SummaryMicroemulsion electrokinetic capillary chromatography (MEEKC) is similar to micellar electrokinetic chromatography (MEKC) in that it separates neutral solutes based on their chromatographic retention factors. In MEEKC solutes partition bek, veen the aqueous phase and oil droplets, which are moving through the solution. The background to MEEKC is described including novel approaches to method development and optimisation. In this case water-immiscible octane forms minute oil droplets that are coated with SDS and butan-1-ok The effects were evaluated using a test-mixture containing nine components of insoluble and soluble acids, bases and neutrals. Selectivity has been adjusted by use of a large number of factors including organic solvent, co-su rfactant, urea, temperature, cyclodextrins, ion-pair reagent. Previous reports on the selectivity in MEEKC have concentrated only on neutral solutes. Separation selectivity was drastically changed with addition of alcohol such as butan-l-ol, propan-2-ol, cyclodextrin or using a low pH buffer. Microemulsion preparation process or filtration of the buffer did not affect the separation. The separation was largely unaffected by the use of methanol or acetonitrile, surfactant concentration, buffer type, oil type, sample diluent or type of the counter-ion. Migration times were dramatically altered with the use of ion-pair reagent and buffer concentration. It was also demonstrated that temperature variations alter the migration time but not the selectivity.

Microemulsion electrokinetic chromatography (MEEKC) is an electrodriven separation technique. Separations are generally achieved using microemulsions consisting of surfactant-coated nanometer-sized oil droplets suspended in aqueous buffer. A cosurfactant such as a short-chain alcohol is generally used to stabilize the microemulsion. This review summarizes the various microemulsion types and compositions that have been used in MEEKC. The effects of key-operating variables such as surfactant type and concentration, cosurfactant type and concentration, buffer pH and type, oil type and concentration, use of organic solvent and cyclodextrin additions, and temperature are described. Specific examples of water-in-oil microemulsions and chirally selective separations are also covered.

JEL classification: G21 G17 D12 C55 Keywords:Credit risk Consumer finance Credit card default model Machine-learning a b s t r a c t Using account-level credit card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer tradeline, credit bureau, and macroeconomic variables to predict delinquency. In addition to providing accurate measures of loss probabilities and credit risk, our models can also be used to analyze and compare risk management practices and the drivers of delinquency across banks. We find substantial heterogeneity in risk factors, sensitivities, and predictability of delinquency across banks, implying that no single model applies to all six institutions. We measure the efficacy of a bank's risk management process by the percentage of delinquent accounts that a bank manages effectively, and find that efficacy also varies widely across institutions. These results suggest the need for a more customized approached to the supervision and regulation of financial institutions, in which capital ratios, loss reserves, and other parameters are specified individually for each institution according to its credit risk model exposures and forecasts.

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