generator modules, respectively. While the charging mechanism will be deployed in FY22, the discharge mechanism was tested in FY21 and is described here. The solid salt tube transfer method was deployed at one of Argonne’s engineering-scale electrorefiners to implement at-line high-throughput pneumatic micro-sample generation capabilities. The approach was used to generate precise uranium- and lanthanide-bearing electrorefiner micro-samples with the specific dimensions requested by researchers at Los Alamos National Laboratory for use in testing their novel microcalorimeter x-ray techniques. The solid salt transfer mechanism proved not only to be an effective means of integrating the precision sample generator with vacuum sampling, but also improved the performance of the sampler generator. To discharge salt from the sample tubes at the sampler generator, tube segments were inserted directly into the sample generator’s Helmholtz chamber and pressure pulse actuations were used to generate precision molten salt samples directly from the tube segments. The direct insertion of sample tubes into the sample generator enabled rapid loading of the salt and prevented salt from contacting most of the interior surfaces of the sample generator, which eliminated cross-contamination between runs. The vacuum sampling-loop tube charging mechanism will support high-throughput tube sampling operations by employing a dynamic vacuum filling process to fill short charge tubes that are configured to be rapidly connected and disconnected from the loop. The dynamic vacuum sampling operation will be automated, and sample tube handling can be executed with simple overhead actuation. Because the modular sampling approach described here eliminates the need for new high-radiation sample handling capabilities, salt-wetted seals, salt-wetted moving parts, and heated transfer lines outside the electrorefiner, it will address most of the remaining technical challenges for the at-line deployment of high-precision analytical techniques which will enable significant reductions in the time delay for sampling-based high-precision accountancy measurements.
Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals
Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.
Analysis of Tank 38H (HTF-38-22-91, -92) and Tank 43H (HTF-43-22-93, -94) Samples for Support of the Enrichment Control and Corrosion Control Programs
SRNL analyzed samples from Tank 38H and Tank 43H to support ECP and CCP. The results indicate the concentrations of most soluble species in the Tank 38H surface sample increased slightly from the previous surface sample. The Tank 38H sub-surface sample shows changes in concentration for soluble species in the solution with some increasing and some decreasing. The current Tank 38H sub-surface sample contains more sludge solids than the previous sample based on visual appearance. The small differences in the concentrations of major components between the Tank 38H surface and sub-surface samples indicate only minimal stratification of solution species within the tank. The Tank 43H surface and sub-surface samples are similar in composition to the previous samples. The similar solution compositions measured in the Tank 43H surface and sub-surface samples indicate a minimal stratification within the tank. The total uranium and plutonium in the current Tank 38H surface sample remains similar to the previous analysis. The Tank 38H sub-surface sample shows an increase in uranium and plutonium concentrations compared to the previous sample likely because of an increase in sludge solids in the current sample. The total uranium concentration in the two Tank 43H samples is essentially unchanged from previous sample results. The plutonium concentration in the Tank 43H surface sample is similar to the previous sample results while the plutonium in the Tank 43H sub-surface sample increased relative to the previous analysis. The sum of the major cations versus the sum of the major anions shows a difference of <10% for both samples from Tank 38H and for both samples from Tank 43H providing an indication of good data quality for the non-radioactive analytes in the samples. The silicon concentrations measured in the Tank 38H sub-surface sample increased compared with the previous sample results likely due to the presence of more sludge solids in the current sample. The Tank 38H surface sample silicon concentrations is similar to the previous sample results. The Tank 43H surface and sub-surface sample silicon concentrations both increased compared to the previous sample results. The samples analyzed from Tanks 38H and 43H show silicon concentrations ranging from 61.5 to 97.3 mg/L.