Work

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

Year: 2017

Type: article

Abstract: 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.

Source: Microscopy

DOI: 10.1093/jmicro/dfx091

Authors: Siyuan Zhang, Christina Scheu

Institutions: Max-Planck-Institut für Nachhaltige Materialien, RWTH Aachen University

Language: English

Topic: Electron and X-Ray Spectroscopy Techniques

Subfield: Surfaces, Coatings and Films

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,494

Funders: Deutsche Forschungsgemeinschaft, Universität Duisburg-Essen