Analytical performance characteristics of a new vacuum ultraviolet (VUV) detector for gas chromatography (GC) are reported. GC-VUV was applied to hydrocarbons, fixed gases, polyaromatic hydrocarbons, fatty acids, pesticides, drugs, and estrogens. Applications were chosen to feature the sensitivity and universal detection capabilities of the VUV detector, especially for cases where mass spectrometry performance has been limited. Virtually all chemical species absorb and have unique gas phase absorption cross sections in the approximately 120-240 nm wavelength range monitored. Spectra are presented, along with the ability to use software for deconvolution of overlapping signals. Some comparisons with experimental synchrotron data and computed theoretical spectra show good agreement, although more work is needed on appropriate computational methods to match the simultaneous broadband electronic and vibronic excitation initiated by the deuterium lamp. Quantitative analysis is governed by Beer-Lambert Law relationships. Mass on-column detection limits reported for representatives of different classes of analytes ranged from 15 (benzene) to 246 pg (water). Linear range measured at peak absorption for benzene was 3-4 orders of magnitude. Importantly, where absorption cross sections are known for analytes, the VUV detector is capable of absolute determination (without calibration) of the number of molecules present in the flow cell in the absence of chemical interferences. This study sets the stage for application of GC-VUV technology across a wide breadth of research areas.

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Cellulose nanocrystals (CNCs) are a biorenewable filler and can be an excellent nucleating agent for the development of microcellular foamed polymeric nanocomposites. However, their relatively low degradation temperature limits their use with engineering resins like polyamide 6 (PA6) in typical melt processing techniques such as injection molding, compounding, and extrusion. A water-assisted extrusion compounding process was investigated to directly compound CNC suspensions with PA6 without the need of predrying the CNCs. By using water as a plasticizer and reducing the processing temperature by 30 ºC, this process can mitigate the degradation of CNCs during compounding. The effects of the CNCs on the mechanical properties, crystal type, and microstructure of solid and microcellular foamed specimens were characterized. The CNCs primarily acted as a nucleating filler, affecting both the matrix crystal structure and, in foamed composites, the cell structure. The CNCs nucleated the α-crystalline form of PA6 and also acted as a foam cell nucleator, increasing cell density by an order of magnitude while significantly reducing cell size. The weight reduction of the foamed specimens was about 15%. Adding small amounts of CNCs also increased matrix orientation in the solid injection molded specimens. These factors helped to improve the mechanical performance, especially the modulus of elasticity. During water-assisted compounding, thermal hydrolysis of PA6 occurred and generated carbon-carbon double bonds, as evaluated by FTIR. However, the molecular weight reduction caused by hydrolysis was less than 5%. The total molecular weight reduction was around 18%, combined with the melt extrusion and injection molding processes.

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A time interval deconvolution (TID) method was devised to integrate a gas chromatography-vacuum ultraviolet (GC-VUV) data set in order to provide bulk characterization and speciation of finished gasoline samples. The method was demonstrated using a commercially available standard and tested on a series of ASTM gasoline proficiency samples. Very good correlation (R ∼ 0.97-0.99) between GC-VUV and measurements using various ASTM methods was achieved. A key advantage of the TID method applied to GC-VUV data sets is that a large number of coelution events can be tolerated, resulting in significantly easier and faster separations, approximately 30 min in the case of gasoline. Methods for determining relative response factors, VUV reference libraries, and generalization to other types of complex samples are also discussed.

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