Estimation of thermal conductivity of ionic liquids using quantitative structure–property relationship calculations

Lazzús, Juan A.; Pulgar-Villarroel, Geraldo

doi:10.1016/j.molliq.2015.08.037

Cited by 27 publications

(19 citation statements)

References 34 publications

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“…One of the major challenges of this work is to model a one-to-many relationship between the chemical compound and property data points, where one chemical has several equilibrium concentration values depending on temperature of experiment, unlike conventional one-to-one QS(P)AR studies. In previous QSPR studies of temperature-dependent IL properties, the impact of temperature on the modeling approach was either ignored [16,17] or separate models were made for every particular temperature. [18,19] In modeling of compounds other than IL, QSPR is applied to predict temperature-independent parameters, which are used in equations with direct temperature impact.…”

Section: Introductionmentioning

confidence: 99%

QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid

Klimenko

Inês

Esperança

et al. 2020

Molecular Informatics

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The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.

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Section: Introductionmentioning

confidence: 99%

QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid

Klimenko

Inês

Esperança

et al. 2020

Molecular Informatics

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“…Therefore, the prediction of the thermal conductivity of an IL based on its structure and other physical properties would be very useful for designing novel ILs. So far, an empirical-prediction method [13,20,21], group-contribution method [22][23][24], quantitative structure-property relationship method [25], prediction Thermal Conductivity of Ionic Liquids http://dx.doi.org/10.5772/intechopen.76559 method using a neural network [26,27], and many other methods [28][29][30][31][32][33][34][35] have been proposed for the prediction of the thermal conductivities of ILs. In this section, the empirical-prediction method based on other physical properties and prediction method using group-contribution method are introduced.…”

Section: Prediction and Correlation Of The Thermal Conductivity Of Anmentioning

confidence: 99%

Thermal Conductivity of Ionic Liquids

Tomida¹

2018

Impact of Thermal Conductivity on Energy Technologies

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Ionic liquids (ILs) have attracted great attention as green solvents, heat carriers, and electrolytes. They can be obtained with specific thermophysical properties and functions by changing the kind of species of cations and anions. Knowledge of the fundamental thermophysical properties of ILs, such as their densities, viscosities, and thermal conductivities, is needed to design ILs with desirable thermophysical properties. In this chapter, we will review the various measurement results for the thermal conductivities of the pure components of ILs and methods for predicting the thermal conductivity of an IL, which are based on its structure and physical properties, by conducting correlations between these parameters. In the recent years, the thermal conductivities of IoNano fluids, which comprise of nanoparticles dispersed in an IL, have attracted great attention. Therefore, we will review the unique thermal conductivities of IoNano fluids.

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“…In case of thermal conductivity of ILs, Chen et al 44 and Lazzus et al 45 proposed a QSPR model to predict the thermal conductivity of ILs under the condition of variable temperature (273.15-390 K) with AARD of 2.0 %-2.3 %. He et al 46 presents a linear QSPR model based on the norm-indexes for predicting ILs thermal conductivity in a wide temperature (273.15-355.07 K) and pressure range (0.1-20.0 MPa) withAARD of 1.45 %.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the properties of ionic liquid at variable temperatures and pressures by QSPR

Zhang

Jia

Yan³

et al. 2020

Preprint

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ILs thermodynamic properties at variable temperature and pressure, such as density, viscosity and thermal conductivity, are necessarily basal parameters of chemical engineering process. In this paper, some norm descriptors-based QSPR models are established to predict the properties of ILs at variable temperature and pressure. The f-T-P models are developed with 9020 data points of 314 ILs for density, 7342 data points of 351 ILs for viscosity and 608 data points of 87 ILs for thermal conductivity. These models have satisfactory statistical results for the calculation of ILs properties. The validation analysis shows that these QSPR models have good stability and predictability. And the norm descriptors are universal for predicting the properties of ILs. Moreover, these QSPR models are applied to predict parameters of f-T-P models for 16329 ILs generated by combing the cations and anions in the dataset, which might be valuable and further used handily by other chemists.

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Estimation of thermal conductivity of ionic liquids using quantitative structure–property relationship calculations

Cited by 27 publications

References 34 publications

QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid

QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid

Thermal Conductivity of Ionic Liquids

Evaluating the properties of ionic liquid at variable temperatures and pressures by QSPR

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