Hydrodynamic Characterization of Bile Salt Host-Guest Complexes by Pulsed Field Gradient NMR Spectroscopy

The work described herein is aimed at understanding primary and secondary aggregation of bile salt micelles and how micelles can perform chiral recognition of binapthyl analytes. Previous work with cholate and deoxycholate using micellar electrokinetic chromatography (MEKC) and nuclear magnetic resonance (NMR) has provided insightinto cholate and deoxycholate micelle formation, especially with respect to the critical micelle concentration (CMC). Chiral separations of the model analyte, 1,1â??-binaphthyl-2,2â??-diyl hydrogen phosphate (BNDHP), via cholate (C) and deoxycholate (DC) mediated MEKC separataions previously have shown the DC CMC to be 7-10 mM andthe cholate CMC at 14 mM at ph 12. A second model analyte,1,1â??-binaphthol (BN), was also previously investigated to probe micellar structure, but the MEKC data for this analyte implied a higher CMC, which may be interpreted as secondary aggregation. Thiswork extends the investigation of bile salts to include pulsed field gradient spin echo (PFGSE) NMR experiments being used to gain information about the size and degree of polydispersity of cholate and deoxycholate micelles. Concentrations of cholate below 10mM show a large variation in effective radius likely due to the existence of transient preliminary aggregates. The onset of the primary micelle shows a dramatic increase in effective radius of the micelle in cholate and deoxycholate. In the region of expectedsecondary aggregation a gradual increase of effective radius was observed with cholate; deoxycholate showed a persistent aggregate size in the secondary micelle region that is modulated by the presence of an analyte molecule. Effective radii of cholate anddeoxycholate (individually) were compared with and without R- and S-BNDHP in order to observe the effective radius difference of micelles with and without analyte present. The presence of S-BNDHP consistently resulted in a larger effective aggregate radius incholate and deoxycholate, confirming previous data of the S-BNDHP interacting more with the micelle than R-BNDHP. In total, various NMR techniques, like diffusion NMR can be used to gain a greater understanding of the bile salt micellization process and chiral resolution.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.