Focused Ion Beam Scanning Electron Microscopy Imaging Research Articles

Multiscale investigation of pore structure heterogeneity in carbonate rocks using digital imaging and SCAL measurements: A case study from Upper Jurassic limestones, Abu Dhabi, UAE.

This study presents a comprehensive analysis of rock properties for a selected group of six carbonate reservoir rock samples revealing complex structures at various length scales. Experimental laboratory methods as well as image analysis techniques were conducted in this study to characterize the macro- and micro-pores in mud- and grain-dominated limestones samples from the Upper Jurassic Arab Formation (Arab D member). Mercury Injection Capillary Pressure (MICP), porosimeter, and permeameter lab measurements were employed to assess the pore network heterogeneity and complexity. In addition, a multiscale rock imaging approach was implemented to detect grain and pore phases at several length scales using Thin Sections (TS), Scanning Electron Microscopy (SEM), Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), as well as 3D X-ray Computed Tomography (CT), and micro-computed tomography images (MCT). Furthermore, the multifractal analysis method was applied on the MICP and FIB-SEM to characterize quantitatively the heterogeneity of the pores in the studied samples. Heterogeneous samples 3R, 4M, 5W, and 6M display the highest non-uniformity degree Δα values, falling within the range of [1.21, 1.39] based on FIB-SEM images. Samples 1G, 2R, 3R, and 5W exhibit more heterogeneous pore structure, with Δα values ranging from 0.73 to 1.49 based on the MICP experiments. The results and findings confirm the effectiveness of multifractal parameters Δα and the asymmetry degree in the vertical axis Δf(α) in quantifying and characterizing rock heterogeneity.

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging.

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.

Quantitative characterization of organic and inorganic pores in shale based on deep learning

Organic matter (OM) maturity is closely related to organic pores in shales. Quantitative characterization of organic and inorganic pores in shale is crucial for rock-physics modeling and reservoir porosity and permeability evaluation. Focused ion beam-scanning electron microscopy (FIB-SEM) can capture high-precision three-dimensional (3D) images and directly describe the types, shapes, and spatial distribution of pores in shale gas reservoirs. However, due to the high scanning cost, wide 3D view field, and complex microstructure of FIB-SEM, more efficient segmentation for the FIB-SEM images is required. For this purpose, a multiphase segmentation workflow in conjunction with a U-net is developed to segment pores from the matrix and distinguish organic pores from inorganic pores simultaneously in the entire 3D image stack. The workflow is repeated for FIB-SEM data sets of 17 organic-rich shales with various characteristics. The analysis focuses on improving the efficiency and relevance of the workflow, that is, quantifying the minimum number of training slices while ensuring accuracy and further combining the fractal dimension (FD) and lacunarity to study a simple and objective method of selection. Meanwhile, the computational efficiency, accuracy, and robustness to noise of the 2D U-net model are discussed. The intersection over the union of automatic segmentation can amount to 80%–95% in all data sets with manual labels as ground truth. In addition, calculated by the FIB-SEM multiphase segmentation, the organic porosity is used to quantitatively evaluate the OM decomposition level. Deep-learning-based segmentation shows great potential for characterizing shale pore structures and quantifying OM maturity.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.

Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.

Fluid Flow in the Gas Diffusion Layer Using Computational Fluid Dynamics and Microscopy Techniques

The utilization of a proton exchange membrane fuel cell (PEMFC) as an energy provider using hydrogen as a fuel has increased drastically. The reasons are the current limitation of fossil fuel-based devices, pollutants-free, high efficiency, and zero-carbon emission. The life-cycle assessment results of this type of fuel cell have also indicated the lowest contribution to global warming, human health, and resource scarcity in comparison to other types of fuel cells. In this regard, improving the performance of PEMFC is of importance.As an important component of the PEMFC, the gas diffusion layer (GDL) transports the gas reactants to the catalyst layer with the least electrical resistance. The GDL is often made of carbon fibers and should have a surface with good electrical contact and hydrophobic properties to facilitate the water removal. Remaining water in the GDL will result in difficulties during the cold-start and enhances the degradation of this layer. It is believed that the water removal of the GDL has a direct relationship with the capillary pressure, which is strongly linked to the GDL’s microstructure and wettability. However, further investigations can be done once a comprehensive simulation model is developed to monitor the changes in the GDL liquid removal by the effective parameters.In this regard, the Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) has been used to perform three-dimensional cross-section imaging of the GDL. The GDL samples are provided by Freudenberg in which the Microporous layer (MPL) is impregnated into the GDL. The average porosity of this sample is in the range of 75% while having the in-plane gas permeability of 2.4 under a compressive stress of 1 MPa. The utilized resin for FIB-SEM imaging is a mixture of Epoxy embedding medium (diglycidyl ether of bisphenol – A) with two different hardeners DDSA (2-Dodecenylsuccinic anhydride) and MNA (Methylnadic anhydride), which will be mixed with DPM – 30 [2,4,6 – Tris(dimethylaminomethyl)phenol] as the accelerator, all provided by Sigma Aldrich. After the preparation of the resin, 0.4 gr of Cobalt (II) acetylacetonate nanoparticles (supported by Sigma Aldrich) were added to 7.14 ml of the resin to improve the contrast of the images. The samples were impregnated with the resin under vacuum to be pressurized (3 MPa) for 20 minutes, and afterward, heated in an oven at 60℃ for 12 hours. The surfaces for analyses were cut by a diamond wire, polished by abrasive plates down to 0.1 and gold coated (20nm).FIB-SEM acquisition consisted of the polishing of cross-sections with a focused ion beam (LMIS Ga+ source) at 30 keV and 1 nA (Zeiss Crossbeam 540), followed by imaging with an electron beam at acceleration voltages of 1.0, 3.0 kV. Milling and imaging were performed at a working distance of 5.2 mm and stage tilt of 54°, i.e., the coincidence point of the electron and ion beams.Once the three-dimensional cross-sections of the GDL are obtained, the segmentation and reconstruction will be made to provide the needed geometry for fluid flow simulation and to calculate the microstructural properties. Fig. 1 shows the generated structure of the GDL after the segmentation and reconstruction of the cross-section images. The geometry will be then used to characterize the effective parameters of the thermal/water management of the PEMFC. This study can also be a valid reference for future computational fluid dynamic analyses in the GDL using the numerical modeling with the conservative equations or the Lattice Boltzmann modeling (LBM) with the kinetic and particle distribution equations. Keywords : Proton exchange membrane fuel cell (PEMFC); Gas Diffusion Layer (GDL); Focused Ion Beam- Scanning Electron Microscopy (FIB-SEM); Three-dimensional simulation; Lattice Boltzmann method (LBM) Figure 1

IMAGE-IN: Interactive web-based multidimensional 3D visualizer for multi-modal microscopy images.

Advances in microscopy hardware and storage capabilities lead to increasingly larger multidimensional datasets. The multiple dimensions are commonly associated with space, time, and color channels. Since "seeing is believing", it is important to have easy access to user-friendly visualization software. Here we present IMAGE-IN, an interactive web-based multidimensional (N-D) viewer designed specifically for confocal laser scanning microscopy (CLSM) and focused ion beam scanning electron microscopy (FIB-SEM) data, with the goal of assisting biologists in their visualization and analysis tasks and promoting digital workflows. This new visualization platform includes intuitive multidimensional opacity fine-tuning, shading on/off, multiple blending modes for volume viewers, and the ability to handle multichannel volumetric data in volume and surface views. The software accepts a sequence of image files or stacked 3D images as input and offers a variety of viewing options ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. We evaluate the performance by comparing the loading and rendering timings of a heterogeneous dataset of multichannel CLSM and FIB-SEM images on two devices with installed graphic cards, as well as comparing rendered image quality between ClearVolume (the ImageJ open-source desktop viewer), Napari (the Python desktop viewer), Imaris (the closed-source desktop viewer), and our proposed IMAGE-IN web viewer.

Assessing microstructural critical quality attributes in PLGA microspheres by FIB-SEM analytics.

The distribution of the active pharmaceutical ingredient (API) within polymer-based controlled release drug products is a critical quality attribute (CQA). It is crucial for the development of such products, to be able to accurately characterize phase distributions in these products to evaluate performance and microstructure (Q3) equivalence. In this study, polymer, API, and porosity distributions in poly(lactic-co-glycolic acid) (PLGA) microspheres were characterized using a combination of focused ion beam scanning electron microscopy (FIB-SEM) and quantitative artificial intelligence (AI) image analytics. Through in-depth investigations of nine different microsphere formulations, microstructural CQAs were identified including the abundance, domain size, and distribution of the API, the polymer, and the microporosity. 3D models, digitally transformed from the FIB-SEM images, were reconstructed to predict controlled drug release numerically. Agreement between the in vitro release experiments and the predictions validated the image-based release modelling method. Sensitivity analysis revealed the dependence of release on the distribution and size of the API particles and the porosity within the polymeric microspheres, as captured through FIB-SEM imaging. To our knowledge, this is the first report showing that microstructural CQAs in PLGA microspheres derived from imaging can be quantitatively and predictively correlated with formulation and manufacturing parameters.

Multicomponent Droplet Drying Modeling Based on Conservation and Population Balance Equations.

The aim of this work is to present a modeling tool to describe drying kinetics and delineate evolving physical and chemical behavior of multicomponent droplets during drying. Conservation equations coupled with population balance equations (PBE) are used to achieve this goal. Modeling results are gauged with single salt-water droplet drying from literature and show congruent trends. This model is then extended to a more complex system: various droplet sizes containing methanol (solvent), Felodipine (active ingredient), and PVP (polyvinylpyrrolidone as excipient). The FIB-SEM (Focused-Ion Beam Scanning Electron Microscopy) imaging results from spray-dried particles produced with similar formulation and processing conditions are consistent with phase behavior predicted by the model. The results show competing impacts of transport phenomena on the intermittent shell formation process and final particle structure and chemical heterogeneity. Solute diffusion, solvent efflux, and intra-drop flow impact the model system. It is found that shell formation follows a fluctuating profile where the initial precipitation of the dissolved species on the droplet surface is dampened, and nucleated particles become dispersed periodically until the shell becomes strong enough to withstand internal circulations. These internal effects are dependent on droplet size and are pronounced for larger droplets. That is, the particle phase behavior and physical nature are functions of the atomized droplet size. Stemming understating from this study would inform an optimized unit, operating in target design space. This would provide better product quality control and minimize discrepancies observed in process development during the early phase vs. commercial scale.

Release Mechanisms and Practical Percolation Threshold for Long-acting Biodegradable Implants: An Image to Simulation Study

The development of long-acting drug formulations requires efficient characterization technique as the designed 6–12 months release duration renders real-time in vitro and in vivo experiments cost and time prohibitive. Using a novel image-based release modeling method, release profiles were predicted from X-Ray Microscopy (XRM) of T0 samples. A validation study with the in vitro release test shows good prediction accuracy of the initial burst release. Through fast T0 image-based release prediction, the impact of formulation and process parameters on burst release rate was investigated. Recognizing the limitations of XRM, correlative imaging with Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) was introduced. A water stress test was designed to directly elucidate the formation of pores through polymer-drug-water interplay. Through an iterative correction method that considers poly(lactic-co-glycolic acid) (PLGA) polymer degradation, good agreement was achieved between release predictions using FIB-SEM images acquired from T0 samples and in vitro testing data. Furthermore, using image-based release simulations, a practical percolation threshold was identified that has profound influence on the implant performance. It is proposed as an important critical quality attribute for biodegradable long-acting delivery system, that needs to be investigated and quantified.

Integration of Multiscale Imaging of Nanoscale Pore Microstructures in Gas Shales

Quantification of the microstructures of shales is difficult due to their complexity which extends across many orders of magnitude of scale. Nevertheless, shale microstructures are extremely important, not only as shale gas resources but also as cap rocks in CCS resources, in geothermal reservoirs, and as a host to the long-term storage of radioactive materials. In this work, we have performed ultrahigh-resolution CT imaging (nano-CT), mercury injection porosimetry (MIP), and nitrogen adsorption experiments on a sample of gas shale for which we already have focused ion beam scanning electron microscopy (FIB-SEM) and high-resolution CT (micro-CT) data sets. The combination of these data sets has allowed us to examine the microstructure of the shale in unprecedented depth across a wide range of scales (from about 20 nm to 0.5 mm). Overall, the sample shows a porosity of 0.67 ± 0.009% from the nano-CT data, 0.0235 ± 0.003% from nitrogen adsorption, and 0.60 ± 0.07% from MIP, which compare with 0.10 ± 0.01%, 0.52 ± 0.05%, and 0.94 ± 0.09% from three FIB-SEM measurements and 0.06 ± 0.008% from one micro-CT measurement. The data vary due to the different scales at which each technique interrogates the rock and whether the pores are openly accessible (especially in the case of the nitrogen adsorption value). The measured kerogen fraction is 32.4 ± 1.45% from nano-CT compared with 34.8 ± 1.74%, 38.2 ± 1.91%, 41.4 ± 2.07%, and 44.5 ± 2.22% for three FIB-SEM and one micro-CT measurement. The pore size imaged by nano-CT ranged between 100 and 5000 nm, while the corresponding ranges were between 3 and 2000 nm for MIP analysis and between 2 and 90 nm for N2 adsorption. The distribution of pore aspect ratio and scale-invariant pore surface area to volume ratio (σ) as well as the calculated permeability shows the sample to have a high shale gas potential. Aspect ratios indicate that most of the pores that contribute significantly to pore volume are oblate, which is confirmed by the range of σ (3–13). Oblate pores have greater potential for interacting with other pores compared to equant and needle-shaped prolate pores as well optimizing surface area for gas to desorb from the kerogen into the pores. Permeability essays provide 2.61 ± 0.42 nD from the nano-CT data, 2.65 ± 0.45 nD from MIP, and (5.07 ± 0.02) × 10–4 nD from nitrogen adsorption, which are consistent with expectations for generic gas shales (i.e., tens of nD) and the measurements made previously on the same sample using FIB-SEM and micro-CT imaging techniques.

FIB-SEM as a Volume Electron Microscopy Approach to Study Cellular Architectures in SARS-CoV-2 and Other Viral Infections: A Practical Primer for a Virologist.

The visualization of cellular ultrastructure over a wide range of volumes is becoming possible by increasingly powerful techniques grouped under the rubric “volume electron microscopy” or volume EM (vEM). Focused ion beam scanning electron microscopy (FIB-SEM) occupies a “Goldilocks zone” in vEM: iterative and automated cycles of milling and imaging allow the interrogation of microns-thick specimens in 3-D at resolutions of tens of nanometers or less. This bestows on FIB-SEM the unique ability to aid the accurate and precise study of architectures of virus-cell interactions. Here we give the virologist or cell biologist a primer on FIB-SEM imaging in the context of vEM and discuss practical aspects of a room temperature FIB-SEM experiment. In an in vitro study of SARS-CoV-2 infection, we show that accurate quantitation of viral densities and surface curvatures enabled by FIB-SEM imaging reveals SARS-CoV-2 viruses preferentially located at areas of plasma membrane that have positive mean curvatures.

Machine-Learning-Assisted Segmentation of Focused Ion Beam-Scanning Electron Microscopy Images with Artifacts for Improved Void-Space Characterization of Tight Reservoir Rocks

SummaryThe modern focused ion beam-scanning electron microscopy (FIB-SEM) allows imaging of nanoporous tight reservoir-rock samples in 3D at a resolution up to 3 nm/voxel. Correct porosity determination from FIB-SEM images requires fast and robust segmentation. However, the quality and efficient segmentation of FIB-SEM images is still a complicated and challenging task. Typically, a trained operator spends days or weeks in subjective and semimanual labeling of a single FIB-SEM data set. The presence of FIB-SEM artifacts, such as porebacks, requires developing a new methodology for efficient image segmentation. We have developed a method for simplification of multimodal segmentation of FIB-SEM data sets using machine-learning (ML)-based techniques.We study a collection of rock samples formed according to the petrophysical interpretation of well logs from a complex tight gas reservoir rock of the Berezov Formation (West Siberia, Russia). The core samples were passed through a multiscale imaging workflow for pore-space-structure upscaling from nanometer to log scale. FIB-SEM imaging resolved the finest scale using a dual-beam analytical system. Image segmentation used an architecture derived from a convolutional neural network (CNN) in the DeepUNet (Ronneberger et al. 2015) configuration. We implemented the solution in the Pytorch® (Facebook, Inc., Menlo Park, California, USA) framework in a Linux environment. Computation exploited a high-performance computing system.The acquired data included three 3D FIB-SEM data sets with a physical size of approximately 20 × 15 × 25 µm with a voxel size of 5 nm. A professional geologist manually segmented (labeled) a fraction of slices. We split the labeled slices into training, validation, and test data. We then augmented the training data to increase its size. The developed CNN delivered promising results. The model performed automatic segmentation with the following average quality indicators according to test data: accuracy of 86.66%, precision of 54.93%, recall of 83.76%, and F1 score of 55.10%. We achieved a significant boost in segmentation speed of 14.5 megapixel (MP)/min. Compared with 0.18 to 1.45 MP/min for manual labeling, this yielded an efficiency increase of at least 10 times.The presented research work improves the quality of quantitative petrophysical characterization of complex reservoir rocks using digital rock imaging. The development allows the multiphase segmentation of 3D FIB-SEM data complicated with artifacts. It delivers correct and precise pore-space segmentation, resulting in little turn-around-time saving and increased porosity-data quality. Although image segmentation using CNNs is mainstream in the modern ML world, it is an emerging novel approach for reservoir-characterization tasks.

Ultrahigh-Resolution 3D Imaging for Quantifying the Pore Nanostructure of Shale and Predicting Gas Transport

The pore and fracture microstructures are key to understanding gas flow in shales. The experimental determination of these microstructures is dependent on the measurement technique employed and its resolution. High-resolution three-dimensional imaging techniques coupled with image analysis and numerical simulations have been employed to characterize the petrophysical properties of shale samples. In this work, our particular focus is on using the Nano-CT and focused ion beam scanning electron microscopy (FIB-SEM) techniques at the same location in a shale rock sample to investigate the effect of their different resolutions and fields of view on the resulting imaged nanopore structure, as well as to determine any differences in the consequent measurements of the shale petrophysical properties. These petrophysical properties include porosity, permeability, pore volume and size distribution, the pore aspect ratio, the surface area to volume, and pore connectivity. The reconstructed matrix, kerogen, and pore space volumes from each approach showed significant scale-dependent differences in the microstructure. The shale sample displayed a high kerogen content with high connectivity. Porosity from the reconstructed shale volumes was observed to be 0.43 and 0.7% for FIB-SEM and Nano-CT approaches, respectively. The pore volume, size, surface area to volume ratio, and two orthogonal pore aspect ratio distributions have also been determined from the reconstructed image data by three-dimensional (3D) image analysis. These data show that voids within the rock are oblate at all scales. Permeabilities have been calculated from both the FIB-SEM and Nano-CT images and fall in the range of 2.55–9.92 nD. A simulation has also been produced based on the permeability calculation and parameters from the image analysis. The results of the simulation show connectivity in the x-, y-, and z-directions for both the FIB-SEM and Nano-CT images, with very low connectivity in the x-direction but higher connectivity in the y- and z-directions.

Semantic Segmentation of SOFC Composite Electrode Images Incorporating Patch-Based Convolutional Neural Networks

Quantitative microstructure analysis is an important task in many fields of material engineering. In solid oxide fuel cell (SOFC), the microstructure of porous electrodes determines electrochemical performance of the cell. Three dimensional analysis by focused ion beam - scanning electron microscopy (FIB-SEM) has been successfully implemented to evaluate the fabrication strategies and to identify degradation mechanisms. For precise evaluation of the electrode microstructures, segmentation of large number of SEM images is required. The conventional method for image processing of raw FIB-SEM dataset consists of semi-automatic filtering, thresholding, edge-detection, etc. Those semi-automatic methods usually require manual corrections in final stages of the processing to ensure highest accuracy, which greatly extends the processing time. Therefore, number of images, size and resolution of the obtained microstructures are limited not only by the physical capabilities of FIB-SEM hardware, but also by the post processing of datasets.In the present research, the framework for automated SOFC 3D microstructure reconstruction form a large dataset of FIB-SEM images is proposed. The framework is based on machine learning, namely convolutional neural networks (CNN). A simple yet effective deep neural network incorporating the patch-based convolutional layers (patch-CNN) organized in the encoder-decoder manner was designed and tested. Clear guidelines are given for the selection and labeling of training images, selection of network architecture, methods for the CNN training from a scratch and transfer learning. The most influential factors to assure short processing time and accurate segmentation are the selection of optimal training dataset and the proper network architecture. Teaching images have to be labelized prior to training, since minimizing their number greatly reduces the preprocessing time. Selections of optimal network architecture and patch size are crucial in providing enough spatial information and maintaining reasonable training time. The data augmentation and random patch extraction allow artificial enlargement of the training dataset. The augmentation techniques, including brightness and contrast random enhancements, rotation and reflection, are discussed in relation to the original features of different microstructures.The proposed methodology was tested for various electrodes including nickel - yttria-stabilized zirconia (Ni-YSZ) anode, nickel - gadolinium doped ceria pattern electrode (Ni-GDC), pure GDC and La0.9Sr0.1Cr0.5Mn0.5O3-δ - Gd0.1Ce0.9O2-δ (LSCM-GDC) nano-composite porous anodes. The tested data had various resolutions (5 nm – 30 nm), different image sizes (500 – 4000 pixels) and had very different measurements artifacts. The CNN-based approach succeeded in accurate binarization, ternarization and quaternarization of micro- and nano-structures as well as impurities detection. The achieved pixel-based accuracy was over 96%, and the 3D microstructural parameters such as phase fractions, connectivity, total and active TPB densities, surface area density and tortuosity factors calculated from ground truth data and CNN-prediction were identical.One of the challenges of image segmentation in handling FIB-SEM image set is the treatment of artifacts such as curtaining, shadows and charge-ups. Proposed patch-CNN can successfully process all of these artifacts. The detection of pores which are not filled with resin prior to the FIB-SEM measurement is another difficult task for the conventional image processing. The results achieved for the samples with a large number of un-filled pores are presented in Fig. 1. Reconstruction of this sample was not possible with the conventional algorithms based on the pixel brightness values. The two separated CNNs were trained to conduct artificial pore-filling operation (CNN1) and to ternarize the phases (CNN2). The amount of labelized data incorporated for training was 3 and 17 images for CNN1 and CNN2, respectively. The final segmentation was conducted for 935 images and the reconstructed volume size was 54 × 54 × 28 µm with resolution of 30 nm in all spatial directions.Deploying artificial intelligence methods into real problems in material engineering requires fairness, high accuracy, transferability and interpretability. The proposed automatic patch-CNN algorithm can significantly shorten the image processing time with maintaining accuracy, resolution and size of the reconstructed microstructures, which were not available by conventional methods. Proposed algorithm can be easily extended for any multiphase, porous materials and different 3-D imagining techniques.Fig. 1 Framework for automated SOFC 3D microstructure reconstruction form a large dataset of FIB-SEM images without pore filling. Figure 1

Cryo-fluorescence microscopy of high-pressure frozen C. elegans enables correlative FIB-SEM imaging of targeted embryonic stages in the intact worm.

Rapidly changing features in an intact biological sample are challenging to efficiently trap and image by conventional electron microscopy (EM). For example, the model organism C. elegans is widely used to study embryonic development and differentiation, yet the fast kinetics of cell division makes the targeting of specific developmental stages for ultrastructural study difficult. We set out to image the condensed metaphase chromosomes of an early embryo in the intact worm in 3-D. To achieve this, one must capture this transient structure, then locate and subsequently image the corresponding volume by EM in the appropriate context of the organism, all while minimizing a variety of artifacts. In this methodological advance, we report on the high-pressure freezing of spatially constrained whole C. elegans hermaphrodites in a combination of cryoprotectants to identify embryonic cells in metaphase by in situ cryo-fluorescence microscopy. The screened worms were then freeze substituted, resin embedded and further prepared such that the targeted cells were successfully located and imaged by focused ion beam scanning electron microscopy (FIB-SEM). We reconstructed the targeted metaphase structure and also correlated an intriguing punctate fluorescence signal to a H2B-enriched putative polar body autophagosome in an adjacent cell undergoing telophase. By enabling cryo-fluorescence microscopy of thick samples, our workflow can thus be used to trap and image transient structures in C. elegans or similar organisms in a near-native state, and then reconstruct their corresponding cellular architectures at high resolution and in 3-D by correlative volume EM.

Two-Stage Alignment of FIB-SEM Images of Rock Samples.

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) tomography provides a stack of images that represent serial slices of the sample. These images are displaced relatively to each other, and an alignment procedure is required. Traditional methods for alignment of a 3D image are based on a comparison of two adjacent slices. However, such algorithms are easily confused by anisotropy in the sample structure or even experiment geometry in the case of porous media. This may lead to significant distortions in the pore space geometry, if there are no stable fiducial marks in the frame. In this paper, we propose a new method, which meaningfully extends existing alignment procedures. Our technique allows the correction of random misalignments between slices and, at the same time, preserves the overall geometrical structure of the specimen. We consider displacements produced by existing alignment algorithms as a signal and decompose it into low and high-frequency components. Final transformations exclude slow variations and contain only high frequency variations that represent random shifts that need to be corrected. The proposed algorithm can operate with not only translations but also with arbitrary affine transformations. We demonstrate the performance of our approach on a synthetic dataset and two real FIB-SEM images of natural rock.

Optimization of Material Contrast for Efficient FIB‐SEM Tomography of Solid Oxide Fuel Cells

AbstractFocused ion beam (FIB) – scanning electron microscopy (SEM) serial sectioning tomography has become an important tool for three‐dimensional microstructure reconstruction of solid oxide fuel cells (SOFC) to obtain an understanding of fabrication‐related effects and SOFC performance. By sequential FIB milling and SEM imaging a stack of cross‐section images across all functional SOFC layers was generated covering a large volume of 3.5·104 μm3. One crucial step is image segmentation where regions with different image intensities are assigned to different material phases within the SOFC. To analyze all relevant SOFC materials, it was up to now mandatory to acquire several images by scanning the same region with different imaging parameters because sufficient material contrast could otherwise not be achieved. In this work we obtained high‐contast SEM images from a single scan to reconstract all functional SOFC layers consisting of a Ni/Y2O3‐doped ZrO2 (YDZ) cermet anode, YDZ electrolyte and (La,Sr)MnO3/YDZ cathode. This was possible by using different, simultaneous read‐out detectors installed in a state‐of‐the‐art scanning electron microscope. In addition, we used a deterministic approach for the optimization of imaging parameters by employing Monte Carlo simulations rather than trial‐and‐error tests. We also studied the effect of detection geometry, detecting angle range and detector type.

Super-Resolution Microscopy and FIB-SEM Imaging Reveal Parental Centriole-Derived, Hybrid Cilium in Mammalian Multiciliated Cells.

Motile cilia are cellular beating machines that play a critical role in mucociliary clearance, cerebrospinal fluid movement, and fertility. In the airways, hundreds of motile cilia present on the surface of a multiciliated epithelia cell beat coordinately to protect the epithelium from bacteria, viruses, and harmful particulates. During multiciliated cell differentiation, motile cilia are templated from basal bodies, each extending a basal foot-an appendage linking motile cilia together to ensure coordinated beating. Here, we demonstrate that among the many motile cilia of a multiciliated cell, a hybrid cilium with structural features of both primary and motile cilia is harbored. The hybrid cilium is conserved in mammalian multiciliated cells, originates from parental centrioles, and its cellular position is biased and dependent on ciliary beating. Furthermore, we show that the hybrid cilium emerges independently of other motile cilia and functions in regulating basal body alignment.

An optimized approach using cryofixation for high-resolution 3D analysis by FIB-SEM

Compared with conventional two-dimensional transmission electron microscopy (TEM), focused ion beam scanning electron microscopy (FIB-SEM) can provide more comprehensive 3D information on cell substructures at the nanometer scale. Biological samples prepared by cryofixation using high-pressure freezing demonstrate optimal preservation of the morphology of cellular structures, as these are arrested instantly in their near-native states. However, samples from cryofixation often show a weak back-scatter electron signal and bad image contrast in FIB-SEM imaging. In addition, it is impossible to do large amounts of heavy metal staining. This is commonly achieved via established osmium impregnation (OTO) en bloc staining protocols. Here, we compared the FIB-SEM image quality of brain tissues prepared using several common freeze-substitution media, and we developed an approach that overcomes these limitations through a combination of osmium tetroxide, uranyl acetate, tannic acid, and potassium permanganate at proper concentrations, respectively. Using this optimized sample preparation protocol for high-pressure freezing and freeze-substitution, perfect smooth membrane morphology, even of the lipid bilayers of the cell membrane, was readily obtained using FIB-SEM. In addition, our protocol is broadly applicable and we demonstrated successful application to brain tissues, plant tissues, Caenorhabditis elegans, Candida albicans, and chlorella. This approach combines the potential of cryofixation for 3D large volume analysis of subcellular structures with the high-resolution capabilities of FIB-SEM.

Scale-span pore structure heterogeneity of high volatile bituminous coal and anthracite by FIB-SEM and X-ray μ-CT

Visualizing and quantifying the pore structure at the nano-micro scale is critical for understanding the micro fluid transport and enrichment in coalbed methane (CBM) reservoirs. In this work, the detailed micro-nano scale pore parameters such as pore counts, pore area, pore volume and pore size distribution can be acquired by the focused ion beam-scanning electron microscopy (FIB-SEM) and X-ray computed micro-tomography (X-ray μ-CT) techniques. Meanwhile, the pore network model (PNM) was adopted to describe and quantify the pore throat characteristics, which found that the pore throats of the sample LHG well developed and were conducive to seepage. Additionally, the three-dimensional fractal dimension (D3) by the box-counting method was used to evaluate the pores spatial heterogeneity. The D3 of sample LHG and sample L-1 are 2.23 and 2.04 (for FIB-SEM with pore size of 10 nm–~1000 nm), 2.69 and 2.51 (for X-ray μCT with pore size over 500 nm), respectively. The results indicate that the pore network has self-similarity with a secondary development. The variable trends from tens of nanometers to micrometers through the FIB-SEM and X-ray μCT images. For the relationship between porosity and D3, two opposite trends have emerged. The positive correlation trend should be related to the complex pore structure. The more complex the pore structure is, the higher the porosity is. The negative correlation should be contributed by a lot of mineral-filled pores. Pores filled with minerals that will increase the proportion of small pores and decrease the porosity, which causes that the spatial complexity of the pore networks is increased, and the D3 is increased. Therefore, this work may provide insights into the gas storage and seepage capabilities of CBM reservoirs, and thus will be favorable for enhancing CBM recovery.