Both regimens showed a good clinical efficacy, but CyA seems to be a better glucocorticoid-sparing agent than AZA.
This article presents a method of simulating molecular transport in capillary gas chromatography (GC) applicable to isothermal, temperature-programmed, and thermal gradient conditions. The approach accounts for parameter differences that can occur across an analyte band including pressure, mobile phase velocity, temperature, and retention factor. The model was validated experimentally using a GC column comprised of microchannels in a stainless-steel plate capable of isothermal, temperature-programmed, and thermal gradient GC separations. The parameters governing retention and dispersion in the transport model were fitted with 12 experimental isothermal separations. The transport model was validated with experimental data for three analytes using four temperature-programmed and three thermal gradient GC separations. The simulated peaks (elution time and dispersion) give reasonable predictions of observed separations. The magnitudes of the maximum error between simulated peak elution time and experiment were 2.6 and 4.2% for temperature-programmed and thermal gradient GC, respectively. The magnitudes of the maximum error between the simulated peak width and experiment were 15.4 and 5.8% for temperature-programmed and thermal gradient GC, respectively. These relatively low errors give confidence that the model reflects the behavior of the transport processes and provides meaningful predictions for GC separations. This transport model allows for an evaluation of analyte separation characteristics of the analyte band at any position along the length of the GC column in addition to peak characteristics at the column exit. The transport model enables investigation of column conditions that influence separation behavior and opens exploration of optimal column design and heating conditions.
This paper compares static (i.e., temporally unchanging) thermal gradient gas chromatography (GC) to isothermal GC using a stochastic transport model to simulate peak characteristics for the separation of C12–C14 hydrocarbons resulting from variations in injection bandwidth. All comparisons are made using chromatographic conditions that give approximately equal analyte retention times so that the resolution and number of theoretical plates can be clearly compared between simulations. Simulations show that resolution can be significantly improved using a linear thermal gradient along the entire column length. This is mainly achieved by partially compensating for loss in resolution from the increase in mobile phase velocity, which approximates an ideal, basic separation. The slope of the linear thermal gradient required to maximize resolution is a function of the retention parameters, which are specific to each analyte pair; a single static, thermal gradient will not affect all analytes equally. A static, non-linear thermal gradient that creates constant analyte velocities at all column locations provides the largest observed gains in resolution. From the simulations performed in this study, optimized linear thermal gradient conditions are shown to improve the resolution by as much as 8.8% over comparative isothermal conditions, even with a perfect injection (i.e., zero initial bandwidth).
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.
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