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


Thermodynamic modeling, Chromatography, Molecules, Colloids, Heat transfer


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 (tR). As shown previously, a static thermal gradient does not improve resolution (Rs) 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 Rs 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 Rs along with slightly shorter tR values compared to simulations for traditional temperature-programmed conditions.

Original Publication Citation

Avila, S., Tolley, H. D., Iverson, B. D., Hawkins, A. R., Johnson, S. L., and Lee, M. L., 2021, “Comparison of the dynamic thermal gradient to temperature-programmed conditions in gas chromatography using a stochastic transport model,” Analytical Chemistry, 93(34), pp. 11785–11791. DOI: 10.1021/acs.analchem.1c02210

Document Type

Peer-Reviewed Article

Publication Date


Permanent URL


Analytical Chemistry




Ira A. Fulton College of Engineering


Mechanical Engineering

University Standing at Time of Publication

Associate Professor