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1. Multicomponent Signal Unmixing from Nanoheterostructures:Overcoming the Traditional Challenges of Nanoscale X-ray Analysisvia Machine Learning.

Author

David Rossouw, Pierre Burdet, Francisco de laPeña, Caterina Ducati, BenjaminR. Knappett, Andrew E. H. Wheatley, and Paul A. Midgley

Subjects
*HETEROSTRUCTURES, *MACHINE learning, *ENERGY dispersive X-ray spectroscopy, *SCANNING transmission electron microscopy, *PRINCIPAL components analysis, *INDEPENDENT component analysis, *CARBON films
Abstract

The chemical composition of core–shellnanoparticle clustershave been determined through principal component analysis (PCA) andindependent component analysis (ICA) of an energy-dispersive X-ray(EDX) spectrum image (SI) acquired in a scanning transmission electronmicroscope (STEM). The method blindly decomposes the SI into threecomponents, which are found to accurately represent the isolated andunmixed X-ray signals originating from the supporting carbon film,the shell, and the bimetallic core. The composition of the latteris verified by and is in excellent agreement with the separate quantificationof bare bimetallic seed nanoparticles. [ABSTRACT FROM AUTHOR]

Published
2015
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