Molar Refractivity and Connectivity Index Correlations for Henry's Law Virial Coefficients of Odorous Sulfur Compounds on Carbon and for Gas-Chromatographic Retention Indices

Rybolt, Thomas R.; Hooper, Dana N; Stensby, Jessica B.; Thomas, Howard E.; Baker, Mitchell L.

doi:10.1006/jcis.2000.7202

Cited by 11 publications

(25 citation statements)

References 31 publications

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“…The column was outgassed under a flow of helium at a maximum temperature of 498 K for at least 14 hours prior to each day's measurements. As in previous studies [26,27], the direction of helium flow through the column was reversed each day. This flow reversal was normally done after a day's measurements and just prior to overnight outgassing.…”

Section: Methodsmentioning

confidence: 99%

“…The experimental procedure used has been described elsewhere [29] and used in previous work [26,27,[30][31][32][33]. The 43 × 0.318 (OD) cm stainless steel column used in the present study was packed by Supelco and contained 0.760 g of 60/80 mesh Carboxen 1000.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the only variation in the gas-solid interaction energies is due to different adsorbates used. If the gas-solid interaction energy is proportional to the molar refractivity, MR, as shown previously [26,27], then Eq. (10) becomes simply (11) ln B 2s = c 0 + c 1 MR.…”

Section: Theorymentioning

confidence: 99%

“…A significant parameter in their work was the Kier and Hall order 1 connectivity index, χ . In prior work [26,27], we showed that the molar refractivity, MR, and χ were effective parameters to correlate chromatographic retention times expressed in terms of second gas-solid virial coefficients. MR is directly proportional to the polarizability [1] as expressed by MR = (4πN A α/3), where N A is the Avogadro constant and α is the polarizability [28].…”

Section: Introductionmentioning

confidence: 98%

See 3 more Smart Citations

Predicting second gas–solid virial coefficients using calculated molecular properties on various carbon surfaces

Rybolt

Janeksela

Hooper

et al. 2004

Journal of Colloid and Interface Science

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Predicting second gas–solid virial coefficients using calculated molecular properties on various carbon surfaces

Rybolt

Janeksela

Hooper

et al. 2004

Journal of Colloid and Interface Science

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“…Refractivity is a combined measure of the size and polarizability of a molecule and can explain the hydrodynamic and dielectric friction contribution in determination of the electrophoretic mobility. 33 According to the beta values (Table 2), the more relevant descriptor is the refractivity of the molecule.…”

Section: The Predicted Results Of Svmmentioning

confidence: 99%

Study of the Quantitative Structure-Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines

Xue

Zhang

Liu

et al. 2004

J. Chem. Inf. Comput. Sci.

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The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (*10), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobility of aliphatic and aromatic carboxylic acids.

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Quantitative structure–(chromatographic) retention relationships

Héberger

2007

Journal of Chromatography A

360

188

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Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

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Molar Refractivity and Connectivity Index Correlations for Henry's Law Virial Coefficients of Odorous Sulfur Compounds on Carbon and for Gas-Chromatographic Retention Indices

Cited by 11 publications

References 31 publications

Predicting second gas–solid virial coefficients using calculated molecular properties on various carbon surfaces

Predicting second gas–solid virial coefficients using calculated molecular properties on various carbon surfaces

Study of the Quantitative Structure-Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines

Quantitative structure–(chromatographic) retention relationships

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