Effect of solvent composition on adsorption isotherms based on multi-component breakthrough curve analysis and application of a multi-component BET model

Abstract Quantitative knowledge of competitive adsorption isotherms is essential for the design and optimization of adsorption-based separation processes. To adjust the retention times of the components to be separated in liquid phase applications, mixtures of two or more solvents are often selected as the mobile phase. The solvent composition thus has an influence on the courses of the adsorption isotherms. The paper presents the results of breakthrough curve (BTC) experiments considering the separation of three benzene derivatives characterized by different aliphatic carbon chains, exploiting conventional reversed-phase chromatography (RP18). As the mobile phase, different mixtures of acetonitrile (ACN) and water (W) were used. Equilibrium loadings were extracted from characteristic features of the observed BTCs for single components, binary mixtures, and ternary mixtures. The analysis ignored kinetic effects and exploited classical equilibrium theory. For an initially selected solvent composition, the courses of the isotherms revealed an anti-Langmuir behavior for the two longer retained components. All single-component and mixture equilibrium data could be well described with a newly derived widely applicable multi-component single-site BET adsorption model, assuming a finite number of adsorbed layers. The analysis was applied again to determine and analyze the courses of BTCs and the corresponding multi-component adsorption isotherms for three other solvent compositions. The slopes of the single-component isotherms changed significantly for the different components. The derived BET-type equation could again be used to describe rather well the isotherms for increasing amounts of ACN with specific numbers of adsorbed layers. For ACN/W = 90/10, the layer number approached unity and, thus, an almost perfect Langmuir behavior was identified. The identified trends between the solvent compositions and the estimated isotherms model parameters could be well described with the LSS model. Finally, suggestions were made for further improving the agreement between measured and predicted isotherms.