Evaluation of Semi-Automatic Compositional and Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Image and Data Processing Framework for Fuel Cell Applications
Evaluation of Semi-Automatic Compositional and Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Image and Data Processing Framework for Fuel Cell Applications
复制标题
通过基于 Python 的燃料电池应用图像和数据处理框架评估能量色散光谱 (EDS) 图的半自动成分和微观结构分析
DOI：
10.1149/1945-7111/acd584
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发表时间：
2023
影响因子：
3.9
通讯作者：
Jankovic, Jasna
中科院分区：
文献类型：
--
作者：
Batool, Mariah;Godoy, Andres O.;Birnbach, Martin;Dekel, Dario R.;Jankovic, Jasna
计算机辅助数据采集、分析和解释在研究的许多方面迅速获得关注。该领域的子集之一，图像处理，最常用于后处理材料微观结构表征数据，以更好地理解和预测材料在多个尺度上的特征，属性和行为。然而，为了解决多组分材料分析的模糊性，光谱数据可以与图像处理结合使用。目前的研究引入了一种新的基于Python的图像和数据处理方法，用于深入分析能量色散光谱（EDS）元素图，以分析多组分团聚体的粒度分布，每个组分的平均面积及其重叠。在这项研究中开发的框架被施加到检查的氧化铈（CeO x）和钯（Pd）粒子的相互作用在膜电极组件（MEA）的阴离子交换膜燃料电池（AEMFC），并调查如果这种方法可以与电池性能。研究还进行了敏感性分析的几个参数和它们对计算结果的影响。所开发的框架是一种很有前途的半自动数据处理方法，可以进一步推进到清洁能源材料和更广泛领域的类似数据类型的全自动分析。
Computer-aided data acquisition, analysis, and interpretation are rapidly gaining traction in numerous facets of research. One of the subsets of this field, image processing, is most often implemented for post-processing material microstructural characterization data to understand better and predict materials' features, properties, and behaviors at multiple scales. However, to tackle the ambiguity of multi-component materials analysis, spectral data can be used in combination with image processing. The current study introduces a novel Python-based image and data processing method for in-depth analysis of energy dispersive spectroscopy (EDS) elemental maps to analyze multi-component agglomerate size distribution, the average area of each component, and their overlap. The framework developed in this study is applied to examine the interaction of Cerium Oxide (CeO x) and Palladium (Pd) particles in the membrane electrode assembly (MEA) of an Anion-Exchange Membrane Fuel Cell (AEMFC) and to investigate if this approach can be correlated to cell performance. The study also performs a sensitivity analysis of several parameters and their effect on the computed results. The developed framework is a promising method for semi-automatic data processing and can be further advanced towards a fully automatic analysis of similar data types in the field of clean energy materials and broader.