Gas chromatography system constant database for 52 wall-coated, open-tubular columns covering the temperature range 60–140 °C

Poole, Colin F.

doi:10.1016/j.chroma.2019.460482

Cited by 35 publications

(6 citation statements)

References 71 publications

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“…For DB-210 and DB-1701, we decided to use the same idea but keep the model as simple as possible with very few fitted parameters. The GC retention can be approximately represented [35] as the sum of the products of analyte-specific and SP-specific parameters:…”

Section: Further Testing and Applicationsmentioning

confidence: 99%

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Matyushin

Sholokhova

Buryak

2021

IJMS

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Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

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Section: Further Testing and Applicationsmentioning

confidence: 99%

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Matyushin

Sholokhova

Buryak

2021

IJMS

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“…Even though column production is a complicated multi-step procedure that includes treating and deactivation of the silica surface, wall-coating, and immobilization of the stationary phase. These steps are implemented by the manufacturers using different technologies giving rise to different qualities of the stationary phase [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…In 1990 Abraham et al introduced the solvation parameter model to describe McReynolds 77-stationary phase set with 5 constants instead of one single polarity index [5]. Based on the solvation parameter model Poole [1] built up a chromatography system constant database for 52 wall-coated capillary columns using multiple linear regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Comparison of 624-type Capillary Columns

Nyerges

Mátyási

Balla

2023

Period. Polytech. Chem. Eng.

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In our research, seven 624-type capillary columns were investigated. All the columns were the same in length, internal diameter, and film thickness (30 m × 0.32 mm × 1.0 µm). However, they were produced by different manufacturers or the same manufacturer but in different batches. Even though the manufacturers recommend them as "equivalent columns" this equivalence did not prevail even in the case of columns produced by the same manufacturer. Our examination criteria centered on the quantitative determination ability of the columns. A homemade column test mixture was compiled to represent all the second-order interactions that can occur between the analyte and stationary phase. Although theoretically these columns have the same stationary phase quality, they did not result in the same chromatograms. In addition to the origin and batch of the column, the "history of the column" contributes likewise to the different peak symmetry, retention order, and even peak areas that affect the quantitative determination. We quantified this quantitative determination ability with the effective carbon number (ECN) and the Limit of Quantitation (LoQ) values. Based on our results the attainable LoQ and ECN values depend at least as much on the origin and actual state of the stationary phase as on the measurement conditions to be optimized. In our paper, we demonstrate the extent to which the same stationary phases offered by different companies and/or different backgrounds can influence our detection limit and detector response even if the relevant columns have theoretically the same chemical structure.

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“…describes solute transfer into a condensed phase from the gas phase. Specific properties that have been successfully described include partition coefficients [20][21][22], molar solubility ratios [19,23,24], aquatic toxicities [25][26][27], nasal pungencies [28], gas-liquid chromatographic and HPLC retention data [18,[29][30][31][32][33], Draize scores and eye irritation thresholds [34,35], human and rat intestinal adsorption data [36,37], human skin permeability [38,39], infinite dilution activity coefficients [40,41], molar enthalpies of solvation [42][43][44], standard molar vaporization [45] and sublimation [46] at 298 K, vapor pressures [47], and limiting diffusion coefficients [48,49]. Unlike many of the QSPRs that have been proposed in the published chemical and engineering literature, Equations ( 1) and ( 2) are based upon a fundamental understanding of how molecules interact in solution.…”

Section: Introductionmentioning

confidence: 99%

Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

Sinha

Yang

et al. 2022

Liquids

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Abraham model L solute descriptors have been determined for 149 additional C11 to C42 monomethylated and polymethylated alkanes based on published Kovat’s retention indices based upon gas–liquid chromatographic measurements. The calculated solute descriptors, in combination with previously published Abraham model correlations, can be used to predict a number of very important chemical and thermodynamic properties including partition coefficients, molar solubility ratios, gas–liquid chromatographic and HPLC retention data, infinite dilution activity coefficients, molar enthalpies of solvation, standard molar vaporization and sublimation at 298 K, vapor pressures, and limiting diffusion coefficients. The predictive computations are illustrated by estimating both the standard molar enthalpies of sublimation and the enthalpies of solvation in benzene for the monomethylated and polymethylated alkanes considered in the current study.

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Gas chromatography system constant database for 52 wall-coated, open-tubular columns covering the temperature range 60–140 °C

Cited by 35 publications

References 71 publications

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Comparison of 624-type Capillary Columns

Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

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