Activity coefficients at infinite dilution for organic solutes dissolved in two 1-alkylquinuclidinium bis(trifluoromethylsulfonyl)imides bearing alkyl side chains of six and eight carbons

Ayad, Amel; Mutelet, Fabrice; Negadi, Amina; Acree, William E.; Jiang, Bihan; Lu, Amber; Wagle, Durgesh V.; Baker, Gary A.

doi:10.1016/j.molliq.2015.12.029

Cited by 46 publications

(6 citation statements)

References 37 publications

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“…A detailed list of all the contributions to ion-specific LSER covering years from 2010 to 2013 can be found in the references list of the paper of Stephens et al, who in 2014 provided a comprehensive revision and extension of the model. In this version of the ion-specific LSER, the parameters table covered 40 different cations and 16 different anions, so that the number of binary ILs that can be tested by the model (40 × 16 = 640) increased approximately by a factor of 9 compared to the pioneering papers of Sprunger et al. − Since the publication of the paper of Stephens et al., the range of applications of the model has additionally increased as the parameters for several novel cationic and anionic structures (like imidazolide anions or quinuclidinium-based cations) have been calculated and reported. − …”

Section: Introductionmentioning

confidence: 99%

In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms

Paduszyński

2016

J. Chem. Inf. Model.

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The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many applications of ILs, particularly in separations. Three new models are proposed, each of them based on distinct machine learning algorithm: stepwise multiple linear regression (SWMLR), feed-forward artificial neural network (FFANN), and least-squares support vector machine (LSSVM). The models were established based on the most comprehensive γ(∞) data bank reported so far (>34 000 data points for 188 ILs and 128 solutes). Following the paper published previously [J. Chem. Inf. Model 2014, 54, 1311-1324], the ILs were treated in terms of group contributions, whereas the Abraham solvation parameters were used to quantify an impact of solute structure. Temperature is also included in the input data of the models so that they can be utilized to obtain temperature-dependent data and thus related thermodynamic functions. Both internal and external validation techniques were applied to assess the statistical significance and explanatory power of the final correlations. A comparative study of the overall performance of the investigated SWMLR/FFANN/LSSVM approaches is presented in terms of root-mean-square error and average absolute relative deviation between calculated and experimental γ(∞), evaluated for different families of ILs and solutes, as well as between calculated and experimental infinite dilution selectivity for separation problems benzene from n-hexane and thiophene from n-heptane. LSSVM is shown to be a method with the lowest values of both training and generalization errors. It is finally demonstrated that the established models exhibit an improved accuracy compared to the state-of-the-art model, namely, temperature-dependent group contribution linear solvation energy relationship, published in 2011 [J. Chem. Eng. Data 2011, 56, 3598-3606].

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Section: Introductionmentioning

confidence: 99%

In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms

Paduszyński

2016

J. Chem. Inf. Model.

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“…We first follow the normal charge-scaling protocol by scanning the mass density as a function of the scaling factor of atomic charges, the results of which are presented in Figure 2 a. The experimental values of the mass densities obtained from literatures are 1.358 g/mL and 1.295 g/mL for [QUIN6][NTF] and [QUIN8][NTF], respectively [ 96 ]. It is clearly shown that the mass density does not show noticeable variations when the charge-scaling factor lies within the range of 0.6~0.9, which is in stark contrast to previous experiences in imidazolium-involved ILs.…”

Section: Resultsmentioning

confidence: 99%

Molecular Modelling of Ionic Liquids: Situations When Charge Scaling Seems Insufficient

et al. 2023

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Charge scaling as an effective solution to the experiment–computation disagreement in molecular modelling of ionic liquids (ILs) could bring the computational results close to the experimental reference for various thermodynamic properties. According to the large-scale benchmark calculations of mass density, solvation, and water-ILs transfer-free energies in our series of papers, the charge-scaling factor of 0.8 serves as a near-optimal option generally applicable to most ILs, although a system-dependent parameter adjustment could be attempted for further improved performance. However, there are situations in which such a charge-scaling treatment would fail. Namely, charge scaling cannot really affect the simulation outcome, or minimally perturbs the results that are still far from the experimental value. In such situations, the vdW radius as an additional adjustable parameter is commonly tuned to minimize the experiment–calculation deviation. In the current work, considering two ILs from the quinuclidinium family, we investigate the impacts of this vdW-scaling treatment on the mass density and the solvation/partition thermodynamics in a fashion similar to our previous charge-scaling works, i.e., scanning the vdW-scaling factor and computing physical properties under these parameter sets. It is observed that the mass density exhibits a linear response to the vdW-scaling factor with slopes close to −1.8 g/mL. By further investigating a set of physiochemically relevant temperatures between 288 K and 348 K, we confirm the robustness of the vdW-scaling treatment in the estimation of bulk properties. The best vdW-scaling parameter for mass density would worsen the computation of solvation/partition thermodynamics, and a marginal decrease in the vdW-scaling factor is considered as an intermediate option balancing the reproductions of bulk properties and solvation thermodynamics. These observations could be understood in a way similar to the charge-scaling situation. i.e., overfitting some properties (e.g., mass density) would degrade the accuracy of the other properties (e.g., solvation free energies). Following this principle, the general guideline for applying this vdW-tuning protocol is by using values between the density-derived choice and the solvation/partition-derived solution. The charge and current vdW scaling treatments cover commonly encountered ILs, completing the protocol for accurate modelling of ILs with fixed-charge force fields.

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“…The data set comprised three series of experimental gas-ionic liquid partition coefficients (log K ) measured at 298 K for benzene, hexane and cyclohexane in various ionic liquids where bis(trifluoromethylsulfonyl)imide ([Tf2N] – ) was the common anion ( Tables S1–S3, Supplementary Material ) [ 42 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 ]. These three data series had similar sizes with 57, 60 and 60 partition coefficients for hexane, cyclohexane and benzene, respectively.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Toots

Sild

Leis

et al. 2022

IJMS

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Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure–property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]−, for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71–0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.

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Activity coefficients at infinite dilution for organic solutes dissolved in two 1-alkylquinuclidinium bis(trifluoromethylsulfonyl)imides bearing alkyl side chains of six and eight carbons

Cited by 46 publications

References 37 publications

In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms

In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms

Molecular Modelling of Ionic Liquids: Situations When Charge Scaling Seems Insufficient

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

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