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[Tailed normal chromatographic peak expressed by plate model].

Authors: Z, Han; Z, He; G, Yu;

[Tailed normal chromatographic peak expressed by plate model].

Abstract

An equation for describing tailed normal chromatographic peak is derived based on the plate model. In terms of the de Moivre-Laplace theorem, which states that a binomial distribution can be approximated by the Gaussian distribution for large sample size, it is suggested that the concentration distribution along the distance downstream from the inlet of chromatographic column conforms to the Gaussian distribution function, but the concentration distribution with elution volume (or elution time) does not. The equation developed here, which is similar to the Gaussian distribution function, indicates that the normal chromatographic elution curve should be a tailing one. It is also shown that the symmetric Gaussian elution curve is an approximate solution of the plate model and can be obtained by the approximation of the equation here. The equation is proved to be congruent to the results of the diffusion model. Thus, the plate model and diffusion model are equivalent to each other in describing chromatographic process although different mechanism is based on.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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