Description of the Thermal Conductivity λ(<i>T</i>, <i>P</i>) of Ionic Liquids Using the Structure–Property Relationship Method

He, Wensi; Yan, Fangyou; Jia, Qi; Wang, Qiang

doi:10.1021/acs.jced.7b00422

Cited by 24 publications

(9 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lazzús 22 using GCM to predict the λ at variable temperature and pressure and got good results (R 2 =0.9843,AARD =2.12%). Compared with Chen et al 44 , Lazzus 45 and He et al 56 , our work has a larger amount of data points, and obtained a higherR 2 of 0.9847, a lower AARD of 1.02 %. Therefore, our QSPR model for λ prediction has the higher precision.…”

Section: Y -Randomization Testcontrasting

confidence: 49%

“…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 %. Indeed, the above QSPR and GCM reference methods have achieved good results in predicting the properties of ILs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang

Jia

Yan³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Y -Randomization Testcontrasting

confidence: 49%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Jia

Yan³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…AD analysis is a crucial concept in QSPRs as it allows for the estimation of (i) the uncertainty in prediction and (ii) the extent of extrapolation of the developed model. , AD is a specific physicochemical, structural, or biological space, where the model is developed. Predictions of new molecules that lie within the same AD space are considered to be highly reliable as these molecules are structurally similar to the molecules used within the training set.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Electrical Conductivity of Deep Eutectic Solvents Using COSMO-RS Sigma Profiles as Molecular Descriptors: A Quantitative Structure–Property Relationship Study

Lemaoui

Darwish

Hammoudi

et al. 2020

Ind. Eng. Chem. Res.

106

View full text Add to dashboard Cite

This work presents the development of molecular-based mathematical models for the prediction of electrical conductivity of deep eutectic solvents (DESs). Two new quantitative structure–property relationship (QSPR) models based on conductor-like screening model for real solvent (COSMO-RS) molecular charge density distributions (S σ-profiles) were developed using the data obtained from the literature. The data comprise 236 experimental electrical conductivity measurements for 21 ammonium- and phosphonium-based DESs, covering a wide range of temperatures and molar ratios. First, the hydrogen-bond acceptors (HBAs) and hydrogen-bond donors (HBDs) of each DES were successfully modeled using COSMO-RS. Then, the calculated S σ-profiles were used as molecular descriptors. The relation between the conductivity and the descriptors in both models has been expressed via multiple linear regression. The first model accounted for the structure of the HBA, the HBD, the molar ratio, and temperature, whereas the second model additionally incorporated the interactions between the molecular descriptors. The results showed that by accounting for the interactions, the regression coefficient (R 2) of the predictive model can be increased from 0.801 to 0.985. Additionally, the scope and reliability of the models were further assessed using the applicability domain analysis. The findings showed that QSPR models based on S σ-profiles as molecular descriptors are excellent at describing the properties of DESs. Accordingly, the obtained model in this work can be used as a useful guideline in selecting DESs with the desired electrical conductivity for industrial applications.

show abstract

“…To assess the performance of the λ-QSPR model, we compared it with other QSPR models in the literature. Table denotes the performance parameters of these models where no more detailed investigations have been performed so far to explain the influence of pressure on the thermal conductivity, except that He et al considered three levels of pressure at 0.1, 10, and 20 MPa only. The thermal conductivity of ILs can be influenced by pressure to some extent but not as substantially as temperature .…”

Section: Resultsmentioning

confidence: 99%

“…Considering the effects of temperature and pressure on the thermal conductivity of ILs, λ shows a linear correlation, , which can be fitted to the equation as follows where λ is the thermal conductivity (W·m –1 ·K –1 ), T is the absolute temperature (K), p is the pressure (MPa), and A , B , and C are constants.…”

Section: Dataset and Methodologymentioning

confidence: 99%

Predicting the Thermal Conductivity of Ionic Liquids Using a Quantitative Structure–Property Relationship

Ren

Song

et al. 2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Thermal conductivity (λ) is an extremely crucial indicator of the heat transfer capability of ionic liquids (ILs) and plays a critical function in their industrial applications. In this study, there are two descriptors for model construction, namely, the charge density distribution area of ions at a specific interval (S σi) obtained using the conductor-like screening model for the segment activity coefficient (COSMO-SAC) and the cavity volume of ILs (V cosmo). Using the multiple linear regression (MLR) approach, a quantitative structure–property relationship (QSPR) model was proposed to describe the thermal conductivity of ILs. Furthermore, 606 experiment data points for 44 ILs at different temperatures and pressures were collected from the literature, which were randomly divided into a training set and a testing set for feasibility analysis. For the model built by the total data, its determination coefficients (R 2), root mean square error (RMSE), and average absolute relative deviation (AARD) are 0.9713, 0.004304, and 2.18%, respectively; thus, the developed λ-QSPR model offers a relatively good prediction of λ for ILs. Meanwhile, the percentage of extraterritorial points in the model’s application domain (AD) analysis is only 3.80% and the double extraterritorial region is blank. Overall, the proposed model reproduces the change of λ with temperature (T) and pressure (p) well and outperforms other models of similar type. Moreover, it provides an effective approach to predicting the thermal conductivity of ILs.

show abstract

Description of the Thermal Conductivity λ(T, P) of Ionic Liquids Using the Structure–Property Relationship Method

Cited by 24 publications

References 62 publications

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

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

Prediction of Electrical Conductivity of Deep Eutectic Solvents Using COSMO-RS Sigma Profiles as Molecular Descriptors: A Quantitative Structure–Property Relationship Study

Predicting the Thermal Conductivity of Ionic Liquids Using a Quantitative Structure–Property Relationship

Contact Info

Product

Resources

About