Unraveling peak asymmetry in chromatography through stochastic theory powered Monte Carlo simulations.

An overarching theory of chromatography capable of modeling all analyte-stationary phase interactions would enable predictive design of pharmaceutically relevant separations. The stochastic theory of chromatography has been postulated as a suitable basis to achieve this goal. Here, we implement Dondi and Cavazzini's Monte Carlo framework that utilizes experimentally accessible single molecule kinetics and use it to correlate heterogenous adsorption statistics at the stationary phase to shifts in asymmetry. The contributions cannot be captured or modeled through ensemble chemometrics. Simulations reveal that peak asymmetry scales non-linearly with longer analyte-stationary phase interactions and migrates towards symmetry across the column length, even without column overloading.