Calculation of the electrophoretic mobility of amines in methanol-aqueous electrolyte systems

Quantitative structure property relationship study of the electrophoretic mobilities of some benzoic acids derivatives in different carrier electrolyte compositions

Quantitative structure-properties relationship (QSPR) has been applied to modeling and predicting the electrophoretic mobilities of a series of benzoic acid derivatives in different carrier electrolyte composition. Descriptors that were selected by stepwise multiple linear regression (MLR) technique are radial distribution function-lag8 (RDF-8), unweighted R-maximal autocorrelation geometry, topology and atomic weight assembly-lag4 (R-GETAWAY-4), geometrical descriptor lag-26 (GEO-26), and the overall dielectric constant of the carrier electrolyte. These descriptors were used as inputs for generated 4-7-1 artificial neural network (ANN). The results obtained using ANN and MLR were compared as well as with the experimental values and showed the superiority of ANN over MLR model. Also the appearance of these descriptors in QSPR models reveals the role of electronic and steric interactions in solutes mobility in capillary electrophoresis due to the dielectric and hydrodynamic friction forces.

Enantioseparation of 2-O-β-d-glucopyranosyl-2H-1,4-benzoxazin-3(4H)-one and its 7-chloro-derivative by capillary zone electrophoresis using native and substituted β-cyclodextrins as chiral additives

Efficient, rapid and inexpensive methods were established for the chiral separation of two glucopyranosyl compounds from plant extracts, by capillary zone electrophoresis (CZE). Baseline separation was achieved for both compounds. Several native cyclodextrins and their derivatives were tried as chiral selectors. CM-beta-CD and HP-beta-CD (with addition of acetonitrile in the buffer) gave rise to optimal chiral separation for the two compounds, respectively, each within a few minutes. The effects of several parameters on the chiral separation were studied.

Prediction of the electrophoretic mobilities of some carboxylic acids from theoretically derived descriptors

A 4-4-1 artificial neural network was constructed and trained for the prediction of the electrophoretic mobilities of some aliphatic and aromatic carboxylic acids based on quantitative structure-property relationships. The inputs of this network are theoretically derived descriptors that were chosen by the stepwise variables selection techniques. These descriptors are: shape factor, molecular surface area, the maximum value of electron density on atom in molecule, and the sum of atomic polarizability. In order to assess the accuracy and predictability of the proposed model, the cross-validation test was employed. The results obtained showed the ability of developed artificial neural network to prediction of electrophoretic mobilities of aliphatic and carboxylic acids. Also result reveals the superiority of the artificial neural network over the multiple linear regression models.