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

Rybolt, Thomas R.; Janeksela, Vanessa E; Hooper, Dana N; Thomas, Howard E.; Carrington, Nathan A; Williamson, Eric J

doi:10.1016/j.jcis.2003.09.026

Cited by 5 publications

(11 citation statements)

References 39 publications

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“…As shown previously [13], the term ln{z * (T /E * ) 1/2 } varies little relative to (E * /T ) as the temperature and the gas adsorption energy are varied. Equation (12) can therefore be approximated as (13) ln B 2s = c 0 + (E * /T ).…”

Section: Theorysupporting

confidence: 56%

“…It has been shown in previous work [13,15] that a variety of surfaces, not just flat uniform structures, fit the above equation, implying that c 0 , and E * roughly incorporate information about the more complex gas-solid adsorption energy potentials and surface structures. If only one type of adsorbent surface is considered in an analysis, there is no variation in the surface and A is constant.…”

Section: Theorymentioning

confidence: 68%

“…Pompe and Novic [12] used 16 different informational and topological indices to predict retention times for 381 organic compounds. In prior work [13][14][15], we showed that the molar refractivity, MR, and connectivity index, χ could correlate chromatographic retention times expressed in terms of B 2s , second gas-solid virial coefficients. MR is proportional to the polarizability [16] where MR = (4¼N A /α 3 ), N A is the Avogadro constant, and α is the polarizability [17].…”

Section: Introductionmentioning

confidence: 84%

“…The experimental procedure has been used in previous studies [13][14][15] and is described in detail elsewhere [28]. The 40 × 0.318-cm stainless steel column used in the current study was packed by Supelco Inc. and contained 0.985 g of 60/80 mesh Carbosieve S-III adsorbent.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Adsorption energies for a nanoporous carbon from gas–solid chromatography and molecular mechanics

Rybolt

Ziegler

Thomas

et al. 2006

Journal of Colloid and Interface Science

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Section: Theorysupporting

confidence: 56%

Section: Theorymentioning

confidence: 68%

Section: Introductionmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Adsorption energies for a nanoporous carbon from gas–solid chromatography and molecular mechanics

Rybolt

Ziegler

Thomas

et al. 2006

Journal of Colloid and Interface Science

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“…The main focus of QSRR is on molecular structural representation and modeling methodology. These molecular representation descriptors often involve the use of physicochemical [14], quantum chemical [15], topological [16,17], geometric [16], and connectivity index parameters [18,19]. The general modeling methodology used includes multiple linear regression, artificial neural networks, partial least squares, random forests, and nearest shrunken centroids [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

et al. 2011

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An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships (QSRR) models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties (SGBP), involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance (ACC). ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonucleotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.oligonucleotide, quantitative structure-retention relationship, scores of generalized base properties, auto cross covariance, genetic algorithm, support vector machine

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Quantitative structure–(chromatographic) retention relationships

Héberger

2007

Journal of Chromatography A

366

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

Cited by 5 publications

References 39 publications

Adsorption energies for a nanoporous carbon from gas–solid chromatography and molecular mechanics

Adsorption energies for a nanoporous carbon from gas–solid chromatography and molecular mechanics

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

Quantitative structure–(chromatographic) retention relationships

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