The infinite dilution molar conductivity
(λB
∞) that represents the interactions
between ions and solvent molecules is an important transfer property
for the utilization of ionic liquids (ILs) in electrochemical applications.
However, employing the quantitative structure–property relationship
(QSPR) model to predict the λB
∞ of unconventional ions remains to be
explored. In this work, new λB
∞-QSPR models were developed to predict
the λB
∞ of ions in aqueous solutions by using multiple linear regression
(MLR) and stepwise linear regression (SLR) methods based on the molecular
descriptors obtained by COSMO-SAC. A total of 132 cations and 158
anions data points at different temperatures were collected and tested.
The results showed that the determination coefficients (R
2) of the QSPR model using MLR for cations and anions
were 0.9515 and 0.9411, and the average absolute relative deviations
(AARD) were 6.79% and 10.42%, respectively, indicating that the proposed
λB
∞-QSPR model was excellent at predicting λB
∞. Moreover, R
2 of the QSPR model using SLR for cations and anions were
0.9450 and 0.9406, and AARD were 7.10% and 10.19%, respectively, implying
that the λB
∞-QSPR model with fewer descriptors could also predict λB
∞ satisfactorily.
We envisage the established λB
∞-QSPR models provide an available method
for obtaining λB
∞ of ions in aqueous solutions.
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.
The self-diffusion coefficient of pure liquids, a fundamental
transport
property, is involved in a wide range of applications. Many methods
have been employed to study the self-diffusion coefficient, with the
most popular being semiempirical models. The quantitative structure–property
relationship (QSPR) has been widely used to predict various physicochemical
properties of substances, but the appropriate molecular descriptors
must be selected first. In this study, the charge density distribution
area of molecules at a specific interval (S
σi
) and cavity volume (V
COSMO) was determined based on the conductor-like screening model for
the segment activity coefficient (COSMO-SAC). Using these molecular
descriptors, a backpropagation artificial neural network (BP-ANN)
method was employed to construct a nonlinear QSPR model that can predict
the self-diffusion coefficients of pure liquids under normal pressure.
The data set used included 2596 data points for 238 compounds, covering
a self-diffusion coefficient range of 8.74 × 10–13 to 8.66 × 10–9 m2·s–1 and a temperature range of 90.5–475.1 K. The coefficients
of determination (R
2) of the BP-ANN model
on the training, validation, and testing sets were all greater than
0.99. For the entire data set, the R
2,
absolute average relative deviation (AARD), and root mean square error
(RMSE) were 0.9940, 7.09%, and 0.1106, respectively. In an application
domain (AD) analysis, 94.67% of the data were within the AD range
of the model. Consequently, the model developed in this study can
satisfactorily predict the self-diffusion coefficients of liquids.
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