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

Authors :
David Rossouw
Pierre Burdet
Francisco de laPeña
Caterina Ducati
BenjaminR. Knappett
Andrew E. H. Wheatley
Paul A. Midgley
Source :
Nano Letters. Apr2015, Vol. 15 Issue 4, p2716-2720. 5p.
Publication Year :
2015

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]

Details

Language :
English
ISSN :
15306984
Volume :
15
Issue :
4
Database :
Academic Search Index
Journal :
Nano Letters
Publication Type :
Academic Journal
Accession number :
102002531
Full Text :
https://doi.org/10.1021/acs.nanolett.5b00449