Computer-assisted modelling and optimisation of reversed-phase high-temperature liquid chromatographic (RP-HTLC) separations

Abstract

The use of high temperatures (above 100 °C) in reversed-phase liquid chromatography (RP-HTLC) has opened up novel and enhanced applications for this essential separation technique. Although the favourable effects of temperature on LC have been extensively studied both theoretically and practically, its potential application to method development has barely been investigated. These favourable effects include enhanced speed, efficiency, resolution and detectability, as well as changes in selectivity, especially for polar and ionisable compounds, and the emergence of new options such as temperature programming and the concomitant use of solvent and temperature gradients, green separations, and so on. The recent availability of silica-based columns that routinely support high temperatures in addition to more conventional temperature-resistant columns (based on graphitised carbon, polymers and zirconium oxide) and dedicated column ovens that allow accurate temperature control up to 200 °C makes it possible to conceive of RP-HTLC as a routine separation technique in the modern analytical laboratory. On the other hand, the addition of temperature as a new optimisable parameter to RPLC adds further complexity to method development. Thus, new computer-assisted optimisation tools that extend the capabilities of current computer-assisted tools are being specifically developed for this type of separation. A new specially developed computer-assisted method development (CAMD) tool is presented herein, and its efficiency is demonstrated. This CAMD is based on the development of a rugged retention model for peaks, allowing the simulation of any kind of RP-HTLC separation, including isocratic, linear, curved, multilinear and stepwise gradients of solvent composition concomitant with constant, linear and multilinear temperature gradients. Both the retention models and the unattended optimisation of separations are driven by evolutionary algorithms, thus providing negligible-cost, rapid, highly efficient, and user-friendly optimisation processes.