Revealing the spatial and temporal distribution of different chemical states of lithium by EELS analysis using non-negative matrix factorization

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Detection of the spatial distribution and temporal evolution of an element in different chemical states is difficult in transmission electron microscopy. Here, taking the lithium element as an example, spatial and temporal distribution of different lithium-containing compounds could be revealed by using electron energy-loss spectroscopy (EELS) combined with the analysis method of non-negative matrix factorization (NMF), which is an algorithm that can accomplish the decomposition of high-dimensional data, especially the data which must be positive to implement its physical significance. NMF algorithms of different forms are adopted in this paper to tackle the problem. It is shown that two types of iteration methods, fast hierarchical alternating least squares (Fast-HALS) and spatial orthogonal (SO)-HALS provide decent NMF results on EELS datasets of lithium element. In particular, the low-loss and the core-loss regions of the EELS data are combined together in the process of NMF analysis, enabling better distinction of different chemical states. The above algorithms are recommended for the purpose of analyzing the EELS datasets containing different chemical states of lithium.