Development and full system testing of novel co-impregnated La0.20Sr0.25Ca0.45TiO3 anodes for commercial combined heat and power units

Funding: Engineering and Physical Sciences Research Council. Grant Numbers: EP/J016454/1, EP/P024807/1; HEXIS AG.Over the past decade, the University of St Andrews and HEXIS AG have engaged in a highly successful collaborative project aiming to develop and upscale La0.20Sr0.25Ca0.45TiO3 (LSCTA-) anode “backbone” microstructures, impregnated with Ce0.80Gd0.20O1.90 (CG20) and metallic electrocatalysts, providing direct benefits in terms of performance and stability over the current state-of-the-art (SoA) Ni-based cermet solid oxide fuel cell (SOFC) anodes. Here, we present a brief overview of previous work performed in this research project, including short-term, durability, and poison testing of small-scale (1 cm2 area) SOFCs and upscaling to full-sized HEXIS SOFCs (100 cm2 area) in short stacks. Subsequently, recent results from short stack testing of SOFCs containing LSCTA- anodes with a variety of metallic catalyst components (Fe, Mn, Ni, Pd, Pt, Rh, or Ru) will be presented, indicating that only SOFCs containing the Rh catalyst provide comparable degradation rates to the SoA Ni/cerium gadolinium oxide anode, as well as tolerance to harsh overload conditions (which is not exhibited by SoA anodes). Finally, results from full system testing (60 cells within a 1.5 kW electrical power output HEXIS Leonardo FC40A micro-combined heat and power unit), will be outlined, demonstrating the robust and durable nature of these novel oxide electrodes, in addition to their potential for commercialization.Publisher PDFPeer reviewe

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

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

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)

Characterization-app : 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 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”

IG-MYC+ neoplasms with precursor B-cell phenotype are molecularly distinct from Burkitt lymphomas

The WHO Classification of Tumours of Haematopoietic and Lymphoid Tissue notes instances of Burkitt lymphoma/leukemia (BL) with IG-MYC rearrangement displaying a B-cell precursor immunophenotype (termed herein preBLL). To characterize the molecular pathogenesis of preBLL, we investigated 13 preBLL cases (including 1 cell line), of which 12 were analyzable using genome, exome, and targeted sequencing, imbalance mapping, and DNA methylation profiling. In 5 patients with reads across the IG-MYC breakpoint junctions, we found evidence that the translocation derived from an aberrant VDJ recombination, as is typical for IG translocations arising in B-cell precursors. Genomic changes like biallelic IGH translocations or VDJ rearrangements combined with translocation into the VDJ region on the second allele, potentially preventing expression of a productive immunoglobulin, were detected in 6 of 13 cases. We did not detect mutations in genes frequently altered in BL, but instead found activating NRAS and/or KRAS mutations in 7 of 12 preBLLs. Gains on 1q, recurrent in BL and preB lymphoblastic leukemia/lymphoma (pB-ALL/LBL), were detected in 7 of 12 preBLLs. DNA methylation profiling showed preBLL to cluster with precursor B cells and pB-ALL/LBL, but apart from BL. We conclude that preBLL genetically and epigenetically resembles pB-ALL/LBL rather than BL. Therefore, we propose that preBLL be considered as a pB-ALL/LBL with recurrent genetic abnormalities