Bo-Yan Li

Application of Sample–Sample Two-Dimensional Correlation Spectroscopy to Determine the Glass Transition Temperature of Poly(ethylene terephthalate) Thin Films

The glass transition temperatures (Tg) of poly(ethylene terephthalate) (PET) thin films with different thicknesses are determined by analyzing their in situ reflection–absorption infrared (RAIR) spectra measured over a temperature range of 28 to 84 °C. The criterion of standard deviation of the covariance matrices is used as a graphical indicator for the determination of the Tg present in the sample–sample two-dimensional (2D) correlation spectra calculated from the temperature-dependent RAIR spectra. After two data pretreatments of the first derivative of the spectral absorbance versus temperature and the mean normalization over the wavenumbers are sequentially carried out on the RAIR spectra, an abrupt change of the first-derivative correlation spectra with respect to temperature is quickly obtained. It reflects the temperature at which the apparent intensity changes in pertinent absorption bands of PET thin films take place due to the dramatic segmental motion of PET chain conformation. The Tg of the thin PET films is accordingly determined. The results reveal that it decreases with a great dependence on the film thickness and that sample–sample 2D correlation spectroscopy enables one to determine the transition temperature of polymer thin films in an easy and valid way.

Multi-Objective Genetic Algorithm-Based Sample Selection for Partial Least Squares Model Building with Applications to Near-Infrared Spectroscopic Data:

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. Multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

Exploring Time-Dependent Structural Changes During the Cold Crystallization Process of Isotactic Polystyrene by Infrared Spectroscopy and Multivariate Curve Resolution

The present study attempts an application of Fourier transform infrared (FT-IR) spectroscopy in conjunction with multivariate curve resolution (MCR) techniques to explore the structural evolution of isotactic polystyrene (iPS) during the cold crystallization process. The focus of the present study is placed on the performance of MCR techniques, e.g., orthogonal projection (OP), alternating least squares (ALS), and fixed-size moving window evolving factor analysis (FSMWEFA), and the interpretability of spectral changes in the investigated chemical process. As a result, valuable information and conclusions about the structural evolution of iPS during the crystallization process can be extracted: when the amorphous phase of iPS changes, the ordering of the phenyl rings takes place first, and then the polymer chains adjust their local conformations to form short 31 helix structures. Furthermore, according to intensity profiles of the spectral variations, the ordering of the phenyl rings proceeds more intensely than the formation of ordered local chains, and the structural evolution of iPS occurs even during the induction period. The spectral variations resulting from the conformational changes in the 31 helical structures depend on the sequence length of the helical chains: the longer the polymer chain is, the smaller the corresponding band variations are. It has been demonstrated that the combination of FT-IR spectroscopy and chemometric MCR techniques is very promising for the analysis of the crystallization process of polymers. MCR is a powerful tool for analyzing and visualizing spectral data and integrating them with other information, making spectral intensity variations more amenable to interpretation in order to explore the molecular dynamics of polymers.