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

• Published in: Nano letters 2015-04, Vol.15 (4), p.2716-2720
• Main Authors: Rossouw, David, Burdet, Pierre, de la Peña, Francisco, Ducati, Caterina, Knappett, Benjamin R, Wheatley, Andrew E. H, Midgley, Paul A
• Format: Article
• Language: English
• Subjects: Astrophysics; Bimetals; Carbon; Chemical Sciences; Condensed Matter; Earth Sciences; Geophysics; Image Interpretation, Computer-Assisted - methods; Image transmission; Imaging, Three-Dimensional - methods; Independent component analysis; Letter; Machine Learning; Magnetite Nanoparticles - chemistry; Magnetite Nanoparticles - ultrastructure; Material chemistry; Materials Science; Materials Testing - methods; Microscopy, Electron, Transmission - methods; Nanostructure; Pattern Recognition, Automated - methods; Physics; Principal component analysis; Scanning electron microscopy; Sciences of the Universe; X-Ray Diffraction - methods; X-rays
• ISSN: 1530-6984; 1530-6992
• DOI: 10.1021/acs.nanolett.5b00449

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.