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22 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Characterization of Candidate Materials for Remote Recession Measurements of Ablative Heat Shield Materials

A method of remotely measuring surface recession of a material sample in a plasma flow through emission spectroscopy of the post shock layer was characterized through experiments in the NASA Langley HYMETS arc jet facility. Different methods for delivering the seed products into the Phenolic Impregnated Carbon Ablator (PICA) material samples were investigated. Three samples were produced by seeding the PICA material with combinations of Al, Si, HfO2, VB2, Al2O3, SiO2, TiC, HfC, NaCl, and MgCl2 through infusing seed materials into a core of PICA, or through encapsulating seed material in an epoxy disk, mechanically bonding the disk to a PICA sample. The PICA samples seeded with the candidate tracers were then tested at surface temperatures near 2400 K under low pressure air plasma. The emission of Al, Ti, V, Na, and Mg in the post-shock layer was observed in the UV with a high resolution imaging spectrometer viewing the whole stagnation line from the side, and from UV to NIR with a fiber-coupled miniaturized spectrometer observing the sample surface in the wavelength range from 200 nm to 1,100 nm from the front through a collimator. Al, Na, and Mg were found to be emitting in the post-shock spectra even before the recession reached the seeding depth - therefore possibly characterizing the pyrolysis process rather than the recession itself. The appearance of Ti and V emission in the spectra was well correlated with the actual recession which was monitored through a video of the front surface of the sample. The applicability of a seed material as an indicator for recession appears to be related to the melting temperature of the seed material. Future parametric studies will be carried out in low power plasma facilities at the University of Kentucky.

Butler, Bradley D.↗
NASA NTRS2020

High-temperature Hydrogen Chloride Releases from Mixtures of Sodium Chloride with Sulfates: Implications for the Chlorine-Mineralogy as Determined by the Sample Analysis at Mars Instrument on the Curiosity Rover in Gale Crater, Mars

Hydrogen chloride releases above 500 °C occurred in several samples analyzed by the Sample Analysis at Mars (SAM) evolved gas analyzer on the Curiosity rover in Gale crater. These have been attributed to reactions between chlorides (original or from oxychlorine decomposition) and water. Some of these HCl releases that peaked below the melting temperature of common chlorides did not co-evolve with oxygen or water, and were not explained by laboratory analog work (Figure 1). Therefore, these HCl releases were not caused by MgCl2 or soley due to reactions between water and melting chlorides. The goal of this work was to explain the HCl releases that did not co-evolve with oxygen or water and occurred below the melting point of common chlorides, which have not been explained by previous laboratory analog work. This work specifically evaluates the role of evolved SO2 in the production of HCl.

Clark, J.V.↗
NASA NTRS2020

The Effect of Europa and Enceladus Analog Seawater Composition on Isotopic Measurements of Volatile CO2

Science goals for icy ocean worlds missions include characterizing the chemical compositionof the surface and interior using remote sensing and in situ techniques. The next class of flightmass spectrometers for these missions will obtain compositional identifications and isotope ratiomeasurements of volatiles evolved from the ice surface (exosphere) and plumes. These massspectra will be combined with data from other flight instruments to infer the composition of theinterior from volatiles. To ensure accurate interpretation of these measurements, it is critical toverify whether the fundamental assumption that volatiles observed in icy ocean world exospheresor plumes will be a direct reflection of the sub-ice ocean. The present study evaluates whether isotopologues from an initial CO2 gas fractionate by interacting with seawater (brine) of varyingsalt composition and concentration. δ13CCO2 are affected by the pH of the brine and subsequentspeciation of {CO2}, where {CO2} represents the combination of CO2, H2CO3, HCO3-, and CO32-.{CO2} for low pH brines hypothesized for Europa will preferentially speciate as CO2. Analyzedδ13CCO2 for low pH brines are within error of the original δ13CCO2, demonstrating that volatile CO2in a low pH system will be a direct reflection of the original CO2. However, Europa’s radiativeenvironment and rapid depressurization due to plume ejection may impose fractionation effects.In contrast, high pH systems relevant to Enceladus or a more alkaline Europa are expected toform all carbonate species, while favoring speciation as HCO3-. High pH brines in theseexperiments include both an original CO2 gas and an isotopically distinct HCO3- (from NaHCO3salt). These alkaline experiments demonstrate that δ13CCO2 values are highly variable, and dependon the concentration of the dominant carbonate species, (Na)HCO3-. Mass balance estimatesindicate that measured δ13CCO2 is a thermodynamically predictable mixture of both carbonsources, suggesting that measurements of δ13C at Enceladus would directly reflect of the sourcesof CO2 and carbonate buffering in the ocean. δ18O measurements for CO2 interacting with KCland MgCl2 follow established models for δ18O-ionic strength. CO2 interacting with MgSO4,Na2SO4, and NaCl demonstrate an offset from established δ18O-ionic strength models dependingon the concentration of initial CO2. These results suggest that current predictive models for δ18Oin brines need to be resolved for changing concentrations of CO2

Ocean Worlds↗
NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗