Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with -OH Moieties.

The retention behavior in supercritical fluid chromatography and its stability over time are still unsatisfactorily explained phenomena despite many important contributions in recent years, especially focusing on linear solvation energy relationship modeling. We studied polar stationary phases with predominant -OH functionalities, i.e.

Fast Optimization of Supercritical Fluid Chromatography-Mass Spectrometry Interfacing Using Prediction Equations.

The effect of makeup solvent composition in ultrahigh-performance supercritical fluid chromatography-triple quadrupole mass spectrometry using electrospray ionization was studied using a set of 91 compounds, 3 stationary phases, and 2 organic modifiers of the mobile phase. The 24 tested makeup solvents included pure alcohols and methanol in combination with commonly used additives such as water, formic and acetic acid, ammonia, and ammonia salts with varying molarity. The behavioral trends for different makeup solvent additives were established in the first step.