Prediction of electrophoretic mobilities. 1. Monoamines

Area of Science:

• Analytical Chemistry
• Physical Chemistry
• Computational Chemistry

Background:

• Electrophoretic mobility is key in capillary electrophoresis.
• Physicochemical properties of monoamines influence their mobility.
• Accurate prediction of mobility is essential for separation science.

Purpose of the Study:

• To develop a predictive model for absolute mobilities of monoamines.
• To identify key molecular descriptors influencing electrophoretic mobility.
• To establish a reliable method for calculating ion mobility in capillary electrophoresis.

Main Methods:

• Molecular modeling was used to determine physicochemical parameters of monoamines.
• Regression analysis was employed to correlate parameters with experimental mobilities.
• A novel equation incorporating molecular weight and hydration was derived.

Main Results:

• Molecular mass is a stronger predictor of mobility than molecular volume.
• Classical models (Hückel, Perrin) were inadequate predictors.
• The derived equation achieved prediction errors of 3.3%–7.2% on various datasets.

Conclusions:

• A new empirical equation accurately predicts monoamine electrophoretic mobilities.
• Incorporating hydration effects significantly improves mobility prediction accuracy.
• The findings offer a valuable tool for capillary electrophoresis method development.