A review of image processing and quantification analysis for solid oxide fuel cell

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-02-28 DOI:10.1016/j.egyai.2024.100354
Kar Shen Tan , Chee Kiang Lam , Wee Choon Tan , Heap Sheng Ooi , Zi Hao Lim
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Abstract

The purpose of this study is to investigate the approaches applied to analyze solid oxide fuel cell (SOFC) microstructural properties. Both manual and automated image processing approaches applied on SOFC microstructural images which are obtained from several types of tomography such as dual-beam focused ion beam with scanning electron microscopy (FIB-SEM), Electron Backscatter Diffraction (EBSD) and others are discussed. In fact, to achieve a realistic and accurate SOFC microstructural properties, such as average diameter, volume fraction, triple phase boundary (TPB), area interface density and tortuosity factor, the approaches of image processing and quantification are crucial for a reliable image generation for quantification purposes. The microstructural properties are optimized to improve SOFC electrode performance. Therefore, the image processing and quantification approaches are outlined and reviewed. Despite the automated image processing and quantification algorithms significantly outperform manual image processing and quantification approaches in terms of computing speed when evaluating and measuring microstructural properties, the efficiency and productivity are still extremely taken into concern. As a result, image processing and quantification approaches are concluded and presented respectively in this paper.

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固体氧化物燃料电池图像处理和量化分析综述
本研究的目的是调查用于分析固体氧化物燃料电池(SOFC)微观结构特性的方法。研究讨论了手动和自动图像处理方法,这些方法适用于从多种层析成像技术(如双光束聚焦离子束扫描电子显微镜(FIB-SEM)、电子背散射衍射(EBSD)等)获得的 SOFC 微观结构图像。事实上,要获得真实准确的 SOFC 微结构特性,如平均直径、体积分数、三相边界(TPB)、面积界面密度和迂回因子,图像处理和量化方法对于生成可靠的量化图像至关重要。微结构特性的优化可提高 SOFC 电极的性能。因此,本文对图像处理和量化方法进行了概述和评述。尽管在评估和测量微观结构特性时,自动图像处理和量化算法在计算速度方面明显优于手动图像处理和量化方法,但效率和生产率仍是极为重要的考虑因素。因此,本文对图像处理和量化方法分别进行了总结和介绍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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