在SEM-EDX化学图中使用正矩阵因数分解进行相位识别
Xiangrui Kong1, Ivana Staničić2, Viktor Andersson1
1Department of Chemistry and Molecular Biology, Atmospheric Science, University of Gothenburg, Gothenburg SE-412 96, Sweden.
MethodsX
|October 12, 2023
概括
积极矩阵分解 (PMF) 有效地分析扫描电子显微镜-能量分散式X射线光谱 (SEM-EDX) 的元素图,以揭示固体材料中的化学组成和相互作用.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 数据分析 数据分析
背景情况:
- 扫描电子显微镜 (SEM) 与能量分散式X射线光谱 (EDX) 结合,提供了固体材料详细的元素组成.
- 了解元素的混合状态和相互作用对于材料的表征至关重要.
研究的目的:
- 适应和应用正矩阵因数分解 (PMF) 来分析由SEM-EDX生成的元素图.
- 展示PMF在识别常见元素特征和可视化它们的空间分布方面的能力.
主要方法:
- 将图形和数字SEM-EDX图像转换为PMF输入文件.
- 应用PMF,一种多变量因子分析技术,根据元素图中的常见发生情况将元素分组.
- 可视化PMF衍生因子图,以说明化学成分和相互作用.
主要成果:
- PMF成功地将元素分组成不同的因子,代表了共同的化学联系.
- 可视化因素图提供了关于灰相互作用和氧载体材料中不同化学层的组成的见解.
- 该方法在处理各种化学测绘数据,包括大型数据集方面表现出有效性.
结论:
- PMF是分析来自SEM-EDX等技术的复杂元素映射数据的宝贵工具.
- 适应的方法增强了对材料组成,混合状态和界面化学的理解.
- 这种方法具有多功能性,适用于SEM-EDX以外的各种化学测绘数据集.
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