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Understanding Retention in Reversed-Phase Liquid Chromatography

Recent research from the group of Professor Ilja Siepmann.

Reversed-phase liquid chromatography (RPLC) is among the most widely used and versatile analytical techniques. However, a detailed, molecular-level understanding of the RPLC retention mechanism has eluded analytical chemists for decades. Through validated, particle-based Monte Carlo simulations of a model RPLC system consisting of dimethyl octadecyl silanes on an explicit silica substrate with unprotected residual silanols in contact with a water/methanol mobile phase, graduate student Jake Rafferty and co-workers show that the molecular-level retention processes for non-polar and polar analytes, such as alkanes and alcohols, are much more complex than what has been previously deduced from thermodynamic and theoretical arguments. In contrast to some previous assumptions, the simulations indicate that both partitioning and adsorption play a key role in the separation process and that the stationary phase in RPLC behaves substantially different from a bulk hydrocarbon phase. The retention of non-polar methylene segments is dominated by lipophilic interactions with the retentive phase, while solvophilic interactions are more important for the retention of the polar hydroxyl group (see Figure).

FIGURE CAPTION: Thermodynamic driving forces for retention. The incremental free energies of transfer for methylene and hydroxyl groups into the stationary phase (ODS) and mobile phases (neat water, 33% mole fraction of methanol, 67% mole fraction of methanol, and neat methanol) are shown with respect to the ideal-gas reference phase (VAPOR). For comparison, incremental free energies of transfer into bulk n-hexadecane phase (C16) and determined from chromatographic experiments (red lines, B.N. Barman, Ph.D. Dissertation, Georgetown University, 1986) are also depicted.

A detailed description of this research has been published in Analytical Chemistry 79, 6551 (2007).

The development of advanced computational strategies for the most challenging problems in chemistry is a theme common to the research endeavors of the Minnesota Computational Chemistry Group, where research includes new theoretical formulations, the development of new computational algorithms, and use of state-of-the-art supercomputers to solve prototype problems to high accuracy and to predict chemically useful results for a wide range of system scales ranging from a few atoms to thousands of atoms.

Financial support from the National Science Foundation is gratefully acknowledged. Part of the computer resources were provided by the Minnesota Supercomputing Institute.

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