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Derivation of empirical XPS relative sensitivity factors from silicate glasses
Author(s) -
Shallenberger Jeffrey R.,
Smith Nicholas J.,
Banerjee Joy
Publication year - 2021
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.6944
Subject(s) - x ray photoelectron spectroscopy , satellite , computational physics , analytical chemistry (journal) , auger , chemistry , computer science , atomic physics , physics , nuclear magnetic resonance , environmental chemistry , astronomy
Quantification of spectral data from X‐ray photoelectron spectroscopy is frequently done using relative sensitivity factors (RSFs) provided by instrument or software vendors. Understanding of the key factors that impact the amount of signal observed from a given photoelectron peak has improved substantially over the past 30+ years, which permits calculation of RSFs from first principles. Here, we have examined a series of in vacuo fractured silicate glass samples covering a wide range of elemental compositions that allow determination of empirical RSFs for the photoelectron peaks of several important elements. RSFs for three common X‐ray‐induced Auger lines (two Mg KLL and one Na KLL) have also been evaluated. These results are then compared with standard RSF libraries provided by instrument manufacturers, as well as those derived from first principles. The empirically derived RSFs differ by up to 10% for several photoelectron peaks and directly impact the accuracy of the resulting measured composition. We explore likely causes of these discrepancies and conclude that satellite losses, as recently discussed by Brundle and Crist, are a significant factor that must be accounted for if first principles RSFs are to be used. In the absence of suitable empirical RSFs for the material family of interest, we propose systematically measuring satellite losses for different materials to develop an empirical table of yield losses for more accurate quantification using first principles RSFs.