Abstract
Carbon deposition is a critical problem for operating solid oxide fuel cells (SOFCs) with carbon containing fuels such as natural gas, biogas, etc. When steam to carbon ratio is below the critical threshold, carbon may form on the surface of nickel (Ni) catalyst. Carbon covers the active triple boundaries and deteriorates the electrochemical performance. In most severe cases, carbon may even lead to an irreversible deformation of a porous cermet. However, quantitative investigations of carbon deposition have been limited due to the difficulties in observing the microstructures. In the present study, the 3D structures of carbon deposition in the SOFC electrode are quantitatively reconstructed for the first time by a machine learning assisted image processing framework. Focused ion beam - scanning electron microscope (FIB-SEM) measurements of anode samples are conducted without resin infiltration. A U-net neural network with multiple inputs is developed to identify the unfilled pores, from which phase segmentations of raw SEM images without resin filling became possible. The mild and severe carbon deposition conditions are tested by exposing the electrodes to humidified and dry methane conditions. It is revealed from the 3D reconstruction that carbon layer formed over Ni particles and Ni pulverizes due to high temperature corrosion (metal dusting). At the same time, YSZ network deformed by the internal stress. The observed microstructural changes are in good agreement with the performance recovery after the regeneration process. The developed microstructure evaluation framework will enable quantitative investigations of carbon deposition and developments of mitigation strategies for SOFC anodes.
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