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

Abstract

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

Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances

“…The reliance on LSER chromatographic parameters differentiates the GCxGC elution model from other GCxGC retention time models. − Due to the broad predictive capability of the UFZ-LSER tool, , users can apply the GCxGC elution model to diverse chemical structures containing a wide variety of functionalities, including all of the hydrocarbon structures in the library.…”

Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update

“…Recently, column selection/experimental design based on use of Visual Basic scripting to optimize GC×GC−TOFMS analysis of middle distillates was validated using either low-polarity/polar vs polar/low-polarity phase column configurations . An approach to reduce the number of experiments in order to obtain thermodynamic data for prediction of peak time and widths in temperature-programmed separation was reported . This approach provided uncertainties in retention time and peak width of <0.5% and <5%, respectively, for separation of PAH and a mixture containing compounds with aldehyde, ketone, alkene, alkane, alcohol, and ester functionalities.…”

“…67 An approach to reduce the number of experiments in order to obtain thermodynamic data for prediction of peak time and widths in temperature-programmed separation was reported. 68 This approach provided uncertainties in retention time and peak width of <0.5% and <5%, respectively, for separation of PAH and a mixture containing compounds with aldehyde, ketone, alkene, alkane, alcohol, and ester functionalities. In order to improve accuracy in retention time prediction in GC×GC, parameters can be added into a simulation model to take into account systematic errors caused by the modulation device.…”

A deep learning‐based simulator for comprehensive two‐dimensional GC applications

“…The first approach performs geometric measurements of orthogonality between dimensions to obtain the best separation, originating from the so-called scattering models [18,19]. The second is based on thermodynamic calculations to predict retention times in the respective dimensions, which is widely adopted [20][21][22]. Finally, the third is the recent and still under-explored computational models that employ algorithms and computational techniques for data processing, like those based on machine learning.…”