Focused Ion Beam Scanning Electron Microscopy Tomography Research Articles

Development and application of a 3D image analysis strategy for focused ion beam - Scanning electron microscopy tomography of porous soft materials.

In recent years, the potential of porous soft materials in various device technologies has increased in importance due to applications in fields, such as wearable electronics, medicine, and transient devices. However, understanding the 3-dimensional architecture of porous soft materials at the microscale remains a challenge. Herein, we present a method to structurally analyze soft materials using Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) tomography. Two materials, polymethyl methacrylate (PMMA) membrane and pine wood veneer were chosen as test-cases. FIB-SEM was successfully used to reconstruct the true topography of these materials in 3D. Structural and physical properties were subsequently deduced from the rendered 3D models. The methodology used segmentation, coupled with optimized thresholding, image processing, and reconstruction protocols. The 3D models generated pore size distribution, pore inter-connectivity, tortuosity, thickness, and curvature data. It was shown that FIB-SEM tomography provides both an informative and visual depiction of structure. To evaluate and validate the FIB-SEM reconstructions, porous properties were generated from the physical property analysis techniques, gas adsorption analysis using Brunauer-Emmett-Teller (BET) surface area analysis and mercury intrusion porosimetry (MIP) analysis. In general, the data obtained from the FIB-SEM reconstructions was well-matched with the physical data. RESEARCH HIGHLIGHTS: Porous specimens of both synthetic and biological nature, a poly(methyl methacrylate) membrane and a pine veneer respectively, are reconstructed via FIB-SEM tomography without resin-embedding. Different thresholding and reconstruction methods are explored whereby shadowing artifacts are present with the aid of free open-source software. Reconstruction data is compared to physical data: MIP, gas adsorption isotherms which are analyzed via BET and Barrett-Joyner-Halenda (BJH) analysis to yield a full picture of the materials.

Characterization of the pore structure in Chinese anthracite coal using FIB-SEM tomography and deep learning-based segmentation

Coalbed methane (CBM) is an unconventional natural gas that possesses significant impacts on energy supply, mining safety, and environmental conservation. CBM is primarily stored within the pores of coal, highlighting the significance of pore structures for both methane storage and migration. However, comprehensively understanding the intricate pore structures in coal pose challenges. This study employed focused ion beam-scanning electron microscopy (FIB-SEM) tomography and deep learning-based segmentation to characterize the pore structures within a Chinese anthracite sample. The obtained pore structures exhibited a considerable degree of disconnection, comprising numerous separate pore components. Isolated pores prevailed in number, while connected pores dominated in surface area and pore volume. Mesopores (100–1000 nm) contributed the most to pore number, surface area, and pore volume. Pore size distribution analysis revealed distinct patterns among different pore structure properties, with pore number exhibiting an intensive distribution while surface area and pore volume displaying dispersed distributions. Pore structure connectivities exhibited a hierarchical nature and held distinct meanings at the levels of pore, pore component, and pore network. The pore structure characteristics observed in this study have implications for primary CBM recovery, emphasizing the necessity to improve connectivity between pore components and fractures to enhance production rates and recoverability.

Three-dimensional visualization of dentine occlusion based on FIB-SEM tomography

The occlusion of dentinal tubules has become a rapid and effective method for treating dentin hypersensitivity. Accurate evaluation of dentin occlusion is critical to illustrate the efficacy of oral care products and to optimize dental therapy in the clinics, which is limited by the conventional two-dimensional (2-D) characterization methods. Here, we demonstrate the visualization of the dentin occlusion via three-dimensional (3-D) characterization using a focused ion beam-scanning electron microscopy (FIB-SEM) tomography. Using the “Slice and View” approach, the material used for occluding dentin tubules is imaged with a very high-resolution voxel (10 nm × 10 nm × 20 nm) from 2-D SEM images and then reconstructed into a 3-D volume, which presents the mode of action of toothpaste for treating dentin hypersensitivity. Meanwhile, quantitative analysis of the depth of occlusion is successfully obtained. This work validates the feasibility of FIB-SEM tomography in the analysis of dentin occlusion within the complicated networks of dentine tubules at the nanoscale, and provides a novel approach to facilitate the research and development of oral care products.

Characterizing the Pore Network of Diffusion Media at the Pore Size Scale

Water management by gas diffusion media and electrodes is a fundamental aspect of the performance of electrochemical cells including proton exchange membrane (PEM) fuel cells. In these devices, product liquid water blocks part of the available space for reactants flow, leading to mass transport losses and lowering of the performance1. Pores saturation has been observed with different experimental techniques for GDL substrates, however a detailed saturation process at the pore size scale of the microporous layer (MPL) is necessary. Previously, it was suggested that large pore network domains are responsible for an enhanced water evacuation2, however, possible existence of narrow size constrictions could still limit water evacuation. In this work, we detail the benefits of three-dimensional imaging of the pore network at the typical MPL pore size scale, i.e. the sub-micrometre scale, which is achieved by focused ion beam scanning electron microscope (FIB-SEM) nanotomography.Two different MPL designs were analysed: an acetylene black based MPL, named Li100, and a vapor grown carbon fiber (VGCF) based MPL, the latter showing better performance under high relative humidity conditions2, 3. FIB-SEM tomography volumes in Fig. 1a-b were used to observe the relative distribution of larger pore domains and narrower pore constrictions. By removing narrower sized pores from the network and analysing the connections of large pore domains, we show the existence of a characteristic size for pore constrictions below which the pore network is well connected. This characteristic size is depicted by the red line in Fig. 1c-d, which plots the fraction of connected pores as a function of their minimum size. Constrictions size values for the two MPL designs were further validated with mercury intrusion experiments, since constrictions affect all the intrusions by capillary pressure, such as water flow in hydrophobic MPLs.The calculated parameters, which are representative of the connections between large pore domains, open up new perspectives on the prediction of gas diffusion under realistic conditions. The approach allows evaluation of the effects of alternatively removing larger and narrower pore domains, to understand the saturation-driven tortuosity in different MPL structures. J. Shen, L. Xu, H. Chang, Z. Tu, and S. H. Chan, Energy Conversion and Management, 207, 112537 (2020).C. Simon, D. Kartouzian, D. Müller, F. Wilhelm, and H. A. Gasteiger, Journal of The Electrochemical Society, 164, F1697–F1711 (2017).C. Simon, J. Endres, B. Nefzger-Loders, F. Wilhelm, and H. A. Gasteiger, Journal of The Electrochemical Society, 166, F1022–F1035 (2019). Figure 1

Fueling the Simulation Machine: Considerations for High-Resolution 3D Microscopy to Support Computational Modeling in Battery Research

To meet the rapidly evolving energy needs of a growing population, energy storage and conversion devices such as batteries must rapidly evolve as well. Battery materials and other functional energy materials derive their properties not from their static state, but from how they interact with other materials in their service environment. Furthermore, the microstructure of these materials strongly dictates their performance and how they interact with other components (like electrolytes, binders, etc). In order to accelerate this development cycle, researchers are increasingly integrating computational modeling into their materials and device development efforts.Computational analysis is progressing to the point where simulations can incorporate real material microstructures, simulate statistically equivalent structures, and predict performance changes based on changes to these microstructures. At the same time experimental techniques have emerged that can deliver representative volumes of high-quality, high-resolution 3D microstructural images from real devices and materials. The interplay between advanced computational analysis and evolving characterization capabilities means we are rapidly approaching the realization of the “materials by design” paradigm where new materials are designed and tested digitally with input and validation provided by advanced materials characterization techniques. Within this paradigm, however, it is critical that the experimental techniques provide accurate, realistic, and representative input to the computational models. In the case of microstructural information, this means generating 3-dimensional images that accurately represents the materials in question.One key example of this is for materials, like battery materials and electrodes, which contain micro- and nano-scale porosity. Acquiring image data that can reproduce real material properties and feed accurate simulations is challenging and relies on satisfying several conditions. For example, the volume must be representative of the “bulk” material microstructure. The (2D) image resolution must capture the relevant scope of material features, and the 3D image must accurately represent the material in all 3 dimensions and at appropriate length scales. The focused-ion beam scanning electron microscope (FIB-SEM) is capable of imaging these micro- and nano-scale pore networks in 3D but care must be taken when considering these experiments, especially when the intent is to use the images to feed computational models.We present here a review of the factors researchers must consider when tackling these demanding tasks and highlight experimental recommendations for leveraging FIB-SEM tomography to produce the most relevant imaging data in this context. We use a lithium-ion battery cathode as a test case to demonstrate this approach, but the outcome and methods can be leveraged across the materials research space and beyond.

FIB-SEM tomography in catalysis and electrochemistry

Tomographic imaging methods have been incorporated, mostly from other scientific disciplines, into catalysis research. They are invaluable tools for the structural diagnostics of solid catalyst and electrode materials, which uniquely provide information on notions of spatial character which remain out of reach for conventional single-projection, i.e. 2D, microscopy methods. Focused-Ion-Beam Scanning-Electron-Microscopy (FIB-SEM) tomography is a destructive, slicing-type tomographic method which offers spatial resolutions down to few nm for inspection volumes up to several tens of µm across. As such, it has attracted a significant deal of attention as a means to study mesoscale features and macropore networks in catalytic materials. In this review, we first provide a succinct account on the recent technical developments in dual-beam technologies and discuss their implications for tomographic imaging experiments. Next, an exemplary experimental workflow for FIB-SEM experiments is discussed, with emphasis on technical aspects which concern specifically work with highly porous, electrically insulating catalyst materials. Contributions of FIB-SEM tomography to the quantification of mass transport-relevant topological parameters in porous catalysts, and multiple-phase boundaries of significance for concomitant mass and charge transport phenomena in electrode materials are surveyed. The application of FIB-SEM tomography for the analysis and rational development of materials in catalysis and electrochemistry has seen a fast surge over the last decade. It promises to continue consolidating as an important diagnostic tool for meso- and nano-spatial structural features, e.g. in multi-functional composite catalyst materials, wherein the relative spatial location of different sub-materials/functionalities are determinant for performance.

Mesoscopic structural analysis via deep learning processing, with a special reference to in vitro alteration in collagen fibre induced by a gap junction inhibitor.

Dense connective tissue, including the ligament, tendon, fascia and cornea, is formed by regularly arranged collagen fibres synthesized by fibroblasts (Fbs). The mechanism by which fibre orientation is determined remains unclear. Periodontal ligament Fbs consistently communicate with their surroundings via gap junctions (GJs), leading to the formation of a wide cellular network. A method to culture Fb-synthesized collagen fibres was previously reported by Schafer etal. ('Ascorbic acid deficiency in cultured human fibroblasts'. J. Cell Biol. 34: 83-95, 1967). This method has been applied to investigate the ability and activity of Fb collagen synthesis/phagocytosis using conventional electron microscopy (EM). However, the three-dimensional mesoscopic architecture of collagen fibres and the influence of GJ inhibitors on collagen fibre formation in vitro are poorly understood. In this study, three-dimensional mesoscopic analysis was used to elucidate the mechanism of directional fibre formation. We investigated the influence of GJ inhibitors on collagen formation driven by periodontal ligament Fbs in vitro, histomorphometrically, and the structural properties of in vitro collagen fibre on a mesoscale quantitatively, using correlative light and EM optimized for picrosirius red staining and focused ion beam-scanning EM tomography. Our results indicate that under culture conditions, in the presence of a GJ inhibitor, the orientation of collagen fibres becomes more disordered than that in the control group. This suggests that the GJ might be involved in determining fibre orientation during collagen fibre formation. Elucidation of this mechanism may help develop novel treatment strategies for connective tissue orientation disorders. Graphical Abstract.

Comparison of segmentation algorithms for FIB/SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation

Abstract Focused-ion-beam/scanning-electron-microscopy (FIB-SEM) tomography has become increasingly important to investigate the three-dimensional (3D) distribution of different phases in inhomogeneous materials. However, quantitative analysis of 3D structures by FIB-SEM tomography requires adequate image segmentation that is typically based on thresholds in the intensity histograms of SEM images. Recently machine learning (ML) segmentation algorithms have emerged with the opportunity to tune the algorithm based on prior knowledge of SEM image contrast. In this work we have performed FIB-SEM tomography of 3D printed nanoporous polymer structures with a special focus on SEM image segmentation and pore size determination. By applying Monte Carlo (MC) simulations, SEM image contrast of the pore/polymer interface was analysed. The performance of several traditional segmentation algorithms on simulated SEM images was found to lead to erroneous results on pore sizes. Training the ML segmentation algorithm by the knowledge obtained from MC simulations yields more reliable segmentation results. Evaluated pore sizes and analysis of further 3D material properties show a strong dependence on the segmentation method, which emphasize the importance of the segmentation process in the 3D reconstruction of FIB-SEM data.

The 3D Reconstruction and Simulation of Solid Oxide Fuel Cell Anode

The 3D Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) technique has been used to study the ceramic microstructures with the accuracy of tens of nanometers. There are several reports about the 3D reconstruction about the Ni-YSZ electrode in SOFC or SOEC mode (1-3). In this work, samples taken from full size, 10×10 cm2, solid oxide fuel cell (SOFC) single cells were reconstructed with the FIB-SEM tomography. Three samples were chosen from the same production batch, including a cell without reduction, a cell with 5 hours reduction and a cell with 100 hours test with humidified hydrogen, respectively. Figure 1 shows the SEM micrograph and reconstructed anode for the cell after 100 hours test. Different from the button cells, the full size anode-supported SOFC need a strong support layer and a smaller porosity is also acceptable. The lattice Boltzmann method (LBM) was used to simulate the physical processed in the reconstructed porous electrode. The 3D microstructure was remeshed to cubic voxels for numerical analysis. The triple-phase-boundary length was automatically identified and the parameters in the exchange current density equation were fitted to the polarization curves. Detailed results will be submitted in the full length draft.

The 3D Reconstruction and Simulation of Solid Oxide Fuel Cell Anode

The 3D Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) technique has been used to study the ceramic microstructures with the accuracy of tens of nanometers. Samples taken from commercial size, 10×10 cm2, solid oxide fuel cell (SOFC) single cells were reconstructed with the FIB-SEM tomography. Three samples were chosen from the same production batch, including a cell without reduction, a cell with 5 hours reduction, and a cell with 80 hours high temperature test with humidified hydrogen, respectively. The triple-phase-boundary length and volume fractions were identified for the samples. The lattice Boltzmann method (LBM) was used to simulate the physical processed in the reconstructed porous electrode. The parameters in the exchange current density equation were fitted to the polarization curves.

Three-Dimensional Characterization of Selective Laser Melted Graphene Oxide-Reinforced Ti-48Al-2Cr-2Nb Alloy

The influence of graphene oxide (GO) on selective laser melting (SLM) of Ti-48Al-2Cr-2Nb (at.%, Ti-4822) has been investigated using three-dimensional (3D) focused ion beam-scanning electron microscopy (FIB-SEM) tomography. The reconstructed 3D images from FIB-SEM tomography reveal the micro-/nano-features in the SLMed Ti-4822, including the dual-phase (α2 + γ) cellular structure and various types of defects. With the addition of 0.3 wt.% GO, the formed α2 cells are significantly refined, the γ phase network becomes thinner and more tightly packed, and the volume fraction of γ phase increases while that of α2 decreases. In addition, micro-cracks and nano-pores are observed in the SLMed Ti-4822, while only nano-pores with dramatically reduced size and number density are characterized in the SLMed Ti-4822 with GO. Our 3D characterization results have provided experimental evidence indicating that the addition of GO can improve the manufacturability of SLM of Ti-4822.

Segmentation of Solid Oxide Cell Electrodes by Patch Convolutional Neural Network

A framework for the automatic segmentation of large 3-D datasets of microscopic images is designed and tested for microstructures of solid oxide cells (SOC) electrodes reconstructed using focused ion beam—scanning electron microscopy (FIB-SEM). The developed algorithm utilizes a simple, yet very effective deep neural network based on the patch-convolutional layers (patch-CNN) in the encoder-decoder configuration. The guidelines for a selection of network architecture and for preparing and minimizing the training dataset are given. The proposed methodology is tested for various SOC electrode microstructures. The analyzed FIB-SEM tomography data have different resolutions, material properties and measurement artefacts. Pixel-based accuracies of the validation datasets are over 97.5%, and the 3-D microstructural parameters calculated from the ground-truth data and CNN-data show good agreement. The proposed automatic patch-CNN microstructure reconstruction shortens the image processing time by two orders of magnitude. The proposed method can achieve high accuracy which has not been reported in previous studies. In addition, the proposed CNN can serve as an artificial pore-infiltration technique that will help to reconstruct samples without epoxy resin infiltration. The developed framework can be easily extended for other multiphase porous materials with different 3-D imagining techniques that require image processing.

Comparison of segmentation algorithms for FIB-SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation

Focused-ion-beam/scanning-electron-microscopy (FIB-SEM) tomography has become increasingly important to investigate the three-dimensional (3D) distribution of different phases in inhomogeneous materials. However, quantitative analysis of 3D structures by FIB-SEM tomography requires adequate image segmentation that is typically based on thresholds in the intensity histograms of SEM images. Recently machine learning (ML) segmentation algorithms have emerged with the opportunity to tune the algorithm based on prior knowledge of SEM image contrast. In this work we have performed FIB-SEM tomography of 3D printed nanoporous polymer structures with a special focus on SEM image segmentation and pore size determination. By applying Monte Carlo (MC) simulations, SEM image contrast of the pore/polymer interface was analysed. The performance of several traditional segmentation algorithms on simulated SEM images was found to lead to erroneous results on pore sizes. Training the ML segmentation algorithm by the knowledge obtained from MC simulations yields more reliable segmentation results. Evaluated pore sizes and analysis of further 3D material properties show a strong dependence on the segmentation method, which emphasize the importance of the segmentation process in the 3D reconstruction of FIB-SEM data.

Optimization of FIB-SEM Tomography and Reconstruction for Soft, Porous, and Poorly Conducting Materials.

Tomography using a focused ion beam (FIB) combined with a scanning electron microscope (SEM) is well-established for a wide range of conducting materials. However, performing FIB–SEM tomography on ion- and electron-beam-sensitive materials as well as poorly conducting soft materials remains challenging. Some common challenges include cross-sectioning artifacts, shadowing effects, and charging. Fully dense materials provide a planar cross section, whereas pores also expose subsurface areas of the planar cross-section surface. The image intensity of the subsurface areas gives rise to overlap between the grayscale intensity levels of the solid and pore areas, which complicates image processing and segmentation for three-dimensional (3D) reconstruction. To avoid the introduction of artifacts, the goal is to examine porous and poorly conducting soft materials as close as possible to their original state. This work presents a protocol for the optimization of FIB–SEM tomography parameters for porous and poorly conducting soft materials. The protocol reduces cross-sectioning artifacts, charging, and eliminates shadowing effects. In addition, it handles the subsurface and grayscale intensity overlap problems in image segmentation. The protocol was evaluated on porous polymer films which have both poor conductivity and pores. 3D reconstructions, with automated data segmentation, from three films with different porosities were successfully obtained.

FIB-SEM Three-Dimensional Tomography for Characterization of Carbon-Based Materials

A review on the recent advances of the three-dimensional (3D) characterization of carbon-based materials was conducted by focused ion beam-scanning electron microscope (FIB-SEM) tomography. Current studies and further potential applications of the FIB-SEM 3D tomography technique for carbon-based materials were discussed. The goal of this paper is to highlight the advances of FIB-SEM 3D reconstruction to reveal the high and accurate resolution of internal structures of carbon-based materials and provide suggestions for the adoption and improvement of the FIB-SEM tomography system for a broad carbon-based research to achieve the best examination performances and enhance the development of innovative carbon-based materials.

Site specific, high-resolution characterisation of porosity in graphite using FIB-SEM tomography

Graphite is used as a moderator of fast neutrons in some types of nuclear reactors and for other industrial applications. The influence of smaller pores on the mechanical and physical properties of graphite remains to be fully understood. In this work, focused ion beam-scanning electron microscopy (FIB-SEM) tomography was applied to characterise the porosity of AGX graphite – an electrode material. FIB-SEM tomography consists in alternating the ion milling and SEM imaging at an area of interest with the objective of creating a 3D reconstruction. Regions containing a filler and a mixture of filler and binder were selected as the areas of interest. Resolutions of a few nanometers were achieved in volumes up to 1400 μm3 for both regions. Typical porous structures were detected in the filler and binder regions such as thermal cracks, gas evolution porosity and lenticular pores. The resolution achieved with these experiments made possible detection of pores smaller than 150 nm in diameter, that is of the length scale of voids generated by neutron irradiation, and an improvement in spatial resolution of traditional x-ray tomography studies. The resolution achieved by FIB-SEM tomography may be essential for the study of the microstructure of graphite, providing complimentary data to x-ray tomography experiments.

Comparative analysis of nanopore structure and its effect on methane adsorption capacity of Southern Junggar coalfield coals by gas adsorption and FIB-SEM tomography

Nano-scale porous structure has a significant influence on methane adsorption in coal seam. This work presents a comparative analysis of different experimental techniques for evaluating pore structure and the relationship between pore surface area and roughness with methane adsorption capacity. This sorption capacity subsequently affects the accuracy of total gas-in-place estimates and feasibility of CO2 injection for enhanced natural gas recovery. To evaluate methane adsorption characteristics of nanopores (<100 nm), various laboratory analyses are presented. Surface area, porosity, pore volume and fractal analysis of adsorption pores were determined in 13 coal samples (maximum vitrinite (huminite) reflectance <1.0%) using gas (CO2, N2 and CH4) adsorption and focused ion beam scanning electron microscopy (FIB-SEM) tomography. FIB-SEM images indicate a clustered distribution of multi-scale adsorption pores with similar orientation. The macropores connect some mesopores, which enhance gas flow and have a positive influence on coal permeability. The percent of connected pores compared to the number of total nanopores is ∼2% in selected samples, as calculated by FIB-SEM. Surface area and pore volume distribution determined from FIB-SEM are consistently higher than those obtained from N2 adsorption methods. In general, the FIB-SEM technique can detect both isolated and connected mesopores and macropores but cannot measure micropores. Surface area, pore volume, and porosity that estimates from N2 adsorption tests are prone to underestimate actual conditions. Langmuir volume (9.92–24.42 m3/t) are seemingly independent of maceral composition and coal ranks in this set of samples, having no direct correlations. Methane adsorption capacity increases with increasing the adsorption pore surface area and fractal dimensions and follow a moderate straight-line relationship. Hence, methane adsorption capacity is directly influenced by micropore surface area and micropore roughness. These results are significant for understanding the interaction of coal with gases.

Multi-scale quantitative characterization of 3-D pore-fracture networks in bituminous and anthracite coals using FIB-SEM tomography and X-ray μ-CT

To study the three-dimensional (3-D) pore-fracture networks of bituminous (BC) and anthracite (AC) coals, a combination of focused-ion beam-scanning electron microscopy (FIB-SEM) tomography and X-ray computed micro-tomography (X-ray μ-CT) was used to characterize 3-D pore-fracture characteristics at different scales. First, as observed in the SEM images, organic-matter (OM) pores, shrinkage-induced pores and dissolution pores are developed in BC samples with pore sizes ranging from ∼25nm to 600nm, and are independently distributed within OM and partial pores filled with minerals. In contrast, a large number of gas pores are widely developed in the AC sample with a cluster distribution in OM, and the pore widths range from ∼10nm to 2μm. Second, we reconstructed the 3-D pore-fracture networks of the BC and AC samples, and quantitatively characterized the porosity, 3-D pore-throat characteristics and its connectivity using the Pore Network Model (PNM) in Avizo. The results indicate that the pores in the BC sample are poorly connected and isolated from each other, whereas the pores in the AC sample are well connected by the throats. There is a logarithmic correlation between the cumulative throat volume and throat size, indicating that the smaller throats make the main contribution to the throat volume. Moreover, the normalized pore size distribution obtained from FIB-SEM and X-ray μ-CT analysis shows that both BC and AC samples exhibit a three-peak structure, but the nano-scale pores are more developed in the AC sample and its 3-D pore morphologies are more complex than those of the BC sample. Furthermore, the macro-pores and micro-fractures in the two coal samples show complex spatial distribution, interconnectivity and tortuosity. The total resolved porosity is 1.108% for BC samples consisting of 0.279% of FIB-SEM and 0.829% of X-ray μ-CT, and 6.082% for AC sample composed of 4.194% of FIB-SEM and 1.888% of X-ray μ-CT, including the connected and non-connected porosity. These results show that the pore-fracture networks of the AC sample are more conducive to CBM storage and flow ability than those of the BC sample. Therefore, this finding provides an accurate method of evaluating the physical properties of coal reservoirs and assists in understanding the mechanisms of CBM storage and transport.

Correlative tomography at the cathode/electrolyte interfaces of solid oxide fuel cells

This paper introduces a correlative tomography technique. It visualizes the spatial organization of primary and secondary phases at the interface of La0.58Sr0.4Co0.2Fe0.8O3-δ cathode/10 mol% Gadolinia doped Ceria/8 mol% Yttria stabilized Zirconia electrolyte. It uses focused ion beam/scanning electron microscope tomography (FIB/SEM), and combines data sets from Everhart-Thornley and Inlens detector differentiating four primary and two secondary material phases. In addition, grayscale information is correlated to elemental distribution gained by energy dispersive X-ray spectroscopy in a scanning transmission electron microscope. Interdiffusion of GDC into YSZ and SrZrO3 as secondary phases depend (in both amount and spatial organization) on the varied co-sintering temperature of the GDC/YSZ electrolyte. The ion-blocking SrZrO3 forms a continuous layer on top of the temperature-dependent GDC/YSZ interdiffusion zone (ID) at and below a co-sintering temperature of 1200 °C; above it becomes intermittent. 2D FIB/SEM images of primary and secondary phases at 1100, 1200, 1300 and 1400 °C were combined with a 3D FIB/SEM reconstruction (1300 °C). This reveals that “preferred” oxygen ion transport pathways from the LSCF cathode through GDC and the ID into the YSZ electrolyte only exist in samples sintered above 1200 °C. The applied correlative technique expands our understanding of this multiphase cathode/electrolyte interface region.

Evaluation of the Influence of Gadolinium Doped Ceria Particle Size on the Electrochemical Performance and Microstructure of Nickel-Gadolinium Doped Ceria Anodes

Composite anodes consisted of nickel-gadolinium doped ceria (Ni-GDC) cermet increasingly gain a lot of attention as they have potential to be a beneficial solution for the intermediate temperature solid oxide fuel cells. In this paper, the influence of the initial microstructure of Ni-GDC anode on the electrochemical performance and its deterioration in 100-h operation are investigated. The initial microstructure of investigated anodes is determined by the different aspect ratios and sizes of GDC powder used in the fabrication, but all anodes had the same initial chemical compositions of NiO:GDC=60:40 vol%. The microstructure of the fabricated anodes has been investigated by focused ion beam - scanning electron microscopy tomography (FIB-SEM) and quantitatively evaluated to deliver the factors expressing the microstructural characteristics, especially important ones for the electrochemical performance: Triple-Phase-Boundary (TPB) density, Double-Phase-Boundary (DPB) density and tortuosity factor. From the experimentation, contribution of the Ni-GDC microstructure to the anode electrochemical performance is discussed.