Reinforcement of bacterial cellulose aerogels with biocompatible polymers.

Bacterial cellulose (BC) aerogels, which are fragile, ultra-lightweight, open-porous and transversally isotropic materials, have been reinforced with the biocompatible polymers polylactic acid (PLA), polycaprolactone (PCL), cellulose acetate (CA), and poly(methyl methacrylate) (PMMA), respectively, at varying BC/polymer ratios. Supercritical carbon dioxide anti-solvent precipitation and simultaneous extraction of the anti-solvent using scCO2 have been used as core techniques for incorporating the secondary polymer into the BC matrix and to convert the formed composite organogels into aerogels. Uniaxial compression tests revealed a considerable enhancement of the mechanical properties as compared to BC aerogels. Nitrogen sorption experiments at 77K and scanning electron micrographs confirmed the preservation (or even enhancement) of the surface-area-to-volume ratio for most of the samples. The formation of an open-porous, interpenetrating network of the second polymer has been demonstrated by treatment of BC/PMMA hybrid aerogels with EMIM acetate, which exclusively extracted cellulose, leaving behind self-supporting organogels.

Ethoximation-silylation approach for mono- and disaccharide analysis and characterization of their identification parameters by GC/MS.

The qualitative and quantitative analysis of complex carbohydrate mixtures is a challenging problem. When tackled by GC/MS, close retention times and largely similar mass spectra with no specific features complicate unambiguous identification, especially of monosaccharides. An optimized pre-capillary ethoximation-silylation GC/MS method for determination of monosaccharides and disaccharides was applied to a wide range of analytes (46 compounds). The two-step derivatization resulted in a pair of syn and anti peaks with specific retention and intensity ratio. The resulting dataset of mass spectra was subjected to a PCA-based pattern recognition. An oxime peak identifier (OPI) of the carbohydrate analytes, based on the combination of an internal standard and the corresponding syn/anti peak ratios, increased the reliability of the identification of reducing carbohydrates. Finally, the introduced EtOx-TMS derivatization method was applied to four different carbohydrate matrices (agave sirup, maple sirup, palm sugar, and honey).

Evaluation of different derivatisation approaches for gas chromatographic-mass spectrometric analysis of carbohydrates in complex matrices of biological and synthetic origin.

Gas chromatographic analysis of complex carbohydrate mixtures requires highly effective and reliable derivatisation strategies for successful separation, identification, and quantitation of all constituents. Different single-step (per-trimethylsilylation, isopropylidenation) and two-step approaches (ethoximation-trimethylsilylation, ethoximation-trifluoroacetylation, benzoximation-trimethylsilylation, benzoximation-trifluoroacetylation) have been comprehensively studied with regard to chromatographic characteristics, informational value of mass spectra, ease of peak assignment, robustness toward matrix effects, and quantitation using a set of reference compounds that comprise eight monosaccharides (C(5)-C(6)), glycolaldehyde, and dihydroxyacetone. It has been shown that isopropylidenation and the two oximation-trifluoroacetylation approaches are least suitable for complex carbohydrate matrices. Whereas the former is limited to compounds that contain vicinal dihydroxy moieties in cis configuration, the latter two methods are sensitive to traces of trifluoroacetic acid which strongly supports decomposition of ketohexoses. It has been demonstrated for two "real" carbohydrate-rich matrices of biological and synthetic origin, respectively, that two-step ethoximation-trimethylsilylation is superior to other approaches due to the low number of peaks obtained per carbohydrate, good peak separation performance, structural information of mass spectra, low limits of detection and quantitation, minor relative standard deviations, and low sensitivity toward matrix effects.