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At least 235 records · Page 13

Characterization of Three Carbon- and Nitrogen-Rich Particles from Comet 81P/WILD

Comets may sample the early solar system s complement of volatile-forming elements - including C and N - more fully and reliably than do the terrestrial planets or asteroids. Until recently, all elemental analyses of unambiguously cometary material were carried out remotely. The return of the Stardust mission makes it possible to analyze documented material from P81/Wild 2 in the laboratory Wild 2 particles fragmented when they stopped in the aerogel collectors. We have studied three fragments thought to be rich in C and N by using several techniques: FTIR to characterize organic matter; synchrotron-induced x-ray fluorescence (SXRF) to determine Fe and certain element/Fe ratios; SEM to image sample morphology and to detect semiquantitatively Mg, Al, Si, Ca, and Fe; and nuclear reaction analysis (NRA) to measure C, N, O, and Si.

Gallien, J.-P.↗

Formulation, Casting, and Evaluation of Paraffin-Based Solid Fuels Containing Energetic and Novel Additives for Hybrid Rockets

This investigation studied the inclusion of various additives to paraffin wax for use in a hybrid rocket motor. Some of the paraffin-based fuels were doped with various percentages of LiAlH4 (up to 10%). Addition of LiAlH4 at 10% was found to increase regression rates between 7 - 10% over baseline paraffin through tests in a gaseous oxygen hybrid rocket motor. Mass burn rates for paraffin grains with 10% LiAlH4 were also higher than those of the baseline paraffin. RDX was also cast into a paraffin sample via a novel casting process which involved dissolving RDX into dimethylformamide (DMF) solvent and then drawing a vacuum on the mixture of paraffin and RDX/DMF in order to evaporate out the DMF. It was found that although all DMF was removed, the process was not conducive to generating small RDX particles. The slow boiling generated an inhomogeneous mixture of paraffin and RDX. It is likely that superheating the DMF to cause rapid boiling would likely reduce RDX particle sizes. In addition to paraffin/LiAlH4 grains, multi-walled carbon nanotubes (MWNT) were cast in paraffin for testing in a hybrid rocket motor, and assorted samples containing a range of MWNT percentages in paraffin were imaged using SEM. The fuel samples showed good distribution of MWNT in the paraffin matrix, but the MWNT were often agglomerated, indicating that a change to the sonication and mixing processes were required to achieve better uniformity and debundled MWNT. Fuel grains with MWNT fuel grains had slightly lower regression rate, likely due to the increased thermal conductivity to the fuel subsurface, reducing the burning surface temperature.

Larson, Daniel B.↗

The Effects of Experimentally Induced Zircon Annealing on Melt Inclusion-Zircon Geochemistry: A Case Study of Archean Zircon From the Barberton Complex

Crystalline melt inclusions (MIs) in Archean zircons can potentially constrain the evolution of magmatic processes. However, to obtain a homogeneous glassy MI representative of the parent melt, devitrified MIs must be heated at temperatures above the liquidus. Although, it is well known that radiation damaged domains in MI-host zircon undergo temperature-time dependent annealing, the effects of annealing on zircon stable isotope and trace element compositions has received less attention. In this study, we heated multiple aliquots of low-magnetism zircons with relatively low levels of radiation damage (Deff<9.6x1014 α-events/mg) from a 3.304 Ga tonalite gneiss xenolith from the Barberton greenstone belt (sample B87-18; Kamo and Davis, 1994) in an internally-heated pressure vessel at 0.4GPa and 1000–1200°C. Zircons were imaged by SEM (CL, BSE) to determine the zoning patterns around the MIs. The radiation damage preserved in the heated and unheated zircons was determined by measuring broadening of the 3(SiO4) Raman stretching band of zircon (FWHM). Oxygen isotope ratios (δ18O), OH/O ratios, and trace and rare earth element (TREE) concentrations were measured in the zircons and MIs using SIMS. The FWHM and Raman shift of the zircon 3(SiO4) band show a systematic increase of zircon crystallinity with increasing experimental temperature. Despite increasing crystallinity, the 3(SiO4) FWHM is not correlated with δ18O(Zrn) values in the heated or unheated zircons, suggesting that δ18O was not altered by laboratory heating in these low initial radiation damage zircons. FWHM and OH/O are positively correlated in both heated and unheated zircons. The unheated zircons have the largest FWHM and OH/O (OH/OMAX=3.5×10-4) suggesting that H2O was lost during experimental heating due to the progressive annealing of radiation-damaged domains in the zircons. Zircon TREE concentrations and FWHM values are not correlated despite the presence of TREE altered domains in both the heated and unheated zircons. Our results demonstrate that pre-experimental zircon TREE concentrations and δ18O(Zrn) remain intact during laboratory heating of crystalline MIs in zircon, while secondary H2O hosted in radiation damaged domains is partially lost. Thus, MIs in low initial radiation damage Archean zircon are a potentially viable tool for constraining melt compositions.

Joseph P. Gonzalez↗

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗

Revealing Phase Heterogeneity in Vertically Aligned Nanocomposites via Plan-View Electron Energy Loss Spectroscopy

Hydrogen utilization in clean energy technologies is challenged by limited storage and transport within materials, owing to the complex hydrogen kinetics at interfaces [1]. Understanding these interfacial mechanisms at the nanoscale is crucial for developing improved materials for hydrogen applications, particularly proton-conducting fuel cells (PCFCs). Vertically aligned nanocomposites (VANs) grown by pulsed laser deposition (PLD) offer a unique platform for investigating the interfacial effects on hydrogen transport due to their well-defined interfaces parallel to the direction of charge transport [2-4]. To investigate hydrogen transport, the two phases within the VANs were chosen as BaZr 0.9 Y 0.1 O 3-x (BZY), a known proton conductor, and Pr 0.1 Ce 0.9 O 2-x (PCO), a mixed ionic-electronic conductor [5]. This PCO-BZY VANs architecture allows the investigation of how the interface between a proton conductor and a mixed conductor influences hydrogen transport. However, because of the small size of hydrogen, it is difficult to discern the nature of its interactions with interfaces from bulk measurements at the macroscale, thus necessitating nanoscale measurements [6]. Electron energy loss spectroscopy (EELS) allows for nanometer-resolution probing of the local atomic structure and chemistry at the BZY/PCO interface. In this study, plan-view analysis of PCO-BZY VANs films was employed to characterize the structure and phase distribution of the VANs and investigate the interface between the nanostructures. The films were imaged using scanning electron microscopy (SEM) in the Hitachi S-4800 SEM, collecting secondary electron images using mixed upper and lower detectors. Then, plan-view transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) EELS were employed using a JEOL ARM300 microscope operated at 300kV with a Gatan K3 GIF Continuum detector to study the distribution of the BZY and PCO phases through the film. As a result, spectrum images were acquired at a dispersion of 0.18eV per channel and denoised afterward by principal component analysis (PCA) method.

Griffin, Elizabeth [Northwestern University, Evans↗

Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks

Abstract For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure, e.g., via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view. Cracks within Li-ion electrode particles are an example of fine features, representative quantification of which requires large volumes of tens of particles. To overcome the trade-off between the imaged volume of the material and the resolution achieved, we deploy generative adversarial networks (GAN), namely SRGANs, to super-resolve SEM images of cracked cathode materials. A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles. This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view, thus enabling representative quantification of fine features.

30 DIRECT ENERGY CONVERSION↗

SEM and STEM X-ray spectrum images

This is supplemental information to a paper tentatively titled Applying Data Analytics Methods to X-ray Spectrum Images. Datasets are in the format of .h5 in the pyUSID flavor. SEM_small.h5 is 5 keV beam energy map of a TiB2 ceramic puck. SEM_large.h5 is a 20 keV map of the same sample. STEM.h5 is a STEM 200 keV map of neutron-irradiated tungsten (due to transmutation burnout, it has become a W-Re-Os alloy).

36 MATERIALS SCIENCE↗

Examination of Scanning Electron Microscope and Computed Tomography Images of PICA

Micrographs of PICA (Phenolic Impregnated Carbon Ablator) taken using a Scanning Electron Microscope (SEM) and 3D images taken with a Computed Tomography (CT) system are examined. PICA is a carbon fiber based composite (Fiberform ) with a phenolic polymer matrix. The micrographs are taken at different surface depths and at different magnifications in a sample after arc jet testing and show different levels of oxidative removal of the charred matrix (Figs 1 though 13). CT scans, courtesy of Xradia, Inc. of Concord CA, were captured for samples of virgin PICA, charred PICA and raw Fiberform (Fig. 14). We use these images to calculate the thermal conductivity (TC) of these materials using correlation function (CF) methods. CF methods give a mathematical description of how one material is embedded in another and is thus ideally suited for modeling composites like PICA. We will evaluate how the TC of the materials changes as a function of surface depth. This work is in collaboration with ETH-Zurich, which has expertise in high temperature materials and TC modeling (including CF methods).

Lawson, John W.↗

Multimodal imaging of oil-in-water bilgewater emulsion and biofilms

Bilgewater emulsions, consisting of oil and surfactant mixtures in water, affect many aquatic species. Thus development of methods and techniques to treat or mitigate the formation and undesired consequences of shipboard emulsions are urgently needed. This work presents new results of the evolution of the bilgewaer chemical and physical properties using in situ imaging. A Navy O/W emulsion consisting of three oils and a detergent mixture was used as the synthetic bilgewater model [1, 2]. Synthetic oil-in-water (O/W) emulsion droplets were seled in a vacuum compatible microfluidic sample holder and imaged using a suite of tools including scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), confocal laser scanning microscopy, and optical microscopy, highlighting the advantage of multiscale analysis and its applications in waste reduction and treatment in the environment. The transferrable and vacuum compatible microfluidic interface, System for Analysis at the Liquid Vacuum Interface (SALVI), was used in this study. SALVI enabled surface analysis of liquids and liquid-solid interactions using ToF-SIMS and SEM [3, 4]. Its detection window is an aperture of 2 ?m in diameter open to vacuum, permitting direct detection of the liquid surface. Liquid is withheld by surface tension within the aperture. The interface is composed of a silicon nitride membrane and polydimethylsiloxane microchannel [3, 4]. A variety of samples including complex liquid mixtures, ionic liquids, single mammalian cells, live biofilms, and solid-electrolyte interface have been analyzed using in situ imaging [5, 6]. The feasibility of using SALVI for in situ SEM imaging of nanoparticles and colloids in liquid was recently demonstrated [7]. This paper shows new findings of multimodal imaging and analysis of synthesized emulsion particles in water. Figure 1 depicts a schematic of multimodal in situ imaging of bilgewater emulsion using SEM and ToF-SIMS. Furthermore, we study the interactions of microbes and bilgewater emulsion. Microbes play an important role in the ocean. We hypothesize that microbes release organics that can act as surfactants that affect bilgewater formation or weakening. We present the first systematic study of emulsions and biofilms and investigate the effects of biofilms on bilgewater emulsions. Pseudomonas was selected as a model strain. Biofilms were cultured in a microchannel to allow culture [8]. Once a thick layer of biofilms was formed, the medium solution was changed to a mixture consisting of 50 % bilgewater emulsion. Dispersed biofilms were collected at 24 hrs. and 48 hrs. after emulsions were introduced into the channel. Bilgewater emulsions, biofilms, and mixtures of bilgewater emulsions and biofilms were analyzed using multiple in situ and ex situ techniques as described earlier. Our findings indicate that biofilms change the chemical makeup of the emulsion surface compositions and emulsion droplet size distribution, confirming the hypothesis that extracellular polymeric substance (EPS) related components released from biofilms can function as surfactants and change the oil-in-water interfaces.

bilgewater, multimodal imaging, biofilms↗

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. Here, the ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model).

08 HYDROGEN↗

Atomic force microscopy imaging of fragments from the Martian meteorite ALH84001

A combination of scanning electron microscopy (SEM) and environmental scanning electron microscopy (ESEM) techniques, as well as atomic force microscopy (AFM) methods has been used to study fragments of the Martian meteorite ALH84001. Images of the same areas on the meteorite were obtained prior to and following gold/palladium coating by mapping the surface of the fragment using ESEM coupled with energy-dispersive X-ray analysis. Viewing of the fragments demonstrated the presence of structures, previously described as nanofossils by McKay et al. (Search for past life on Mars--possible relic biogenic activity in martian meteorite ALH84001. Science, 1996, pp. 924-930) of NASA who used SEM imaging of gold-coated meteorite samples. Careful imaging of the fragments revealed that the observed structures were not an artefact introduced by the coating procedure.

Carbonates/analysis↗

Conformal Pressure and Fast-Charging Li-Ion Batteries

Batteries capable of extreme fast-charging (XFC) are a necessity for the deployment of electric vehicles. Material properties of electrodes and electrolytes along with cell parameters such as stack pressure and temperature have coupled, synergistic, and sometimes deleterious effects on fast-charging performance. We develop a new experimental testbed that allows precise and conformal application of electrode stack pressure. We focus on cell capacity degradation using single-layer pouch cells with graphite anodes, LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) cathodes, and carbonate-based electrolyte. In the tested range (10–125 psi), cells cycled at higher pressure show higher capacity and less capacity fading. Additionally, Li plating decreases with increasing pressure as observed with scanning electron microscopy (SEM) and optical imaging. While the loss of Li inventory from Li plating is the largest contributor to capacity fade, electrochemical and SEM examination of the NMC cathodes after XFC experiments show increased secondary particle damage at lower pressure. We infer that the better performance at higher pressure is due to more homogeneous reactions of active materials across the electrode and less polarization through the electrode thickness. Our study emphasizes the importance of electrode stack pressure in XFC batteries and highlights its subtle role in cell conditions.

25 ENERGY STORAGE↗

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host–pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum–Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral ( x-y) pixel resolution of 10 nm and an axial ( z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum–Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Biochemistry & Molecular Biology↗

Secondary electron yields of solar system ices

The secondary electron yields of H2O, CO2, NH3, and CH3OH ices have been measured as a function of electron beam energy in the 2- to 30-keV energy range. The ices were produced on a liquid-nitrogen-cooled cold finger and transferred under vacuum to a SEM where the yield measurements were made. The imaging capabilities of the SEM provide a means of correlating the yield measurements with the morphology of the ices and are also used to monitor charging effects. The yields were determined by measuring the amplified current from a secondary electron detector and calibrating this current signal with the amplified current signal from samples of metals with known secondary electron yields. Each of the measured yields is found to decrease with an increase in energy in the 2- to 30-keV range. Estimates are given for the maximum secondary electron yield Y(max) of each ice and the energy at which this maximum yield occurs. Implications for the charging of solar system ice grains are discussed.

Suszcynsky, David M.↗

Organic Analysis of Catalytic Fischer-Tropsch Type Synthesis Products: Are they Similar to Organics in Chondritic Meteorites?

Fischer-Tropsch Type (FTT) synthesis of organic compounds has been hypothesized to occur in the early solar nebula that formed our Solar System. FTT is a collection of abiotic chemical reactions that convert a mixture of carbon monoxide and hydrogen over nano-catalysts into hydrocarbons and other more complex aromatic compounds. We hypothesized that FTT can generate similar organic compounds as those seen in chondritic meteorites; fragments of asteroids that are characteristic of the early solar system. Specific goals for this project included: 1) determining the effects of different FTT catalyst, reaction temperature, and cycles on organic compounds produced, 2) imaging of organic coatings found on the catalyst, and 3) comparison of organic compounds produced experimentally by FTT synthesis and those found in the ordinary chondrite LL5 Chelyabinsk meteorite. We used Pyrolysis Gas Chromatography Mass Spectrometry (PY-GCMS) to release organic compounds present in experimental FTT and meteorite samples, and Scanning Electron Microscopy (SEM) to take images of organic films on catalyst grains.

Yazzie, Cyriah A.↗

Reliability of CGA/LGA/HDI Package Board/Assembly (Final Report)

Package manufacturers are now offering commercial-off-the-shelf column grid array (COTS CGA) packaging technologies in high-reliability versions. Understanding the process and quality assurance (QA) indicators for reliability are important for low-risk insertion of these advanced electronics packages. The previous reports, released in January of 2012 and January of 2013, presented package test data, assembly information, and reliability evaluation by thermal cycling for CGA packages with 1752, 1517, 1509, and 1272 inputs/outputs (I/Os) and 1-mm pitch. It presented the thermal cycling (-55C either 100C or 125C) test results for up to 200 cycles. This report presents up to 500 thermal cycles with quality assurance and failure analysis evaluation represented by optical photomicrographs, 2D real time X-ray images, dye-and-pry photomicrographs, and optical/scanning electron Microscopy (SEM) cross-sectional images. The report also presents assembly challenge using reflowing by either vapor phase or rework station of CGA and land grid array (LGA) versions of three high I/O packages both ceramic and plastic configuration. A new test vehicle was designed having high density interconnect (HDI) printed circuit board (PCB) with microvia-in-pad to accommodate both LGA packages as well as a large number of fine pitch ball grid arrays (BGAs). The LGAs either were assembled onto HDI PCB as an LGA or were solder paste print and reflow first to form solder dome on pads before assembly. Both plastic BGAs with 1156 I/O and ceramic LGAs were assembled. It also presented the X-ray inspection results as well as failures due to 200 thermal cycles. Lessons learned on assembly of ceramic LGAs are also presented.

Ghaffaroam. Reza↗

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗