Hyperspectral Raman Line Imaging of Syndiotactic Polystyrene Crystallinity

Abstract

The crystallinity of syndiotactic polystyrene (sPS) is studied by hyperspectral Raman line imaging. Images of a 140 × 1200 μm region of an sPS test piece containing 39 200 pixels/image were generated from spectra taken over the wavenumber interval between 300 and 875 cm−1. The spectral region includes the moderate-intensity crystallinity-sensitive bands in the 770 800 cm−1 region, as well as other useful but weaker marker bands. Factor analysis was used to extract structure information from the set of spectra. Four non-noise factors were extracted; two were assigned to crystalline and amorphous sPS and the other two to background. Score images of the crystalline and amorphous sPS factors were used to visualize distribution of those species. The results were compared to the integrated Raman intensity image for the 773 cm−1 band of crystalline sPS.

Spatial Distribution of Phosphate Species in Mature and Newly Generated Mammalian Bone by Hyperspectral Raman Imaging

“…[18][19][20] In the current application, a 250 mW 785 nm laser (SDL, Inc.) was used as the excitation source. A 5ϫ/0.25 numerical aperture (NA) Fluar objective (Zeiss) was used to illuminate the specimen and it collected backscattered Raman-shifted light.…”

“…17 We have applied the same technique to Raman imaging to generate chemical composition contrast in bone 14 and in aluminosilicate glasses 18 and crystallinity contrast in syndiotactic polystyrene. 19 …”

Two-dimensional correlation spectroscopy as a tool for analyzing vibrational images

“…To resolve these issues, chemometrics routines such as principal component analysis, Simplisma, target factor analysis, etc. have all been utilized to find the most characteristic spectra in an image and/or for noise elimination [3][4][5][6][7][8][9][10][11][12][13][14][15]. Though every image is essentially a purposefully represented three-dimensional data set, all the indicated methods that deal with bi-linear, two-dimensional (2D) data can nevertheless be applied for imaging because one of the two spatial dimensions of the image, x or y (the third dimension is spectral, w) can be re-arranged so that it is no longer an independent coordinate.…”

“…The multivariate approaches can, on the other hand, handle such situations, as basically they do not necessarily need any prior www.elsevier.com/locate/vibspec Vibrational Spectroscopy 37 (2005) [217][218][219][220][221][222][223][224] information to produce an image. With respect to this, there are several applications by Morris' group [10][11][12][13], Andrew and Hancewicz [14], and Wang et al [15] that illustrate the advantages of the multivariate approach. A descriptive example is that by Timlin et al [13] who employed nonnegativity and second derivative constraints based principal component analysis to investigate images of bone specimens.…”

Application of multivariate curve resolution analysis to FTIR kinetics data

“…Multivariate curve resolution (MCR) analysis represents a chemometrics approach capable of reducing and analyzing matrices of vibrational spectra [1][2][3][4]. The analysis is most useful in applications where spectra are measured as a function of a perturbation to the chemical system under study [5,6].…”