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Nano Lett. 2015 Apr 8;15(4):2716-20. doi: 10.1021/acs.nanolett.5b00449. Epub 2015 Mar 17.

Multicomponent signal unmixing from nanoheterostructures: overcoming the traditional challenges of nanoscale X-ray analysis via machine learning.

Author information

1
†Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom.
2
‡Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.

Abstract

The chemical composition of core-shell nanoparticle clusters have been determined through principal component analysis (PCA) and independent component analysis (ICA) of an energy-dispersive X-ray (EDX) spectrum image (SI) acquired in a scanning transmission electron microscope (STEM). The method blindly decomposes the SI into three components, which are found to accurately represent the isolated and unmixed X-ray signals originating from the supporting carbon film, the shell, and the bimetallic core. The composition of the latter is verified by and is in excellent agreement with the separate quantification of bare bimetallic seed nanoparticles.

KEYWORDS:

EDX; ICA; TEM; electron microscopy; nanoparticle

PMID:
25760234
PMCID:
PMC4440406
DOI:
10.1021/acs.nanolett.5b00449
[Indexed for MEDLINE]
Free PMC Article

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