Search

In the present study, Ni-M (M = Fe, Cu) bimetallic fuel electrodes are applied to investigate the effects of transition metal elements on nickel (Ni) migration and Ni coarsening under SOFC and SOEC operations. Ni-Fe, Ni-Cu and pure Ni patterned fuel electrodes are sputtered on YSZ pellets. The electrochemical performance of these Ni-M bimetallic fuel electrodes are lower than pure Ni fuel electrode, while the degradation rate of Ni-Fe fuel electrodes is smaller than the others. The spreading of Ni film on YSZ surface is observed for all samples under anodic polarization, and such Ni migration is suppressed by Fe doping, whereas it was enhanced by Cu doping. The adhesion between the electrode/electrolyte interface is weakened for Ni-Cu and pure Ni fuel electrodes under cathodic polarization, while good adhesion at the interface is maintained for Ni-Fe, which correlates with the smaller performance degradation rate.

Gadolinium doped ceria (GDC) is a promising material for solid oxide cells. GDC is conventionally used as an ionic conductor in composites with electron-conducting metal or perovskite materials. However, recent studies demonstrated that pure GDC fuel electrodes can achieve substantial power density if their microstructure is appropriately designed. Four different types of GDC electrodes are investigated in this study, including conventional Nickel (Ni)-GDC composite, GDC-perovskite composite, GDC deposited with Ni nanoparticles and pure GDC. The test results show a clear correlation between GDC surface area and electrode performance. At the same time, the electrode active thickness depends on the electrode microstructure. It is in the range of 5 µm for the nano-size GDC electrodes. When the GDC electrode is thicker than the active thickness, it results in large ohmic resistance due to the electronic conduction in the outer layer.

The optimal anode mass fraction of La0.9Sr0.1Cr0.5Mn0.5O3-δ (LSCM) and Gd0.1Ce0.9O2-δ (GDC) is evaluated in this study. The anodes with GDC share of 30 – 100 wt.% are investigated. Initial polarization resistance decreased as the GDC share increased. However, anodes with GDC share over 80 wt.% significantly deteriorated in the degradation tests. Nano-scale cracks were observed in the GDC phase at the grain boundaries after the test. These nano-cracks were not observed in composite anodes, from which it is implied that LSCM has stabilization effect on GDC structure. The mass fraction of LSCM : GDC = 30 : 70 wt.% is found to be optimal in terms of initial electrochemical performance and stability. The optimal LSCM-GDC shows lower polarization resistance than conventional Ni-YSZ at low temperatures, which is comparable to Ni-GDC anode.

In this paper, we propose a framework for studying the linkage between microstructure and performance of Ni-YSZ anode in solid oxide fuel cells (SOFCs) by using meso-scale modeling and deep learning. The discrete element method (DEM) and the mesoscale kinetic Monte Carlo (KMC) method were respectively used to model the particle packing and sintering of composite Ni-YSZ anodes. Anodes with various microstructures were numerically synthesized by varying volume fraction, sizes and size distributions of solid phases and pores. Then, representative volume elements (RVEs) of anode microstructures were meshed for finite element modeling of the effective elastic modulus and thermal coefficient of expansion of the anode based on the homogenization theory. The triple phase boundary length density ( L TPB), tortuosity factor for each phase were also calculated based on RVEs. Based on a large dataset which consists of various anode microstructures (represented as ternary image stacks) and corresponding macroscopic properties, a deep convolutional neural network (CNN) was trained to capture the nonlinear functional relationship between anode microstructures and macroscopic properties. The deep learning CNN model was finally well validated against extra new samples with excellent predictive performances. This indicates that the CNN model can be used as a surrogate model for rapid direct access to macroscopic properties of composite anodes, if the microstructure of the anode is known. The macroscopic properties can be further used for continuum modeling of SOFCs. This framework is highly extendable for other single-phase or composite electrodes of SOFCs. In addition, the proposed framework provides a new ideal for multiscale and multiphysics modeling of SOFCs.

The correlation between microstructure and performance of La0.75Sr0.25Cr0.5Mn0.5O3 - Gd0.2Ce0.8Ox (LSCM-GDC) solid oxide fuel cell (SOFC) anode is investigated. The fuel electrode is fabricated with the nano-composite powder prepared through colloidal-processing-based approach. The well dispersed nano-powder results in the limited particle coarsening during sintering and fine electrode microstructure. The influences of sintering temperature and electrode thickness on polarization resistance are investigated. The lowered sintering temperature results in particle size below 100 nm, which improves electrochemical performance. On the other hand, porosity over 57% inhibits percolation of the solid phases, in particular the LSCM. The performance of thinner composite electrode is superior, which is in good agreement with the deteriorated LSCM and GDC percolations. The electrochemical performance of the optimized electrode is competitive to the conventional Ni-YSZ, Ni-GDC, and LSCM-GDC anodes fabricated with submicron powders.

Understanding and modelling complex phenomenon in solid oxide fuel cell electrodes require information about their three dimensional microstructures. However, measuring the 3-D micro- and nano-structures requires complex and time-consuming data acquisition technique such as focused ion beam - scanning electron (FIB-SEM) tomography. Meanwhile, obtaining a single 2-D cross-sectional SEM image is relatively simple. A new algorithm for synthetic 3-D microstructure generation from 2-D cross-section is proposed in the present study. The algorithm is based on the generative adversarial network (GAN) composed of a 3-D generator and a 2-D discriminator, which uses only 2-D images as a training data. The applicability of the proposed method is tested on nickel (Ni) – gadolinium doped ceria (GDC) anodes. The synthetic 3-D microstructures are compared with those obtained from FIB-SEM. The proposed method can be easily adapted for any type of materials and imagining techniques.

Electrochemical reaction mechanism of an LSM-YSZ composite cathode is investigated using a combined measurement of oxygen labeling experiment and 3D microstructural reconstruction. First, oxygen isotope gas 18O2 was exposed to the LSM-YSZ cathode at 750 °C and a current density of 0.052 A/cm2. After 2 minutes of 18O2 supply, the gas was switched to water-cooled He to quench the cell and fix the incorporated isotope at the operating condition. After the oxygen labeling experiment, the cell piece was filled with an epoxy resin and polished to prepare the sample. Three-dimensional microstructure was obtained using a focused ion beam-scanning electron microscopy (FIB-SEM). Furthermore, between the measurement of the FIB-SEM, secondary ion mass spectroscopy (SIMS) analysis was conducted at the middle plane of the observed volume to correlate 18O distribution with the 3D microstructure. Based on percolation analysis of the microstructure, several isolated LSMs showed quite low 18O concentration, whereas some isolated LSMs showed 18O incorporation. For a further understanding of the correlation between the 18O distribution and microstructure, 3D numerical simulation using the reconstructed microstructure was carried out. First, potential distribution was obtained by solving gas diffusion and charge transportations which are coupled with the reaction at the reaction sites using the lattice Boltzmann method. Then, 18O distribution was predicted by solving oxygen diffusion along the calculated potential field. The electrochemical reaction mechanism was discussed by comparing the results of numerical simulation and experiment.

During high-temperature operation of a solid oxide fuel cell, the stresses caused by mismatch of thermal expansion coefficients between different materials and external mechanical loads may cause the rising of damage risk of nickel-yttria-stabilized zirconia anode. It is quite difficult to quantify the mechanical characteristics of a composite anode without investigating on the stress distribution in its real microstructure. However, the high operating temperature and extremely complex microstructure in micro-scale determine the high difficulty in in-situ measurement of thermo-mechanical stress distribution. In this work, the microstructures of six different anode samples, fabricated by using identical nickel oxide-yttria-stabilized zirconia powder mixture, are reconstructed in three-dimension based on the dual-beam focused-ion-beam-scanning-electron-microscopy. The three-dimensional thermo-mechanical stress distributions of different microstructures are conducted at operating temperature based on the finite element method. The effects of both thermal expansion coefficients mismatch between nickel and yttria-stabilized zirconia and external mechanical loads are analyzed. The mechanical failure probabilities of yttria-stabilized zirconia phase in different reconstructions are estimated based on the obtained stress distributions to investigate the influence of microstructure characterizations on nickel-yttria-stabilized zirconia anode strength.

Porous media play important roles in a wide range of scientific and engineering problems. Recently, with their increasing application in energy conversion and storage devices, such as fuel cells, batteries and supercapacitors, it has been realized that transport processes and reactions occurring in the pores and at the interfaces of different constituents significantly affect the performance of the porous media, yet these pore-scale transport phenomena are not well described or even neglected in the conventional numerical models based on the representative element volume (REV). Pore-scale modeling is an efficient tool for the simulation of pore-scale transport and reactions in porous media because of its ability to accurately characterize these processes and to provide the distribution details of important variables which are challenging for current experimental techniques to provide either due to lack of in-situ measurement capability or due to the limited spatial and temporal resolution. In the present review, the advances and challenges of the state-of-the-art pore-scale modeling are summarized. The practical applications of pore-scale modeling in the fields of geoscience, polymer exchange membrane fuel cells (PEMFC) and solid oxide fuel cells (SOFC) are discussed. Notable results from the pore-scale modeling are presented, and the challenges facing the pore-scale model development are discussed. This in-depth review is intended to give a well-rounded introduction of critical aspects on which the pore-scale modeling can shed light in the development of relevant scientific and engineering systems. ISSN:0360-1285 ISSN:0360-3202

A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttria-stabilized zirconia anodes in solid oxide fuel cells (SOFCs) to their effective macroscopic properties. A variety of microstructures are generated by the discrete element method and the meso‑scale kinetic Monte Carlo method. Then, the finite element method and the homogenization theory are used to calculate the effective elastic modulus (E), Poisson's ratio (υ), shear modulus (G) and coefficient of thermal expansion (CTE) of representative volume elements. In addition, the triple-phase boundary length density (LTPB) is also calculated. The convolutional neural network (CNN) based deep learning model is trained to find the potential relationship between the microstructures and the five effective macroscopic properties. The comparison between the ground truth and the predicted values of the new samples proves that the CNN model has an excellent predictive performance. This indicates that the CNN model could be used as an effective alternative to numerical simulations and homogenization because of its accurate and rapid prediction performance. Hence the deep learning-based homogenization framework could potentially accelerate the continuum modeling of SOFCs for microstructure optimization.

La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) cathodes on rigid Gd0.1Ce0.9O1.95 (GDC) substrate were sintered to different densities, and their three dimensional (3D) microstructures were characterized using focused ion beam-scanning electronic microscopy (FIB-SEM) tomography. The microstructure anisotropies of the green and constrainedly sintered cathodes were studied using 3D mean intercept length (MIL) method and the two dimensional (2D) best-fit ellipse method and 3D best-fit ellipsoid method. It shows that in the constrained sintering of LSCF films, the microstructures were transversely isotropic in the plane parallel to the substrate, while the microstructures in the thickness direction were anisotropic. Density and grain size gradients were observed and quantified along the cathode thickness direction during constrained sintering. The pores were preferentially oriented and elongated in the thickness direction. The anisotropy factor increased as increasing the sintering time or sintering temperature in the current density range. Cross-sectional 2D measurements underestimated the pore anisotropy, but showed qualitative agreement with 3D measurements.

A new SOFC electrode characterization technique combining active sites imaging by oxygen isotope labeling and three-dimensional (3D) microstructural observation by focused-ion beam-scanning electron microscopy (FIB-SEM) is carried out. A YSZ electrolyte-supported cell with Ni/YSZ anode and LSM/YSZ cathode was operated at 700℃ supplied with 18O2 under a constant current, then the cell was quenched by water-cooled helium gas impinging jet. The 3D microstructure of the quenched cathode was obtained by orthogonally-aligned FIB-SEM. During the FIB-SEM observation, 18O concentration on the milled surface was measured by secondary ion mass spectroscopy (SIMS). As a result, 3D-microstructure of the LSM/YSZ cathode with a 2D-18O concentration mapping in the intermediate plane of the characterized volume was obtained. Finally, the relationship between 18O concentration distribution and 3D-microstructure of the cathode was discussed.

In the present study, the numerical model is developed to optimize the solid oxide fuel cell (SOFC) electrode-electrolyte interface structure. The computational domain consists of La0.6Sr0.4Co0.2Fe0.8O3 (LSCF) cathode and Gd0.1Ce0.9O1.95 (GDC) electrolyte regions. In the electrode region, electrode materials and pore phases are assumed to be homogeneously distributed. Adjoint method is applied for optimization, and the interface structure is transformed until the total amount of reaction current in the whole computational domain is maximized. It is found from the computational results that the cathode with optimized electrode-electrolyte interface structure has better electrochemical performance than the microstructure with flat electrode-electrolyte interface. In addition, the optimal structures are specified for different electrolyte conductivities and electrode microstructural parameters.

Among the gas, ion, and electron diffusion processes in solid-oxide fuel cell (SOFC) electrodes, it is generally known that ionic conduction has the most impact on their electrochemical performance. Therefore, enhancement of the effective ionic conductivity of electrodes is a useful approach to reduce the overpotential. Yttria-stabilized zirconia (YSZ) pillars can be effective solutions to enhance the effective ionic conductivity of SOFC anodes. In this study, the influence of YSZ pillar structures on the electrochemical performance of SOFC anodes was evaluated by numerical simulation and experiments. First, to reveal the electrochemical reaction kinetics of anodes with pillar structures, a three-dimensional electrochemical simulation was conducted by the lattice Boltzmann method. The microstructure without pillars obtained by a focused ion beam scanning electron microscopy (FIB-SEM) measurement was used as the reference structure. Then, the original structure was replaced with YSZ phase to obtain virtual microstructures with YSZ pillars. With YSZ pillars, predicted area specific resistance became smaller than that of the reference structure, in spite of decrease in percolated TPB density. The electrochemical potential distribution of oxide ion and charge-transfer currents clearly show increase in the effective ionic conductivity. Relationships between overpotential and pillar geometries were parametrically discussed. Then, electrochemical performance of Ni-YSZ anode with the YSZ pillar structure formed by modifying the YSZ electrolyte surface was evaluated. By sputtering Ni-YSZ on pillar structures, stable electrochemical performance was obtained.

In the current study, electrochemical performances of La0.6Sr0.4Co0.2Fe0.8O3-Gd0.1Ce0.9O1.95 (LSCF-GDC) composite cathode microstructures are numerically simulated in order to clarify the effects of GDC pillar which is introduced to enhance the effective ionic conductivity of the solid oxide fuel cell (SOFC) cathode. The numerical simulation is carried out based on the three-dimensional microstructure reconstructed with focused ion beam scanning electron microscopy (FIB-SEM). In order to further investigate the characteristics of the GDC pillars, we examine the electrochemical effects of GDC pillars inside the pure LSCF and LSCF-GDC composite cathode microstructures with different particle sizes. According to the simulation results, the GDC pillars effectively improve the performance of the pure LSCF cathode, and the improvements by the GDC pillars are more pronounced in the microstructures with small particle sizes.

Nickel-gadolinium doped ceria (Ni-GDC) composite anodes 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 polarization characteristics and its deterioration in 100h operation are investigated. The initial microstructures of the investigated anodes are determined by the different sizes and aspect ratios of the GDC powders, but all anodes having the same initial compositions of NiO:GDC = 60:40 vol.%. It was found that the anode with the smallest GDC particle had the best initial performance, but also underwent the most severe degradation. The microstructures of the fabricated anodes were investigated by focused ion beam - scanning electron microscopy (FIB-SEM), and microstructural parameters were quantified to relate the electrochemical performance. From the experimentation, contribution of the Ni-GDC microstructure to the anode polarization characteristics is discussed. The morphological evolution after the operation is also in discussion, especially those of the GDC phase. It was concluded that the mobility of GDC could be an influential factor determining the change in polarization resistance.

Recent progress of manufacturing techniques in the field of solid oxide fuel cells (SOFCs) enables fabrications of complex multi-sized gradient microstructures with the spatially varied properties. Methods using additive manufacturing, layer-by-layer deposition, tape casting, nano-imprint, pulse laser deposition and laser engraving, etc. provide possibilities to fabricate 3D structures with flexible design. Moreover, the spatially optimized structures can provide better mechanical and thermal properties, enhance diffusion and improve electrochemical reaction kinetics. In particular, multi-layered electrode designs are attracting increasing interest. The layered electrode design and optimization require effective methods to fabricate synthetic microstructures with property gradients. The generative adversarial network (GAN) proves its usabilities in fabricating artificial microstructures belonging to the same class as the training data [1] and reconstructing 3D structures from 2D image [2], etc. The applications primarily focus on the structures with uniform spatial properties identical to the training sample. The Wasserstein GAN was recently applied for the fast inverse design of two-phase homogenous microstructures with user-defined properties [3]. In this study, an approach using conditional GAN (C-GAN) is proposed to flexibly generate multi-phase structures with predefined gradients. The proposed method is tested on the microstructures of porous nickel-gadolinium doped ceria (Ni-GDC) SOFC anodes. The C-GAN training dataset consists of real Ni-GDC microstructures with varied Ni and GDC compositions (GDC share in the range of 30 – 70 vol%) and with varied porosity [4]. In total, 10 different samples were prepared and sintered at 1350 oC in air atmosphere and further reduced in the H2 flow at 800 oC. The fabricated electrodes had significantly different microstructural properties and electrochemical performance. The samples were infiltrated with epoxy resin to enable clear phase recognition, polished to expose cross-section and characterized by the scanning electron microscope (SEM). The SEM images were segmented prior to the C-GAN training. The C-GAN was trained with patches of 256 x 256 pixels which were randomly extracted from SEM images of each sample. The sample with Ni : GDC = 50 : 50 vol% was excluded from the training data and used only for testing. The generator network input consists of 4 variables: random noise vector, Ni share in composite, porosity and particle size (Fig. 1A). The particle size is controlled by the magnification of the training images. The Ni volume fraction and porosity are calculated separately for each patch during the iterative training process. The primary version of the trained C-GAN network generates homogenous artificial microstructures with the patch size of 256 x 256 pixels identical to the training data. The fabricated microstructures (Fig. 1B) show excellent visual and good statistical agreements with the real samples. It is possible to reproduce not only the volumetric fractions, but also the surface area density and triple phase boundary density of the real Ni-GDC electrodes (Fig. 1C). The C-GAN can fabricate not only the microstructures belonging to the training dataset, but also samples with other compositions. Although the C-GAN generator was trained based on patches with 256 × 256 pixels, it can be used for the fabrication of larger structures by increasing the size of the generator input. By adjusting the generator input matrix consisting of the Ni share, porosity and particle size, various microstructure patterns can be fabricated including linear gradients (Fig. 1D) and layered structures (Fig. 1E). Those structures have a significance for the SOFC anodes design as layered designs are conventionally incorporated in SOFC electrodes, e.g. support and active anode layers. In addition, it was shown that the graded structures have superior performance compared with the isotropic anodes [5]. The proposed framework provides a convenient tool for generating realistic microstructures with wide range of predefined properties. Further, it can be coupled with the existing simulation tools to evaluate the wide range of graded and layered microstructural designs. [1] A. Gayon-Lombardo, L. Mosser, N. P. Brandon, and S. J. Cooper, npj Comput. Mater., 6, 1–11 (2020). [2] A. Sciazko, Y. Komatsu, and N. Shikazono, ECS Trans., 103, 1363–1373 (2021). [3] X. Lee, et al., Nature Computational Science, 1, 229, (2021). [4] Y. Komatsu, A. Sciazko, and N. Shikazono, J. Power Sources, 485, 229317 (2021). [5] Z. Yan, et al., ECS Trans., 91, 2055, (2019). Fig. 1. A) Schema of C-GAN network, B) synthetic homogenous microstructures fabricated by C-GAN, C) TPB dependence on the Ni and pore volume fractions, D) synthetic structure with predefined gradient and E) synthetic layered structure. Figure 1

The anisotropic microstructure develops during powder processing and constrained sintering. In the present study, the microstructural evolutions of Au submicron particles during free sintering and constrained sintering were investigated by FIB-SEM tomography. The decrease in total surface area and the coarsening, i.e. the increase of mean intercept length of solid phase, were observed during the densification process. The anisotropic packing structure of spherical particles induced by casting process was characterized by area weighted fabric tensor, which represented the bond orientation and the anisotropy in contact area. In free sintering, the initial anisotropy in packing structure decreased with densification as indicated by the decrease in deviatoric component of surface energy tensor. In constrained sintering, the initial anisotropy observed by the mean intercept length of pore was reversed in the later stage. The microstructure evolved so as to form the elongated pores along the thickness direction.