An Image Processing Approach for Quantitative Microstructural Analysis of Solid Oxide Fuel Cell Anodes

Abstract

Solid oxide fuel cells are emerging as promising devices for electrochemical energy conversion because they offer high efficiency and fuel flexibility. However, their performance is highly dependent on complex microstructural features that are difficult to quantify accurately. This research presents an image processing pipeline designed for segmenting and quantifying the microstructures of solid oxide fuel cells. The pipeline incorporates tailored techniques such as preprocessing, segmentation and morphological quantification analysis to handle the complexity of multiphase structures. Despite these efforts, segmentation accuracy remains a challenge due to issues like intensity overlap between different phases, surface or texture imperfections, and unclear boundaries. Consequently, the quantification accuracy ranged from 83.22 % to 99.49 %. Although some variation exists among the quantification parameters like particle size, volume fraction, and area interfacial density, the overall strong performance demonstrates the capability of automating solid oxide fuel cell image analysis. This work establishes a foundational framework for future improvements with the integration of machine learning or deep learning techniques to enable more accurate and reproducible characterization of solid oxide fuel cells.