2020
Benito-González, Isaac; Martínez-Sanz, Marta; López-Rubio, Amparo; Gómez-Mascaraque, Laura G.
Confocal Raman imaging as a useful tool to understand the internal microstructure of multicomponent aerogels Artículo de revista
En: Journal of Raman Spectroscopy, vol. 51, no 10, pp. 2022-2035, 2020.
Resumen | Enlaces | BibTeX | Etiquetas: Cellulose, hyperspectral imaging, multivariate analysis, Raman microscopy, renewable
@article{https://doi.org/10.1002/jrs.5936,
title = {Confocal Raman imaging as a useful tool to understand the internal microstructure of multicomponent aerogels},
author = {Isaac Benito-González and Marta Martínez-Sanz and Amparo López-Rubio and Laura G. Gómez-Mascaraque},
url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/jrs.5936},
doi = {https://doi.org/10.1002/jrs.5936},
year = {2020},
date = {2020-01-01},
journal = {Journal of Raman Spectroscopy},
volume = {51},
number = {10},
pages = {2022-2035},
abstract = {Abstract This work shows the characterization of (nano)cellulosic aerogels prepared from Posidonia oceanica waste biomass by means of confocal Raman microscopy (CRM). For this aim, aerogels were prepared by simple freeze-drying of aqueous dispersions of four (nano)cellulosic fractions with different purification degrees, tested at two different concentrations (0.5% and 2%). These were then coated with polylactic acid (PLA) in order to improve their hydrophobicity and subjected to oil sorption–desorption experiments. Both univariate and multivariate analyses, including an approach based on comparing the spectra with those of reference materials and another one based on automatic detection of components, were compared in terms of the quality and the accuracy of the information provided. Univariate analysis only provided accurate information in the simplest systems (native (nano)cellulosic aerogels), while multivariate analyses facilitated the detection of the different components even for the most complex structures. Automatic identification of components was selected as the optimal methodology, although it also underestimated the abundance of the components with the least intense Raman spectra (cellulosic clusters) in the presence of PLA and oil. Comparison with the reference materials resulted in unrealistic images for the most complex systems. Micron-sized regions of concentrated cellulose were detected using CRM, being more abundant in the denser aerogels. Results also confirmed that PLA was preferentially located close to the surface, while oil could penetrate deeper along the matrix. Overall, the results showed the potential of Raman imaging as a novel approach for the characterization of complex biopolymeric aerogels.},
keywords = {Cellulose, hyperspectral imaging, multivariate analysis, Raman microscopy, renewable},
pubstate = {published},
tppubtype = {article}
}
Abstract This work shows the characterization of (nano)cellulosic aerogels prepared from Posidonia oceanica waste biomass by means of confocal Raman microscopy (CRM). For this aim, aerogels were prepared by simple freeze-drying of aqueous dispersions of four (nano)cellulosic fractions with different purification degrees, tested at two different concentrations (0.5% and 2%). These were then coated with polylactic acid (PLA) in order to improve their hydrophobicity and subjected to oil sorption–desorption experiments. Both univariate and multivariate analyses, including an approach based on comparing the spectra with those of reference materials and another one based on automatic detection of components, were compared in terms of the quality and the accuracy of the information provided. Univariate analysis only provided accurate information in the simplest systems (native (nano)cellulosic aerogels), while multivariate analyses facilitated the detection of the different components even for the most complex structures. Automatic identification of components was selected as the optimal methodology, although it also underestimated the abundance of the components with the least intense Raman spectra (cellulosic clusters) in the presence of PLA and oil. Comparison with the reference materials resulted in unrealistic images for the most complex systems. Micron-sized regions of concentrated cellulose were detected using CRM, being more abundant in the denser aerogels. Results also confirmed that PLA was preferentially located close to the surface, while oil could penetrate deeper along the matrix. Overall, the results showed the potential of Raman imaging as a novel approach for the characterization of complex biopolymeric aerogels.
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