Introduction
Ionic liquids (ILs) are a kind of common low melting point organic
salts. Recently, ILs have attracted much attention for their unique
physical and chemical
properties,
such as good thermal stability, conductivity, wide electrochemical
window, incombustibility and recyclability.1,2 Because
of these characteristics, ILs have been successfully applied in the
fields of electrochemistry,3-5 separation and
extraction procedures,6-8 biological field9,10 and chemical engineering.2,11
The
thermodynamic properties of ILs basically determine and affect their
applications. Density (ρ ) is indispensable in the process design
of material science and chemical engineering. In particular, the density
of ILs can be used to calculate other properties (viscosity, surface
tension, thermal conductivity, etc.). The viscosity (η ) of ILs
can be used to determine the feasible operation conditions of the actual
chemical process, such as fluids pumping and stirring, liquid-liquid
extraction, distillation process.12 Thermal
conductivity (λ ) is very important for obtaining the heat
transfer coefficient of fluid, which is essential for the design of heat
transfer fluid and equipment.13,14 Until now, large
amount of ILs have been synthesized by a variety of cations, anions and
substituents.15 However, the existing experimental
properties data of ILs cannot meet the requirements for guiding chemical
industrial applications. Moreover, density, viscosity and thermal
conductivity are significantly affected by the temperature and the
pressure, and it is even impossible for obtaining all these experiment
data under different temperatures and pressures through the
time-consuming and expensive experimental methods. Therefore, it is
urgent to provide a model to calculate these characteristics of ILs
under variable temperature and pressure as an alternative to the
experimental measurement.
To date, different methods have been reported in literatures to
calculate the properties of ILs.16-20 These methods
are mainly based on: (i) the group contribution model (GCM), and (ii)
the quantitative structure−property relationship (QSPR). GCM is a very
important method to predict various physical and thermodynamic
properties with satisfy prediction results21. For the
thermal conductivity of ionic liquids, Lazzús 22established a GCM with 400 data point of 41 ILs at a wide range of
temperature (253-395 K) and pressure (0.1-20 MPa)
(R 2=0.9843, AARD =2.12 %). Recently,
Chen et al.16 developed a GCM model to calculate
properties at variable temperatures of ILs including the density (7360
data points,143 ILs) and the viscosity (1090 data points, 76 ILs) with
good prediction results. It is undeniable that these models provide
accurate predictions for the physical properties of ILs. However, GCM
depends on the group contribution value, and due to the lack of some
group contribution values, the properties of some ILs cannot be
calculated for some ILs.
In the last decade, QSPR has been profusely employed to study the
properties of ILs, such as heat capacity,23-25viscosity,26-29 thermal
conductivity,30,31 surface
tension,32,33 and toxicity.34-37Lazzús38 estimated the density in a wide of
temperature (253-473 K) and pressure (0.1-250 MPa) range with
satisfactory results (AARD =2.00 %). Yan et
al.39 established a QSPR model to predict the density
of ILs under variable temperature and pressure by using topological
index with AARD of 0.42 %. Recently,
AguirrePaduszyński40 established a reference term
model and a modified QSPR model to predict the density (temperature
(217−473 K) and pressure (0.1−250 MPa)) of ILs, and good results could
be obtained with AARD of 0.9 %. Paduszyński41has proposed a new QSPR method for calculating ILs density and viscosity
with temperature (217-473 K) and pressure (0.1-250 MPa) by MLR and LSSVM
with good results.
Beckner et al. 42 used neural networks to get a
high-precision model to predict the ILs viscosity at variable
temperatures and pressure with satisfactory results (AARD= 7.10
%). Zhao et al.43 established a new QSPR model using
multiple linear regression (MLR) and support vector machine (SVM)
algorithm to predict the viscosity of ILs at variable temperature
(253.15-395.32 K) and pressure (0.1-300 MPa) with the overallAARD of 6.58 %. Yan et al.29 predicted the
viscosity of ILs under a wide range of temperature (253.15-573 K) and
pressure (0.06-300 MPa), and obtained satisfactory prediction results
with AARD of 4.62 %.
In case of thermal conductivity of ILs, Chen et al.44and 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.
However,
in their modelling process, the descriptors used to build the model were
made up of anion and cation descriptors, and their interactions were
often neglected to some
extent.
In
order to study the interaction between cations and anions, our previous
work regarded the interaction between cations and anions as a
mathematical formula composed of ion descriptors.37,47Compared with the results in the above literatures, the accuracy of our
previous works on the interaction between anions and cations have been
significantly improved. Recently, Yan et al.25,48proposed a new method to express the interaction between anions and
cations by calculating descriptors from ILs molecule, which have been
successfully applied for QSPR modelling for predicting the heat capacity
and the eco-toxicity of ILs. The improved prediction accuracy and
stability of the model further suggests that the interaction between
anion and anion has an important impact on the properties of ILs, which
is essential in the construction of the prediction model.
At
present, there are hundreds of common cations and about 100 anions, and
the combination of these anions and anions thus can produce tens of
thousands of ILs. Although many literatures have reported
QSPR
models to predict thermodynamic properties, the descriptors used in
these models need to be calculated by complicate software, which will
lead to the inconvenience of model application. Therefore, the
convenient and successful application of QSPR models for predicting the
properties of density, viscosity and thermal conductivity is of great
value for the design, development and application of ILs.
The focus of this work is to establish QSPR models for predicting the
density, viscosity and thermal conductivity properties of ILs under
variable temperature and pressure. There are four main works in this
paper: (1) the f-T-P model of ILs under variable temperature and
pressure was established; (2) the cationic, anionic and ILs descriptors
were calculated to build the QSPR model; (3) three QSPR models were
established for three properties of ILs at variable temperatures and
pressures; (4) f-T-P model parameters of 16329 ILs were predicted
by the QSPR models.