An expanded cellular automata model for enantiomer separations using a β-cyclodextrin stationary phase

Chromatographic scale enantiomer separation has not been modeled using cellular automata (CA). CA uses easy to adjust equations to different enantiomers under various chromatographic conditions. Previous work has demonstrated that CA modeling can accurately predict the strength of one-to-one binding interactions between enantiomers and β-cyclodextrin (CD) [1]. In this work, the model is expanded to a chromatographic scale grid environment in order to transform model output into HPLC chromatograms. The model accurately predicted the lack of chromatographic selectivity of mandelic enantiomers (1.05 published, 1.01 modeled) and the separation of brompheniramine enantiomers (1.13 published, 1.12 modeled) previously modeled in one-to-one interactions. By examining cyclohexylphenylglycolic acid (CHPGA) enantiomers, the model accurately predicted both the selectivity and resolution of the enantiomer peaks at varying chromatographic temperatures. Modeled changes in mobile phase pH agree with laboratory outcomes when examining peak resolution and selectivity. Changes in injection volume resulted in an increase in retention time of the modeled enantiomers as was observed in the published laboratory results.