Retention Modeling in Liquid Chromatography
Accurate prediction of solute retention is essential for the development and optimization of liquid chromatographic separations. Retention models provide a quantitative relationship between chromatographic retention and operating conditions, particularly mobile-phase composition, enabling efficient solvent selection, separation optimization, and process design. Although various empirical and semi-empirical models have been widely adopted, their predictive capabilities are often limited by complex molecular interactions, nonideal solvent behavior, and changes in the chromatographic environment. A deeper understanding of the fundamental mechanisms governing retention is therefore crucial for improving model accuracy and extending their applicability across different separation systems.
Our laboratory focuses on investigating the thermodynamic and molecular mechanisms underlying retention behavior in both normal-phase and reversed-phase liquid chromatography. By combining experimental retention measurements, adsorption thermodynamics, and theoretical modeling, we systematically examine the interactions among solutes, solvents, and stationary phases, including the effects of solvent adsorption and chromatographic hold-up volume. Our research aims to evaluate the validity and limitations of existing retention models and to develop more accurate, physically grounded models for predicting retention behavior, ultimately facilitating rational chromatographic method development and efficient purification process design.