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The additivity assumption underlying Giddings’ coupling model for the eddy-dispersion in laminar flows through heterogeneous media is critically analyzed and a potential solution for its non-additivity in the high velocity limit is presented. Whereas the unit cell in Giddings’ model only consists of a single velocity bias step, the unit dispersion cell of the newly proposed model comprises two consecutive velocity bias steps. Consequently, the unit cell of this new model allows to account for the occurrence of an internal velocity bias rectification at high reduced velocities and is therefore additive in both the low and high velocity limit. First, a mathematical expression for the velocity- and diffusion-dependency of the model’s dispersion characteristics has been established. Subsequently, the physical behavior of the model is discussed. It is shown the relation between the eddy-dispersion plate height h and the reduced velocity ν can be expected to display a local maximum in systems where the transversal dispersion purely occurs by molecular diffusion, as is the case in perfectly ordered flow-through media. In disordered media, where the transversal dispersion also contains a significant advective component, the model predicts a velocity-dependency that is qualitatively similar to that described by Giddings’ coupling model but, all other conditions being equal, converges to a significantly smaller horizontal asymptote at high reduced velocity. The latter might shed new light on earlier eddy-dispersion studies pursuing a quantitative agreement between experimental data and the Giddings model.We report on a series of high-accuracy computational fluid dynamics band broadening simulations in three different 2-D flow systems a 2-D pillar array and 2-D lumped packed bed geometries with different checkerboard velocity bias patterns. These media display a local maximum in the relationship between the eddy-dispersion plate height and the mobile phase velocity. The occurrence of such a dispersion maximum has not been reported before but appears to be a characteristic of regular chromatographic media with alternating velocity bias, at least in 2-D geometries. This newly observed behavior can be fully understood and modelled using the checkerboard model established in part I of the present study.Polyimide (PI) microspheres assembled by nanosheets were used for bar adsorptive microextraction (BAμE) for the first time. The PI microsphere possessed self-organized hierarchical nanostructure, large specific surface area (170 m2/g) and good thermostability (up to 400 °C). The BAμE device was prepared by adhering the PI microspheres on a quartz bar with Kapton double sided tape. Trace nitroaromatic explosives in environmental waters were extracted by the BAμE device, desorbed by thermal desorption (TD), and analyzed by gas chromatography-mass spectrometry (GC-MS). The reproducibility of five BAμE devices prepared in parallel was less than 13.0% (expressed as relative standard deviation, RSD). The BAμE device could stand up to 30 extraction/desorption cycles without decrease of extraction efficiency. The results of method validation showed that the BAμE-TD/GC-MS method possessed wide linearity (0.05-50 μg/L or 0.05-20 μg/L), high correlation coefficients (> 0.9987), good precision (RSDs less then 11.8%), low detection limits (0.005-0.013 μg/L) and high enrichment factors (528-1410). Relative recoveries were in the range of 72.2-122.6% with RSDs between 0.1% and 10.5% for real water samples. These results proved that the proposed method was a good choice for determination of organic pollutants in water samples.Trapping volatiles is a convenient way to study aroma compounds but it is important to determine which volatile trapping method is most comprehensive in extracting the most relevant aroma components when investigating complex food products. Awareness of their limitations is also crucial. (Un)targeted metabolomic approaches were used to determine the volatile profiles of two commercial flavourings. Four trapping techniques were tested as was the addition of salt to the mixture. Comprehensiveness and repeatability were compared and SBSE proved particularly suitable for extracting components such as polysulfides, pyrazines and terpene alcohols, and provided an overall broader chemical spectrum. SPME proved to be more suitable in extracting sesquiterpenes and DHS in extracting monoterpenes. Adding salt to the sample had only quantitative effects on volatiles as detected by SPME. These results help clarify the advantages and limitations of different trapping techniques and hence deliver a valuable decision tool for food matrix analysis.The basis of interpretive optimisation in liquid chromatography is the prediction of resolution, from appropriate solute retention models. The reliability of the process depends critically on the quality of the experimental design. This work develops, validates and applies a general methodology aimed to evaluate the quality of any training experimental design, which will be applied in Part II to design optimisation. The methodology is based on the systematic evaluation of the uncertainties associated to the prediction of retention times in comprehensive scans of both isocratic and gradient experimental conditions. It is able to evaluate comprehensively experimental designs of arbitrary complexity. Five common training experimental designs were used to model the retention, according to the Linear Solvent Strength (LSS) and the Neue-Kuss (NK) equations, using a set of 14 sulphonamides of different polarity. The results are presented in terms of relative uncertainties in predictions, which provide significant and interpretable results. The magnitude of such uncertainties, together with the systematic, coherent and logical changes observed at increasing solute hydrophobicity, give support to the results. The NK model gave smaller errors and unbiased predictions, whereas the LSS model gave rise to lack of fit. Isocratic training designs, which are widely accepted as the most informative, are confirmed as the best. As a general conclusion, gradients are predicted with intrinsically smaller uncertainties, independently of the training experimental design. In addition, gradients are more insensitive than isocratic predictions with regard to the type of training design used. Infigratinib Isocratic predictions deteriorate quickly with mobile phase composition. This explains the better performance of gradient predictions, even with biased models.