Surface Tension Of Ionic Liquids Research Articles

Prediction of ionic liquid surface tension via a generalized interpretable Structure‐Surface Tension Relationship model

AbstractIonic liquids' (ILs) surface tension, vital in liquid interface research, faces challenges in measurement methods—time‐consuming and labor‐intensive. The Structure‐Surface Tension Relationship (SSTR) is crucial for understanding the surface tension laws of ionic liquids, helping to predict surface tension and design ionic liquids that meet target requirements. In this study, SMILES string and group contribution methods were used to generate descriptors, and the random forest and multi‐layer perceptron (MLP) models were cross combined with the two descriptor generation methods to establish the SSTR model, providing a comprehensive framework for predicting the surface tension of ionic liquids. String‐MLP excels with high accuracy (R2 = 0.995, RMSE = 0.686, AARD% = 0.71%) for diverse ILs' surface tension values. Meanwhile, the Shapley Additive exPlanning (SHAP) method was used to test the impact of different features on model prediction, increasing the transparency and interpretability of the model.

Surface tension measurement of FAP-based ionic liquid pendant drops in a high vacuum/gas cell

Abstract The surface tension of ionic liquids with the tris(pentafluoroethyl)trifluorophosphate (FAP) anion were measured using a home-built surface tensiometer. A high-vacuum line was used to pre-evacuate the ionic liquids prior to analyses, ensuring that the samples were free of dissolved gases, water, and volatile impurities. Using the pendant drop method, measurements were performed in a custom-built surface tension vacuum or gas cell, in the presence of nitrogen and carbon dioxide gas. To calibrate the instrument, surface tension measurement of known liquids was also performed. Results show that the presence of saturated carbon dioxide led to the lowering of measured surface tension values, indicating the adsorption of CO2 on the ionic liquid surface.

Ionic liquid binary mixtures: Machine learning‐assisted modeling, solvent tailoring, process design, and optimization

AbstractThis work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)‐IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML‐based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML‐based GC models are sequentially integrated into computer‐aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL‐IL binary mixtures in practical applications.

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction.

Ionic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship between the IL structure and properties has been the subject of many research studies. Recently, special attention has been paid to machine learning tools, especially multilayer perceptron and convolutional neural networks, among many other algorithms in the field of artificial neural networks. For the latter, graph neural networks (GNNs) seem to be a powerful cheminformatic tool yet not well enough studied for dual molecular systems such as ILs. In this work, the usage of GNNs in structure-property studies is critically evaluated for predicting the density, viscosity, and surface tension of ILs. The problem of data availability and integrity is discussed to show how well GNNs deal with mislabeled chemical data. Providing more training data is proven to be more important than ensuring that they are immaculate. Great attention is paid to how GNNs process different ions to give graph transformations and electrostatic information. Clues on how GNNs should be applied to predict the properties of ILs are provided. Differences, especially regarding handling mislabeled data, favoring the use of GNNs over classical quantitative structure-property models are discussed.

High-Temperature Measurements on Viscosity and Surface Tension of the Ionic Liquid 1-Ethyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide ([EMIM][NTf2]) up to 473 K

In the present study, the viscosity and surface tension of the commercial low-viscosity ionic liquid (IL) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)-imide ([EMIM][NTf2]) were determined by a combination of surface light scattering (SLS) and the pendant-drop (PD) method between (303 and 473) K. Both methods operating at thermodynamic equilibrium were applied inside the same measurement cell at identical experimental conditions under an argon atmosphere at 0.1 MPa. For SLS measurements performed in the temperature range from (303 to 323) K, the overdamped behavior of the surface fluctuations allowed access to the viscosity with an expanded uncertainty of 2.3 % using the surface tensions determined via the PD method with an uncertainty of 2 % as input quantities. Above 323 K up to 473 K, the oscillatory behavior of the probed surface fluctuations by SLS enabled a simultaneous determination of viscosity and surface tension with average expanded uncertainties of (4 and 3) %, respectively. Multiple repetition measurements by both methods have been conducted at 323 K after experiments at larger temperatures up to 473 K, and the consistency of their results could indicate a sound thermal stability of the IL sample. The mutual agreement of the surface tension results from SLS and the PD method between (353 and 473) K demonstrates that both techniques analyze the very same IL surface. For both properties, the results from this work agree well with the majority of experimental data reported in the literature typically up to 353 K. In summary, the viscosity and surface tension results presented in this study contribute to an improved data landscape for this particular IL and ILs in general at elevated temperatures.

Prediction surface tension of ionic liquid–water mixtures using a hybrid group contribution and artificial neural network method

In this work, a database was established to collect relevant surface tension data (4578 sets of data points in total) for 138 ionic liquids (ILs)-H2O hybrid systems. An ANN-GC model for predicting the surface tension of IL-H2O hybrid systems is proposed to be constructed by combining the group contribution (GC) method and artificial neural network (ANN) algorithm. This model correlates surface tension to temperature together with IL structure and is developed by using machine learning algorithms. The results show that the model with 4, 5 and 7 neurons in the hidden layer is able to provide reliable predictions for the surface tension of the IL-H2O hybrid system. When 4, 5 and 7 neurons are used in the hidden layer of the model, the squared correlation coefficients (R2) of the training set are 0.92, 0.93 and 0.93; the MAE are 0.0021, 0.0020 and 0.0020, respectively. While the squared correlation coefficients (R2) of the test set are 0.94, 0.91,0.91 and the MAE are 0.0023, 0.0029, 0.0027, respectively. Meanwhile, the ANN-GC model of the hybrid system was extended and an ANN-GC model of pure ILs was constructed based on 172 surface tension data of pure ionic liquids. The comparison shows that the model with 4 and 5 neurons in the hidden layer can be extended to the pure IL system to provide reliable prediction of the surface tension for the pure IL system.

A low-surface-energy design to allogeneic sulfide heterostructures anchored on ultrathin graphene sheets for fast sodium storage

Metallic sulfides, endowing high electrochemical reactivity, have been regarded as a kind of promising anode materials for sodium-ion batteries (SIB). However, the low inner conductivity, as well as the inevitable structural collapse during long-term services impede their commercial application. Here, we propose a low-surface-energy strategy to fabricate ultrathin MoS2/SnS/rGO nanoflake with multi-dimensional hierarchical electron transport pathway to mitigate the above issues. The lower surface tension of ionic liquid could release the high surface energy of graphene layers and then induces the uniform growth of bimetallic sulfide. Taking advantage of synergy effect among ternary hybrids, the promoted ion/electron transfer capability on heterointerface and the localized π electrons of sp2 carbon guarantee rapid charge transfer and ions diffusion. Meantime, the compact bonding behavior between graphene layers and bimetallic sulfides shapes a stable microstructure. As a result, the MoS2/SnS/rGO exhibits a high conductivity (4.458 S cm−1) and excellent structural robustness, which facilitate the MoS2/SnS/rGO with a high-rate capability and the long lifespan (a capacity of 255.4 mA h g−1 is maintained even after 500 cycles at 5 A g−1). The as-assembled MoS2/SnS/rGO//Na3V2(PO4)2O2F full cell could maintain the specific capacity as high as 366.1 mA h g−1 after 50 cycles at 1 A g−1.

Modeling surface tension of ionic liquids by chemical structure-intelligence based models

Surface tension is one of the most significant physicochemical characteristics in process design, industrial applications of heterogeneous systems, and scientific applications. To estimate the surface tension of ionic liquids, acquiring a reliable and accurate model is crucial since experimental measurements are costly and time-consuming. In this regard, the main objective of this study is to estimate the surface tension of ionic liquids (ILs) by utilizing two mathematical models based on the radial basis function (RBF) and least square-support vector machine (LSSVM), coupled with two optimization algorithms namely firefly algorithm (FFA) and differential evolution (DE). In this work, a huge experimental dataset including 1042 data points from 69 ILs was utilized. The dataset consists of surface tension and temperature over a range of 18.5–70.3 mN/m and 268.29–532.4 K, respectively, where the pressure is constant (0.101 MPa). The input parameters were chosen to be chemical structure and temperature, whereas the surface tension was the output parameter. Sensitivity analysis, statistical, and graphical error were used in order to evaluate the performance and accuracy of the proposed models. The results showed that the LSSVM-FFA model accurately predicts the surface tension of ILs with the percentage of average absolute relative deviation of 1.8440% and determination coefficient of 0.9828. The sensitivity analysis showed that surface tension is affected by some substrates such as NH2 and SO2 more dominantly compared to other substructures. Furthermore, statistical and graphical error analyses of the models developed in this study along with those obtained from the literature demonstrate that the LSSVM-FFA model significantly outperforms all the existing models in terms of accuracy and range of validity. Finally, conclusions drawn from understanding the impact of temperature on the surface tension using the proposed models reveals a declining surface tension with increasing temperature owing to the effect of intermolecular forces. The outcome of this study can help not only circumvent the challenges of predicting surface tension of ILs, but also establish modern and reliable predictive approaches for an extensive dataset over a wide range of temperatures.

Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence

The surface tension (ST) of ionic liquids (ILs) and their accompanying mixtures allows engineers to accurately arrange new processes on the industrial scale. Without any doubt, experimental methods for the specification of the ST of every supposable IL and its mixtures with other compounds would be an arduous job. Also, experimental measurements are effortful and prohibitive; thus, a precise estimation of the property via a dependable method would be greatly desirable. For doing this task, a new modeling method according to artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching–learning-based optimization (TLBO), particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA), has been suggested to estimate ST of the binary ILs mixtures. For training and testing the applied network, a set of 748 data points of binary ST of IL systems within the temperature range of 283.1–348.15 K was utilized. Furthermore, an outlier analysis was used to discover doubtful data points. Gained values of MSE & R2 were 0.0000007 and 0.993, 0.0000002 and 0.998, 0.0000004 and 0.996 and 0.0000006 and 0.994 for the ICA-ANN, TLBO-ANN, PSO-ANN and GA-ANN, respectively. Results demonstrated that the experimental data and predicted values of the TLBO-ANN model for such target are wholly matched.

Synthesis and characterization of physicochemical properties of new ether-functionalized amino acid ionic liquids

Abstract Since the effect of ether functionalities on physicochemical properties varies strongly, So the experimental study of in properties is still primarily important for ether-functionalized ionic liquids to be properly applied, as well as for providing reference data for more reliable predictions. In this work, two novel ether-functionalized amino acid ionic liquids 1-(2-methoxyethyl)-3-methylimidazolium threonine and 1-(2-ethoxyethyl)-3-methylimidazolium threonine were synthesized and characterized. Physicochemical properties, including density, surface tension, viscosity and refractive index were measured at different temperatures and atmospheric pressure. According to the volumetric properties of ether-functionalized ionic liquids, the contribution of an ether O atom in the alkoxy side chain to molecular volume, standard entropy and lattice energy were estimated to be 0.0102 nm3, 13 J·K−1·mol−1 and 3 kJ·mol−1, respectively. Combined the molar surface Gibbs energy with Lorentz-Lorenz relationship, an equation for predicting surface tensions of ionic liquids was obtained, and the predicted values agree well with the experimental data. Moreover, an equation of the polarity coefficient P was proposed and the polarity of other ionic liquids was estimated. The polarity of the same series of ionic liquids decreases with the length of the alkoxy chain, the hydrophilicity becomes relatively weak, and the hydrophobicity is relatively enhanced. Using Eyring's viscosity equation and thermodynamic formula, an expression for calculating the viscous flow activation energy of ionic liquids was obtained, and the values were in good agreement with the values calculated by the Arrhenius equation.

The molar surface Gibbs energy and polarity of ether-functionalized ionic liquids

Ether groups are well-known for their unique contribution to low viscosity and high conductivity, hence ether-functionalized ionic liquids (EFILs) have been widely studied and successfully employed in various applications. Three EFILs 1-(2-methoxyethyl)-3-methylimidazolium chloride, 1-(2-ethoxyethyl)-3-methylimidazolium chloride and 1-(2-ethoxyethyl)-3-methylimidazolium bromide were prepared and characterized. Their surface tension, density and refractive index in the range of T = (288.15–328.15) K were measured and the molar surface Gibbs energy, gs, were calculated. Combined gs with Lorentz-Lorenz equation, an expression for predicting surface tensions of ionic liquids was obtained, and the predicted values were in good agreement with the experimental ones. According to the gs, the vaporisation enthalpies were predicted and the association enthalpies of ionic liquids were estimated. A new method for estimating the difference of heat capacities between the vapour phase and the liquid phase of ionic liquids was put forward, and the estimated values were in the same order of magnitude as the Verevkin method. Moreover, the concept of the polarity coefficient P for ionic liquids was proposed and the polarity of many ionic liquids was estimated.

Surface tension investigation of ionic liquids by using the Pseudolattice theory

The pseudolattice theory can be used to describe the thermodynamic and transport properties of ionic liquids. To determine the surface tension of these systems, the surface area can be related to the pseudolattice cell size. In the pure ionic liquids, due to the great number of ionic contacts, the short-range repulsive ion-ion interactions are significant and the ion-solvent interactions are absent. According to the pseudolattice theory and by using some approximations we obtain a simple linear correlation for the surface tension of ionic liquids. The parameters of this linear regularity can be used to estimate the surface tension in a wide temperature range and to predict the surface tension of binary mixtures of ionic liquids. The trend of change of the calculated surface tensions of mixtures and their values are consistent with the experimental data.

Understanding the Friction of Nanometer-Thick Fluorinated Ionic Liquids

Ionic liquids (ILs) show promise as lubricants for nano- and microelectromechanical systems (NEMS/MEMS) and hard disc drives (HDDs). When deposited on solid substrates as nanometer-thick films, these liquids can exhibit a solid-like layering structure, which makes them ideal as lubricants. Moreover, ILs are “designer” materials with many possible molecular structures and tunable properties. In this work, we have experimentally investigated the friction of various nanometer-thick fluorinated ILs on a silica surface using a nanotribometer. We found that surface tension has a significant effect on the friction. When the surface tension of ILs is low, ILs wet the silica surface, and the friction is low. However, when the surface tension is high, ILs form droplets and do not wet the silica surface, which results in higher friction. The effect of moisture at the interface has also been investigated, and the results suggested that water promotes the surface charging of silica, which induces more ordered cation/anion layering and thus reduces the friction. The finding here is critical in designing novel nanometer-thick IL lubricants.

Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable IL and its mixture with other compounds would be a herculean task. Besides, experimental measurements are intrinsically laborious and expensive; therefore, accurate prediction of the property using a reliable technique would be overwhelmingly favorable. To do so, a modeling method based on artificial neural network (ANN) trained by Bayesian regulation back propagation training algorithm (trainbr) has been proposed to predict surface tension of the binary ILs mixtures. A total set of 748 data points of binary surface tension of IL systems within temperature range of 283.1-348.15 K was used to train and test the applied network. The obtained results indicated that the predictive values and experimental data are quite matching, representing reliability of the used ANN model for such purpose. Also, compared with other methods, such as SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN and ANN trained with trainlm algorithm the proposed model was better in terms of accuracy.

Probing the Surface Tension of Ionic Liquids Using the Langmuir Principle.

At 298 K, the surface tension of ionic liquids (ILs) of the 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide series, [CnC1Im][NTf2], ranges from around 35 mN·m–1 for [C2C1Im][NTf2] to just below 30 mN·m–1 for [C12C1Im][NTf2]. However, the decrease rate along the series is not constant: a large decrease from [C2C1Im][NTf2] to [C8C1Im][NTf2] is followed by almost constant values from [C8C1Im][NTf2] to [C12C1Im][NTf2]. Such behavior is hard to interpret from a molecular point of view without suitable information about the free-surface structure of the different ILs. In this work, we have successfully used the Langmuir principle in combination with structural data obtained from angle-resolved X-ray photoelectron spectroscopy experiments and molecular dynamics simulations, to predict the correct surface tension trend along the IL series. The concepts unveiled for this particular homologous IL family can be easily extended to other systems.

Effect of potential attraction term on surface tension of ionic liquids

In this work, we have studied the effect of attraction term of molecular potential on surface tension of ionic liquids (ILs). For this purpose, we have introduced two different potential models to obtain analytical expressions for the surface tension of ILs. The introduced potential models have different attraction terms. The obtained surface tensions in this work have been compared with other theoretical methods and also experimental data. Using the calculated surface tension, the sound velocity is also estimated. We have studied the structural effects on the surface tensions of imidazolium-based ionic liquids. It is found that the cation alkyl chain length and the anion size play important roles to the surface tension of the selected ionic liquids. The calculated surface tensions show a good harmony with experimental data. It is clear that the attraction term of molecular potential has an important role on surface tension and sound velocity of our system.

A Simple Prediction Method for the Surface Tension of Ionic Liquids as a Function of Temperature

In this study, a simple prediction method for the surface tension of ionic liquids (ILs) as a function of temperature is developed. Based on a database of experimental surface tension values collected from the literature, first a prediction scheme for the surface tension at a reference temperature of 298.15 K using only information on the density, molar mass, and anion type of the IL is suggested. By combination of this approach with the temperature dependence of the density, an extended prediction scheme describing the temperature dependence of the surface tension of ILs is recommended. The optimized prediction model for the surface tension allows for the prediction of about 3500 temperature-dependent experimental surface tension data of 226 different ILs with a standard deviation of about 7 %. In comparison with fluid-specific prediction methods found in the literature, the developed simple empirical prediction model requires only easily accessible parameters and can be applied for ILs with arbitrary cation and anion combinations. Thus, the proposed prediction method seems to be a valuable engineering tool for the quantitative estimation of the surface tension of ILs.

Development of a group contribution method for estimating surface tension of ionic liquids over a wide range of temperatures

A group contribution method for estimating the surface tension of ionic liquids as a function of the absolute temperature, is presented. A total of 2286 experimental data points from 226 data sets of 154 ionic liquids were collected from the specialized literature. This database covers a temperature range of 263–533K and a surface tension range of 0.015–0.062N⋅m−1, for a heterogeneous set of ionic liquid-types such as imidazolium, ammonium, phosphonium, pyridinium, pyrrolidinium, and piperidinium. A correlation set containing the 75% of the overall database was used in order to calculate the contribution values of 10 structural cation groups and 30 structural anion groups. Then, a prediction set containing the other 25% of the overall database with data sets not used in the correlation phase was used in order to test the capabilities and accuracy of the proposed method. The results show that the proposed method can estimate the surface tension of several ionic liquids with a better accuracy than other methods available in the literature, with an average absolute relative deviation of 2.8% and a correlation coefficient of 0.98.

A robust modeling approach to predict the surface tension of ionic liquids

The objective of the present study is to develop a mathematical model based on the least square support vector machine to predict the surface tension of ionic liquids (ILs). Molecular weight, reduced temperature, reduced pressure, critical compressibility factor and acentric factor are selected as input parameters and the surface tension is designated as the output parameter. An extensive database including 868 experimental data points for surface tension of 61 ILs are considered to develop the LSSVM model which the adjustable parameters of the model are calculated by a genetic algorithm programming. The proposed model exhibits good accuracy with the average absolute relative deviation of 1.41, 1.74 and 1.54% for train set, test set, and total data, respectively. The Leverage method is employed to check the validity of the model. The results show that the majority of data points are located in a standard error domain demonstrating statistical acceptability of the model and only 3.6% of data points are recognized as suspected data. To evaluate the effects of input parameters on the surface tension of ILs, a sensitivity analysis is performed. The results show that the critical compressibility factor of ILs with relative importance of 22.97% has the greatest effect on the surface tension of ionic liquids. Finally, the effects of temperature and the total number of carbon in cation side alkyl chain on the surface tension are predicted by the proposed model. The model exhibits a monotonic decrease for surface tension with increasing temperature. Moreover, it well predicts the effects of the total number of carbon atoms in cation side alkyl chain on the surface tension.

Accurate Prediction of the Surface Tensions of Ionic Liquids–Amines–Water Mixtures Regarding CO2 Recovery and Sequestration Processes

A support vector machine (SVM) model is presented concerning accurate prediction of the surface tension of the complex mixtures of ionic liquids (ILs)–amines–water using the most important types of amines employed in the gas sweetening plants, such as monoethanolamine, diethanolamine, and methyldiethanolamine. Different ILs, such as [bmim] [BF4], [bmim] [DCA], [mmim] [dmp], [emim] [dep], [bmim] [BF4], [bmim] [Br], [bmim] [BF4], and [bmim] [Br], are considered in the presented model. The effects of most important influencing parameters, such as temperature and mass fraction of amines and ILs, on the surface tension of the aqueous mixtures of ILs and amines are well represented. A comparison of the presented model and the most important models reveals that the suggested SVM model has better performance in terms of accuracy and generalizability. The percentage average absolute deviation between the predicted and experimental surface tensions is about 0.44% suggesting the superiority of the presented model over wide ranges of temperatures and species concentrations.