Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. Exploring the influence of ionic liquid anion structure on gas-ionic liquid partition coefficients of organic solutes using machine learning. Langmuir, 2024, 40, 23714–23728

QsarDB Repository

Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. Exploring the influence of ionic liquid anion structure on gas-ionic liquid partition coefficients of organic solutes using machine learning. Langmuir, 2024, 40, 23714–23728

QDB archive DOI: 10.15152/QDB.262   DOWNLOAD

QsarDB content

Property logK_benzene-EMIm: Gas-ionic liquid partition coefficient of benzene in EMIm+ ionic liquids

Eq13: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of benzene in EMIm+ ionic liquids

Regression model (regression)

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R2

σ

Eq13 model datasettraining120.9550.040

Property logK_benzene-BMIm: Gas-ionic liquid partition coefficient of benzene in BMIm+ ionic liquids

Eq14: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of benzene in BMIm+ ionic liquids

Regression model (regression)

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Eq14 model datasettraining100.6590.077

Property logK_benzene-HMIm: Gas-ionic liquid partition coefficient of benzene in HMIm+ ionic liquids

Eq15: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of benzene in HMIm+ ionic liquids

Regression model (regression)

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Eq15 model datasettraining90.8910.051

Property logK_cyclohexane-EMIm: Gas-ionic liquid partition coefficient of cyclohexane in EMIm+ ionic liquids

Eq16: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of cyclohexane in EMIm+ ionic liquids

Regression model (regression)

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Eq16 model datasettraining130.8510.137

Property logK_cyclohexane-BMIm: Gas-ionic liquid partition coefficient of cyclohexane in BMIm+ ionic liquids

Eq17: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of cyclohexane in BMIm+ ionic liquids

Regression model (regression)

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Eq17 model datasettraining100.9860.045

Property logK_cyclohexane-HMIm: Gas-ionic liquid partition coefficient of cyclohexane in HMIm+ ionic liquids

Eq18: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of cyclohexane in HMIm+ ionic liquids

Regression model (regression)

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Eq18 model datasettraining70.9970.010

Property logK_methanol-EMIm: Gas-ionic liquid partition coefficient of methanol in EMIm+ ionic liquids

Eq19: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of methanol in EMIm+ ionic liquids

Regression model (regression)

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Eq19 model datasettraining130.5800.336

Property logK_methanol-BMIm: Gas-ionic liquid partition coefficient of methanol in BMIm+ ionic liquids

Eq20: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of methanol in BMIm+ ionic liquids

Regression model (regression)

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Eq20 model datasettraining100.9840.052

Property logK_methanol-HMIm: Gas-ionic liquid partition coefficient of methanol in HMIm+ ionic liquids

Eq21: Multiple Linear Regression QSAR model for gas-ionic liquid partition coefficient of methanol in HMIm+ ionic liquids

Regression model (regression)

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Eq21 model datasettraining80.9250.134

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. Data for: Exploring the influence of ionic liquid anion structure on gas-ionic liquid partition coefficients of organic solutes using machine learning. QsarDB repository, QDB.262. 2024. https://doi.org/10.15152/QDB.262

  • Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. Exploring the influence of ionic liquid anion structure on gas-ionic liquid partition coefficients of organic solutes using machine learning. Langmuir 2024, 40, 23714–23728. https://doi.org/10.1021/acs.langmuir.4c02628

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Title: Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. Exploring the influence of ionic liquid anion structure on gas-ionic liquid partition coefficients of organic solutes using machine learning. Langmuir, 2024, 40, 23714–23728
Abstract:This article presents an in-depth investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquid partition coefficients (log K) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K, additionally it was looked at whether the molecular descriptors of the anion in the relationships encode possible molecular interactions during the miscibility and partitioning in IL. The research involves the compilation of data series of experimental log K values, where the cation component is constant. Such representative data series were obtained for three solutes — benzene, cyclohexane, and methanol — in three ILs with a uniform cationic component of methyl imidazolium. Using multiple linear regression models enhanced with machine learning techniques, the relationship between anionic structures and log K values was successfully quantified and modeled. Systematically selected molecular descriptors describing the anion structure show that in the case of methanol log K is strongly dependent on hydrogen bonds and Coulomb-dipolar interactions with the anion component, while in the case of benzene and cyclohexane the dispersion forces of the anion component are dominant. The outlier analysis and data interpretation highlight the need for extensive experimental data. The results confirm the initial hypothesis and provide valuable information on the role of the structure of anionic component in determining the partitioning behavior of organic solutes. This knowledge is important for the design and optimization of ILs for specific applications, particularly as solvents in various industrial processes. The research also provides useful information about molecular interactions taking place in the interfaces of IL and organic additives in complex liquid media such as multicomponent electrolyte solutions, for example in energy storage applications.
URI:http://hdl.handle.net/10967/262
http://dx.doi.org/10.15152/QDB.262
Date:2024-09-02
Funding:Authors are grateful for support from the Ministry of Education and Research, Republic of Estonia, through the Estonian Research Council (grant number PRG1509)


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