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Modelling the relation between the species retention factor and the C‐term band broadening in pressure‐driven and electrically driven flows through perfectly ordered 2‐D chromatographic media
Author(s) -
De Wilde Daan,
Detobel Frederik,
Billen Jeroen,
Deconinck Johan,
Desmet Gert
Publication year - 2009
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.200900458
Subject(s) - van deemter equation , diffusion , analytical chemistry (journal) , chemistry , range (aeronautics) , geometry , porosity , materials science , mathematics , physics , thermodynamics , chromatography , composite material , high performance liquid chromatography , organic chemistry
The band broadening that can be expected in perfectly ordered cylindrical pillar arrays has been calculated for a wide range of intra‐particle diffusion coefficients ( D sz ) and zone retention factors (0< k ″<10) using an in‐house‐developed computational fluid dynamics‐algorithm. Both pressure‐driven and electrically driven (PD and ED) flows are considered. In the case of a large intra‐pillar diffusion coefficient ( D sz / D mol =0.5), the minimal reduced plate height varies between h =0.8 (low k ″) and h =1.2 (high k ″), while the C‐term range of the Van Deemter curve depends only slightly on k ″. In the case of a small intra‐pillar diffusion coefficient ( D sz / D mol =0.1), h is nearly independent of k ″ and approximately equals h min =1.1, while the C‐term band broadening decreases strongly with increasing k ″. The difference between ED and PD flows in perfectly ordered porous pillar arrays is much smaller than in packed beds, generally only 3% difference around the minimum of the plate–height curve and maximally 10% at high‐reduced velocities. The generated data sets were also ideal to verify the accuracy of k ″ and D sz dependency of the general plate height model of chromatography. Determining the values of the geometrical parameters appearing in the model through curve fitting, the average fitting error can be reduced to a value of about 1%.