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Multivariate Auger Feature Imaging (MAFI) – a new approach towards chemical state identification of novel carbons in XPS imaging
Author(s) -
Barlow Anders J.,
Scott Oliver,
Sano Naoko,
Cumpson Peter J.
Publication year - 2015
Publication title -
surface and interface analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 90
eISSN - 1096-9918
pISSN - 0142-2421
DOI - 10.1002/sia.5738
Subject(s) - x ray photoelectron spectroscopy , auger , chemical state , carbon fibers , multivariate statistics , spectral line , chemical imaging , materials science , identification (biology) , feature (linguistics) , chemistry , analytical chemistry (journal) , computer science , artificial intelligence , nuclear magnetic resonance , physics , atomic physics , machine learning , environmental chemistry , hyperspectral imaging , composite material , linguistics , philosophy , botany , astronomy , composite number , biology
The clear identification of allotropes and similar chemical states of carbon in XPS imaging can be made difficult because of the subtle differences observed in spectra, particularly when varying from sp 2 to sp 3 hybridised carbon. By shifting focus from the commonly analysed C1s region in XPS spectra to the often ignored C KVV region, we utilise the so‐called D‐Parameter to identify different forms of carbon in a surface. When this methodology is applied to XPS imaging, the result is a powerful and unambiguous tool for the chemical state identification of carbon in XPS images. Further enhancement by multivariate statistics improves XPS spectral and image quality, and we call this technique Multivariate Auger Feature Imaging. Herein, we have applied this technique to clearly identify in XPS imaging a graphite film mounted on carbon tape. Copyright © 2015 John Wiley & Sons, Ltd.

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