Ideal versus real automated twin column recycling chromatography process

Area of Science:

• Analytical Chemistry
• Chromatography
• Separation Science

Background:

• Baseline separation of compounds with low selectivity factors (α<1.03) is often impractical or impossible with single-column liquid chromatography (LC) due to pressure limitations.
• Single-column efficiency is fundamentally limited by system pressure, column permeability, eluent viscosity, and analyte diffusivity.
• Alternative methods are needed to overcome the inherent efficiency limits of conventional LC for challenging separations.

Purpose of the Study:

• To demonstrate the superior speed-resolution performance of the twin-column recycling separation process (TCRSP) compared to single-column approaches.
• To develop an automated TCRSP for the challenging separation of polycyclic aromatic hydrocarbon (PAH) isomers.
• To investigate and model the discrepancies between ideal and real TCRSP performance, particularly concerning pressure-dependent retention.

Main Methods:

• Development and automation of a twin-column recycling separation process (TCRSP).
• Utilized reversed-phase chromatography with specific C18 columns (XBridge BEH-C18 and XSelect HSST3) and an acetonitrile-water mobile phase.
• Employed a revisited retention and efficiency model to account for pressure-dependent retention and changes in partial molar volume.

Main Results:

• The automated TCRSP successfully separated challenging PAH isomers (benzo[a]anthracene and chrysene) at pressures below 5000 psi.
• Significant differences were observed between predicted ideal TCRSP performance and actual experimental results.
• A revised model accurately predicted real TCRSP performance by incorporating a change in partial molar volume (Δv_m=-10cm³/mol) for the PAH isomers.

Conclusions:

• TCRSP offers an intrinsically superior speed-resolution performance for difficult chromatographic separations.
• Pressure-dependent retention and changes in partial molar volume are critical factors influencing real TCRSP performance.
• The developed revisited model provides accurate predictions for real TCRSP, enabling optimized separation strategies.