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Article Dans Une Revue Nano Letters Année : 2015

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

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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.
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hal-04434660 , version 1 (12-02-2024)

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David Rossouw, Pierre Burdet, Francisco de la Peña, Caterina Ducati, Benjamin R. Knappett, et al.. Multicomponent Signal Unmixing from Nanoheterostructures: Overcoming the Traditional Challenges of Nanoscale X-ray Analysis via Machine Learning. Nano Letters, 2015, Nano Letters, 15 (4), pp.2716-2720. ⟨10.1021/acs.nanolett.5b00449⟩. ⟨hal-04434660⟩
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