Wide injection zone compression in gradient reversed-phase liquid chromatography.
Chromatographic zone broadening is a common issue in microfluidic chromatography, where the sample volume introduced on column often exceeds the column void volume. To better understand the propagation of wide chromatographic zones on a separation device, a series of MS Excel spreadsheets were developed to simulate the process. To computationally simplify these simulations, we investigated the effects of injection related zone broadening and its gradient related zone compression by tracking only the movements of zone boundaries on column. The effects of sample volume, sample solvent, gradient slope, and column length on zone broadening were evaluated and compared to experiments performed on 0.32mm I.D. microfluidic columns. The repetitive injection method (RIM) was implemented to generate experimental chromatograms where large sample volume scenarios can be emulated by injecting two discrete small injection plugs spaced in time. A good match between predicted and experimental RIM chromatograms was observed. We discuss the performance of selected retention models on the accuracy of predictions and use the developed spreadsheets for illustration of gradient zone focusing for both small molecules and peptides.
Waters Corporation, 34 Maple Street, Milford, MA 01757, USA. Electronic address: Martin_Gilar@waters.com.
Martin
M
Gilar
Waters Corporation, 34 Maple Street, Milford, MA 01757, USA.
Thomas S
TS
McDonald
Waters Corporation, 34 Maple Street, Milford, MA 01757, USA.
Jay S
JS
Johnson
Waters Corporation, 34 Maple Street, Milford, MA 01757, USA.
James P
JP
Murphy
Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
James W
JW
Jorgenson
D056148:Chromatography, Reverse-Phase / Q000379:methods*; D009842:Oligopeptides / Q000302:isolation & purification
D009842:Oligopeptides
Chromatography; Gradient compression; Linear solvation strength theory; Microfluidic; Repetitive injection method; Retention prediction