Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with –OH Moieties
Résumé
The retention behavior in supercritical fluid chromatog-raphy and its stability over time are still unsatisfactorily explainedphenomena despite many important contributions in recent years,especially focusing on linear solvation energy relationship modeling. Westudied polar stationary phases with predominant −OH functionalities, i.e.,silica, hybrid silica, and diol columns, and their retention behavior overtime. We correlated molecular descriptors of analytes with their retentionusing three organic modifiers of the CO2-based mobile phase. Thedifferences in retention behavior caused by using additives, namely, 10mmol/L NH3 and 2% H2O in methanol, were described in correlation toanalyte properties and compared with the CO2/methanol mobile phase.The structure of >100 molecules included in this study was optimized bysemiempirical AM1 quantum mechanical calculations and subsequentlydescribed by 226 molecular descriptors including topological, constitutional, hybrid, electronic, and geometric descriptors. Anartificial neural networks simulator with deep learning toolbox was trained on this extensive set of experimental data andsubsequently used to determine key molecular descriptors affecting the retention by the highest extent. After comprehensivestatistical analysis of the experimental data collected during one year of column use, the retention on different stationary phases wasfundamentally described. The changes in the retention behavior during one year of column use were described and their explanationwith a proposed interpretation of changes on the stationary phase surface was suggested. The effect of the regeneration procedure onthe retention was also evaluated. This fundamental understanding of interactions responsible for retention in SFC can be used forthe evidence-based selection of stationary phases suitable for the separation of particular analytes based on their specificphysicochemical properties.
Domaines
Chimie
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