In Silico Screening of Two-Dimensional Separation Selectivity for Ion Chromatography × Capillary Electrophoresis Separation of Low-Molecular-Mass Organic Acids.

A prerequisite for ordered two-dimensional (2D) separations and full utilization of the enhanced 2D peak capacity is selective exploitation of the sample attributes, described as sample dimensionality. In order to take sample dimensionality into account prior to optimization of a 2D separation, a new concept based on construction of 2D separation selectivity maps is proposed and demonstrated for ion chromatography × capillary electrophoresis (IC×CE) separation of low-molecular-mass organic acids as test analytes. For this purpose, 1D separation selectivity maps were constructed based on calculation of pairwise separation factors and identification of critical pairs for four IC stationary phases and 28 levels of background electrolyte pH in CE. The derived IC and CE maps were then superimposed and the effectiveness of the respective 2D separations assessed using an in silico approach, followed by testing examples of one successful and one unsuccessful 2D combination experimentally. The results confirmed the efficacy of the predictions, which require a minimal number of experiments compared to the traditional one-at-a-time approach. Following the same principles, the proposed framework can also be adapted for optimization of separation selectivity in various 2D combinations and for other applications.