Fernandez, V., Kiani, D., Fairley, N., Felpin, F.-X. & Baltrusaitis, J. (2020) Curve fitting complex X-ray photoelectron spectra of graphite-supported copper nanoparticles using informed line shapes. Appl. Surf. Sci. 505 143841.
Added by: Richard Baschera (2020-03-13 14:50:49) Last edited by: Richard Baschera (2020-03-13 15:03:23)
|Type de référence: Article
Numéro d'identification (ISBN etc.): 0169-4332
Clé BibTeX: Fernandez2020
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|Catégories: IMN, INTERNATIONAL
Mots-clés: ambient-pressure xps, binding-energy calibration, carbon-dioxide, catalyst, chemistry, Copper nanoparticles, Data driven spectral analysis, graphite, nickel metal, oxide, reduction, Spectral envelope, spectroscopy, Surface, X-ray photoelectron spectroscopy
Créateurs: Baltrusaitis, Fairley, Felpin, Fernandez, Kiani
Collection: Appl. Surf. Sci.
Consultations : 1/294
Indice de consultation : 4%
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Complex spectral envelopes of transition metal photo-excitations obtained using X-ray Photoelectron Spectroscopy (XPS) contain extensive information on the oxidation states and chemical bonding but pose multiple challenges for extracting reliable data due to the presence of multiple closely lying binding energy peaks. In this work, we outlined a procedure for graphite supported copper nanoparticles (Cu NP/graphite) XPS data interpretation that involves constructing spectral envelopes of the potential copper components (Cu2O, CuO and Cu(OH)(2)) extracted from the diverse set of Cu NP/graphite samples and using Linear Least Squares (LLS) fitting to reconstruct the exact surface composition of Cu NP/graphite samples. We utilized Informed Amorphous Sample Model (IASM) to calculate spectral envelopes using a physical process affecting the series of Cu NP/graphite samples, namely their synthesis procedure, to construct an informed line shape necessary to complete data reproduction by the model. The method described herein can be used to interpret crucial XPS data obtained in many science and engineering disciplines, including chemistry, fundamental and applied surface science, catalysis, semiconductors and many others. A brief discussion is also provided on the opportunities and pitfalls of deriving standard model line shapes from user sourced online databases.