Literature DB >> 19013576

Foundations of retention in partition chromatography.

Colin F Poole1, Salwa K Poole.   

Abstract

The connection between the observable output in column chromatography (retention time, retention volume, retention factor, separation factor, etc.) and system properties (hold-up volume, pressure, temperature, isotherm behavior, etc.) is discussed from a practical and mechanistic perspective for gas-liquid chromatography, reversed-phase liquid chromatography, supercritical fluid chromatography, micellar electrokinetic chromatography, and capillary electrochromatography. The unifying feature of these techniques is that retention can be described by a partition model, although not always exclusively. When over simplistic system models are used to explain variation in retention parameters they frequently mask the true reasons for poor repeatability and difficulties in transfer between system. Methods employing relative retention afford higher precision but may contain residual uncorrected errors. For those systems with several separate mechanisms contributing to retention the effective retention parameters can no longer be interpreted by simple partition models. The broadly based and practically focused material in this article affords an illustration of the often complicated relationship between system properties and retention, and the dangers that lurk in simplified retention models if the validity of their underlining approximations is not appropriate for the system under study.

Mesh:

Year:  2008        PMID: 19013576     DOI: 10.1016/j.chroma.2008.10.092

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  6 in total

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Authors:  Jonell N Smith; Gregory V White; Michael I White; Robert Bernstein; James M Hochrein
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2.  Difference equation model for isothermal gas chromatography expresses retention behavior of homologues of n-alkanes excluding the influence of holdup time.

Authors:  Liejun Wu; Yongli Chen; Sarah A L Caccamise; Qing X Li
Journal:  J Chromatogr A       Date:  2012-08-01       Impact factor: 4.759

3.  Mobile Affinity Sorbent Chromatography.

Authors:  ZhiYu Li; JinHee Kim; Fred E Regnier
Journal:  Anal Chem       Date:  2018-01-09       Impact factor: 6.986

4.  A new accurate quadratic equation model for isothermal gas chromatography and its comparison with the linear model.

Authors:  Liejun Wu; Maoxue Chen; Yongli Chen; Qing X Li
Journal:  J Chromatogr A       Date:  2012-08-28       Impact factor: 4.759

5.  Determination and evaluation of gas holdup time with the quadratic equation model and comparison with nonlinear models for isothermal gas chromatography.

Authors:  Liejun Wu; Maoxue Chen; Yongli Chen; Qing X Li
Journal:  J Chromatogr A       Date:  2013-04-30       Impact factor: 4.759

6.  From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Authors:  Saer Samanipour; Phil Choi; Jake W O'Brien; Bob W J Pirok; Malcolm J Reid; Kevin V Thomas
Journal:  Anal Chem       Date:  2021-11-29       Impact factor: 6.986

  6 in total

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