2015
DOI: 10.1016/j.fluid.2015.07.015
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A group contribution method to predict the thermal conductivity λ(T,P) of ionic liquids

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Cited by 36 publications
(25 citation statements)
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“…These models include predictive quantitative structure–activity relationship (QSAR) models, molecular docking, structure–activity relationship (SAR) systems, read‐across models, physiology‐based pharmacokinetic models, and quantitative toxicity–toxicity relationship (QTTR) models . In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, surface tensions, infinite dilution activity coefficients, viscosities, conductivities, solubilities, glass transition temperatures, and decomposition temperatures …”
Section: Computational Prediction Of the Toxicity Of Ionic Liquidsmentioning
confidence: 99%
See 1 more Smart Citation
“…These models include predictive quantitative structure–activity relationship (QSAR) models, molecular docking, structure–activity relationship (SAR) systems, read‐across models, physiology‐based pharmacokinetic models, and quantitative toxicity–toxicity relationship (QTTR) models . In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, surface tensions, infinite dilution activity coefficients, viscosities, conductivities, solubilities, glass transition temperatures, and decomposition temperatures …”
Section: Computational Prediction Of the Toxicity Of Ionic Liquidsmentioning
confidence: 99%
“…These models include predictive quantitative structure-activity relationship (QSAR) models, [42][43][44][45] molecular docking, [46] structure-activity relationship (SAR) systems, [47] read-across models, [48][49][50] physiology-based pharmacokinetic models, [51,52] and quantitative toxicity-toxicity relationship (QTTR) models. [53] In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, [54,55] surface tensions, [56,57] infinite dilution activity coefficients, [58][59][60] viscosities, [61,62] conductivities, [63,64] solubilities, [65,66] glass transition temperatures, [64] and decomposition temperatures. [67] Determining the relationship between toxicity and structural features is one of the most basic processes of a model and this is made possible through computational chemistry.…”
Section: Computational Prediction Of the Toxicity Of Ionic Liquidsmentioning
confidence: 99%
“…To overcome this problem, several researchers are developing predictive tools to model structure-property relationships in ionic liquids which allow the physical and chemical properties to be estimated without the need for expensive and time-consuming synthesis and characterization. For example, ionic liquid properties such as melting point [32], decomposition temperature [33], density [34], heat capacity [35], viscosity [36], surface tension [37], and thermal conductivity [38] can be estimated using group contribution models with relatively high degrees of accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…Considerable attention has been given in recent years towards developing mathematical expressions for estimating the physical properties and solubilising characteristics of IL solvents based on both group contribution methods and quantitative structure-property relationships. To date group contribution methods have been developed for predicting infinite dilution activity coefficients and gas-liquid partition coefficients of solutes dissolved in ILs, [1][2][3] for predicting enthalpies of solvation of organic solutes dissolved in ILs, [4] and for estimating viscosities, [5,6] thermal conductivities [7,8] isobaric heat capacities, [9][10][11] refractive indices, [12] static dielectric constants, [13] surface tensions, [14] densities, [15] and Daphnia magna water flea toxicities [16] of ILs at both 298 K and as a function of temperature.…”
Section: Introductionmentioning
confidence: 99%