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2D microstructure reconstruction for SEM via non-local patch-based image inpainting.
Abstract not provided.
NEUP IMEBM Project: Solubility Experiments on Carbon Fibers, Results from SEM-EDS & XRD.
Abstract not provided.
Slides on AFC-4C-R5 MNT20E destructive PIE :SEM, Micro-tensile testing, Post-test TEM Characterization
Slides containing updated PIE results for AFC-4C-R5 (MNT20E)
Chemo-Mechanical Instabilities in Lithium Cobalt Oxide at Higher State-Of-Charge in Li-Ion Batteries
Transition metal oxide cathodes are widely used in commercial Li-ion batteries. However, their practical charge capacity is limited due to severe chemo-mechanical instabilities at higher charge voltage, and state-of-charge condition. Here, in situ stress and strain measurements were synchronized to probe mechanical deformations in the lithium cobalt oxide (LCO) cathode via a multi-beam stress sensor and digital image correlation, respectively. In situ mechanical measurements revealed how Li removal from the electrode structure induces deformations on the LCO composite cathodes during cycling. The structure and morphology of the LCO cathodes were further investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies. Two distinct electrochemical and mechanical behaviors were identified when the LCO was charged up to 4.65 V. The LCO undergoes a compressive stress generation when charged up to 4.2 V and surface fractures on the LCO particles were detected by SEM. LCO cathode experienced significantly large contractions (negative strains) when charged up to 4.65 V, where intergranular crack formation and phase transformation were detected on the LCO particles via SEM and XRD, respectively. Overall, the study bridges complicated structural deformations with in situ analysis of mechanical degradations in LCO cathodes charged at higher voltages. The correlation is vital to understanding instability mechanisms in transition metal oxides at high voltages for alkali metal ion batteries.
Resolving the Electron Plume within a Scanning Electron Microscope
Scanning electron microscopy (SEM), a century-old technique, is today a ubiquitous method of imaging the surface of nanostructures. However, most SEM detectors simply count the number of secondary electrons from a material of interest, and thereby overlook the rich material information contained within them. Here, by simple modifications to a standard SEM tool, we resolve the momentum and energy information on secondary electrons by directly imaging the electron plume generated by the electron beam of the SEM. Leveraging these spectroscopic imaging capabilities, our technique is able to image lateral electric fields across a prototypical silicon p–n junctions and to distinguish differently doped regions, even when buried beyond depths typically accessible by SEM. Intriguingly, the subsurface sensitivity of this technique reveals unexpectedly strong surface band bending within nominally passivated semiconductor structures, providing useful insights for complex layered component designs, in which interfacial dynamics dictate device operation. Further, these capabilities for noninvasive, multimodal probing of complicated electronic components are crucial in today’s electronic manufacturing but is largely inaccessible even with sophisticated techniques. These results show that seemingly simple SEM can be extended to probe complex and useful material properties.
Paw-Net: Stacking ensemble deep learning for segmenting scanning electron microscopy images of fine-grained shale samples
Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, various machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images of fine-grained shale samples. However, we found that general CNNs do not yield optimal performance due to insufficient training data and imbalanced objects in SEM images. This work has revised the U-Net architecture, a popular approach for biomedical image analyses, by incorporating a loss function that addresses the imbalance issue. Furthermore, we used the ensemble learning method to train multiple models and combined the results to improve the overall performance of segmentation. We prepared 2162 sub-images from raw SEM images in our experiments and divided them into training, validation, and testing datasets. The overall results show that our method improves the average Intersection over Union (IOU) of mineral objects from 0.49 to 0.58, compared to the original U-Net model. Our method can clearly distinguish each object from others with boundaries, even in highly imbalanced images. Training our models takes less than three minutes using a single GPU, while manual labeling can take up to three hours for each image. Furthermore, the method helps geoscientists gain insights quickly and effectively by building neural network models from a small dataset of SEM images.
A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel
Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.
Proposed Analytical Methods for Determining Filter Media Properties
High Efficiency Particulate Air (HEPA) filters, commonly used in nuclear filtration applications, are an integral part of the waste management processes in nuclear plants. HEPA filters are 99.97% efficient filtration devices characterized by their high resistance to air flow, or pressure drop. From theoretical models, the initial pressure drop across a clean filter is proven to be a function of the filter media fiber diameters and porosity of the media. These filter properties are relatively difficult to obtain with traditional manual methods; therefore, there is a need to develop analytical methods to find the fiber diameter and porosity to determine the pressure drop. Typically, these values can be found by substituting a calculated representative value based upon measured values, such as equivalent fiber diameter based upon a clean pressure drop. However, these values may also be directly recorded and measured by incorporating Scanning Electron Microscope (SEM) image analysis and other measurement methods to analyze the filter media and determine its physical properties without the need for media testing. DiameterJ, an open source Java plug-in, used with ImageJ, can process images of filter media taken by an SEM to find statistical data such as mean fiber diameter and porosity. To produce the raw data, SEM images of two filter media type samples are taken and segmented in DiameterJ using the traditional and statistical region merging segmentation algorithms. Manual segmentation is necessary after the initial segmentation by the algorithms as the images tend to be too complex for the algorithms to output with the necessary accuracy. However, complications exist in the manual segmentation process as these methods can be time intensive and prone to the individual bias of the user. This in turn can skew the final mean fiber diameter result, and lead to either an over or under prediction of the pressure drop. It was also discovered that the porosity data produced by DiameterJ is inaccurate, as the SEM analyzes the three-dimensional filter media by projecting its geometry onto a plane and analyzing it as a two-dimensional binary image. Thus, the porosity is artificially inflated through the segmentation process, rendering this result incorrect. Alternatively, density determination, gravimetric analysis, and thickness testing of the filter media is collectively used to determine the filter fiber porosity. Together, the SEM image analysis and analytical lab methods produce results through direct measurements which allow for the prediction of the initial pressure drop from clean filter media without the need for prior media testing to collect pressure data. By improving upon this proposed analytical method in the future, there is potential to streamline the process of finding these filter properties into a more direct methodology for determining the pressure drop across HEPA filters.
Exploring 3D leaf anatomical traits for C 4 photosynthesis: chloroplast and plasmodesmata pit field size in maize and sugarcane
Summary Volume and surface area of chloroplasts and surface area of plasmodesmata pit fields are presented for two C 4 species, maize and sugarcane, with respect to cell surface area and cell volume. Serial block face scanning electron microscopy (SBF‐SEM) and confocal laser scanning microscopy with the Airyscan system (LSM) were used. Chloroplast size estimates were much faster and easier using LSM than with SBF‐SEM; however, the results were more variable than SBF‐SEM. Mesophyll cells were lobed where chloroplasts were located, facilitating cell‐to‐cell connections while allowing for greater intercellular airspace exposure. Bundle sheath cells were cylindrical with chloroplasts arranged centrifugally. Chloroplasts occupied c. 30–50% of mesophyll cell volume, and 60–70% of bundle sheath cell volume. Roughly 2–3% of each cell surface area was covered by plasmodesmata pit fields for both bundle sheath and mesophyll cells. This work will aid future research to develop SBF‐SEM methodologies with the aim to better understand the effect of cell structure on C 4 photosynthesis.
Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis
In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.
Development of Parameters for the Particle Size Distribution of TATB
Laser light scattering (LLS), manual counting of scanning electron microscopy (SEM) images, and reduction of SEM images using ImageJ (open source program) to determine Feret (caliper) diameters were applied to determine particle size distribution (PSD) of four preparations of 1,3,5‐triamino‐2,4,6‐trinitrobenzene (TATB) and yttria‐stabilized zirconia (YSZ), an SEM certified standard. Mie theory was used to reduce the LLS data. The spherical nature of the YSZ made it a good candidate for LLS. Variations in n , the refractive index, and iκ, the imaginary component, produced very little change in the PSD. However, changing the carrier liquid from H 2 O to a 40% aqueous sucrose solution, thereby changing the carrier refractive index, n 0 , substantially affected the PSD. The Mie complex refractive indices for the YSZ were n = 2.200, iκ = 0.100, with a 40% aqueous sucrose solution, n 0 = 1.400. The triclinic crystal structure of TATB made refractive index determinations more difficult, so a study was conducted varying Mie parameters and comparing them to the same data reduced using the Fraunhofer theory. Changing the n and κ parameters produced PSD with a small concentration of particles less than 1 µm in size or none in this range. SEM images, Feret data, manual counting, and Fraunhofer data reduction indicate particles less than 1 µm are probably < 5% in concentration. The final selection of Mie parameters for TATB was n = 2.283, iκ = 0.1, and suspension medium, n 0 = 1.330. Finally, computations, using density functional theory produced similar parameters.
Multimodal imaging and machine learning to enhance microscope images of shale
A machine learning based image processing workflow is presented to enhance shale source rock microscopic images obtained using diverse imaging platforms. Images were acquired from a 30 μm diameter cylindrical Vaca Muerta shale sample using both nondestructive Transmission X-Ray Microscopy (TXM, alternately referred to as nano computed tomography) and destructive Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM). Output cross-sectional images from each modality were aligned using a combination of manual and automated registration techniques to create a registered image dataset. We then apply this dataset for two image processing tasks: prediction of image cross sections with SEM-like resolution from nondestructive TXM data and repair of charged region artifacts (localized accumulation of electrons) within SEM images. The image processing algorithms for both tasks use deep learning models, specifically image-to-image Convolutional Neural Networks (CNNs) and conditional Generative Adversarial Networks (cGANs). In the image enhancement tasks, we are able to achieve significant qualitative and quantitative improvement in TXM images. Here, the best model reaches an average Peak Signal to Noise Ratio (PSNR) of 15.8 dB. Conditioning on TXM data is also shown to reduce artifacts from SEM charging, achieving an average PSNR of 25.8 dB. Furthermore, our results suggest that properly trained and validated networks are capable of significant enhancement of images obtained using nondestructive techniques, thereby improving interpretation of two- and three-dimensional images while preserving samples for future use.
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.
Confined Growth of DNA-Assembled Superlattice Films
Here, we study the assembly of DNA-functionalized nanocubes under lateral confinement in microscale square trenches on a DNA-functionalized substrate. Microfocus small-angle X-ray scattering (SAXS) and scanning electron microscopy (SEM) are used to characterize the superlattices (SLs). The results indicate that nanocubes form simple-cubic SLs with square-prism morphology and a (100) out-of-plane orientation to maximize DNA bonding. In-plane, SLs align with the template, exposing their {100} side facets, and the degree of alignment depends on trench size. Interestingly, the distribution of in-plane orientations determined from SAXS and SEM do not agree, indicating that the internal and external structures of the SLs differ. To understand this discrepancy, X-ray ptychography is employed to image the internal structures of the SLs, revealing that SLs which appear to be single-crystalline in SEM may have subsurface grain boundaries, depending on trench size. SEM reveals that the SLs grow via nucleation and growth of randomly oriented domains, which then coalesce; this mechanism explains the observed dependence of alignment and defect structure on size. Interestingly, crystallization occurs via an unusual growth mode, whereby continuous SL layers grow on top of several misoriented islands. Overall, this work elucidates the effect of lateral confinement on the crystallization of DNA-functionalized nanoparticles and shows how X-ray ptychography can be used to gain insight into nanoparticle crystallization.
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 .
Preserving Superconvergence of Spectral Elements for Curved Domains via h and p-Geometric Refinement [Slides]
Spectral element methods (SEM) are extensions of finite element methods (FEM) that employ Gauss-Lobatto or similar nodes instead of equidistant nodes for high-order elements. SEM can deliver superior accuracy compared to equidistant FEM due to potential superconvergence. However, significant challenges remain for domains with curved boundaries, which have limited the advantages of SEM for real-world applications. In this work, we propose a novel approach to bolster the overall accuracy and preserve the superconvergence of SEM over curved domains.
Preserving Superconvergence of Spectral Elements for Curved Domains [Slides]
Finite Element Methods (FEM) and Spectral Element Methods (SEM) are crucial for solving partial differential equations (PDEs) on complex geometries. SEM offers superior accuracy due to potential superconvergence for simple domains. Challenges persist for domains with curved boundaries, restricting SEM’s advantages in real-world applications. A proposed solution is the introduction of a novel strategy to enhance accuracy and maintain superconvergence of SEM in curved domains. The strategy includes a mesh-generation procedure with geometrically refined elements near curved boundaries and a post-processing phase using the Adaptive Extended Stencil Finite Element Method (AES-FEM). The method, named AES-FEM post-processed Spectral Element Method (ApSEM), aligns the accuracy of non-tensor-product elements with superconvergent spectral elements.