Multicomponent signal unmixing from nanoheterostructures: overcoming the traditional challenges of nanoscale X-ray analysis via machine learning.
Change log
Authors
Rossouw, David
Burdet, Pierre
de la Peña, Francisco
Knappett, Benjamin R
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.
Description
Keywords
EDX, ICA, TEM, electron microscopy, nanoparticle, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Machine Learning, Magnetite Nanoparticles, Materials Testing, Microscopy, Electron, Transmission, Pattern Recognition, Automated, X-Ray Diffraction
Journal Title
Nano Lett
Conference Name
Journal ISSN
1530-6984
1530-6992
1530-6992
Volume Title
15
Publisher
American Chemical Society (ACS)
Publisher DOI
Sponsorship
European Research Council (291522)
European Research Council (259619)
European Commission (312483)
European Research Council (259619)
European Commission (312483)
D.R. acknowledges support from the Royal Society’s Newton
International Fellowship scheme. B.R.K. thanks the U.K.
EPSRC for financial support (EP/J500380/1). F.d.l.P. and
C.D. acknowledge funding from the ERC under grant no.
259619 PHOTO EM. P.A.M and P.B. acknowledges financial
support from the European Research Council under the
European Union’s Seventh Framework Programme (FP7/
2007-2013)/ERC grant agreement 291522-3DIMAGE. P.A.M.
also acknowledges financial support from the European Union’s
Seventh Framework Programme of the European Commission:
ESTEEM2, contract number 312483.