Parallel factor (PARAFAC) kernel analysis of temperature- and composition-dependent NMR spectra of poly(lactic acid) nanocomposites

Abstract

The parallel factor (PARAFAC) kernel matrix to analyze a sample system stimulated by more than one type of perturbation is described. PARAFAC kernel is a quantitative representation of the synchronicity and asynchronicity observed within the PARAFAC score matrices generated by carrying out two-dimensional (2D) correlation analyses. Thus, kernel matrix representation provides more intuitively understandable interpretation to the conventional PARAFAC trilinear model. In this study, the utility of PARAFAC kernel is demonstrated by the study of poly(lactic acid)-nanocomposite undergoing a structural change depending on the temperature as well as the clay content in the sample. Seemingly complicated variation of nuclear magnetic resonance (NMR) spectra induced by the change in the temperature and clay content are readily analyzed by the multiple-perturbation 2D correlation spectroscopy and PARAFAC kernel. PARAFAC kernel revealed that crystalline and amorphous structures of the PLA substantially undergo thermal deformation, and these variations are also influenced by the presence of the clay.