Surface Tension Of Binary Mixtures Research Articles

Physicochemical properties of extraction solvents for the advanced recycling of spent nuclear fuel

A comprehensive understanding of solvent physicochemical properties is essential to developing advanced solvent extraction processes, such as Advanced PUREX (Plutonium Uranium Reduction Extraction) and GANEX (Group Actinide Extraction), that can safely and efficiently recycle spent nuclear fuel. The densities, viscosities and surface tensions of binary mixtures of tributyl phosphate (TBP) (0.92–3.66 M) in n-dodecane and N,N-di (2-ethyl hexyl)isobutyramide (DEHiBA) (0.69–2.78 M) in n-dodecane were measured at atmospheric pressure from 278.15 to 333.15 K. Through these measurements, empirical expressions to predict the density and viscosity at any molar composition of these solvents within the studied temperature range were constructed and shown to be in good correlation with all experimental data across the studied variables. The relationship between temperature and viscosity of pure TBP and DEHiBA can be described by the Arrhenius equation for viscosity in the temperature range studied. Surface tension measurements (air-liquid interface) were obtained for varying extractant concentrations between 278.15 and 333.15 K, which showed that DEHiBA acts as a surfactant when mixed with n-dodecane whilst TBP exhibits a higher surface tension than n-dodecane.

Measurement and modeling of surface tension of binary mixtures containing N,N-dimethylcyclohexylamine and alcohols

In this work, the surface tension of the six binary systems containing N, N-dimethylcyclohexylamine (DMCHA) and alcohol (1-pentanol, 1-hexanol, 1-heptanol, 1-octanol, 1-nonanol or 1-decanol) was measured over the whole DMCHA concentration at the temperatures of (293.15 to 323.15) K and 101 kPa. Based on the experimental data, surface tension deviation (Δσ) was calculated. The values are positive for all the binary mixtures and decreased with chain length of alcohols. The results were correlated using different models. The surface tensions were fitted by Fu-Li-Wang (FLW) model with stand deviations (S) less than 0.122. The surface tension deviations were well fitted by Myers-Scott (MS) and Redlich-Kister (RK) equations with S ≤ 0.052. Additionally, according to the relationship of surface tension and temperature, the thermodynamic properties, surface enthalpy (Hγ) and surface entropy (Sγ), were also estimated. All the above results were further analyzed and discussed in terms of temperature, composition and chain length of alcohol.

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Precise estimation of the physical properties of both ionic liquids (ILs) and their mixtures is crucial for engineers to successfully design new industrial processes. Among these properties, surface tension is especially important. It’s not only necessary to have knowledge of the properties of pure ILs, but also of their mixtures to ensure optimal utilization in a variety of applications. In this regard, this study aimed to evaluate the effectiveness of Stochastic Gradient Boosting (SGB) tree in modeling surface tensions of binary mixtures of various ionic liquids (ILs) using a comprehensive dataset. The dataset comprised 4010 experimental data points from 48 different ILs and 20 non-IL components, covering a surface tension range of 0.0157–0.0727 N m−1 across a temperature range of 278.15–348.15 K. The study found that the estimated values were in good agreement with the reported experimental data, as evidenced by a high correlation coefficient (R) and a low Mean Relative Absolute Error of greater than 0.999 and less than 0.004, respectively. In addition, the results of the used SGB model were compared to the results of SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN, three based ANNs, PSO-ANN, GA-ANN, ICA-ANN, TLBO-ANN, ANFIS, ANFIS-ACO, ANFIS-DE, ANFIS-GA, ANFIS-PSO, and MGGP models. In terms of the accuracy, the SGB model is better and provides significantly lower deviations compared to the other techniques. Also, an evaluation was conducted to determine the importance of each variable in predicting surface tension, which revealed that the most influential factor was the mole fraction of IL. In the end, William’s plot was utilized to investigate the model's applicability range. As the majority of data points, i.e. 98.5% of the whole dataset, were well within the safety margin, it was concluded that the proposed model had a high applicability domain and its predictions were valid and reliable.

Assessment of a Parachor Model for the Surface Tension of Binary Mixtures

We compiled an experimental database for the surface tension of binary mixtures containing a wide variety of fluids, from the chemical classes (water, alcohols, amines, ketones, linear and branched alkanes, naphthenes, aromatics, refrigerants, and cryogens). The resulting data set includes 65 pure fluids and 154 binary pairs with a total of 8205 points. We used this database to test the performance of a parachor model for the surface tension of binary mixtures. The model uses published correlations to determine the parachors of the pure fluids. The model has a single, constant binary interaction parameter for each pair that was found by fitting experimental mixture data. It can be also used in a predictive mode when the interaction parameters are set to zero. We present detailed comparisons on the performance of the model for both cases. In general, the parachor model in a predictive mode without fitted interaction parameters can predict the surface tension of binary mixtures of non-polar mixtures such as linear and branched alkanes, linear and branched alkanes with naphthenes, aromatics with aromatics, aromatics with naphthenes, and mixtures of linear alkanes of similar sizes with an average absolute percentage deviation of about 3 % or less. Polar mixtures of halocarbons with other halocarbons and also polar/nonpolar mixtures of alkanes with halocarbons could be modeled with an average absolute deviation of less than 0.35 mN·m−1 with the use of a binary interaction parameter. The parachor model even with a fitted binary interaction parameter performs poorly for mixtures of water and organic compounds and is not recommended.

Surface tension of binary and ternary mixtures mapping with ASP and UNIFAC models based on machine learning

Modeling predictions of surface tension for binary and ternary liquid mixtures is difficult. In this work, we propose a machine learning model to accurately predict the surface tension of binary mixtures of organic solvents-ionic liquids and ternary mixtures of organic solvents-ionic liquids–water and analytically characterize the proposed model. In total, 1593 binary mixture data points and 216 ternary mixture data points were collected to develop the machine learning model. The model was developed by combining machine learning algorithms, UNIFAC (UNIversal quasi-chemical Functional group Activity Coefficient) and ASP (Abraham solvation parameter). UNIFAC parameters are used to describe ionic liquids, and ASP is used to describe organic solvents. The effect of each parameter on the surface tension is characterized by SHAP (SHapley Additive exPlanation). We considered support vector regression, artificial neural network, K nearest neighbor regression, random forest regression, LightGBM (light gradient boosting machine), and CatBoost (categorical boosting) algorithms. The results show that the CatBoost algorithm works best, MAE = 0.3338, RMSE = 0.7565, and R2 = 0.9946. The SHAP results show that the surface tension of the liquid decreases as the volume and surface area of the anion increase. This work not only accurately predicts the surface tension of binary and ternary mixtures, but also provides illuminating insight into the microscopic interactions between physical empirical models and physical and chemical properties.

Volumetric Properties, Molar Surface Gibbs Energy, and Its Application of Binary Systems Containing Ether-Group Functionalized Ionic Liquid with Monohydric Alcohols

Ether-group functionalized ionic liquid N-(2-methoxyethyl)-N-methylpyrrolidinium bis(trifluoromethylsulfonyl)amide, [C1OC2pyrr][NTf2], was synthesized and characterized by 1H NMR, 13C NMR, and TGA. The density and surface tension of binary mixtures including [C1OC2pyrr][NTf2] and monohydric alcohols were measured over the entire range of mole fractions in different temperatures. Based on the experimental data of density and surface tension, the volumetric and surface properties of [C1OC2pyrr][NTf2] and its binary mixtures were obtained, and Vm, αp, and VE were explored. The effects of temperature and composition on these properties were investigated, and the excess molar volume of two binary mixtures was well fitted by the Redlick-Kister equation. The molar surface Gibbs energy (gs) of [C1OC2pyrr][NTf2] and its binary mixtures was calculated, and the traditional Eötvös equation was modified; moreover, all parameters had a clear physical meaning. Finally, based on gs, the surface tension of [C1OC2pyrr][NTf2] and its binary mixtures was estimated, and it is in good agreement with experimental values.

Surface Tensions for Binary Mixtures of Alkyl Levulinate + Alkanol: Measurement and Modeling

Surface Tensions for Binary Mixtures of Alkyl Levulinate + Alkanol: Measurement and Modeling

Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

AbstractIonic liquids (IL) are a new class of liquids considered as green solvents; less toxic, less flammable, and less polluting which retain their liquid state over wide temperature ranges and are considered alternatives to volatile organic solvents. The surface tension of IL‐organic solvent mixtures plays an important role in the design and development of many industrial processes. This work investigated the capability and feasibility of four ANN model topologies (“trainbr, logsig”; “trainbr, tansig”; “trainlm, logsig”; “trainlm, tansig”), a PSO‐SVM model, and an LSSVM model to predict the surface tension of binary systems containing IL. For this purpose, 1623 data points corresponding to the experimental surface tension values of binary mixtures containing IL were collected from the literature. The surface tension values were between 18.9 and 72.7 mN m−1. The temperature, the composition in mole fraction of IL (XIL), descriptors based on the sigma profiles, relating to the H‐bond donor and to the H‐bond acceptor character, the anion, the cation and the solvent were used as input variables of the model in order to differentiate the different compounds involved in the binary systems. A comparison of the experimental and the predicted values in terms of several statistical metrics showed good agreement, however, the prediction (trainbr, logsig) was better than the other approaches with an overall average absolute relative deviation of .8466% and a mean square error of .4952. These results are very encouraging for future projects modeling other physical and chemical properties of ILs.

Thermodynamic properties of binary systems containing 1-allyl-3-methylimidazolium dicyanamide [Amim][DCA] with short chain alcohols and prediction of surface tension based on molar surface Gibbs free energy

BackgroundAllyl functionalized ionic liquids (ILs) are widely used because of their excellent properties, to increase their applications in industry and academic community, preparing binary mixtures containing ILs and organic solvents is an effective way, and it is essential to further explore the thermodynamic properties of them. MethodsHere, we reported the synthesis and characterization of the ILs 1-allyl-3-methylimidazolium dicyanamide [Amim][DCA]. Then, in the temperature range of 288.15 K–318.15 K, the values of density and surface tension of 1-allyl-3-methylimidazolium dicyanamide, methanol, ethanol, propanol and corresponding binary systems were measured at every 5 K in the full molar concentration. The values of surface tension deviation Δγ, excess molar volume VE and molar surface Gibbs free energy gs of the mixtures were calculated. In addition, the modified Eӧtvӧs equation is derived based on gs, and the values of surface tension of binary mixtures are estimated. Significant findingsThe results show VE are negative, which is mainly due to the tight packing and hydrogen bond, the formation of hydrogen bond has been confirmed by infrared spectroscopy. The Δγ values of 1-allyl-3-methylimidazolium dicyanamide + methanol are positive, while that of the other two are negative. The modified Eӧtvӧs equation has a clear physical meaning. Moreover, the predicted values are in good agreement with the measured ones.

Density and surface tension of binary mixtures of 1, 2-propylene glycol and glycerol under atmospheric pressure from 303.15 K to 393.15 K and correlating with the Jouyban–Acree model

ABSTRACT In this study, the density and surface tension of binary mixtures of 1,2-propylene glycol (1) + glycerol (2) at the temperature range of 303.15 K to 393.15 K under atmospheric pressure are reported. In addition, the excess molar volume, surface tension deviation, molar volume and molar thermal expansion of the mixture were calculated. Finally, the Jouyban–Acree model was used to correlate the experimental data mathematically, and the accuracy of the model was verified by using the mean relative deviation (MRD). The results showed that the MRD between the experimental data and the calculated data of density and surface tension were 0.003 ± 0.007 and 0.023 ± 0.092, respectively. The accuracy of the model is high and can be directly applied to the production and application of some pharmaceutical and liquid industrial products.

Surface tension of binary mixtures of benzene + propionic acid and butanoic acid between 298.15 K and 318.15 K: Measurement and modeling

Experimental measurement of the surface tension of benzene + propanoic acid and benzene + butanoic acid has been carried out in the temperature range of 298.15 K to 318.15 K and atmospheric pressure. 170 new experimental points of surface tension have been obtained. Three theoretical models, i.e., Linear gradient theory (LGT), Shereshefsky model (SM), and the extended Langmuir model (LM), were used to modeling the surface tension and adsorption phenomenon of these binary mixtures. The three models correlated the surface tension of the benzene + carboxylic acid mixtures, and the average percent deviation was, 0.04% for LGT, 0.08% for SM, and 0.05% for LM. The Helmholtz density term and the liquid and vapor densities required in computing density with LGT were obtained with perturbed chain-statistical associating fluid theory equation of state (PC-SAFT EOS). Based on SM, the standard Gibbs energy of adsorption and the free energy change in the surface region were calculated. The free energy change was used to obtain the excess number of molecular layers in the surface region. The required parameters of the LM were adjusted, the standard Gibbs energy of adsorption was calculated, and negative deviations from ideality were determined. Also mole fractions of solute 0at surface were calculated by using LM. Additionally, by using surface tensions at different temperatures, specific surface enthalpy and specific surface entropy were calculated for (benzene + carboxylic acid) mixtures.

Surface thermodynamics of 2-chloroethanol with 2-dimethylaminoethanol and 2-diethylaminoethanol at different temperatures

ABSTRACT The surface tension of binary mixtures of 2-chloroethanol with 2-dimethylaminoethanol (DMAE) and 2-diethylaminoethanol (DEAE) was measured over the entire composition range at the temperatures 293.15 K to 313.15 K and atmospheric pressure. The excess values of surface tension were correlated with the Redlich-Kister polynomial equation. The experimental surface tension was correlated with five different theoretical models. The Hilderbrand and Scott theories for ideal solutions (HSIS) and their extensions of Guggenheim’s equation (HSEG), Volume Fraction Statistics, Sudgen method and modified New Parachor method were applied to determine the values of surface tension in the binary solvent systems. The surface tension data were further used to determine the surface thermodynamic properties such as surface entropy (SS) and surface enthalpy (HS) per unit surface area. The results have brought about some useful information on the extent of molecular interactions between the unlike molecules occurring at the surface and bulk.

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.

A simple correlation to predict surface tension of binary mixtures containing ionic liquids

Ionic liquids are in a developing situation in nowadays research and industrial atmosphere. In some industrial applications and academic researches, one will face with binary mixtures containing ionic liquids, therefore; many studies have been done evaluating the properties of binary mixtures containing ionic liquids. In this study, it has been tried to find a general trend and precise correlation to predict surface tension of binary mixtures containing ionic liquids. To do this, Multi-Gene Genetic Programming (MGGP), which is one of the most powerful techniques of soft computing, has been used. Mole fraction, Temperature, Molecular weight of two components and boiling point have been used as input parameters of the network, where surface tension of the mixture was the output parameter. Utilizing the mentioned parameters and MGGP, precise networks obtained. On top of that, using MGGP, a general correlation has been generated for obtaining surface tension of binary mixtures containing Ionic Liquids variable with just mole fraction and in constant temperature. Moreover, adding one term to the mentioned correlation gave a precise correlation for the surface tension variable with mole fraction and temperature. These two correlations are very promising and simplifying for determining the surface tension of binary mixtures containing ionic liquids. The precision of these correlations has been evaluated using correlation coefficient (R2) and AARD, which was respectively, average 0.994 and 0.9567% for all used binary mixtures.

Surface tension of binary mixtures composed of N, N-dimethylcyclohexylamine and alcohols at different temperatures

Surface tension (γ) for five binary mixtures containing N, N-dimethylcyclohexylamine (DMCA) and alcohol, ethanol, n-propanol, iso-propanol, n-butanol or iso-butanol were measured over the whole range of composition at 0.1 MPa and different temperatures ranging from (288.15 to 308.15) K. From the experimental data, the surface tension deviations (δγ) for the binary mixtures were calculated. The δγ values are positive for all the studied systems and increase with the decreasing of alky chain length of alcohol. For the mixture containing the branched alkyl alcohol, the increase is significant compared with the equivalent molecules with straight chains. According to temperature dependence of surface tension, the thermodynamic parameters, surface enthalpy (Hγ) and surface entropy (Sγ) were also estimated and further discussed.

Surface tension of binary mixtures of (ionic liquid + tributyl phosphate)

In this work, surface tension was determined for binary mixtures of imidazolium based ionic liquids (ILs) and tributyl phosphate (TBP) over the whole composition range at temperatures from (293.15 to 323.15) K and atmospheric pressure. The ILs, 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim]BF4), 1-butyl-3-methylimidazolium hexafluorophosphate ([Bmim]PF6) and 1-butyl-3-methyl imidazolium bis(trifluoromethylsulfonyl)imide ([Bmim]Tf2N) were used in this work. According to the experimental data, surface tension deviations (δγ) and thermodynamic properties, including surface enthalpy (Hγ), surface entropy (Sγ), enthalpy of vaporisation △lgHmo and Hildebrand solubility parameter (SH), were also calculated. The results show that γ values of ILs are significantly higher than that of TBP. And the contribution of the anion size to the surface tension of ILs follows the order of BF4− > PF6− > Tf2N−, which indicates the low negative charge delocalization of small anion can lead to high γ values. Moreover, the strong electrostatic interactions between cation and anion in ILs result in the higher concentration of TBP at surface. As a result, δγ values for all the studied systems are all negative over the entire range of composition. Additionally, due to the high surface organization of ILs, surface entropies for ILs are much lower than that of TBP.

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.

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.

Theoretical Estimation of Surface Tension for Binary Mixtures at Different Temperatures

Theoretical Estimation of Surface Tension for Binary Mixtures at Different Temperatures

Surface tension of binary mixtures of dimethylsulfoxide + methanol, ethanol and, propanol between 293.15 and 308.15 K

The main aim of this work is to study the interface of dimethylsulfoxide+n-alkanol mixtures. To achieve this aim, first, the surface tension of (dimethylsulfoxide+methanol, ethanol, and propanol) was measured as a function of mole fraction and temperature. These measurements were conducted in the temperature range of (293.15–308.15) K and atmospheric pressure. Then the surface tensions of binary mixtures were correlated by Shereshefsky model and excellent results were obtained. The average percent deviation in correlation of surface tension of studied mixtures was 0.7%. Based on Shereshefsky model, the standard Gibbs energy of adsorption and the free energy change in the surface region were calculated. The free energy change was used to obtain the excess number of molecular layers in the surface region. Additionally, the extended Langmuir model was applied. The required parameters of the model were adjusted, the standard Gibbs energy of adsorption was calculated, and positive or negative deviations from ideality were determined. Also surface compositions were calculated by using extended Langmuir model. In addition to these two methods, a predictive model based on the equality of the chemical potential of species in the interface and bulk liquid was used to predict surface compositions and surface tension of mixtures. Additionally, by using surface tensions of studied mixtures at different temperaturs, specific surface enthalpy and specific surface entropy were calculated for (dimethylsulfoxide+n-alkanol) mixtures.