Recent research quanitifies plasma treatment of PP

Plasma treatment of PP

Feature-Article_june_14_1

Recent research quanitifies plasma treatment of PP

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

 Plasma treatment of PP

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 four-factor 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.

1. Introduction

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful and sensitive surface analysis technique, which provides information about materials composition, molecular structure, and chemical bonding. [1–5] ToF-SIMS datasets are information-rich. However, matrix effects and the complexities of the ionization processes occurring make quantification extremely difficult. Quantitative information may be extracted in some cases with the use of standards and well-defined matrices. The interpretation of ToF-SIMS data is often greatly assisted by complementary information from techniques such as X-ray photoelectron spectroscopy (XPS), [3,5,6–8] and contact angle analysis. [8,9] Multivariate analysis (MVA) is a useful tool for analyzing large datasets, such as those generated by ToF-SIMS. [1,2,10,11] Multivariate techniques, such as principal components analysis (PCA) and partial least squares regression (PLSR) methods, are successfully used to examine ToF-SIMS data. [12–16]

PLSR is a proven method with certain qualities, which makes it suitable for the calibration and prediction of ToF-SIMS data. PLSR assumes a linear relationship between variables and response(s), and it also compensates for any nonlinearity by including more components in the prediction model. PLSR is also used to handle data sets with a high number of variables and noise parameters. PLSR is superior to other bilinear calibration methods in which most of the data relevant to the response are represented in the first few components. This property of the PLSR algorithm justifies the inclusion of the intensity of many peaks in the ToF-SIMS spectrum as variables, and thereby minimizes the bias inherent in selecting a small subset of variables assumed to be of physical significance. The theory and application of multivariate calibration are described in detail elsewhere. [17–19]

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

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