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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Measurement of Heat of Vaporization for Research Gasolines and Ethanol Blends by DSC/TGA

The heat of vaporization (HOV) is a relatively poorly studied fuel property that can be related to a fuel's evaporation characteristics and knock resistance and, therefore, the emissions and efficiency of direct-injection spark-ignition engines. Methods for measuring the HOV of complex gasoline mixtures and blends of gasoline with ethanol and other oxygenates require additional development. Recently, we described a differential scanning calorimetry/thermogravimetric (DSC/TGA) method to perform HOV measurements. Herein we describe a detailed investigation of factors affecting the precision and accuracy of the DSC/TGA method and examine enthalpy evolution and mass loss rate for research gasolines as a function of fraction evaporated. Examination of n-hexane evaporation using several DSC/TGA pan/lid configurations showed that initial sample loss can be reduced using a well-fitting pinhole lid on the sample pan, which reduces the evaporation rate during the balance of the experiment. However, additional experiments with research gasolines revealed that tight lid placement on the pans is not always achieved. Nevertheless, accurate total HOV measurements, with less than 10% initial sample loss, could be achieved for low volatility gasolines regardless of lid placement. A higher mass loss might be expected for higher vapor pressure (wintertime) gasolines. Because of variations in pan lids and their placement and their significant impact on results, quantitative comparisons between different samples of mass loss rate and heat flow curves versus time or fraction evaporated are not yet possible. However, features in the mass loss rate and heat flow curves are reproducible. Furthermore, results show that ethanol-gasoline blend evaporation is highly influenced by the formation of near-azeotropic mixtures of ethanol and specific gasoline components, causing a flattening of the mass and heat curves, with a sharp evaporation rate reduction after the ethanol is evaporated.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

The Comet Astrobiology Exploration Sample Return (CAESAR) Mission

The Comet Astrobiology Exploration Sample Return (CAESAR) mission will acquire and return to Earth for laboratory analysis a minimum of 80 grams of surface material from the nucleus of comet 67P/Chur-yumov-Gerasimenko (67P). CAESAR will characterize the surface region sampled, preserve the collected sample in a pristine state, and return evolved volatiles by capturing them in a separate gas reservoir. NASA Goddard Space Flight Center provides project management, systems engineering, safety and mission assurance, contamination control, mission operations, and many other important functions. Northrop Grumman Space Systems will build the spacecraft, based on Dawn mission heritage, which like CAESAR, uses solar electric propulsion. CAESAR was selected by for Phase A study in the New Frontiers 4 Competition and will be proposed to New Frontiers 5.Collection of a sample from the surface of comet 67P is facilitated by a set of cameras that together provide images to support sample site selection, perform optical navigation, and document the sample before, during, and after col-lection. The sample is collected at the end of an arm during a 5-second touch-and-go (TAG) maneuver with the Sample Acquisition System (SAS)designed by Honeybee Robotics for the surface properties of comet 67P observed by the Rosetta mission. After sample collection, and while the sample is still cold (< -80°C), the TAG Arm inserts the sample container into the Sample Containment System (SCS) mounted inside the Sample Return Capsule (SRC). The SCS is sealed, preventing the sample from escaping into space. The sample is slowly warmed inside the SCS to enable sublimation of volatiles, which are collected in the Gas Containment System (GCS), a passively cooled gas reservoir. Separating the volatiles from the solid sample protects the solid sample from alteration. Once all sublimated H2O is transferred to the GCS, the GCS is sealed to capture the volatile sit contains, and the SCS is vented to space to maintain the solid sample under vacuum. The SCS vent is closed before Earth entry to prevent atmospheric contamination. Detailed laboratory analyses of the sample from 67P will trace the history of volatile reservoirs, delineate the chemical pathways that led from simple interstellar species to complex molecules, constrain the evolution of the comet, and evaluate the role of comets in delivering water and prebiotic organics to the early Earth. CAESAR will achieve these goals by carrying out coordinated sample analyses that will link macroscopic properties of the comet with microscale mineralogy, chemistry, and isotopic studies of volatiles and solids. Most of the sample (≥75%) will be set aside for analyses by generations of scientists using continually advancing tools and methods, yielding an enduring scientific treasure that only sample return can provide. This presentation will review development conducted during NF4 Phase A and discuss the NF5 mission concept.

A G Hayes↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗
DOE OSTI2019

Process Modeling of U-10wt% Mo Alloys Using Integrated Computational Materials Engineering

Low-enriched uranium alloyed with 10 wt% molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium as fuel for the nation’s high-performance research reactors. Manufacturing the U-10Mo alloy consists of multiple complex thermomechanical processes, which is highly challenging for computational modeling. The integrated computational materials engineering (ICME) concept supports building a microstructure-based framework to investigate the effect of manufacturing processes on the material microstructure. In this report, an ICME model that combines homogenization, hot rolling, annealing, and cold rolling is presented as an example of implementing the ICME concept for modeling the manufacturing processes of the U-10Mo alloy. The integrated model enables information to be passed between the different model components, leading to an improved prediction and a better understanding of microstructure across multiple processes. The integrated model was also implemented to investigate the variation of chemical composition, thickness of the zirconium interlayer, grain growth, and carbide redistribution and fracture of U-10Mo during the thermomechanical processes. With the homogenization model, a molybdenum concentration map can be reconstructed from any given microstructure, and the homogenization time needed to reach a desired homogenization level can also be approximated. The microstructure-based finite-element rolling model, shows that the homogenization process reduces the nonuniformity in the thickness of the zirconium interlayers on the U-10Mo. With the output from the rolling model, a statistical analysis of second-phase hard particles was conducted to quantify the particle redistribution and interparticle spacing between carbides during the deformation. Also, new stringer identification criteria were proposed to predict the correlation between stringer volume fractions and hot-rolling reduction. A macro-finite-element method hot-rolling model showed that higher reduction passes can be achieved within the mill separation force range, and a new “aggressive” rolling schedule was proposed. The model also showed the importance of can material on the quality of rolled foils, with stronger can materials resulting in hot-rolled U-10Mo free of defects (waviness, dog-boning). The deformation-induced recrystallization model demonstrated capability to use inputs from deformation simulations of strain accumulation in U-10Mo samples to predict grain growth behavior of the fuel during thermal treatment stages. Finally, a plane-strain compression mode simulated particle fracture and calculates the void volume fraction generated during cold rolling. The macro-finite-element method cold-rolling model showed that smaller diameter rolls are necessary to achieve the desired foil thickness.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques

Abstract. The chemical composition of ambient organic aerosols plays a critical role in driving their climate and health-relevant properties and holds important clues to the sources and formation mechanisms of secondary aerosol material. In most ambient atmospheric environments, this composition remains incompletely characterized, with the number of identifiable species consistently outnumbered by those that have no mass spectral matches in the literature or the National Institute of Standards and Technology/National Institutes of Health/Environmental Protection Agency (NIST/NIH/EPA) mass spectral databases, making them nearly impossible to definitively identify. This creates significant challenges in utilizing the full analytical capabilities of techniques which separate and generate spectra for complex environmental samples. In this work, we develop the use of machine learning techniques to quantify and characterize novel, or unidentifiable, organic material. This work introduces Ch3MS-RF (Chemical Characterization by Chromatography–Mass Spectrometry Random Forest Modeling), an open-source, R-based software tool, for efficient machine-learning-enabled characterization of compounds separated in chromatography–mass spectrometry applications but not identifiable by comparison to mass spectral databases. A random forest model is trained and tested on a known 130 component representative external standard to predict the response factors of novel environmental organics based on position in volatility–polarity space and mass spectrum, enabling the reproducible, efficient, and optimized quantification of novel environmental species. Quantification accuracy on a reserved 20 % test set randomly split from the external standard compound list indicates that random forest modeling significantly outperforms the commonly used methods in both precision and accuracy, with a median response factor percent error of −2 %, for modeled response factors, compared to > 15 %, for typically used proxy assignment-based methods. Chemical properties modeling, evaluated on the same reserved 20 % test set and an extrapolation set of species identified in ambient organic aerosol samples collected in the Amazon rainforest, also demonstrate robust performance. Extrapolation set property prediction mean absolute errors for carbon number, oxygen to carbon ratio (O : C), average carbon oxidation state (OSc‾), and vapor pressure are 1.8, 0.15, 0.25, and 1.0 (log(atm)), respectively. Extrapolation set out-of-sample R2 for all properties modeled are above 0.75, with the exception of vapor pressure. While predictive performance for vapor pressure is less robust compared to the other chemical properties modeled, random-forest-based modeling was significantly more accurate than other commonly used methods of vapor pressure prediction, decreasing the mean vapor pressure prediction error to 0.24 (log(atm)) from 0.55 (log(atm)) (chromatography-based vapor pressure prediction) and 1.2 (log(atm)) (chemical formula-based vapor pressure prediction). The random forest model significantly advances an untargeted analysis of the full scope of chemical speciation yielded by two-dimensional gas chromatography (GCxGC-MS) techniques and can be applied to gas chromatography coupled with electron ionization mass spectrometry (GC-MS) as well. It enables the accurate estimation of key chemical properties commonly utilized in the atmospheric chemistry community, which may be used to more efficiently identify important tracers for further individual analysis and to characterize compound populations uniquely formed under specific ambient conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Determining Coal Ash Impacts on Radium and Thorium Background Levels in Soil Using Rare Earth Element Analyses - 18604

One challenge at large remediation sites contaminated with radiological materials is differentiating between low-level site-related contamination and NORM. This is because sites contaminated with radiological materials, such as thorium processing and mining and milling facilities, often generate wastes that contain contaminants that are similar to NORM deposits. For sites in urban areas, this issue is further compounded due to the presence of historic industrial fill (historic fill) containing materials such as fly ash, coal ash, and red brick, that exhibit elevated radioactivity levels. These materials often have activity levels near, or even well above, a site's cleanup goal. At a large Superfund Site in the eastern US (Site), many of the suspected Site-related fill areas are commingled with coal and fly ash. As the cleanup criteria for the radionuclides of concern (ROC), radium (Ra) and thorium (Th), is 5 pico-Curies per gram above background, a four-pronged approach was developed to determine if the coal and fly ash are impacting the background levels of the ROCs. This evaluation included a review of boring logs to visually confirm the presence of historic fill, as well as coal fragments and coal ash; a literature review to establish the typical concentrations of Ra and Th found in coal and fly ash from US sources; the use of rare earth analyses to establish chemical signatures for non-Site related historic fill containing coal ash versus Site-related fill; and a quantitative analysis of Site-related data to determine if the radioactivity levels in historic fill containing coal were elevated. Soil samples were collected from two locations at the Site; fill areas containing coal ash that were not suspected to be impacted by Site activities, as well as fill areas containing coal ash that were associated with the former processing facility. These samples were analyzed for rare earth elements (REE), as well as Ra-228 and Th-232 and the ratios of lanthanum, cerium, and neodymium to Ra-228 and Th-232 were established for each sample. Based on a comparison of these data, a strong correlation was established between the rare earth/thorium decay chain ratios and the classification of material as either Site or non-Site related. These data were used to develop a higher background activity for fill areas containing non-Site related NORM. While the cleanup level defined in the Record of Decision remained unchanged, the higher background derived from the fly ash/coal ash material, as identified by REE analyses, resulted in higher project screening levels, which ensured that excavation activities did not 'chase' material that was not Site-related. To date, use of the coal ash background level has saved millions of dollars in cleanup costs since non-Site related contamination is not being remediated. Use of this approach to determine the appropriate background level for a site is relatively straightforward since it relies on visual observation of the soil for coal and fly ash, along with REE analyses, which is an inexpensive, quick, and established analytical method for soil samples. As long as a good rare earth profile of both contaminated and natural (in this case fly ash and coal ash) material can be established, the results can be quite definitive, which could result in significant volume reduction by minimizing the amount of material excavated and disposed of. This will lead to cost and time savings for what could be an otherwise costly and potentially complex excavation. Anyone performing environmental remediation of NORM contaminated sites may benefit from this paper and presentation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm 2 ). Finally, the model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2009

Detection of Cometary Amines in Samples Returned by the Stardust Spacecraft

The delivery of amino acids to the early Earth by comets and their fragments could have been a significant source of the early Earth's prebiotic organic inventory that led to the emergence of life (Chyba and Sagan, 1992). Over 20 organic molecules including methane, ethane, ammonia, cyanic acid, formaldehyde, formamide, acetaldehyde, acetonitrile, and methanol have been identified by radio spectroscopic observations of the comae of comets Hale-Bopp and Hyakutake (Crovisier et al. 2004). These simple molecules could have provided the organic reservoir to allow the formation of more complex prebiotic organic compounds such as amino acids. After a 7-year mission, the Stardust spacecraft returned to Earth samples from comet Wild 2 on January 15, 2006 providing the opportunity to analyze the organic composition and isotopic distribution of cometary material with state-of-the-art laboratory instrumentation. The Preliminary Examination Team analyses of organics in samples returned by Stardust were largely focused on particles that impacted the collector aerogel and aluminum foil (Sandford et al. 2006). However, it is also possible that Stardust returned a "diffuse" sample of gas-phase organic molecules that struck the aerogel directly or diffused away from the grains after impact. To test this possibility, samples of Stardust flight aerogel and foil were carried through a hot water extraction and acid hydrolysis procedure to see if primary amine compounds were present in excess of those seen in controls. Here we report highly sensitive liquid chromatography time-of-flight mass spectrometry measurements of amino acids and amines in samples returned from a comet (Glavin et al. 2008). A suite of amino acids and amines including glycine, L-alanine, methylamine (MA), and ethylamine (EA) were identified in the Stardust bulk aerogel. With the exception of MA and EA, all other primary amines detected in comet-exposed aerogels were also present in the aerogel witness tile that was not exposed to Wild 2, suggesting that most amines are terrestrial in origin. However, the enhanced abundances of MA, EA, and possibly glycine in comet-exposed aerogel compared to controls, coupled with MA to EA ratios (1 to 2) that are distinct from preflight aerogels (7 to 10), suggest that these amines were captured from Wild 2. It is possible that MA and EA were formed on energetically processed icy grains containing methane, ethane, and ammonia. The presence of cometary amines in Stardust material supports the hypothesis that comets were an important source of prebiotic organics on the early Earth. To better understand their origin, a systematic compound specific carbon isotopic analysis (C-CSIA) via gas chromatography quadrupole mass spectrometry in with parallel with combustion isotope ratio mass spectrometry (GCQMS/ IRMS) is being conducted. We will discuss our latest C-CSIA measurements and what they indicate about the origin of amino acids extracted from Stardust samples.

Glavin, Daniel P.↗
DOE OSTI2024

Effect of Heat Treatment on Microstructure and Mechanical Property of 316L Stainless Steel Produced by Laser Powder Bed Fusion

The advanced non-light water reactor designs (Gen IV reactors), including molten salt/ very high temperature/ sodium-cooled and lead-cooled fast reactors, typically operate at higher temperatures and more extreme radiation conditions than light water reactors. An intrinsic part of the deployment and progress of Gen IV reactor designs is selecting the most suitable structural material for a specific application. Additive manufacturing (AM), a fairly new process of making physical, three-dimensional objects from a computer design file, is going to completely change the way of design, build and certify nuclear systems. It offers a range of opportunities to produce complex geometries from existing materials, offers new routes for processing of previously difficult to process materials, allows for design of new high-performance materials, and finally facilitates hybridization of dissimilar materials. This emerging technology has successfully produced cars, wind turbine blade molds and even live cells. It could also open up big opportunities for the nuclear industry to quickly deploy technologies at a fraction of the cost. So far, AM techniques have been preliminarily applied in the field of nuclear reactors, including the classical parts such as the pressure vessel of a small reactor with 508-III steel, the bottom nozzle of a fuel assembly with 304L steel, the fuel cladding with zirconium alloy and the integrated impeller of a pump and the multi-channel valve body with 316L steel [6,7]. The AM applications for operating nuclear reactors started in auxiliary plant components and have slowly migrated to metallic reactors and core components, but many of these are not safety critical components. Although many parts used for nuclear reactors have been fabricated by AM techniques, practical applications in engineering are still a long way off due to the uncertainty factors focused on the processing, material properties, analysis methods and application standards, which feeds the safety and life-cycle of the nuclear reactor. Due to rapid, repeated heating and cooling during production, a high dislocation density was present in the AM material. This microstructure feature is unstable at elevated temperature while high temperature is one of the typical operation environments for nuclear reactors. Thus, it is important to understand the thermal effect on the microstructure of AM material. The objectives of this study are to investigate the effect of heat treatment on the microstructure and mechanical properties of 316L stainless steel produced by laser powder bed fusion additive manufacturing, and to determine an appropriate heat treatment practice that will be applied to the lightweight AM lattice-structured material with the same chemistry. The heat treatment study consisted of annealing the samples at a temperature range of 800 to 1200 oC with a 50 oC increment for different times (1-24 hours), followed by vacuum or air cooling. Microstructural characterization was carried out by Scanning Electron Microscope (SEM). Grain size and crystallographic orientation were investigated by Electron Backscatter Diffraction (EBSD). Vickers hardness tests with a 0.5 kg load were employed to determine the hardness of samples after different heat treatments. After heat treatment, the random crystallographic orientation was preserved, and the volume fraction of high-angle grain boundaries (grain boundary misorientation =15 oC) remained the same. The dislocation density decreased with annealing temperature due to recovery. The fine subgrain structures in the as-printed specimen were quite stable up to 1200 oC. Minimal recrystallization was observed up to 1200 oC. Recrystallization initiated only after 8.5 hours at 1200 oC. The SEM images did not show obvious dependence of microstructure on cooling rate. The hardness of the specimens decreased with increasing annealing temperature as a result of the decrease in dislocation density. It is interesting to note that the AM material showed very similar hardness to the wrought material when annealing at similar temperature, although the microstructures are very different. Annealing at 1050 oC for 1 hour followed by air cooling was selected as the heat treatment procedure for the lattice designed lightweight AM 316L material.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Detection of Cometary Amines in Samples Returned by the Stardust Spacecraft

The delivery of amino acids to the early Earth by comets and their fragments could have been a significant source of the early Earth's prebiotic organic inventory that led to the emergence of life (Chyba and Sagan, 1992). Over 20 organic molecules including methane, ethane, ammonia, cyanic acid, formaldehyde, formamide, acetaldehyde, acetonitrile, and methanol have been identified by radio spectroscopic observations of the comae of comets Hale-Bopp and Hyakutake (Crovisier et al. 2004). These simple molecules could have provided the organic reservoir to allow the forn1ation of more complex prebiotic organic compounds such as amino acids. After a 7 -year mission, the Stardust spacecraft returned to Earth samples from comet Wild 2 on January 15, 2006 providing the opportunity to analyze the organic composition and isotopic distribution of cometary material with state-of-the-art laboratory instrun1entation. The Preliminary Examination Team analyses of organics in samples returned by Stardust were largely focused on particles that impacted the collector aerogel and aluminum foil (Sandford et al. 2006). However, it is also possible that Stardust returned a "diffuse" sample of gas-phase organic molecules that struck the aerogel directly or diffused away from the grains after impact. To test this possibility, samples of Stardust flight aerogel and foil were carried through a hot water extraction and acid hydrolysis procedure to see if primary amine compounds were present in excess of those seen in controls. Here we report highly sensitive liquid chromatography time-of-flight mass spectron1etry measurements of amino acids and amines in samples returned from a comet (Glavin et al. 2008). A suite of amino acids and amines including glycine, L-alanine, methylamine (MA), and ethylamine (EA) were identified in the Stardust bulk aerogel. With the exception of MA and EA, all other primary amines detected in comet-exposed aerogels were also present in the aerogel witness tile that was not exposed to Wild 2, suggesting that most amines are terrestrial in origin. However, the enhanced abundances of MA, EA, and possibly glycine in comet-exposed aerogel compared to controls, coupled with MA to EA ratios (1 to 2) that are distinct from preflight aerogels (7 to 10), suggest that these amines were captured from Wild 2. It is possible that MA and EA were formed on energetically processed icy grains containing methane, ethane, and ammonia. The presence of cometary amines in Stardust material supports the hypothesis that comets were an important source of pre biotic organics on the early Earth. To better understand their origin, a systematic compound specific carbon isotopic analysis (C-CSIA) via gas chromatography quadrupole mass spectrometry in with parallel with combustion isotope ratio mass spectrometry (GC-QMS/IRMS) is being conducted. We will discuss our latest C-CSIA measurements and what they indicate about the origin of amino acids extracted from Stardust samples.

Glavin, Daniel P.↗
NASA NTRSDate not supplied

On the Hunt for Detectable Biosignatures in Jezero Crater: What to Look for and Where

Introduction: The Perseverance rover, which is currently exploring Jezero crater on Mars, is equipped with seven instruments that allow for observation of textures, minerals, color, structure, and chemistry of rocks and sediments in order to search for signs of ancient life, understand the geologic history of the crater, and identify candidates for sample return (Farley et al. 2020). The first of these aims includes the direct detection of potential biosignatures, including textures, organic molecules, minerals, and elemental chemistries that are of biogenic origin (Mustard et al. 2013). The presence of these biosignatures will be constrained by the habitability of the local region, the preservation potential of the host rocks, and the sensitivity of the instrument suite, and their biogenicity will be investigated after return to Earth as part of the Mars Sample Return campaign. Here, we examine key targets on the three planned campaigns, potential biosignatures that may be present, and the capabilities of key rover instruments. High Potential Biosignature Sites in Jezero: To date, measurements have been made on multiple sites that both contain minerals known in terrestrial settings to preserve biosignatures and likely were habitable settings (Williford et al. 2021). Two examples include the fine-grained rocks at the base of the delta fan and the NW inner margin of the crater. Fine-grained Rocks at Base of Delta Fan. Fine-grained, clay-bearing rocks may have been deposited as muddy lake sediments that could have hosted life and preserved biosignatures settling out of the water column. The report of organic molecules by the Curiosity rover in the Sheepbed mudstone and Murray formation has highlighted this site in particular. Potential biosignatures in mudstones, especially those rich in silica (McMahon et al. 2018), microbialites and complex organics. NW Inner Margin of Crater. This unit, located along the inner margin of the crater, contains strong carbonate signatures and may have been the littoral zone of a lake (Horgan et al. 2020). The potential biosignatures here include microfossils, microbialites, biominerals, and complex organics. Detectability of Biosignatures by the Mars 2020 Instrument Suite: The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument comprises a Deep UV spectrometer, context imager, and color camera to generate spatially resolved chemical maps (Bhartia et al. 2020). It is sensitive to trace organics as well as a range of minerals and can detect native fluorescence from aromatic organics; the WATSON and ACI cameras can be used to observe morphologies such as stromatolitic laminations or filaments ranging from the tens of micron to millimeter scale. SHERLOC can detect organics that may be present in either of the high potential biosignature targets, as well as detect carbonates in the latter. However, the presence of high amounts of iron, such as in iron-rich clays, would cause attenuation of spectral response through UV absorption. The Planetary Instrument for X-ray Lithochemistry (PIXL) comprises an X-ray fluorescence spectrometer and camera that can scan rock surfaces to generate elemental maps (Allwood et al. 2020). PIXL can detect chemical biosignatures such as spatial variations of elemental abundances that may have resulted from biological activity. PIXL would be particularly useful in detecting fine textures and elemental chemistries in either high potential biosignature target, but cannot directly detect minerals such as carbonate. The SuperCam instrument performs three types of spectroscopy, color imaging, and acoustic recording to remotely examine elemental composition, minerals, organics, and textures (Maurice et al. 2021). Using laser induced breakdown spectroscopy and time resolved luminescence spectroscopy, SuperCam can detect major elemental building blocks of organics (i.e., C, H, N, O, P, S) and conjugated organic structures, respectively, which may be found in either site. While in other contexts, luminescence is a useful tool for biosignature identification, luminescence generated by the 532 nm laser may obscure the Raman signal. Conclusion: The three instruments discussed can be used collaboratively to establish the presence of potential biosignatures in samples. These high-priority samples may then be returned to Earth for detailed laboratory analysis.

S. Sharma↗
DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Discovery top-down proteomics in symbiotic soybean root nodules

Proteomic methods have been widely used to study proteins in complex biological samples to understand biological molecular mechanisms. Most well-established methods (known as bottom-up proteomics, BUP) employ an enzymatic digestion step to cleave intact proteins into smaller peptides for liquid chromatography (LC) mass spectrometry (MS) detection. In contrast, top-down proteomics (TDP) directly characterizes intact proteins including all possible post-translational modifications (PTMs), thus offering unique insights into proteoform biology where combinations of individual PTMs may play important roles. We performed TDP on soybean root nodules infected by the symbiotic Bradyrhizobium japonicum in both the wildtype bacterium and a nifH- mutant, which lacks the ability to fix nitrogen in the soybean root nodule. TDP captured 1648 proteoforms derived from 313 bacterial genes and 178 soybean genes. Leghemoglobin, the most abundant protein in the sample, existed in many truncated proteoforms. Interestingly, these truncated proteoforms were considerably more abundant in the wildtype relative to the nifH- mutant, implicating protease activity as an important factor in nitrogen fixation. Proteoforms with various PTMs and combinations thereof were identified using an unrestricted open modification search. This included less common PTMs such as myristoylation, palmitoylation, cyanylation, and sulfation. In parallel, we collected high resolution MS imaging (MSI) data of intact proteins and biopolymers (<20 kDa due to current technical limitations) from sections of the soybean root nodules using matrix-assisted laser desorption/ionization (MALDI) coupled to high resolution Orbitrap. Several detected proteoforms exhibited unique spatial distributions inside the infection zone and cortex, suggesting functional compartmentalization in these regions. A subset of peaks from the MALDI-MSI were assigned to proteoforms detected in TDP LCMS data based on matching accurate masses. Many of the proteins detected in both LCMS and MALDI-MSI are currently uncharacterized in UniProt: the PTM and spatial information presented here will be valuable in understanding their biological functions. Taken together, our study demonstrates how untargeted TDP approach can provide unique insights into plant proteoform biology. On-going technology developments are expected to further improve TDP coverage for more comprehensive high-throughput analysis of proteoforms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Technical Advances for Sustainable Remediation: In Situ Monitoring and Climate Resiliency - 18436

Recent advances in sustainable remediation techniques show promise as solutions for contaminant removal in soil and groundwater media. Although passive remediation (or natural attenuation) has gained momentum as an acceptable technique, there are still challenges in regulatory frameworks as well as in public acceptance. Leaving contaminants in subsurface requires increased burden of proof to show that plumes are stable and residual contaminants do not pose a significant health risk. Climate changes and frequent extreme climate events contribute another level of complexity and make it difficult to demonstrate system stability and predict future plume conditions. In this study, we show two coupled approaches to tackle this challenge: (1) in situ groundwater monitoring based on advanced sensor and data analytics technologies (i.e., Big Data), and (2) state-of-art modeling for optimizing monitoring and site closure strategies. In the new monitoring approach, in situ sensors are used to continuously measure the key variables that control contaminant plume mobility and its spatial and temporal distribution (such as pH, redox potential, electrical conductivity, and groundwater level). Coupled with a limited number of groundwater sampling, the data analytics methods - data mining and machine learning - allow us to estimate contaminant concentrations, and also to detect any significant changes associated with the plume mobility. In parallel, the state-of-art numerical flow and reactive transport model - ASCEM software under DOE Office of Environmental Management - provides a physical and mechanistic understanding of the system behavior, and predicts the long-term plume migration for optimizing the monitoring and closure strategies. In particular, we focus on the effects of extreme precipitation or drought on the site closure as well as monitoring strategies. We demonstrate this approach at the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Structural and magnetic properties of La2Ni1−xCoxMnO6 compounds

Highlights: • Monoclinic La2Ni1−xCoxMnO6 samples have been prepared using Sol-gel technique. • Rietveld analysis shows an octahedrally distorted double perovskite structure. • The nature of FM interaction and TC depend on the B-site ions. • The short range FM ordering above TC is induced by antisite disorders. • The existence of any spin glass like phase in such system is discarded. - Abstract: Single phase monoclinic La{sub 2}Ni{sub 1−x}Co{sub x}MnO{sub 6} (x = 0.2, 0.4, 0.8) compounds, henceforth, referred as LNC{sub x}MO (x = 0.2, 0.4, 0.8) have been prepared by Sol–gel technique. The structural study and redox titration suggest that the samples are both La and O deficient. The generated atomic models using Rietveld refined structural parameters show octahedral distortion in the double perovskite structure. X-ray photoelectron spectroscopy (XPS) study has been used to determine the predominant valence states of Ni, Mn and Co ions. LNC{sub x}MO (x = 0.2, 0.4) are found to be Heisenberg ferromagnets whereas LNC{sub 0.8}MO is a phase segregated system. In LNC{sub 0.4}MO and LNC{sub 0.8}MO, at temperatures above T{sub C}, χ{sup −1}(T) curve shows the characteristics of Griffiths phase. The study of ac susceptibility and isothermal remanent magnetization (IRM) discard the possibility of any glassy state at low temperatures. The antiphase boundaries (APBs), antisite disorders (ASDs) and oxygen vacancies play important roles in this system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Mars Sample Return, Sample Retrieval Lander, Reaction Control System Jet Interaction Supersonic Wind Tunnel Test Overview with CFD Predictions

NASA's Mars Sample Return campaign will be launching several missions over the next decade that will work together to return rock samples from Mars back to Earth. The Sample Retrieval Lander (SRL) will deliver the Mars Ascent Vehicle and fetch rover to the surface of Mars in 2006. Rock samples collected by the Mars 2020 Perseverance rover, landing in early 2021, will be loaded on the the ascent vehicle to be launched into Mars orbit for retrieval by yet another spacecraft. The Sample Retrieval Lander will be a blunt entry capsule similar to past Mars entry vehicles like Mars Science Laboratory and Viking. The vehicle will fly a guided entry, using a small lift vector produced by a non-zero trim angle of attack to eliminate downrange and crossrange position errors at the point of parachute deploy. This energy and heading management is achieved with a reaction control system (RCS) that directs the bank angle of the vehicle and also minimizes unwanted capsule dynamics. The reaction control system and control design is based on the Mars Science Laboratory and Mars 2020 RCS systems. However, due to packaging constraints, the backshell of this new entry vehicle has a different geometry than those earlier designs. To certify the RCS system for flight the project must characterize the jet plume interactions with the capsule backshell that could impair or significantly augment the RCS control authority. This characterization will be done through a combination of computational fluid dynamics (CFD) analysis and wind tunnel test. Two candidate arrangements of the RCS jets have been identified for the SRL vehicle and are currently under evaluation before final selection. The aero/RCS plume interactions of these candidate configurations have been measured in a supersonic wind tunnel test in NASA Langley's Unitary Plan Wind Tunnel. The test was conducted in the fall of 2020 and data is currently being reduced. An overview of the candidate RCS configurations are presented here with an overview of the wind tunnel model design, jet scaling and scaled nozzle design, and the test matrix. Preliminary CFD runs are presented with an assessment of the predicted plumes and their interaction with the wake flow of the vehicle. The predicted effects of the model sting is provided as well. This high fidelity wind tunnel test is being conducted much earlier in the SRL project than would normally be done. The test was funded as part of a CFD evaluation task funded by NASA's Aerosciences Evaluation and Test Capabilities Project. The objective of the evaluation task was to compare the ability of CFD to predict complex flows with data that can be measured in the Langley Unitary Plan Wind Tunnel. RCS Jet interactions were selected as a type of complex flow that is important to NASA missions. In addition to providing useful data to the SRL project, there was added emphasis on quantifying the accuracy of the CFD predictions and wind tunnel test data. An overview of the uncertainty quantification methodologies for computational and experimental portions of this test is presented.

blunt body↗
NASA NTRSDate not supplied

Impact of NASA’s Entry Systems Modeling Project on Planetary Mission Design

Planetary missions continue to grow larger and more complex. Furthermore, the current focus on human exploration of the Moon and Mars, as well as Mars Sample Return(MSR), place increas-ingly stringent requirements on the reliability of the entry, descent, and landing (EDL) system that ensures the safe delivery of payload or crew to their destination. Planetary EDL is an area in which mission designers are critically reliant on modeling and simulation to demonstrate the reliability of the system, as there are no ground facilities that are able to fully test these systems in a flight-relevant environment. NASA’s state-of-the-art modeling and simulation capability must continually evolve to meet the needs of the next generation of planetary EDL. To accomplish this aim, NASA’s Entry Systems Modeling (ESM) Project was formed in 2013and is funded bythe Space Technology Mission Directorate(STMD) and Science Mission Directorate (SMD). ESM is the Agency’s only cross-cutting effort for advancing entry systems modeling and simulation capabilities across a range of technical disciplines and Solar System destinations. ESM is a portfolio project covering a variety of mid-TRL research efforts within four core EDL-related areas of investment: (1) Thermal protection material modeling, (2) Shock layer kinetics and radiation, (3) Aerosciences, and (4) Guidance, navigation, and con-trol. The material modeling group creates detailed material response modelsof thermal protection systems (TPS)from the micro to macro scale, and at the fun-damental and engineering levels. Shock layer kinetics and radiation focuses on radiative heating of space-craft, quantum chemistry and benchmark experiments for validation.Aerosciences is a broad research area that impacts many aspects of entry systems, including parachutes, aerodynamics, and turbulent heating augmentation due to TPS roughness.The guidance, navigation,and control effort under ESM is expanding the capabilities of NASA’s main flight mechanics tool, POST2, for use on high-performance computing architectures and to generalize interoperability with external applications for more detailed end-to-end simula-tion.In addition, several focusedresearch topics have been approvedto augment ESM’s core portfolio. These include efforts for deep post-flight analysis of Mars 2020/MEDLI2 flight data; development ofTPS failure models; improvement of hypersonic wakeflow models; and a recently concluded effort to provide material response models for NuSil-coated PICA heat-shield material. This presentation will discuss each of these investment areas and demonstrate via real mission examples how advances to the state-of-the-art enabled by ESM are directly impacting the missions of today and tomorrow, including InSight, Mars 2020, Mars Sample Return, Orion, and Dragonfly.

Entry Systems Modeling↗
NASA NTRSDate not supplied

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as various means to download and access the data including programmatically through the GeneLab Open API (GLOpenAPI). The open access of datasets in NASA’s OSDR provides a unique opportunity for the scientific community, as well as citizen scientists and students, to continue using OSDR resources to further unlock profound insights into the consequences of space travel on the human body. Through implementation of security measures to protect sensitive human data, the OSDR seeks to strengthen the science exchange between the Biological and Physical Sciences Program and the Human Research Program, per recommendation 4-1 of the 2023-2032 Decadal Survey, and encourage further sharing and dissemination of astronaut data to provide the scientific community with the resources needed to lay the groundwork for developing targeted mitigation strategies to help withstand the rigors of long-duration spaceflight.

Amanda Marie Saravia-butler↗