Unmixing noisy co-registered spectrum images of multicomponent nanostructures

espm: A Python library for the simulation of STEM-EDXS datasets

We present two open-source Python packages: "electron spectro-microscopy" (espm) and "electron microscopy tables" (emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes, based on user-defined chemical compositions and spatial abundance maps of constituent phases. The simulation process uses X-ray emission cross-sections generated via state-of-the-art calculations made with emtables. These tables are designed to be easily modifiable, either manually or using espm. The simulation framework is designed to test the application of decomposition algorithms for the analysis of STEM-EDX spectrum images with access to a known ground truth. We validate our approach using the case of a complex geology-related sample, comparing raw simulated and experimental datasets and the outputs of their non-negative matrix factorization. In addition to testing machine learning algorithms, our packages will also help experimental design, for instance, predicting dataset characteristics or establishing minimum counts needed to measure nanoscale features.

Dimensionality reduction and unsupervised clustering for EELS-SI

A novel combination of machine learning algorithms is proposed for the differentiation of distinct spectra in a large electron energy loss spectroscopy spectrum image (EELS-SI) dataset. For clustering of the EEL spectra including similar fine structures in an efficient space, linear and nonlinear dimensionality reduction methods are used to project the EEL spectra onto a low-dimensional space. Then, a density-based clustering algorithm is applied to distinguish the meaningful data clusters. By applying this strategy to various experimental EELS-SI datasets, differentiation of several groups of EEL spectra representing specific fine structures was achieved. It is possible to investigate particular fine structures by averaging all of the spectra in each cluster. Also, the spatial distributions of each cluster in the scanning regions can be observed, which enables investigation of the locations of different fine structures in materials. This method does not require any prior knowledge, i.e., it is a data-driven analysis; therefore, it can be applied to any hyperspectral image.

Extraction of physically meaningful endmembers from STEM spectrum-images combining geometrical and statistical approaches

This article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms of data distribution in a multi-dimensional factor space, which allows for a straightforward and intuitively clear comparison of various approaches. A new computationally efficient and robust method for finding physically meaningful endmembers in spectrum-image datasets is presented. The method combines the geometrical approach of Vertex Component Analysis with the statistical approach of Bayesian inference. The algorithm is described in detail at an example of EELS spectrum-imaging of a multi-compound CMOS transistor.

Dissecting complex nanoparticle heterostructures via multimodal data fusion with aberration-corrected STEM spectroscopy

With nanostructured materials such as catalytic heterostructures projected to play a critical role in applications ranging from water splitting to energy harvesting, tailoring their properties to specific tasks requires an increasingly comprehensive characterization of their local chemical and electronic landscape. Although aberration-corrected electron spectroscopy currently provides sufficient spatial resolution to study this space, an approach to concurrently dissect both the electronic structure and full composition of buried metal/oxide interfaces remains a considerable challenge. In this manuscript, we outline a statistical methodology to jointly analyze simultaneously-acquired STEM EELS and EDX datasets by fusing them along their shared spatial factors. We show how this procedure can be used to derive a rich descriptive model for estimating both transition metal valency and full chemical composition from encapsulated morphologies such as core-shell nanoparticles. We demonstrate this on a heterogeneous Co-P thin film catalyst, concluding that this system is best described as a multi-shell phosphide structure with a P-doped metallic Co core.