Fast and accurate method for the automatic prediction of programmed-temperature retention times

Vezzani, S.; Moretti, Paolo; Castello, Gianrico

doi:10.1016/0021-9673(94)80161-4

Cited by 40 publications

(7 citation statements)

References 33 publications

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“…Equation is not suitable for describing the temperature dependence of Kováts indices over a wide temperature range (regardless of whether the target compound is polar or nonpolar). If the fourth term in eq is added to the first two, an empirical equation utilized by Castello’s group , is obtained. Numerous other empirical forms of the I x = f ( T ) function have also been adopted including linear, , simple quadratic, , polynomial, and the three-parameter Antoine-type hyperbolic equation. − …”

Section: Methodsmentioning

confidence: 99%

Extracting Vapor Pressure Data from Gas–Liquid Chromatography Retention Times. Part 2: Analysis of Double Reference Approach

et al. 2018

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The vapor pressures of 46 compounds including n-alkanes and their monofunctionalized derivatives, some monoterpenes, as well as chloro-and alkylbenzenes were determined by GLC exploring the Kovats retention indices (KI) technique, primarily in its basic form using two alkane references (GLC-RTKI). The fundamental advantage of the GLC-RTKI method is that it guarantees appropriate selection of alkane reference compounds. Typical relative errors in vapor pressures accompanying this method were below 30% for compounds of low polarity, while for polar compounds (nitriles, alcohols) the errors can reach percentages in the hundreds. In contrast, vapor pressures obtained from the modified Kovats method, relying on reference standards whose functional group matches that of the test compound (GLC-RTmKI), were found mostly within 5% of directly measured values. However, the success of this method is dependent on the availability of reliable vapor pressure data for compounds to serve as reference standards. As our methodology eliminates possible secondary error sources (e.g., data extrapolation, or recalculation from supercooled liquid-to-solid phase or vice versa), we propose that the results obtained are as accurate as is feasibly possible with GLC-RTKI and GLC-RTmKI methods. This study also yielded a novel set of directly measured (static method) data on the vapor pressures of C7−C9 alkyl nitriles.

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Section: Methodsmentioning

confidence: 99%

Extracting Vapor Pressure Data from Gas–Liquid Chromatography Retention Times. Part 2: Analysis of Double Reference Approach

et al. 2018

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“…Numerical values of parameters of ideal thermodynamic model gradually change if T changes within a wide temperature range [8,[12][13][14][15][16][17][18][19][20]. This means that the ideal thermodynamic model (fixed parameters in Eqs.…”

Section: Alternative Expressions Of Ideal Retention Modelmentioning

confidence: 99%

Solute–Liquid Interaction in Gas Chromatography

Blumberg¹

2010

Temperature‐Programmed Gas Chromatography

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Solute separation in GC results from the difference in their migration velocities in a column. That difference, in turn, is a result of the difference in the solute interaction with the columns stationary phase. The stronger is the interaction, the lower is the migration velocity. Depending on the stationary phase typesolid or liquida solute can be adsorbed on the solid surface, or it can be absorbed by the liquid (dissolved in it). This chapter focuses on the GC-related parameters of interaction of solutes with liquid stationary phases. Distribution Constant and Retention FactorConsider a layer of a liquid (the liquid phase) and a layer of an inert gas (the gas phase) that are in contact with each other, Figure 5.1. In this chapter, the layers represent organic liquid polymer film and a carrier gas in a capillary (open tubular) column, respectively, Figure 2.1a. Suppose that a solute was added to the gas, and the entire three-component system was sealed. A fraction of the solute added to the gas can be absorbed by the liquid (dissolved in it). Of the primary concern for this chapter is a chromatographically meaningful description of equilibrium of a solute solvationevaporation (solvation in the liquid and evaporation into the gas) or, in broader terms, equilibrium of a solute-liquid interaction. The equilibrium takes place at a certain distribution of a solute between the gas and the liquid. The gas is inert and has no effect on the equilibrium distribution of a solute between the two phases. This means that replacement of one gas with another has no effect on the equilibrium.The equilibrium distribution of a solute between a gas and a liquid, Figure 5.1, can be described by the distribution constant [1] (partition coefficient [2-8]), K c , defined as [2][3][4][5][6][7]9] K c ¼ C sol;f C sol;g ð5:1Þwhere C sol,g and C sol,f are concentrations (mass per volume, mole per volume, and so forth) of the solute in a liquid film and a gas, respectively.Temperature-Programmed Gas Chromatography. Leonid M. Blumberg

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“…Among the approaches to predictive modeling of GC separations, thermodynamic‐based models have received a great deal of attention over the years. Much of the early work focused on establishing relationships between thermodynamic parameters and retention , and using these relationships to calculate retention factors and predict retention times (and sometimes, peak width) . These earlier studies typically required that the retention indices of the compounds of interest on the columns being investigated were known or determined experimentally (usually through several isothermal separations).…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

Stevenson

Harynuk

2019

J of Separation Science

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Thermodynamics-based models have been demonstrated to be useful for predicting retention time and peak widths in gas chromatography and two-dimensional gas chromatography separations. However, the collection of data to train the models can be time consuming, which lessens the practical utility of the method. In this contribution, a method for obtaining thermodynamic-based data to predict peak widths in temperature-programmed gas chromatography is presented. Experimental work to collect data for peak width prediction is identical to that required to collect data for retention time prediction using approaches that we have presented previously. Using this combined approach, chromatograms including retention times and peak widths are predicted with very high accuracy. Typical errors in retention time are < 0.5%, while errors in peak width are typically < 5% as demonstrated using polycycic aromatic hydrocarbons and a mixture containing compounds with aldehyde, ketone, alkene, alkane, alcohol, and ester functionalities. K E Y W O R D Speak width prediction, retention prediction, thermodynamic modeling As such, over the last few decades, researchers have developed methods and tools for predicting and simulating GC separations. Among the approaches to predictive modeling of GC separations, thermodynamic-based models have received a great deal of attention over the years. Much of

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Fast and accurate method for the automatic prediction of programmed-temperature retention times

Cited by 40 publications

References 33 publications

Extracting Vapor Pressure Data from Gas–Liquid Chromatography Retention Times. Part 2: Analysis of Double Reference Approach

Extracting Vapor Pressure Data from Gas–Liquid Chromatography Retention Times. Part 2: Analysis of Double Reference Approach

Solute–Liquid Interaction in Gas Chromatography

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

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