Digital materials design of solid oxide fuel cell anodes

This PhD-Thesis was presented to the Faculty of Science and Medicine of the University of Fribourg (Switzerland) in consideration for the award of the academic grade of Doctor of Philosophy in Physics (Thesis No: 5369). The doctorate was mainly pursued at the Institute of Computational Physics ICP at Zurich University of Applied Sciences ZHAW in Winterthur, Switzerland PhD Presentation: https://zhaw.mediaspace.cast.switch.ch/mediashare/d430305eb29a01af/media/t/0_8py1hjfn GeoDict User Meeting 2021 Presentation: https://www.youtube.com/watch?v=AIROVKq5yocThe storage and efficient conversion of energy is one of the key issues for a successful transition to renewable energies. Solid oxide cell (SOC) technology is a promising solution for the conversion of electrical energy to storable chemical energy (power-to-gas) in the solid oxide electrolysis cell (SOEC) mode, and for the on-demand supply of electrical energy using synthetic gas or biogas (or natural gas) as input in the solid oxide fuel cell (SOFC) mode. To significantly improve on the unavoidable degradation of state-of-the-art anodes like Ni-YSZ, we elaborate on new nickel-free electrode concepts, which are based on mixed ionic and electronic conductors (MIEC) like doped ceria and perovskite (e.g., titanate) materials. However, the anode performance is governed by complex physico-chemical processes including transport of gas in the pores, transport of ions and electrons in both solid phases and fuel oxidation reaction on the surface of the MIECs, which are not yet fully understood. Hence, there are numerous conflicting requirements and lack of knowledge complicating the development and optimization process. These challenges are addressed in this thesis in two ways. First, a Digital Materials Design (DMD) framework for the systematic and model-based optimization of MIEC SOC-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 basis for the DMD process is a set of fabricated solid oxide cells. Their real microstructures are reconstructed using FIB-SEM tomography. Stochastic digital microstructure twins with matching microstructure properties are then constructed for each real structure using a pluri-Gaussian method. On that basis, the microstructure can be virtually varied for a large parameter space in a realistic way. The real and subsequently the virtual 3D structures need to be characterized quantitatively by means of image analysis and numerical simulations. Hence, a standardized and automated microstructure characterization has been developed, which enables the fast determination of an extensive set of microstructure properties relevant for SOC electrodes. Moreover, specific microstructure properties like the ‘composite conductivity’ crucial for novel composite MIEC electrodes are introduced and discussed. A multiphysics continuum simulation model is then used to predict the impact of the microstructure variation on the electrode performance, using the previously determined microstructure properties as an input. In addition, the kinetic reaction parameters of the model are calibrated to the experimental performance characterizations of the 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. Due to the integration of stochastic modeling (pluri-Gaussian method) and its combination with automated characterization and model-based performance prediction, the number of the involved 3D microstructures can be significantly increased. This approach is thus capable to explore a much larger design space than it would be possible with experimental methods only. On this basis, design guidelines for the fabrication of electrodes with improved performances can be provided, which closes the loop of this iterative workflow. This DMD workflow is made available for the research community by the release of two software apps for the standardized microstructure characterization and stochastic microstructure modeling for SOC electrodes. Detailed information on these methodologies is also provided by the corresponding publications. Second, this DMD workflow is applied for the optimization of titanate based LSCT-CGO SOFC-anodes with a noble metal catalyst impregnation. Based on the performance and microstructure characterization of fabricated cells, several DMD studies are performed. Thereof, design guidelines for titanate-CGO anodes are provided including different microstructure design concepts and parameter specifications like appropriate material compositions and porosity. Moreover, the new opportunities as well as the current limitations of these nickel-free electrodes are discussed in great detail

Shear-induced migration in colloidal suspensions

Using Brownian dynamics simulations, we perform a systematic investigation of the shear-induced migration of colloidal particles subject to Poiseuille flow in both cylindrical and planar geometry. We find that adding an attractive component to the interparticle interaction enhances the migration effect, consistent with recent simulation studies of platelet suspensions. Monodisperse, bidisperse and polydisperse systems are studied over a range of shear-rates, considering both steady-states and the transient dynamics arising from the onset of flow. For bidisperse and polydisperse systems, size segregation is observed

Towards model-based optimization of CGO/Ni 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 model-based approach to link bulk material properties, fabrication parameters, microstructure effects and operating conditions with the cell performance on button cell level. Moreover, the model can be extended to different scales like thin film electrodes, used for fundamental material characterization, as well as to large area cells used for industrial devices with stack architecture. By using a stochastic model for virtual structure variation, also the influence of the microstructure can be assessed in a fully digital way (digital materials design). Hence, with the integration of detailed physicochemical properties over different scales into a single model framework, findings from basic and applied research can be directly used for the industrial development, enabling a systematic optimization of SOFC devices

Modelling the effects of using gas diffusion layers with patterned wettability for advanced water management in proton exchange membrane fuel cells

We present a macrohomogeneous two-phase model of a pro- ton exchange membrane fuel cell (PEFC). The model takes into account the mechanical compression of the gas diffusion layer (GDL), the two-phase flow of water, the transport of the gas species and the electrochemical reaction of the reactand gases. The model was used to simulate the behavior of a PEFC with a patterned GDL. The results of the reduced model, which considers only the mechanical compression and the two-phase flow, are compared to the experimental ex-situ imbibition data obtained by neutron radiography imaging. The results are in good agreement. Additionally, by using all the model features, a simulation of an operating fuel cell has been performed to study the intricate couplings in an operating fuel cell and to examine the patterned GDL effects. The model confirms that the patterned GDL design liberates the pre-defined domains from liquid water and thus locally increases the oxygen diffusivity.

Modeling the impedance response and steady state behaviour of porous CGO-based MIEC anodes

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

Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method

Zugehörige Dateien: https://zenodo.org/records/7744110 https://doi.org/10.1039/D3YA00132F https://doi.org/10.21256/zhaw-28430Digital 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

Standardized microstructure characterization of SOC electrodes as a key element for Digital Materials Design

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

Composite conductivity of MIEC-based SOFC anodes : implications for microstructure optimization

Fully ceramic anodes such as LSTN-CGO offer some specific advantages compared to conventional cermet anodes. Ceria- and titanate-based phases are both mixed ionic and electronic conductors (MIEC), which leads to very different reaction mechanisms and associated requirements for the microstructure design compared to e.g. Ni-YSZ. Due to the MIEC-property of both solid phases, the transports of neither the electrons nor the oxygen ions are limited to a single phase. As a consequence, composite MIEC electrodes reveal a remarkable property that can be described as ‘composite conductivity’ (for electrons as well as for ions), which is much higher than the (hypothetical) single phase conductivities of the same microstructure. In composite MIEC anodes, the charge carriers can reach the reaction sites even when the volume fraction of one MIEC phase is below the percolation threshold, because the missing contiguity is automatically bridged by the second MIEC phase. The MIEC properties thus open a much larger design space for microstructure optimization of composite electrodes. In this contribution, the composite conductivities of MIEC-based anodes are systematically investigated based on virtual materials testing and stochastic modeling. For this purpose, a large number of 3D microstructures, representing systematic compositional variations of composite anodes, is created by microstructure modeling. The underlying stochastic model is fitted to experimental data from FIB-SEM tomography. For the fitting of the stochastic model, digital twins of the tomography data are created using the methodology of gaussian random fields. By interpolation between and beyond the digital twin compositions, the stochastic model then allows to create numerous virtual 3D microstructures with different compositions, but with realistic properties. The effect of microstructure variation on the composite conductivity is then determined with transport simulations for each 3D microstructure. Furthermore, the corresponding microstructure effects on the cell-performance are determined with a Multiphysics model that describes the anode reaction mechanism. Especially the impact of the composite conductivities on the cell performance is studied in detail. Finally, microstructure design regions are discussed and compared for three different anode materials systems: titanate-CGO (with composite conductivities), Ni-YSZ (with single-phase conductivities), Ni-CGO (with single-phase ionic and composite electronic conductivities)

Optimization of MIEC-based SOFC anodes by digital microstructure design (DMD)

Reference: 1. P. Marmet et al., Phys. Chem. Chem. Phys., 2021, 23(40), 23042–23074, doi: 10.1039/d1cp01962g.Fully ceramic anodes such as LST-CGO offer some specific advantages compared to conventional Ni-based cermets. Ceria- and titanate-based phases are both mixed ionic and electronic conductors (MIEC), which leads to very different reaction mechanisms and associated requirements for the microstructure design compared to e.g. Ni-YSZ. In MIEC anodes, the reaction mainly takes place on the twophase boundaries of ceria (instead of the three-phase boundaries). Due to the MIEC-property of both solid phases, the transports of neither the electrons nor the oxygen ions are limited to a single phase. This leads to an effective composite conductivity (for electrons as well as for ions), which is much higher than the (hypothetical) single phase conductivity. In such a system, the charge carriers can reach the reaction sites even when the phase volume fraction(s) is/are below the percolation threshold. In this contribution, methodologies for the digital materials design (DMD) are presented to investigate the much larger design space opening for composite MIEC electrodes. Stochastic digital twins representing the 3D microstructure are constructed based on Gaussian random fields for real structures obtained from 3D-tomography. Based on stochastic parameters of digital twins, a large variation of virtual 3D microstructures is then realized using massive simultaneous cloud computing (MSCC) with GeoDict software. All relevant microstructure characteristics are determined by image analysis and/or transport simulation (e.g. the relative ionic composite conductivity and the specific pore-CGO interface area). The effect of the microstructure properties on the cell-performance is then determined with a suitable multiphysics model (see Marmet et al.1). This combination of DMDmethodologies (3D imaging and image analysis, stochastic modelling, numerical simulation) allows for a systematic and data driven optimization to provide robust design guidelines for MIEC-based anodes