Microstructural characterization of solid oxide fuel cell electrodes by image analysis technique

The paper deals with the microstructural characterization of electrodes of solid oxide fuel cells based on processing of 2D images. The interest relies on finding the reliable description of the structures which determine the microscopic image, by means of parameters involving the morphology, the shape and the size of elementary structures, and the microscopic topology in terms of spatial connectivity functions. The use of detailed mathematical methods allowed one to reconstruct the 3D structure of both fuel and air electrodes having 2D images as input.The analysis was applied to an anode-supported cell with NiO based anode, 8YSZ electrolyte and LSM/YSZ cathode. The microscopic analysis was performed by means of both a SEM and an optical microscope before and after the electrical testing of the cell.The obtained images were processed and a quantitative analysis was performed for achieving information concerning the microstructure and including: phases’ fraction, grain size, granulometry law, constituent shape factors, phase spatial organization and descriptive functions. The microstructure features were analyzed by means of one-point and two-point statistics. It was possible to build 3D structures of the electrodes: anode and the double-layer cathode.The work also points out some issues related to the proper use of the observed microscopic parameters and topology functions as inputs for electrodes’ modelling. In the work, the results of the image analysis are used with a simple analytical model with the aim to estimate the optimal design of the cathode current functional layer (CFL). It is proved that the actual cell design with 15 μm thick CFL was optimized for a temperature as high as 850 °C, with a charge transfer and total ohmic resistance of around 0.2 Ω cm2. More generally, results of image analysis can be used efficiently as input in the multi-scale modelling of SOFC electrodes considering macroscopic and mesoscopic models.