Search

The improvement of supercritical fluid chromatography (SFC) instrumentation enhanced its reliability and utility over the past decade. The further development of high speed and high resolution separations is however obstructed by the lack of accurate models for axial dispersion in SFC. In a first step to enhance prediction and understanding of separation performance in SFC, methods need to be developed to measure molecular and longitudinal diffusion in SFC and to model their behavior. These parameters are essential to predict the separation efficiency in chromatography as it affects all contribution (A, B and C-term). Using a commercially available SFC instrument with the addition of only a single rotorstator valve, a flexible method to measure Dmol was developed and validated. By using a chromatographic column to separate the solutes from the injection solvent plug, and a small volume loop to capture and reinject the solute in the appropriate mobile phase, typical practical limitation of this technique could be overcome. The effective diffusion coefficient (Deff) was measured using stop-flow experiments, also called peak parking, where the mobile phase flow is halted when the solute is halfway through the column. In SFC, the set-up needs to be modified since during the parking the mobile phase would decompress and elute from the column. In addition, due to the stronger compressibility of the mobile phase, restarting the flow rate is accompanied by transient start-up effects that vary flowrate and retention in a non-reproducible way. Therefore a two-column variant of the set-up was developed where two identical columns are coupled in parallel using rotor-stator valves, to maintain pressure in the parking column and flow and stable operating pressure in the SFC system.

In the present numerical study, we investigate the fine details of the viscous heating band broadening under extreme high-pressure conditions (2500 bar). We first show that viscous heating leads to two clearly distinguishable band broadening effects, one originating from the radial differences in the species migration velocity and the other from the axial variation in velocity, diffusion coefficient and retention. It was found that the radial contribution is independent of the intrinsic band broadening of the bed. On the other hand, the axial contribution is strongly dependent on it and it is found to be 4 to 5 times lower than the radial contribution. A number of potential column technology solutions to minimize the additional plate height originating from the viscous heating effect were investigated. For the considered column and conditions, it is found that, in order to keep the global plate height as measured at the column outlet (Hvh,glob,out) below 1 μm at an operating pressure of 2500 bar, the bed conductivity would need to be raised to 2.4 W/m·K, i.e., 4 times higher than a typical packed bed of fully-porous or core-shell silica particles. An equivalent effect on the band broadening could be obtained if it would be possible to replace the steel column wall with a low conductivity material. In this case, a wall conductivity of 0.25 W/m·K, i.e., 64 times smaller than the conductivity of steel, would be needed to keep Hvh,glob,out below 1 μm.

Choosing a liquid chromatography (LC) column for a particular application can be a surprisingly challenging task. On one hand, column manufacturers give us many options to choose from, including particle types, pore sizes, particle sizes, and different lengths and diameters. On the other hand, we usually don’t have time to experimentally evaluate many combinations of these parameters, and sometimes we end up picking something similar to the columns that are already in the drawer. The “kinetic plot” is a powerful graphical tool that can help leverage the best available theory to help us understand how different combinations of parameters (that is, particle size and length) will perform in terms of the time needed to get to a particular column efficiency (and thus resolution), and therefore make well-informed decisions when choosing columns.

Choosing a liquid chromatography (LC) column for a particular application can be a surprisingly challenging task. On the one hand, column manufacturers provide us many options to choose from, including particle types, pore sizes, particle sizes, and different lengths and diameters. On the other hand, we usually do not have time to experimentally evaluate many combinations of these parameters, and sometimes we end up picking something similar to the columns that are already available. The “kinetic plot” is a powerful graphical tool that can help leverage the best available theory to help us understand how different combinations of parameters (that is, particle size, length, among others) will perform in regard to the time needed to get to a particular column efficiency (and thus resolution), and therefore make well-informed decisions when choosing columns.

Choosing a liquid chromatography (LC) column for a particular application can be a surprisingly challenging task. On the one hand, column manufacturers give us many options to choose from, including particle types, pore sizes, particle sizes, and different lengths and diameters. On the other hand, we usually do not have time to experimentally evaluate many combinations of these parameters, and sometimes we end up picking something similar to the columns that are already in the drawer. The “kinetic plot” is a powerful graphical tool that can help leverage the best available theory to help us understand how different combinations of parameters (such as particle size and length) will perform in relation to the time needed to get to a particular column efficiency (and thus resolution), and therefore make well-informed decisions when choosing columns.