116 research outputs found
Equivalence of the microscopic and macroscopic models of chromatography: Stochastic-Dispersive versus Lumped Kinetic Model
The microscopic model of chromatography is a stochastic model that consists of two fundamental processes: (i) the random migration of the molecules in the mobile phase, and (ii) the random adsorption-desorption of molecules on the stationary phase contained in a chromatographic column. The diffusion and drift of the molecules in the mobile phase is described with a simple one-dimensional random walk. The adsorption-desorption process is modeled by a Poisson process that assumes exponential sojourn times of the molecules in both the mobile and the stationary phases. The microscopic, or molecular model of chromatography studied here turns out to be identical to the macroscopic lumped kinetic model of chromatography, whose solution is well known in Chromatography. A complete equivalence of the two models is established via the identical expressions they provide for the band profiles
Adsorption equilibria of proline in hydrophilic interaction chromatography
The adsorption behavior of proline under hydrophilic interaction
chromatography conditions was investigated from six aqueous
solutions of acetonitrile. Proline adsorption isotherms were
recorded at each mobile phase composition by frontal analysis and
inverse method. The BET model was found to be the best choice to
describe the nonlinear behavior of proline adsorption under
hydrophilic interaction chromatography conditions. The adsorption
isotherm parameters were derived from two independent parameter
estimation methods. The parameters derived from regression analysis
of the frontal analysis data and from overloaded elution bands were
found to be in good agreement with the excess isotherm of water. The
mobile phase composition at which the maximum excess adsorption of
water was observed corresponded to the maximum saturation capacity
measured for proline
Decoding complex multicomponent chromatograms
This paper describes two mathematical approaches applied for decoding the complex signal of GC separations of multicomponent mixtures.
The methods are helpful in extracting analytical information since separation of all the components present in the sample is still far from being achieved.
One methos is based on the Statistical Degree of Peak Overlapping, the other studies the autocovariance function computed on the experimental digitized GC signal
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