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At least 127 records · Page 7

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NASA NTRS2005

Low cost environmental sensors for Spaceflight : NMP Space Environmental Monitor (SEM) requirements

An outstanding problem in spaceflight is the lack of adequate sensors for monitoring the space environment and its effects on engineering systems. By adequate, we mean low cost in terms of mission impact (e.g., low price, low mass/size, low power, low data rate, and low design impact). The New Millennium Program (NMP) is investigating the development of such a low-cost Space Environmental Monitor (SEM) package for inclusion on its technology validation flights. This effort follows from the need by NMP to characterize the space environment during testing so that potential users can extrapolate the test results to end-use conditions. The immediate objective of this effort is to develop a small diagnostic sensor package that could be obtained from commercial sources. Environments being considered are: contamination, atomic oxygen, ionizing radiation, cosmic radiation, EMI, and temperature. This talk describes the requirements and rational for selecting these environments and reviews a preliminary design that includes a micro-controller data logger with data storage and interfaces to the sensors and spacecraft. If successful, such a sensor package could be the basis of a unique, long term program for monitoring the effects of the space environment on spacecraft systems.

Galileo↗

NASA NTRS2005

Low Cost Environmental Sensors for Spaceflight: NMP Space Environmental Monitor (SEM) Requirements

New Millennium Program (NMP)↗

DOE OSTI2026

Conversion of real-world aluminum scrap streams into high-performance Al–Mg–Si–Cu automotive alloys using shear assisted processing and extrusion

The conversion of post-consumer aluminum (Al) scrap into usable Al alloys without adding primary Al is challenging because of excess impurities. In this work, >99% post-consumer Twitch, used beverage cans (UBCs), and remelt scrap ingots (RSIs) were used as feedstock materials. As-cast and solution heat-treated feedstock billets were extruded using Shear Assisted Processing and Extrusion (ShAPE) at ~510°C, followed by press quenching. To explore the development of the microstructure, texture, and underlying mechanisms and how they contribute to the overall strengthening in as-extruded and artificially aged samples, scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) were used to collect microstructure and texture data. The enhanced strength and ductility were corroborated with the microstructural features and crystallographic texture. Simple shear $\textrm{A}/\bar{\textrm{A}}$, $\textrm{A}_1^*/\textrm{A} _2^*$ texture components along with weak $\textrm{C}$ and $\textrm{B}/\bar{\textrm{B}}$ texture components were formed during extrusion; the texture was strengthened after heat treatment. The refined second-phase particles helped to retain the deformed microstructure and texture. The contributions of dislocation and precipitate strengthening were maximized when billets were solution-heat-treated prior to extrusion. This is attributed to the formation of effective supersaturated solid solutions during the ShAPE process, which precipitate out during the peak age treatment. Overall, the highest yield strength of 305 MPa, ultimate tensile strength of 350 MPa, and elongation of 12% were achieved in artificially aged samples, which are comparable to those of Al 6082-T6.

Al alloy↗

NASA NTRSDate not supplied

Radar Observations from the Haystack Ultrawideband Satellite Imaging Radar in 2019

The NASA Orbital Debris Program Office (ODPO) conducts radar measurements of the low Earth orbit (LEO) orbital debris environment on a continual basis for monitoring and to enable modeling of the environment over time. Radar observations from the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR) in 2019 are the most recent snapshot of the environment to date that has been both measured and analyzed. HUSIR provides data on orbital debris in LEO down to a NASA size estimation model (SEM) size of approximately 5.5 mm, depending upon altitude and year-to -year variation in the sensitivity of the radar. This is of interest as it is the millimeter-sized orbital debris that drives mission-ending risk to robotic spacecraft in LEO. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.

James Murray↗

NASA NTRS1994

Aubrite basalt vitrophyres: High sulfur silicate melts and a snapshot of aubrite formation

Two aubrite basalt vitrophyre clasts have been found within AMNH thin sections from the Parsa EH3 chondrite and the Khor Temiki aubrite. Polished sections of the Parsa Aubrite Inclusion (PAI) and the Khor Temiki Inclusion (KTI) were studied by optical, electron probe microanalysis (EPMA), and scanning electron microscopy (SEM) techniques with broad-beam and low absorbed EPMA currents used to minimize glass volatile loss. Some data have previously been reported for PAI and KTI may possibly correlate to a previously reported inclusion in Khor Tiimiki. In polished sections, PAI and KTI are approximately equal 4 mm in diameter and contain a large volume of glass. The clasts have similar textural characteristics and are akin to lunar vitrophyre textures. The glasses have high alkali rhyodacitic compositions Al-though PAI is peraluminous, KTI is significantly peralkaline. Additionally, the glasses have elevated sulfur concentrations that are extremely high by geochemical standards. SEM examination for beam overlap of microscopic CaS, FeS, and (Mg, Mn, Fe) S inclusions showed no such contamination. Furthermore, homogeneity of glass S content and low FeO contents help rule out contamination. Materials research data show that under reducing conditions alumino-silicate melts can dissolve up to several weight percent sulfur in the absence of Fe. The high S and alkali contents, the lack of associated high shock features, and the rationalized phase equilibria suggest that PAI and KTI are igneous melting products of an E-chondrite-like source material. Although large-scale impact melting cannot totally be ruled out, the above observations eliminate the possibility of in-situ shock melting.

Fogel, R. A.↗

DOE OSTI2020

Microanalytical Approaches to Characterizing REE in Appalachian Basin Underclays

The search for a reliable U.S. domestic source of rare earth elements (REE) is necessary to support the demand of advanced energy applications (e.g., catalysts, electronics, magnets). Sedimentary deposits may be sources for selectively recovering REE and critical metals—specifically the interbedded seat rock, or underclay, that underlies or forms the floor of a coal seam. This material is often a major component of coal waste fines and refuse and thus readily available. This study examines several Appalachian Basin underclays associated with actively mined coal seams as potential feedstocks for the REE. Multimodal microanalytical electron microscopy (SEM, FIB-SEM, EMPA) synchrotron-based µXRF, and image processing techniques are coupled with detailed elemental and mineral data to classify the 2D and 3D petrophysical properties of the materials. The REE contents of Appalachian Basin underclays were measured from 235–399 ppm and predominantly observed as discrete REE-bearing minerals such as monazite and xenotime on the order of 10–100 µm in size. These REE-bearing minerals typically accounted for less than 1% of the scanned areas and volumes under SEM and FIB-SEM analysis, with the exception of regions enriched in crandallite. Synchrotron-based µXRF elemental maps further identified several REE deposition environments in different underclays, including micro-scale (10–100 µm) light REEs co-localizing with Ca and P, micro-scale heavy REEs with Fe, and large-scale light REEs (>200 µm) co-localizing with Sr, Ba, Ca and P.

36 MATERIALS SCIENCE↗

NASA NTRS2009

Development of a Modified Vacuum Cleaner for Lunar Surface Systems

The National Aeronautics and Space Administration (NASA) mission to expand space exploration will return humans to the Moon with the goal of maintaining a long-term presence. One challenge that NASA will face returning to the Moon is managing the lunar regolith found on the Moon's surface, which will collect on extravehicular activity (EVA) suits and other equipment. Based on the Apollo experience, the issues astronauts encountered with lunar regolith included eye/lung irritation, and various hardware failures (seals, screw threads, electrical connectors and fabric contamination), which were all related to inadequate lunar regolith mitigation. A vacuum cleaner capable of detaching, transferring, and efficiently capturing lunar regolith has been proposed as a method to mitigate the lunar regolith problem in the habitable environment on lunar surface. In order to develop this vacuum, a modified "off-the-shelf" vacuum cleaner has been used to determine detachment efficiency, vacuum requirements, and optimal cleaning techniques to ensure efficient dust removal in habitable lunar surfaces, EVA spacesuits, and air exchange volume. During the initial development of the Lunar Surface System vacuum cleaner, systematic testing was performed with varying flow rates on multiple surfaces (fabrics and metallics), atmospheric (14.7 psia) and reduced pressures (10.2 and 8.3 psia), different vacuum tool attachments, and several vacuum cleaning techniques to determine the performance requirements for the vacuum cleaner. The data recorded during testing was evaluated by calculating percent removal, relative to the retained simulant on the tested surface. In addition, Scanning Electron Microscopy (SEM) imaging was used to determine particle size distribution retained on the surface. The scope of this paper is to explain the initial phase of vacuum cleaner development, including historical Apollo mission data, current state-of-the-art vacuum cleaner technology, and vacuum cleaner testing that has focused on detachment capabilities varying pressure environments.

Toon, Katherine P.↗

DOE OSTI2024

Corrosion of Zinc Cold Spray Coatings in a Wet Sweet and Sour Gas Environment

Internal corrosion is a problem for steel pipelines transporting natural gas or CO 2 containing water and partial pressures of H 2 S higher than 0.3 kPa (0.05 psi). This work aims to mitigate internal corrosion in steel pipelines transporting natural gas containing H 2 S using cold spray coatings. Two types of the cold spray binary metallic coatings (zinc chromium [ZnCr]. zinc niobium [ZnNb]) were studied using electrochemical techniques: potentiodynamic polarization, linear polarization resistance, and electrochemical impedance spectroscopy. The corrosion resistance of cold spray coatings (ZnCr, ZnNb) was evaluated in an environment containing 4 bar CO 2 pressure, simulating the partial pressures found in gas transmission lines over a solution of 3.5 wt% NaCl heated to 40°C. A concentration of 0.003 M Na 2 S 2 O 3 ·5H 2 O, corresponding to H 2 S partial pressures around 0.079 bar (1.146 psi), was used to simulate sour conditions. Postcorrosion surface characterization was performed using a scanning electron microscope (SEM) equipped with an energy-dispersive x-ray spectroscope (EDS) and x-ray diffraction analysis. The data showed that the presence of 0.003 M Na 2 S 2 O 3 ·5H 2 O shifted the corrosion potential to more anodic values and decreased the corrosion current density. Both coatings showed similar behavior after 1 h of exposure in the CO 2 /H 2 S environment, indicating that similar electrochemical reactions were occurring on ZnNb and ZnCr. SEM images and EDS surface analyses for specimens showed a significant change in the surface chemical composition of carbon steel coated with ZnNb and ZnCr after 24 h of immersion. In the presence of thiosulfate (under sour conditions), the formation of corrosion product layers (ZnCO 3 and ZnS) on top of ZnNb and ZnCr coatings increased their corrosion resistance, which helped to reduce their corrosion by a factor of 2. Under a sweet environment, the corrosion rates for steel coated with cold spray coatings after 14 d of exposure are lower than that for galvanized steel by a factor of 5 due to the ZnCO 3 layer formed on top of the coatings. The ZnCO 3 layer formed on the steel surface acts as a physical barrier against corrosion by blocking the diffusion of corrosive species to the surface. No localized attack was observed. ZnCr Cold spray coating with defect showed promising corrosion protection against CO 2 corrosion (sweet corrosion) after 14 d of exposure to a CO 2 environment. Here, the scratch on the coating simulated damage created in service, and it was deep enough to expose the substrate material (steel). The formation of zinc oxide (ZnO) and zinc carbonate (ZnCO 3 ) on the scratch confirmed the cathodic protection of the steel by ZnCr and ZnNb coatings.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Improving microstructures segmentation via pretraining with synthetic data

Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been considerable interest in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.

36 MATERIALS SCIENCE↗

NASA NTRS2015

Characterization of Oribtal Debris via Hyper-Velocity Ground-Based Tests

Existing DoD and NASA satellite breakup models are based on a key laboratory-based test, Satellite Orbital debris Characterization Impact Test (SOCIT), which has supported many applications and matched on-orbit events involving older satellite designs reasonably well over the years. In order to update and improve the break-up models and the NASA Size Estimation Model (SEM) for events involving more modern satellite designs, the NASA Orbital Debris Program Office has worked in collaboration with the University of Florida to replicate a hypervelocity impact using a satellite built with modern-day spacecraft materials and construction techniques. The spacecraft, called DebriSat, was intended to be a representative of modern LEO satellites and all major designs decisions were reviewed and approved by subject matter experts at Aerospace Corporation. DebriSat is composed of 7 major subsystems including attitude determination and control system (ADCS), command and data handling (C&DH), electrical power system (EPS), payload, propulsion, telemetry tracking and command (TT&C), and thermal management. To reduce cost, most components are emulated based on existing design of flight hardware and fabricated with the same materials. All fragments down to 2 mm is size will be characterized via material, size, shape, bulk density, and the associated data will be stored in a database for multiple users to access. Laboratory radar and optical measurements will be performed on a subset of fragments to provide a better understanding of the data products from orbital debris acquired from ground-based radars and telescopes. The resulting data analysis from DebriSat will be used to update break-up models and develop the first optical SEM in conjunction with updates into the current NASA SEM. The characterization of the fragmentation will be discussed in the subsequent presentation.

Cowardin, H.↗

DOE OSTI2024

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

Abstract The Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench .

Levine, L. E. (ORCID:0000000334484229)↗

DOE OSTI2020

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. Here we report a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1980

The SEM description of interaction of a transient electromagnetic wave with an object

The singularity expansion method (SEM), proposed as a means for determining and representing the transient surface current density induced on a scatterer by a transient electromagnetic wave is described. The resulting mathematical description of the transient surface current on the object is discussed. The data required to represent the electromagnetic scattering properties of a given object are examined. Experimental methods which were developed for the determination of the SEM description are discussed. The feasibility of characterizing the surface current induced on aircraft flying in proximity to a lightning stroke by way of SEM is examined.

Pearson, L. W.↗

DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A study on the creep behavior of alloy 709 using in-situ scanning electron microscopy

In this research, an experimental evaluation of creep properties of Alloy 709 in the temperature range of 750–850 °C was undertaken. Alloy 709 is a novel austenitic stainless steel with 20% Cr and 25% Ni by wt% that was developed for application in structural components of nuclear power plants. Creep rupture tests were conducted in an in-situ heating-loading and Scanning Electron Microscope (SEM) unit equipped with Electron Backscatter Diffraction (EBSD) detector and Energy Dispersive Spectroscopy (EDS). “Real-time” creep damage mechanisms of Alloy 709 at various stresses and temperatures using a flat, un-notched sample with continuously reducing cross-section is studied so that the failure and maximum creep damage occurred at the center of the sample where the in-situ SEM imaging could be focused. Accelerated creep tests at temperatures and stresses above service conditions were performed by employing multiple blocks of constant loads where the loads were increased once the sample attained constant creep rate, indicating a secondary creep regime. This technique ensures multiple data points can be obtained from the same test, saves the time required for an otherwise long-term creep test and usage of SEM. Further, Coincident Site Lattice (CSL) boundary maps were collected as control maps before testing, and the grain boundaries were observed during the creep test to understand the effect of grain boundary character on the creep damage mechanism. Void growth, grain boundary separation, and sliding were found to be the main creep mechanisms whose rate is dependent on stress and temperature. Failure mechanisms studied on the fracture surface using SEM fractography were correlated to the sample surface observations to create complementary information to better understand the underline creep mechanism of Alloy 709.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Visualizing defects and pore connectivity within metal–organic frameworks by X-ray transmission tomography

Metal–Organic Frameworks (MOFs) have the potential to change the landscape of molecular separations in chemical processes owing to their ability of selectively binding molecules. Their molecular sorting properties generally rely on the micro- and meso-pore structure, as well as on the presence of coordinatively unsaturated sites that interact with the different chemical species present in the feed. In this work, we show a first-of-its-kind tomographic imaging of the crystal morphology of a metal– organic framework by means of transmission X-ray microscopy (TXM), including a detailed data reconstruction and processing approach. Corroboration with Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) images shows the potential of this strategy for further (non-destructively) assessing the inner architecture of MOF crystals. By doing this, we have unraveled the presence of large voids in the internal structure of a MIL-47(V) crystal, which are typically thought of as rather homogeneous lattices. This challenges the established opinion that hydrothermal syntheses yield relatively defect-free material and sheds further light on the internal morphology of crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1990

Calibration correction of an active scattering spectrometer probe to account for refractive index of stratospheric aerosols

The use of the active scattering spectrometer probe (ASAS-X) to measure sulfuric acid aerosols on U-2 and ER-2 research aircraft has yielded results that are at times ambiguous due to the dependence of particles' optical signatures on refractive index as well as physical dimensions. The calibration correction of the ASAS-X optical spectrometer probe for stratospheric aerosol studies is validated through an independent and simultaneous sampling of the particles with impactors; sizing and counting of particles on SEM images yields total particle areas and volumes. Upon correction of calibration in light of these data, spectrometer results averaged over four size distributions are found to agree with similarly averaged impactor results to within a few percent: indicating that the optical properties or chemical composition of the sample aerosol must be known in order to achieve accurate optical aerosol spectrometer size analysis.

Pueschel, R. F.↗