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Updated: Oct 15, 2025

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Modelling of analyte profiles and band broadening generated by interface loops used in multi-dimensional liquid
Ali Moussa1, Thomas Lauer2, Dwight Stoll2
1Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
Abstract:
Currently, the shape and variance of the analyte band entering the second dimension column when injected from an open loop interface in two-dimensional liquid chromatography is not fully understood. This is however important as it is connected to several other variables encountered when developing 2D-LC methods, including the first dimension flow rate, the sampling (modulation) time and the loop volume. Both numerical simulation methods and experimental measurements were used to understand and quantify the dispersion occurring in open tubular interface loops. Variables included are the analyte diffusion coefficient (Dmol), loop filling and emptying rates (Ffill & Fempty), loop inner diameter or radius (Rloop) and loop volume (Vloop). For a straight loop capillary, we find that the concentration profile (as measured at the loop outlet) depends only on a single dimensionless parameter tempty*=VloopFempty·DmolRloop2 and the ratio of the filling and emptying flow rates Fempty/Ffill. A model depending only on these two parameters was developed to predict of the peak variance resulting from the filling and emptying of a straight capillary operated in the first-in-last-out (FILO) modulation mode. Comparison of the concentration profiles and the corresponding variances obtained by either numerical simulation or experiments with straight capillaries shows the results generally agree very well. When the straight capillary is replaced by a tightly coiled loop, significantly smaller (20-40%) peak variances are observed compared to straight capillaries. The magnitude of these decreases is not predicted as well by simulations, however the simulation results are still useful in this case, because they represent an upper boundary (i.e., worst-case scenario) on the predicted variance.
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