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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2021

Chemiresistor sensor based on ion-imprinted polymer (IIP)-functionalized rGO for Cd(II) ions in water

This study reports the design and development of a novel chemiresistor (CR) sensor using ion imprinted polymer (IIP)-functionalized reduced graphene oxide (rGO) [IIP/rGO-CR] for cadmium ions (Cd(II)) determination in water. The sensor consisted of a CR transducer made of rGO channel bridging source and drain electrodes prepared by self-assembly and thermal reduction of graphene oxide (GO) on Au interdigitated electrodes chip fabricated on Si/SiO 2 substrate. The IIP was then grafted on rGO using surface-initiated reversible addition-fragmentation chain transfer (RAFT) polymerization with polyethylenimine (PEI) and methylacrylic acid (MAA) as dual functional monomers and Cd(II) ions as template through UV light-initiated copolymerization. The IIP functionalized on rGO acted as an effective recognition element that modulated the resistance of rGO-CR upon binding of Cd(II), enabling Cd(II) detection at ppb level in aqueous solutions. The prepared IIP/rGO-CR sensor worked effectively in the linear range of 2~200ppb and achieved a limit of detection (LOD) of 0.83 ppb, which is lower than the World Health Organization guidelines of 3ppb for drinking water quality. The developed sensor of IIP/rGO-CR showed a high selectivity against a variety of trace and heavy metal ions found in water and good stability for up to 60 days when stored at room temperature for Cd(II) determination in water. Further, the sensor was successfully applied to analyzing Cd(II) spiked in tap, lake and river waters with a 94.5%–113.5% recovery, demonstrating a high degree of accuracy even in complex water samples. Finally, our results illustrated that the CR sensor of IIP functionalized rGO provides a potential platform for sensitive, robust and low-cost environmental analysis of Cd(II) in water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Trace Impurities Identified as Forensic Signatures in CMX-5 Fuel Pellets Using X-ray Spectroscopic Techniques

We report that small-particle analysis is a highly promising emerging forensic tool for analysis of interdicted special nuclear materials. Integration of microstructural, morphological, compositional, and molecular impurity signatures could provide significant advancements in forensic capabilities. We have applied rapid, high-sensitivity, hard X-ray synchrotron chemical imaging to analyze impurity signatures in two differently fabricated fuel pellets from the 5th Collaborative Materials Exercise (CMX5) of the IAEA Nuclear Forensics International Working Group. The spatial distributions, chemical compositions, and morphological and molecular characteristics of impurities were evaluated using X-ray absorption near-edge structure (XANES) and X-ray fluorescence chemical imaging to discover principal impurities, their granularity, particle sizes, modes of occurrence (distinct grains vs incorporation in the UO 2 lattice), and sources and mechanisms of incorporation. Differences in UO 2+x stoichiometry were detected at the microscale in nominally identical UO 2 ceramics (CMX5-A and CMX5-B), implying the presence of multiple UO 2 host phases with characteristic microstructures and feedstock compositions. Al, Fe, Ni, W, and Zr impurities and integrated impurity signature analysis identified distinctly different pellet synthesis and processing methods. For example, two different Al, W, and Zr populations in the CMX5-B sample indicated a more complex processing history than the CMX5-A sample. K-edge XANES measurements reveal both metallic and oxide forms of Fe and Ni but with different proportions between each sample. Altogether, these observations suggest multiple sources of impurities, including fabrication (e.g., force-sieving) and feedstock (mineral oxides). This study demonstrates the potential of synchrotron techniques to integrate different signatures across length scales (angstrom to micrometer) to detect and differentiate between contrasting UO 2 fuel fabrication techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

WA-IsoC_MSC1.1.0

The soil microbiome is central to the cycling of carbon and other nutrients and to the promotion of plant growth. Despite its importance, analysis of the soil microbiome is difficult due to its sheer complexity, with thousands of interacting species. Here, we reduced this complexity by developing model soil microbial consortia that are simpler and more amenable to experimental analysis but still represent important microbial functions of the native soil ecosystem. Samples were collected from an arid grassland soil and microbial communities (consisting mainly of bacterial species) were enriched on agar plates containing chitin as the main carbon source. Chitin was chosen because it is an abundant carbon and nitrogen polymer in soil that often requires the coordinated action of several microorganisms for complete metabolic degradation. Several soil consortia were derived that had tractable richness (30-50 OTUs) with diverse phyla representative of the native soil, including Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria and Verrucomicrobia. The resulting consortia can be stored as glycerol or lyophilized stocks at -80° C and revived while retaining community composition, greatly increasing their use as tools for the research community at large. One of the consortia that was particularly stable was chosen as a model soil consortium (MSC-1) for further analysis. MSC-1 species interactions were studied using both pairwise co-cultivation in liquid media and during growth in soil under several perturbations. Co-abundance analyses highlighted interspecies interactions and helped to define keystone species, including Mycobacterium, Rhodococcus, and Rhizobiales taxa. These experiments demonstrate the success of an approach based on naturally enriching a community of interacting species that can be stored, revived, and shared. The knowledge gained from querying these communities and their interactions will enable better understanding of the soil microbiome and the roles these interactions play in this environment.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

X-ray observations of spiral galaxies. II - Images and spectral parameters of 13 galaxies

The results of a spatial and spectral analysis of a sample of spiral galaxies observed in X-rays with the Einstein satellite indicate that the X-ray emission is generally extended and complex. In some cases, discrete, very bright X-ray sources are detected, some of these coincident with the nuclear regions. The spectral fit of the IPC data to a thermal bremsstrahlung spectrum with a low-energy absorption cutoff shows that the average spectrum could be rather hard with kT greater than 2 keV, and with low-energy cutoff consistent with line-of-sight absorption. Results on NGC 4631, NGC 6946, and the nuclei of IC 342 and NGC 1313 are discussed and compared with previous observations in different energy ranges.

Fabbiano, G.↗
NASA NTRSDate not supplied

The Search for Chiral Asymmetry as a Potential Biosignature in Samples from Mars

The search for evidence of extraterrestrial life in our solar system has been guided by our under-standing of terrestrial biology and its associated biosignatures. The observed homochirality in all life on Earth, that is, the predominance of “left-handed” or L-amino acids and “right-handed” or D-sugars, is a unique property of life that is crucial for molecular recognition, enzymatic function, information storage and structure, and is thought to be a prerequisite for the origin or early evolution of life. Therefore, the detection of L- or D-excesses of chiral amino acids or sugars could be a powerful indicator of extant or extinct life on Mars or other habitable environments in our solar system. However, studies of primitive meteorites have revealed that they contain extraterrestrial amino acids and sugar acids with large enantiomeric excesses (60% and higher) that resulted from non-biological processes [1], complicating the use of chiral asymmetry by itself as a definitive biosignature. The exploration of habitable environments on Mars, including an assessment of the preservation potential for complex organics of either abiotic or biological origin, is an objective of both current and future Mars missions. Now with the unambiguous detection of indigenous organic matter in sedimentary rocks by the Sample Analysis at Mars (SAM) instrument suite on Mars [2-5], NASA’s Curiosity rover has found evidence of the preservation of potential chemical biosignatures in the martian near surface. Although amino acids have not yet been identified by in situ measurements on Mars [5], indigenous achiral amino acids have been identified in one martian meteorite [6]. It is expected that amino acid racemization would be very slow and any chiral or isotopic signatures from an extinct martian biota could be preserved for billions of years, given the extremely cold and dry surface conditions [7]. The ESA/Roscosmos ExoMars mission scheduled for launch next year includes the Rosalind Franklin rover designed to acquire samples from a depth of ~2 m and deliver them to a suite of instruments, including the Mars Organic Molecule Analyzer (MOMA). The MOMA instrument contains a wet chemistry experiment designed specifically for the detection of amino acids and measurement of their enantiomeric compositions [8]. The complexity and limited duration of spaceflight operations, and the known analytical challenges associated with in situ extraction and characterization of trace reduced organic com-pounds in ancient rocks, make it challenging to determine the origins of martian organic matter found to date. Coordinated state-of-the-art laboratory measurements of returned samples from Mars that include spatially resolved chemical, mineralogical, bulk and molecule-specific isotopic, and enantiomeric measurements will be required to firmly establish whether the complex organic matter detected on Mars derives from bio-tic or abiotic processes. Ultimately, Mars Sample Return of rock cores collected by NASA’s Perseverance rover may be our best chance of identifying chemical biosignatures, including any chiral amino acid asymmetry resulting from a past or present martian biota, if one ever existed on Mars. Here we review our current knowledge of the distributions, and enantiomeric and isotopic com-positions of amino acids found in meteorites compared to terrestrial biochemistry. We also propose a set of measurement criteria that should be used to help establish the sources of any amino acids detected in samples returned from Mars using state-of-the-art gas and liquid chromatography mass spectrometry techniques [1].

D P Glavin↗
DOE OSTI2021

Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning

Development and preservation of carbonate porosity and permeability are critical to characterizing reservoirs. However, secondary porosity, such as vugs and fractures, are difficult to identify and require collection of expensive wireline tools or core sampling. Wireline logs and cores have traditionally been used to identify the presence of secondary porosity but fail to quantify the contribution to total reservoir porosity. Additionally, advanced wireline logs and core are not readily available for most wells. Dual energy CT scans were collected on whole core from the A-1 Carbonate and Brown Niagaran formations drawn from six wells in northern Michigan. 3D analysis techniques were applied to identify and isolate secondary porosity features. A series of machine learning and data analytics techniques were applied to the dataset to predict secondary porosity features on basic wireline logs. The predictive model successfully predicted secondary porosity with high confidence.

02 PETROLEUM↗
DOE OSTI2022

Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics

The bacterial exometabolome consists of a vast array of specialized metabolites, many of which are only produced in response to specific environmental stimuli. For this reason, it is desirable to control the extracellular environment with a defined growth medium composed of pure ingredients. However, complex (undefined) media are expected to support the robust growth of a greater variety of microorganisms than defined media. Here, we investigate the trade-offs inherent to a range of complex and defined solid media for the growth of soil microorganisms, production of specialized metabolites, and detection of these compounds using direct infusion mass spectrometry. We find that complex media support growth of more soil microorganisms, as well as allowing for the detection of more previously discovered natural products as a fraction of total m/z features detected in each sample. However, the use of complex media often caused mass spectrometer injection failures and poor-quality mass spectra, which in some cases resulted in over a quarter of samples being removed from analysis. Defined media, while more limiting in growth, generated higher quality spectra and yielded more m/z features after background subtraction. These results inform future exometabolomic experiments requiring a medium that supports the robust growth of many soil microorganisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

Probabilistic Classification Using Elemental Abundance Distributions and Lossless Image Compression in Apollo 17 Lunar Dust Samples from Mare Serenitatis

We have previously outlined a strategy for the detection of fossils [Storrie-Lombardi and Hoover, 2004] and extant microbial life [Storrie-Lombaudi and Hoover, 20051 during robotic missions to Mars using co-registered structural and chemical signatures. Data inputs included image lossless compression indices to estimate relative textural complexity and elemental abundance distributions. Two exploratory classification algorithms (principal component analysis and hierarchical cluster analysis) provide an initial tentative classification of all targets. Nonlinear stochastic neural networks are then trained to produce a Bayesian estimate of algorithm classification accuracy. The strategy previously has been successful in distinguishing regions of biotic and abiotic alteration of basalt glass from unaltered samples. [Storrie-Lombardi and Fisk, 2004; Storrie-Lombardi and Fisk, 2004] Such investigations of abiotic versus biotic alteration of terrestrial mineralogy on Earth are compromised by .the difficulty finding mineralogy completely unaffected by the ubiquitous presence of microbial life on the planet. The renewed interest in lunar exploration offers an opportunity to investigate geological materials that may exhibit signs of aqueous alteration, but are highly unlikely to contain contaminating biological weathering signatures. We here present an extension of our earlier data set to include lunar dust samples obtained during the Apollo 17 mission. Apollo 17 landed in the Taurus-Littrow Valley in Mare Serenitatis. Most of the rock samples from this region of the lunar highlands are basalts comprised primarily of plagioclase and pyroxene and selected examples of orange and black volcanic glass. SEM images and elemental abundances (C6, N7, O8, Na11, Mg12, Al13, Si14, P15, S16, Cll7, K19, Ca20, Fe26) for a series of targets in the lunar dust samples are compared to the extant cyanobacteria, fossil trilobites, Orgueil meteorite, and terrestrial basalt targets previously discussed. The data set provides a first step in producing a quantitative probabilistic methodology for geobiological analysis of returned lunar samples or in situ exploration.

Storrie-Lombardi, Michael C.↗
NASA NTRS2004

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Hoover, Richard B.↗
DOE OSTI2019

Characterization Techniques for TRU Waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory - 19610

Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical laboratory (AL) at the INL Materials and Fuels Complex (MFC) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste as Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RHTRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization of RHTRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all Acceptable Knowledge (AK) documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical Laboratory (AL) at the Materials and Fuels Complex (MFC) at Idaho National Laboratory (INL) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RH-TRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization if RH-TRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all AK [define AK] documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2015

Contemporary Impact Analysis Methodology for Planetary Sample Return Missions

Development of an Earth entry vehicle and the methodology created to evaluate the vehicle's impact landing response when returning to Earth is reported. NASA's future Mars Sample Return Mission requires a robust vehicle to return Martian samples back to Earth for analysis. The Earth entry vehicle is a proposed solution to this Mars mission requirement. During Earth reentry, the vehicle slows within the atmosphere and then impacts the ground at its terminal velocity. To protect the Martian samples, a spherical energy absorber called an impact sphere is under development. The impact sphere is composed of hybrid composite and crushable foam elements that endure large plastic deformations during impact and cause a highly nonlinear vehicle response. The developed analysis methodology captures a range of complex structural interactions and much of the failure physics that occurs during impact. Numerical models were created and benchmarked against experimental tests conducted at NASA Langley Research Center. The postimpact structural damage assessment showed close correlation between simulation predictions and experimental results. Acceleration, velocity, displacement, damage modes, and failure mechanisms were all effectively captured. These investigations demonstrate that the Earth entry vehicle has great potential in facilitating future sample return missions.

Perino, Scott V.↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission's Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

thermal protection systems↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission’s Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

Justin B. Haskins↗
DOE OSTI2024

Real-time breath analysis towards a healthy human breath profile

Abstract The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human ‘profile’ against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant ( p < 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Coupling of nitric acid digestion and anion-exchange resin separation for the determination of methylmercury isotopic composition within organisms

Isotope ratios of methylmercury (MeHg) within organisms can be used to identify sources of MeHg that have accumulated in food webs, but these isotopic compositions are masked in organisms at lower trophic levels by the presence of inorganic mercury (iHg). To facilitate measurement of MeHg isotope ratios in organisms, we developed a method of extracting and isolating MeHg from fsh and aquatic invertebrates for compound-specifc isotopic analysis involving nitric acid digestion, batch anion-exchange resin separation, and pre-concentration by purge and trap. Recovery of MeHg was quantifed after each step in the procedure, and the average cumulative recovery of MeHg was 93.4±2.9% (1 SD, n=28) for biological reference materials and natural biota samples and 96.9±1.8% (1 SD, n=5) for aqueous MeHgCl standards. The amount of iHg impurities was also quantifed after each step, and the average MeHg purity was 97.8±4.3% (1 SD, n=28) across all reference materials and natural biota samples after the fnal separation step. Measured MeHg isotopic compositions of reference materials agreed with literature values obtained using other MeHg separation techniques, and MeHg isotope ratios of aqueous standards, reference materials, and natural biota samples were reproducible. On average, the reproducibility associated with reference material process replicates (2 SD) was 0.10‰ for δ 202 MeHg and 0.04‰ for Δ 199 MeHg. This new method provides a streamlined, reliable technique that utilizes a single sample aliquot for MeHg concentration and isotopic analysis. Furthermore, this promotes a tight coupling between MeHg concentration, %MeHg, and Hg isotopic composition, which may be especially benefcial for studying complex food webs with multiple isotopically distinct sources of iHg and/or MeHg.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗