Comparison of the Dynamic Thermal Gradient to Temperature-Programmed Conditions in Gas Chromatography Using a Stochastic Transport Model

This paper compares dynamic (i.e., temporally changing) thermal gradient gas chromatography (GC) to temperature-programmed GC using a previously published stochastic transport model to simulate peak characteristics for the separation of C12-C40 hydrocarbons. All comparisons are made using chromatographic conditions that give approximately equal analyte retention times (t(R)). As shown previously, a static thermal gradient does not improve resolution (R-s) equally for all analytes, which highlights the need for a dynamic thermal gradient. An optimal dynamic thermal gradient should result in constant analyte velocities at any instant in time for those analytes that are actively being separated (i.e., analytes that have low retention factors). The average separation temperature for each analyte is used to determine the thermal gradient profile at different times in the temperature ramp. Because many of the analytes require a similar thermal gradient profile when actively being separated, the thermal gradient profile in this study was held fixed; however, the temperature of the entire thermal gradient was raised over time. From the simulations performed in this study, optimized dynamic thermal gradient conditions are shown to improve R-s by up to 13% over comparative temperature-programmed conditions, even with a perfect injection (i.e., zero injection bandwidth). In the dynamic thermal gradient simulations, all analytes showed improvements in R-s along with slightly shorter t(R) values compared to simulations for traditional temperature-programmed conditions.

Automated summary

This study demonstrates that optimized dynamic thermal gradient conditions can improve resolution by up to 13% compared to traditional temperature-programmed conditions, while also slightly shortening retention times.