School of Biosciences, Cardiff University, Cardiff, UK.
School of Physics and Astronomy, Cardiff University, Cardiff, UK.
J Microsc. 2023 Feb;289(2):91-106. doi: 10.1111/jmi.13155. Epub 2022 Nov 30.
Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyse. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components (FSC ) for identifying distinct physical surface phases. Importantly, FSC is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The FSC concentrations are providing the features for a support vector machine-based supervised classification of the surface types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both example LEEM I-V data sets.
低能电子显微镜（LEEM）作为强度-电压（I-V）曲线，可以提供表面的高光谱图像，这些图像可用于识别表面类型，但难以进行分析。在这里，我们展示了一种用于将数据分解为特征成分的光谱和浓度的算法（FSC），以识别不同的物理表面相。重要的是，FSC 是一种无监督且快速的算法。作为示例数据，我们使用了在钕氧化物或钌氧化物在钌单晶衬底上生长的实验，这两种实验都具有共存表面成分的复杂分布，其化学成分和晶体结构都有所不同。通过对分解结果进行稀疏采样方法的演示，将测量时间减少了 1-2 个数量级，这对于动态表面研究非常重要。FSC 浓度提供了基于支持向量机的表面类型监督分类的特征。在这里，通过其衍射图案在结构上以及通过互补的光谱显微镜技术在化学上已被识别的特定表面区域被用作训练集。在两个示例 LEEM I-V 数据集上都证明了可靠的分类。
