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Solid oxide fuel cell (SOFC) technology is a promising solution for the on-demand supply of electrical energy using synthetic gas or biogas (or natural gas) as input. To significantly improve on the unavoidable degradation of state-of-the-art anodes like Ni-YSZ, we elaborate on fully ceramic composite electrodes, which are based on mixed ionic and electronic conductors (MIEC) like doped ceria and perovskite (e.g., titanate) materials. To accelerate the development of these novel electrodes, a Digital Materials Design (DMD) framework for the systematic and model-based optimization of MIEC SOFC-electrodes is elaborated. In our DMD approach we combine stochastic microstructure modeling, virtual testing of 3D microstructures and a multiscale-multiphysics electrode model to explore the available design space by performing parametric studies. The multiphysics electrode model is thereby used to predict the impact of a virtual microstructure variation on the electrode performance. The model captures all the relevant physico-chemical processes involved like the transport of charge carriers in the two MIEC solid phases, transport of the gas species in the pore-phase (described by the dusty-gas model) and the reaction kinetics (calibrated to the experimental performance characterization of the cells). A special emphasize is laid to the appropriate description of the microstructure effects. Thereby, a 1D FEM continuum model implemented in Comsol Multiphysics is used to describe the electrode on a button cell level. The microstructure effects are then captured using effective transport and interface properties, determined from 3D microstructure data. The model results are validated on experimental performance characterizations of the button cells (e.g., EIS results). This model-based performance prediction enables to establish the relationship between materials choices and compositions, fabrication parameters, microstructure properties and cell-performance. This approach is thus

Zugehörige Dateien: https://zenodo.org/records/7744110 https://doi.org/10.1039/D3YA00132F https://doi.org/10.21256/zhaw-28430, Digital Materials Design (DMD) offers new possibilities for data-driven microstructure optimization of solid oxide cells (SOC). Despite the progress in 3D-imaging, experimental microstructure investigations are typically limited to only a few tomography analyses. In this publication, a DMD workflow is presented for extensive virtual microstructure variation, which is based on a limited number of real tomography analyses. Real 3D microstructures, which are captured with FIB-tomography from LSTN-CGO anodes, are used as a basis for stochastic modeling. Thereby, digital twins are constructed for each of the three real microstructures. The virtual structure generation is based on the pluri-Gaussian method (PGM). In order to match the properties of selected virtual microstructures (i.e., digital twins) with real structures, the construction parameters for the PGM-model are determined by interpolation of a database of virtual structures. Moreover, the relative conductivities of the phases are optimized with morphological operations. The digital twins are then used as anchor points for virtual microstructure variation of LSTN-CGO anodes, covering a wide range of compositions and porosities. All relevant microstructure properties are determined using our standardized and automated microstructure characterization procedure, which was recently published. The microstructure properties can then e.g., be used as input for a multiphysics electrode model to predict the corresponding anode performances. This set of microstructure properties with corresponding performances is then the basis to provide design guidelines for improved electrodes. The PGM-based structure generation is available as a new Python app for the GeoDict software package.

Carbon steel samples covered with initially crack-free zinc-nickel coatings were polarized with a small anodic overpotential in moderately alkaline NaCl solution. Along with zinc dissolution, the coatings developed a mud-crack pattern due to tensile stress release, allowing the electrolyte to access the underlying steel surface. Simonkolleite grew on both the zinc-nickel coating and the steel substrate. The resulting current density, that was first strongly anodic, switched to small cathodic values when the coating surface was almost fully covered by a compact simonkolleite layer.

Performance and durability of solid oxide cell (SOC) electrodes are closely linked to their microstructure properties. Thus, the comprehensive characterization of the 3D microstructures e.g., obtained by FIB-SEM tomography is essential for SOC electrode optimization. Recent advances and trends call for a standardized and automated microstructure characterization. Advances in FIB-SEM tomography enable the acquisition of more samples. In order to make reasonable comparisons of 3D microstructures from different sources and to make reliable statistical analyses, these structures need to be analyzed with standardized 3D image processing tools. In addition, the emerging methods for Digital Materials Design (DMD) enable to create numerous virtual but realistic microstructure variations using stochastic microstructure modeling. For such a DMD workflow, many virtual microstructures need to be characterized. Thus, the availability of a standardized, efficient and automated microstructure characterization tool is a crucial prerequisite for the data-driven optimization of energy materials. This dataset provides a standardized microstructure characterization tool for SOC electrodes, which is implemented as a Python app for the GeoDict software-package. A large number of microstructure characteristics can be determined with this app, which are relevant for the performance of conventional electrodes like Ni-YSZ and for more recent MIEC-based electrodes like Ni-CGO or titanate-CGO anodes. This dataset consists of the following files: - The SOC characterization app is described in detail in the file “1_Read_Me_SOC_Characterization_App.pdf” including a description of three examples (testcases with 3D structures). - The scripts for the characterization-app are provided in the folder “2_SOC_Characterization_App”. - Three characterization examples are provided in the folder “3_Characterization_Examples”.

Performance and durability of solid oxide cell (SOC) electrodes are closely linked to their microstructure properties. Thus, the comprehensive characterization of 3D microstructures e.g., obtained by FIB-SEM tomography is essential for SOC electrode optimization. Recent advances and trends call for a standardized and automated microstructure characterization. Advances in FIB-SEM tomography enable the acquisition of more samples, which are also more frequently shared within the research community due to evolving open science concepts. In addition, the emerging methods for Digital Materials Design (DMD) enable to create numerous virtual but realistic microstructure variations using stochastic microstructure modeling. In this publication, a standardized microstructure characterization tool for SOC electrodes is presented, which is implemented as a Python app for the GeoDict software-package. A large number of microstructure characteristics can be determined with this app, which are relevant for the performance of conventional electrodes like Ni-YSZ and for more recent MIEC-based electrodes. The long list of 3D characteristics that can be determined selectively includes morphological characteristics, interface properties and effective transport properties deduced from morphological predictions and from numerical simulations. The extensive possibilities of the standardized microstructure characterization tool are illustrated for a dataset of three LSTN-CGO anode microstructures reconstructed with FIB-SEM tomography and for a dataset of three virtual sphere-packing structures. The automated microstructure characterization is a key element to exploit the full potential of open science, Digital Materials Design (DMD) and artificial intelligence (AI) for the data-driven optimization of SOC electrodes by providing standardized high quality microstructure property data.

Digital Materials Design (DMD) offers new possibilities for data-driven microstructure optimization of solid oxide cells (SOC). Despite the progress in imaging technology, 3D-imaging still represents a bottleneck for the application of DMD. Experimental microstructure variation studies are typically limited to a few 3D datasets from tomography. In contrast, stochastic microstructure modeling allows to explore a much larger design space by performing parametric studies. Therefore, the availability of an appropriate virtual structure generator is a crucial prerequisite for realistic design studies. The stochastic microstructure modeling based on the pluri-Gaussian method (PGM) has proven to be well-suited for the virtual reconstruction of SOC electrodes. This dataset provides a Python app for the stochastic microstructure modeling of SOC electrodes in GeoDict based on a pluri-Gaussian method (PGM). The PGM-app allows for an efficient construction of virtual but realistic SOC microstructures consisting of three phases (two solid-phases and one pore-phase). This dataset consists of the following files: 1. The PGM-app is decribed in detail in the file "01_Read_Me_PGM_SOC_App_Zenodo.pdf". 2. The script of the PGM-app is provided in the file "02_PGM_SOC_App.zip".

This dataset contains 3D data acquired with FIB-tomography from porous Zr-oxide. The pore structures of the ceramic materials were investigated in context with optimization of transport properties (aimed for low viscous flow/permeability and high electric conduction via liquid electrolyte in the pores), so that the Zr-oxide is suitable for application as liquid junction (diaphragm) in pH-sensors. Further details on the scientific background are given in Holzer et al., 2016, Materials & Design, 99, 314–327. (http://doi.org/10.1016/j.matdes.2016.03.034). The tomographs represent 9 ceramic materials that were produced with different sintering temperatures (1250, 1300, 1325 and 1350°C) and different sintering times (1, 2 and 3 hours). The study includes quantitative descriptions of relevant microstructure characteristics (porosity, tortuosity, constrictivity, hydraulic radius) and corresponding effective transport prorperties (permeability, conductivity). Each tomograph is presented as stack of 2D-tiff-images in two different versions: as gray-scale images (raw data) and as binarized images (segmented, with solid=black and pores=white). The voxel resolution is 10x10x10 nm for all 18 image stacks.

Mixed ionic and electronic conducting (MIEC) materials recently gained much interest for use as anodes in solid oxide fuel cell (SOFC) applications. However, many processes in MIEC-based porous anodes are still poorly understood and the appropriate interpretation of corresponding electrochemical impedance spectroscopy (EIS) data is challenging. Therefore, a model which is capable to capture all relevant physico-chemical processes is a crucial prerequisite for systematic materials optimization. In this contribution we present a comprehensive model for MIEC-based anodes providing both the DC-behaviour and the EIS-spectra. The model enables one to distinguish between the impact of the chemical capacitance, the reaction resistance, the gas impedance and the charge transport resistance on the EIS-spectrum and therewith allows its appropriate interpretation for button cell conditions. Typical MIEC-features are studied with the model applied to gadolinium doped ceria (CGO) anodes with different microstructures. The results obtained for CGO anodes reveal the spatial distribution of the reaction zone and associated transport distances for the charge carriers and gas species. Moreover, parameter spaces for transport limited and surface reaction limited situations are depicted. By linking bulk material properties, microstructure effects and the cell design with the cell performance, we present a way towards a systematic materials optimization for MIEC-based anodes.

Gadolinium doped Ceria (CGO) is a promising material for SOFC anodes because of its mixed ionic electronic conductivity, its high catalytic activity for the hydrogen oxidation reaction (HOR) and its robustness against degradation. In SOFC research, electrochemical impedance spectroscopy (EIS) is an essential characterization tool, which serves as a basis for materials optimization on the electrode, cell and stack levels. However, for CGO based electrodes, there is no consensus how to interpret the impedance spectra yet. In the literature, especially the low frequency arc is often either depicted as gas impedance or as chemical capacitance process, without conclusive evidence. Further uncertainties in the interpretation of impedance spectra arise with respect to the operating conditions (especially pO2, pH2O) and to their impact on the HOR resistance. Hence, reliable interpretation of impedance spectra for SOFC with CGO-based anodes requires a detailed model, which captures a) the relevant physico-chemical processes, b) the associated material laws and c) the dependencies on varying operating conditions. In the present contribution, we present an approach for a systematic materials optimization for CGO-based anodes, including EIS measurements, microstructure analysis and finite element modelling with AC and DC mode. The model captures all previously mentioned effects and their impact on the performance of a CGO/Ni-based anode. The computational model is validated and calibrated with EIS-measurements and the impacts of the chemical capacitance and gas impedance on the EIS spectra are illustrated for button cell conditions. The calibrated model is exemplarily used to optimize the CGO/Ni layer thickness. DC results of the extension of the reaction zone are thereby used to understand the different resistive contributions (e.g. from electrochemical conversion, from transport of charge carriers or from gas diffusion) to the total anode impedance. In summary, we present a m

References: 1. A novel approach for analyzing electrochemical properties of mixed conducting solid oxide fuel cell anode materials by impedance spectroscopy, A. Nenning, A. K. Opitz, T. M. Huber, and J. Fleig, Phys. Chem. Chem. Phys., vol. 16, no. 40, pp. 22321–22336, 2014. 2. Modeling the impedance response of mixed-conducting thin film electrodes, C. Chen, D. Chen, W. C. Chueh, and F. Ciucci, Phys. Chem. Chem. Phys., vol. 16, no. 23, pp. 11573–11583, 2014., Gadolinium doped Ceria (CGO) is a promising material for SOFC anodes because of its mixed ionic electronic conductivity (MIEC), its high catalytic activity for the hydrogen oxida-tion reaction (HOR) and its robustness against degradation. However, many processes in CGO based anodes are still poorly understood and there is no consensus yet on how to inter-pret the corresponding electrochemical impedance spectroscopy (EIS) data. In the literature, especially the low frequency arc is often either depicted as gas impedance or as chemical capacitance process, without conclusive evidence. Moreover, with EIS, charge transport re-sistances in CGO are often not distinguishable from other overlapping processes. For dense thin film MIEC electrodes many sophisticated models are available using transmission line (e.g. [1]) and finite element method (FEM) models (e.g. [2]). In contrast, for porous high per-formance MIEC anodes there are hardly any modelling approaches published, which are ca-pable to capture the complex physico-chemical processes. This is, however, a crucial pre-requisite for a systematic materials optimization. In this contribution we present a model, which enables physical insight into the complex processes involved in a porous CGO based anode on the button cell level. The FEM model is implemented in 1D in the commercial software package Comsol Multiphysics. In this model, the full Nernst-Planck-Poisson equations are implemented for the transport of Ce3+-ions and oxygen ion vacancies. Steady state and linear perturbation simulations are performed in order to compute the DC-behaviour and the EIS-spectra, respectively. The model captures the spa-tial distribution of the reaction zone and associated transport pathways of the charge carriers. Thereby, parameter sets that result in transport limited and surface reaction limited cases are studied. Moreover, the model enables to distinguish the impact of chemical capacitance, HOR resistance and gas impedance on the EI