OPTIMIZING A SHORT-END ELECTROPHORETICALLY MEDIATED MICRO-ANALYSIS (EMMA) ASSAY FOR CREATININE

In this work electrophoretically mediated micro-analysis (EMMA) is used in conjunction with short end injection to improve the in-capillary Jaffé assay for creatinine. Key advances over prior work include (i) using simulation to ensure intimate overlap of reagent plugs, (ii) using OH- to drive the reaction, (iii) using short-end injection to minimize analysis time and in-line product degradation. The potential-driven overlapping time with the EMMA approach, as well as the borate buffer background electrolyte (BGE) concentration and pH are optimized with the short end approach. The best conditions for short-end analyses would not have been predicted by the prior long end work, owing to a complex interplay of separation time and product degradation rates. Raw peak areas and flow-adjusted peak areas for the Jaffé reaction product (at 505 nm) are used to assess the sensitivity of the short-end EMMA approach. Optimal overlap conditions depend heavily on local conductivity differences within the reagent zone(s), as these differences cause dramatic voltage field differences, which effect reagent overlap dynamics. Simul 5.0, a dynamic simulation program for capillary electrophoresis (CE) systems, is used to understand the ionic boundaries and profiles that give rise to the experimentally obtained data for EMMA analysis. Overall, fast migration of hydroxide ions from the picrate zone makes difficult reagent overlap. In addition, the challenges associated with the simultaneous overlapping of three reagent zones are considered, and experimental results validate the predictions made by the simulation. With one set of “optimized” conditions including OH- (253 mM) as the third reagent zone the response was linear with creatinine concentration (R2 = 0.998) and reproducible over the clinically relevant range (0.08 to 0.1 mM) of standard creatinine concentrations. An LOD (S/N = 3) of 0.02 mM and LOQ (S/N=10) of 0.08 mM were determined. A significant improvement (43%) in assay sensitivity was obtained compared to prior work that considered only two reagents in the overlap.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.