The multivariate analysis techniques of principal components analysis (PCA), principal component regression (PCR), and partial least squares regression (PLSR) were used to calibrate time-of-flight secondary ion mass spectrometry (ToF-SIMS) data against X-ray photoelectron spectroscopy (XPS) data obtained from plasma-treated polypropylene. This establishes correlations between quantitative information obtained from XPS with the molecular information indicated by ToF-SIMS, allowing the relative concentration of CO functional groups and C:O atomic concentration ratio on the surfaces of plasma-treated polypropylene to be predicted from ToF-SIMS data alone. A fourfactor prediction model was constructed, and was deemed as adequate to predict the concentrations of the surface CO functional groups, and of the C:O atomic ratio with root mean square error of prediction (RMSEP) values of 0.445 and 0.671 at%, respectively.

Multivariate Calibration of ToF-SIMS and XPS Data from Plasma-Treated Polypropylene Thin Films

Awaja, Firas
2014

Abstract

The multivariate analysis techniques of principal components analysis (PCA), principal component regression (PCR), and partial least squares regression (PLSR) were used to calibrate time-of-flight secondary ion mass spectrometry (ToF-SIMS) data against X-ray photoelectron spectroscopy (XPS) data obtained from plasma-treated polypropylene. This establishes correlations between quantitative information obtained from XPS with the molecular information indicated by ToF-SIMS, allowing the relative concentration of CO functional groups and C:O atomic concentration ratio on the surfaces of plasma-treated polypropylene to be predicted from ToF-SIMS data alone. A fourfactor prediction model was constructed, and was deemed as adequate to predict the concentrations of the surface CO functional groups, and of the C:O atomic ratio with root mean square error of prediction (RMSEP) values of 0.445 and 0.671 at%, respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/264219
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