Analysis of electron energy loss spectroscopy data using geometric extraction methods

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.

Quaternary Core-Shell Oxynitride Nanowire Photoanode Containing a Hole-Extraction Gradient for Photoelectrochemical Water Oxidation

A nanowire photoanode SrTaO₂N, a semiconductor suitable for overall water-splitting with a band gap of 2.3 eV, was coated with functional overlayers to yield a core-shell structure while maintaining its one-dimensional morphology. The nanowires were grown hydrothermally on tantalum, and the perovskite-related oxynitride structure was obtained by nitridation. Three functional overlayers have been deposited on the nanowires to enhance the efficiency of photoelectrochemical (PEC) water oxidation. The deposition of TiO _x protects the oxynitride from photocorrosion and suppresses charge-carrier recombination at the surface. Ni(OH) _x acts a hole-storage layer and decreases the dark-current contribution. This leads to a significantly improved extraction of photogenerated holes to the electrode-electrolyte surface. The photocurrents can be increased by the deposition of a cobalt phosphate (CoP_i) layer as a cocatalyst. The heterojunction nanowire photoanode generates a current density of 0.27 mA cm^-2 at 1.23 V vs the reversible hydrogen electrode (RHE) under simulated sunlight (AM 1.5G). Simultaneously, the dark-current contribution, a common problem for oxynitride photoanodes grown on metallic substrates, is almost completely minimized. This is the first report of a quaternary oxynitride nanowire photoanode in core-shell geometry containing functional overlayers for synergetic hole extraction and an electrocatalyst.

Unsupervised machine learning applied to scanning precession electron diffraction data

Scanning precession electron diffraction involves the acquisition of a two-dimensional precession electron diffraction pattern at every probe position in a two-dimensional scan. The data typically comprise many more diffraction patterns than the number of distinct microstructural volume elements (e.g. crystals) in the region sampled. A dimensionality reduction, ideally to one representative diffraction pattern per distinct element, may then be sought. Further, some diffraction patterns will contain contributions from multiple crystals sampled along the beam path, which may be unmixed by harnessing this oversampling. Here, we report on the application of unsupervised machine learning methods to achieve both dimensionality reduction and signal unmixing. Potential artefacts are discussed and precession electron diffraction is demonstrated to improve results by reducing the impact of bending and dynamical diffraction so that the data better approximate the case in which each crystal yields a given diffraction pattern.

Evaluation of EELS spectrum imaging data by spectral components and factors from multivariate analysis

Multivariate analysis is a powerful tool to process spectrum imaging datasets of electron energy loss spectroscopy. Most spatial variance of the datasets can be explained by a limited numbers of components. We explore such dimension reduction to facilitate quantitative analyses of spectrum imaging data, supervising the spectral components instead of spectra at individual pixels. In this study, we use non-negative matrix factorization to decompose datasets from Fe2O3 thin films with different Sn doping profiles on SnO2 and Si substrates. Case studies are presented to analyse spectral features including background models, signal integrals, peak positions and widths. Matlab codes are written to guide microscopists to perform these data analyses.