Results for “Direct Air Capture” — Page 5

NASA NTRS1997

Comparative Soot Diagnostics: Preliminary Results

The motivation for the Comparative Soot Diagnostics (CSD) experiment lies in the broad practical importance of understanding combustion generated particulate. Depending upon the circumstances, particulate matter can affect the durability and performance of combustion equipment, can be a pollutant, can be used to detect fires and, in the form of soot, can be the dominant source of radiant energy from flames. The nonbuoyant structure of most flames of practical interest makes understanding of soot processes in low gravity flames important to our ability to predict fire behavior on earth. These studies also have direct applications to fire safety in human-crew spacecraft, since smoke is the indicator used for automated detection in current spacecraft. In the earliest missions (Mercury, Gemini and Apollo), the crew quarters were so cramped that it was considered reasonable that the astronauts would rapidly detect any fire. The Skylab module, however, included approximately 20 UV-sensing fire detectors. The Space Shuttle has 9 particle-ionization smoke detectors in the mid-deck and flight deck and Spacelab has six additional particle-ionization smoke detectors. The designated detectors for the ISS are laser-diode, forward-scattering, smoke or particulate detectors. Current plans for the ISS call for two detectors in the open area of the module, and detectors in racks that have both cooling air flow and electrical power. Due to the complete absence of data concerning the nature of particulate and radiant emission from incipient and fully developed low-g fires, all three of these detector systems were designed based upon l-g test data and experience. As planned mission durations and complexity increase and the volume of spacecraft increases, the need for and importance of effective, crew-independent, fire detection grows significantly. To provide this level of protection, more knowledge is needed concerning low-gravity fire phenomena and, in particular, how they might be detected and suppressed. Prior to CSD, no combustion-generated particulate samples had been collected near the flame zone for well-developed microgravity flames. All of the extant data either came from drop tower tests and therefore only corresponded to the early stages of a fire or were collected far from the flame zone. The fuel sources in the drop tower tests were restricted to laminar gas-jet diffusion flames and very rapidly overheated wire insulation. The gas-jet tests indicated, through thermophoretic sampling, (2) that soot primaries and aggregates (groups of primary particles) in low-gravity may be significantly larger than those in normal gravity (1-g). This raises new scientific questions about soot processes as well as practical issues for particulate size sensitivity and detection alarm threshold levels used in on-orbit smoke detectors. Preliminary tests in the 2.2 second drop tower suggest that particulate generated by overheated wire insulation may be larger in low-g than in 1-g. Transmission Electron Microscope (TEM) grids downstream of the fire region in the Wire Insulation Flammability experiment as well as visual observation of long string-like aggregates, further confirm this suggestion. The combined impact of these limited results and theoretical predictions is that, as opposed to extrapolation from l-g data, direct knowledge of low-g combustion particulate is needed for more confident design of smoke detectors for spacecraft. This paper describes the operation and preliminary results of the CSD, a project conceived and developed at NASA Lewis Research Center. The CSD flight experiment was conducted in the Middeck Glovebox Facility (MGBX) on USMP-3. The project is support by NASA Headquarters Microgravity Science and Applications Division and Code Q. The results presented here are from the microgravity portion of the experiment, including the temporal response of the detectors and average sizes of the primary and aggregate particles captured on the thermophoretic probes.

Urban, David L.↗

NASA NTRSDate not supplied

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).

A. B. Regberg↗