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

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.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.This is the final published version. It first appeared at http://pubs.acs.org/doi/abs/10.1021/acs.nanolett.5b00449

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