Solubility Of Ionic Liquids Research Articles

Implementation of CP-PC-SAFT and CS-SAFT-VR-Mie for Predicting the Thermodynamic Properties of C1–C3 Halocarbon Systems. II. Inter-Relation between Solubilities in Ionic Liquids, Their Pressure, Volume, and Temperature, and Critical Constants

Implementation of CP-PC-SAFT and CS-SAFT-VR-Mie for Predicting the Thermodynamic Properties of C₁–C₃ Halocarbon Systems. II. Inter-Relation between Solubilities in Ionic Liquids, Their Pressure, Volume, and Temperature, and Critical Constants

Density functional theory-based analyses on selective gas separation by β-PVDF-supported ionic liquid membranes

Finding proper candidates for polymer-supported ionic liquid (IL)-based gas separating membranes is a challenge. The current article elucidates the quantum chemical perspective of the selective gas adsorption efficiency, from a mixture of CO2, CO, CH4, and H2, of α- and β-polyvinylidene fluoride (PVDF)-supported imidazolium- and pyridinium-based six ionic liquid membranes. Although IL-based membrane efficiency mainly depends on the gas solubility of ILs, IL/support binding and gas adsorption on the support material are also studied to describe the overall gas adsorption properties of the PVDF/IL complexes. β-PVDF exhibits better binding with the ILs, and better gas affinity, thus, qualified as a more suitable membrane component as compared to α-PVDF. Dispersion-corrected density functional calculations are performed to provide a detailed insight into the energetic interactions, nonbonding intermolecular interactions based on symmetry adapted perturbation theory (SAPT), natural bond orbitals (NBO), Bader's quantum theory of atoms in molecules (QTAIM), reduced density gradient (RDG), frontier orbital interactions, density of states (DOS), and thermochemical analyses of the gas-adsorbed systems. Gas molecules interact with the membrane components through weak hydrogen bonds and exhibit low interaction energies, indicating physisorption of the gases. Gas adsorption energies are more negative than the mutual interaction energies of the gas molecules, ensuring effective gas adsorption by the membrane components. All the β-PVDF/IL systems have shown the highest and lowest affinity for CO2 and H2, respectively, leading to effective separation of CO2 and H2 from the other gases.

Insight into the inhibitory mechanism of soluble ionic liquids on the transport of TiO2 nanoparticles in saturated porous media: Roles of alkyl chain lengths and counteranion types

Column experiments were carried out to investigate the transport of TiO2 nanoparticles (nTiO2) in water-saturated porous media in the presence of various imidazolium-based ionic liquids (ILs) with different alkyl chain lengths and counteranions. The results indicated that the effects of ILs on nTiO2 transport were considerably dependent upon IL species. In general, the transport-inhibition effects increased with the increasing length of branched alkyl chain on the ILs (i.e., [C6mim]Cl > [C4mim]Cl > [C2mim]Cl). The trend was dominated by the hydrophobicity effects of ILs. Meanwhile, the inhibitory effects of ILs were strongly related to the counteranions and followed the order of [C4mim]Cl > [C4mim][TOS] > [C4mim][PF6], mainly due to different electrostatic repulsion force between nanoparticles and porous media in the presence of various ILs. Furthermore, the inhibitory role of [C4mim][TOS] in nTiO2 transport under acidic conditions (i.e., pH 6.5) was greater than that under alkaline conditions (i.e., pH 8.0). The dominant mechanism was that the differences in the extent of electrostatic repulsion between sand grains and nTiO2 with or without ILs at pH 6.5 were larger than that at pH 8.0. Moreover, two-site kinetic retention model and DLVO theory provided good descriptions for the transport behaviors of nTiO2 with different ILs.

A new approach for correlating of H2S solubility in [emim][Lac], [bmim][ac] and [emim][pro] ionic liquids using two-parts combined models

Two bi-part combined models containing reaction equilibrium thermodynamic model and an equation of state (EoS) including cubic plus association (CPA) or modified Soave-Redlich-Kwong (SRK), were employed to correlate H 2 S solubility in ionic liquids [emim][Lac], [bmim][Ac] and [emim][Pro]. RETM and CPA/mSRK are responsible for chemical and phase equilibria calculations, respectively. The RETM defines a complex formation reaction between an H 2 S and two IL molecules into the liquid phase so that a complex in the form of H 2 S(IL) 2 is probable to produce. On the other side, CPA/mSRK EoSs perform phase equilibria computations to find pressure and vapor phase concentrations. In the case of CPA, both of H 2 S and the ionic liquids were considered as self-associating components which pursue 4C and 2B schemes, respectively. This model computes the pure IL parameters using experimental density data. In this work, critical properties were estimated through the modified Lydersen-Joback-Reid method, Eotvos and Guggenheim relations. Afterward, the binary systems were investigated by applying RETM. CPA presents average absolute deviations (AADs) equal to 2.41%, 13.42% and 3.52% for [emim][Lac], [bmim][Ac], and [emim][Pro], respectively. Moreover, the AADs obtained by mSRK are 3.75%, 5.07%, and 6.06%, respectively. As it is evident from the results, the combination of RETM with both CPA and mSRK EoSs will result in good correlation accuracy.

Solubility Behavior of CO2 in Ionic Liquids Based on Ionic Polarity Index Analyses.

Ionic liquids (ILs) can serve as effective CO2 solvents with an appropriate selection of different anions and cations. However, due to the large library of potential IL compositions, rapid screening methods are needed for characterizing and ranking the expected properties. We have recently proposed the ionic polarity index (IPI) parameter, effectively connecting volume-based approaches and electrostatic potential analyses and providing a single metric that can potentially be used to rapidly screen for desirable IL properties. In this work, the corresponding anion and cation IPIs are used to generate correlations with respect to the CO2 volumetric solubility in ILs. The relationships are generally applicable to groups of ILs within a homologous ion series, and this can be particularly valuable for prescreening different ion pairings for maximizing gas solvation performance.

Predicting water solubility in ionic liquids using machine learning towards design of hydro-philic/phobic ionic liquids

In this work, a database containing the solubility data for water in 16,137 ionic liquids (ILs), which were formed by a combination of the most commonly used 163 cations (in nine groups) and 99 anions, were analyzed using machine learning. The water solubility in ILs was computed by COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) while the molecular descriptors for the individual cations and anions were determined by semi-empirical PM3 method. Association rule mining, decision tree and multilayer fully connected neural network (a deep learning model) were employed as machine learning techniques. The association rule mining analysis clearly identified the descriptors leading to low water solubility in ILs, while the decision tree analysis provided heuristic rules for the selection of cations and anions to form ILs with low water capacity. The prediction accuracy of fully connected neural network model was also high; even the model constructed from a small fraction of data was successful to predict the water solubility in other ILs in the dataset indicating that the anionic and cationic descriptors used were sufficient to represent the performance of ILs. The classification ability of decision trees was verified by the experimental water solubility data for 49 ILs extracted from 13 published papers in literature; the decision tree model correctly classified the experimental solubility of 46 of these ILs. The deep learning predictions of solubility were also in agreement with the experimental data within the accuracy level of COSMO-RS calculations. It was generally found that the anionic descriptors were more influential to predict the water capacity of ILs, while the cationic descriptors made limited contribution.

An electrochemical setup designed for carbon dioxide solubility measurements in ionic liquids.

An electrochemical setup was designed and built to evaluate carbon dioxide solubility in ionic liquids. The setup can simultaneously measure amounts of CO2 in the gas and in the ionic liquid phase, making it very useful for in situ electrochemical measurements. The home-built glass cell is able to withstand high vacuum allowing the ionic liquid samples to be properly evacuated before characterization and kept free of contaminants during experiments. A pressure gauge attached to the setup enables continuous monitoring of gas added to the system. This kind of configuration can measure gas solubility in ionic liquids expressed as Henry's constants determined from generated plots of dissolved gas concentration in the ionic liquid vs headspace gas pressure. It also serves as a more economical alternative to other gas solubility measurement techniques, as it is predominantly made of glass, and requires minimal sample amounts. The setup can be useful in determining the solubility of various gases in ionic liquids.

Extremely Low Vapor-Pressure Data as Access to PC-SAFT Parameter Estimation for Ionic Liquids and Modeling of Precursor Solubility in Ionic Liquids.

Precursor solubility is a crucial factor in industrial applications, dominating the outcome of reactions and purification steps. The outcome and success of thermodynamic modelling of this industrially important property with equations of states, such as Perturbed‐Chain Statistical Associating Fluid Theory (PC‐SAFT), vastly depends on the quality of the pure‐component parameters. The pure‐component parameters for low‐volatile compounds such as ionic liquids (ILs) have been commonly estimated using mixture properties, e. g. the osmotic pressure of aqueous solutions. This leads to parameters that depend on the solvent, and transferability to other mixtures often causes poor modeling results. Mixture‐independent experimental properties would be a more suitable basis for the parameter estimation offering a way to universal parameter sets. Model parameters for ILs are available in the literature [10.1016/j.fluid.2012.05.029], but they were estimated using pure‐IL density data. The present work focuses on a step towards a more universal estimation strategy that includes new experimental vapor‐pressure data of the pure IL. ILs exhibit an almost negligible vapor pressure in magnitude of usually 10−5 Pa even at elevated temperatures. In this work, such vapor‐pressure data of a series of 1‐ethyl‐3‐methyl‐imidazolium‐based [C2mim]‐ILs with various IL‐anions (e. g. tetrafluoroborate [BF4]−, hexafluorophosphate [PF6]−, bis(trifluoromethylsulfonyl)imide [NTf2]−) were experimentally determined and subsequently used for PC‐SAFT parameter estimation. The so‐determined parameters were used to predict experimental molecular precursor solubility in ILs and infinitely diluted activity coefficients of various solvents in ILs. The parameters were further compared to modeling results using classical parametrization methods (use of liquid‐density data only for the molecular PC‐SAFT and the ion‐based electrolyte PC‐SAFT). As a result, the modeled precursor solubilities using the new approach are much more precise than using the classical parametrization methods, and required binary parameters were found to be much smaller (if needed). In sum, including the pure‐component vapor‐pressure data of ILs opens the door towards parameter estimation that is not biased by mixture data. This procedure might be suitable also for polymers and for all kind of ionic species but needs extension to ion‐specific parametrization in the long term.

Selection of ionic liquid for extraction processes: Special case study of extractive desulfurization

Selecting of appropriate ionic liquids (ILs) as extracting agents is a key to ensuring an extraction process. Therefore, this study illustrates a systematic method for an extractive desulfurization (EDS) process to select the most promising ILs. The proposed method combines: 1) COSMO-RS calculation of the mutual solubility of ILs and fuel oil, and the capacity of ILs for the S-compounds, 2) experimental investigation of the prescreened ILs for their desulfurization ability, 3) and Aspen Plus simulation of developing a new insight into the industrial application of EDS. Based on the obtained results, [C1pyr][H2PO4] and [C1pyr][HSO4] having small mutual solubility with fuel oil, high extraction capacity for S-compounds and high desulfurization efficiency are selected as two promising ILs. Considering the qualification of the low sulfur content standard and real industrial applications, [C1pyr][H2PO4] is recognized as the most favorable solvent for the EDS process. The proposed method initiating from micro-level to experimental stage and finally to industrial stage can preferably be extended over a variety of extraction application for selection of the promising ILs.

On the evaluation of solubility of hydrogen sulfide in ionic liquids using advanced committee machine intelligent systems

Ionic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H2S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H2S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H2S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R2) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H2S solubility in ILs with respect to pressure and temperature.

Polymer solubility in ionic liquids: dominated by hydrogen bonding.

Polymer solubility in ionic liquids (ILs) cannot be predicted by the solubility parameter approach based on the "like dissolves like" principle. According to the Kamlet-Abraham-Taft (KAT) multi-parameter polarity scale, ILs can be categorized on the basis of hydrogen-bond acidity or basicity ones. The experimental observations, that acidic ILs easily dissolve basic polymers and basic ILs dissolve acidic polymers, reflect the complementary nature of hydrogen-bonding interactions. A quantitative hydrogen-bonding analysis is proposed for predicting the solubility by taking the product of ΔαΔβ as an indicator of the competition between cross-association and self-association hydrogen bonding (H-bonding), where Δα is the difference of acidity parameters between the polymer and IL, and Δβ is the difference of basicity. This solubility criterion has been validated by the solubility data of 19 polymers (11 acidic and 8 basic) in 11 ILs (7 acidic and 4 basic). These principles based on KAT parameters can be applied to other systems dominated by hydrogen bonding.

Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling

Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing.. Several techniques have been used to eliminate CO2 from the output gas mixtures. One of these methods is CO2 capturing by ionic liquids (ILs). Computational models for estimating the CO2 solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO2 solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO2 in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (Tc ), critical pressure (Pc ) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO2 solubility estimation in ILs and is capable of being used in different industries.

Solubility of H2S in ammonium-based ionic liquids

This work is inserted in a research program that consists mainly in the experimental and theoretical study of gas/liquid solubility in ionic liquids (ILs). In this study the experimental data of hydrogen sulfide solubility in ammonium-based ionic liquids, namely 2-hydroxyethylammonium acetate [2-HEA][Ace], bis(2-hydroxyethyl)ammonium acetate [B-2-HEA][Ace] and 2-hydroxyethyldiethylammonium hydrogen diacetate [2-HEDEA][H(Ace)2], were determined using a volumetric method in the 298 K to 318 K temperature range and at atmospheric pressure. The ionic liquids are functionalized with the OH group in the ammonium-based cations, in order to study the influence of hydroxyl group in the formation of hydrogen bonds between the IL-IL and IL-gas. The data gathered is modelled with the Cubic Plus Association Equation of State (CPA EoS), considering the association schemes four-sites (4C) for hydrogen sulfide and two-sites (2B) for the ILs ([2-HEA][Ace], [B-2-HEA][Ace] and [2-HEDEA][H(Ace)2]).

Development of trace analysis for alkyl methanesulfonates in the delgocitinib drug substance using GC-FID and liquid–liquid extraction with ionic liquid

AbstractAlkyl methanesulfonates are genotoxic impurities that should be limited to an intake of not more than 1.5 µg/day, as regulated by the International Council for Harmonization guideline M7. We herein report a trace analysis of methyl methanesulfonate (MMS), ethyl methanesulfonate (EMS), and isopropyl methanesulfonate (IPMS) in the delgocitinib drug substance using liquid–liquid extraction, with an ionic liquid as the sample-solving medium, and direct injection gas chromatography detected with a flame-ionization detector. The proposed method takes advantage of the fine solubility of ionic liquids toward the drug substance, the good extraction efficiency of alkyl methanesulfonates in liquid–liquid extraction using the Chem Elut cartridge with low-polar organic solvents, and the ability of alkyl methanesulfonates to concentrate in minimum amounts of organic solvent, resulting in excellent sensitivity and selectivity. Specifically, for the preparation of the sample solution, a mixture of 1-butyl-3-methylimidazolium chloride, water, and acetonitrile was used as the sample-solving media, extracted with diethyl ether, and the eluent was concentrated to 1 mL. The method showed good linearity, accuracy, and precision from 1 to 5 ppm, and the limits of detection of MMS, EMS, and IPMS were 0.1, 0.05, and 0.05 ppm, respectively.

Promising Thiolanium Ionic Liquid for Extraction of Aromatics from Aliphatics: Experiments and Modeling

Four ionic liquids (ILs) consisting of the bis(trifluoromethylsulfonyl)imide anion paired with a phosphonium, pyridinium, imidazolium, or thiolanium cation were investigated as potential solvents to separate aromatic from aliphatic compounds in a liquid–liquid extraction process. The thiolanium IL was chosen due to its structural similarity to sulfolane, which is the most widely used organic solvent for aromatic/aliphatic separation in the industry. Interestingly, ternary liquid–liquid equilibrium data shows that 1-n-butylthiolanium bis(trifluoromethylsulfonyl)imide performs as well as the equivalent imidazolium IL despite the fact that it is not aromatic and cannot use π–π interactions (which are available to imidazolium ILs) to enhance aromatic solubility and selectivity. Moreover, we provide quantification of the IL solubility in the organic-rich phase, which is on the order of 10–4 mole fraction. This quantity is important because it would represent IL loss and product contamination in a real extraction process; however, it is commonly reported to be nondetectable. The nonrandom two-liquid (NRTL), perturbed-chain statistical associating fluid theory (PC-SAFT), COSMO-RS, and COSMO-SAC models are appropriate for the mixtures explored, and each model’s strengths and weaknesses are discussed.

Hydrogen sulfide solubility in different ionic liquids: an updated database and intelligent modeling

Hydrogen sulfide is an acidic, corrosive, toxic, and flammable gas, which is found extensively in the oil and gas industry. For processing of oil and gas and also for the sake of environmental consideration, the sour gas must be removed. This is usually performed through the application of the gas-liquid absorption processes. Ionic liquids are comparatively novel solutions with desirable properties and do not exhibit the disadvantages, which are associated with common amine-based solutions. Their remarkable properties such as negligible vapor pressure, high thermal and electrochemical stabilities, high solubility, and non-flammability make them a good alternative to classical liquid solvents in the field of gas separation and adsorption. Establishing models for accurate estimation of solubility is an appropriate alternative for costly and time-consuming laboratory measurements. In this study, a multi-layer perceptron model is proposed to predict the hydrogen sulfide solubility in 33 different ionic liquids in a wide range of pressures and temperatures. Critical pressure and temperature as well as the acentric factor were used as the parameters to distinct between different ionic liquids. As unprecedented steps in the prediction of hydrogen sulfide solubility in ionic liquids, dimensionless pressures and temperatures are used as the inputs in addition to the aforementioned characteristic parameters. Moreover, the natural logarithm of solubility values is considered for modeling. The developed model resulted in the average absolute relative deviation of 2.57%, which is indicative of acceptable accuracy. Furthermore, the results were compared with those for the proposed models in the literature. Although some models have provided lower error values, they were trained and tested for a limited number of datapoints and ionic liquids. Compared with recent models with similar number of datapoints and ionic liquids, the proposed model in this study resulted in a much lower error for even a wider range of ionic liquids.

Estimation of CO2 solubility in ionic liquids using connectionist tools based on thermodynamic and structural characteristics

Ionic Liquids (ILs) are a promising alternative to conventional amine-based solvents in CO2 absorption processes. Relatively high cost and viscous nature of ILs tempt researchers to find appropriate combination of cations and anions for targeting a competitive absorbent close to amines in terms of CO2 absorption capacity. Conducting extensive experimental studies appears to be time-consuming and expensive for a large number of common CO2/IL systems. One of the fast and reliable approaches to predict the solubility of CO2 in ILs is Machine Learning (ML)-based models or smart tools where the thermodynamic-based and structure-based property relationships can be explored. Four ML methods including Least Squares Support Vector Machine (LSSVM), Decision Tree (DT), Random Forest (RF), and Multilinear Regression (MLR) are employed in this study to obtain CO2 solubility in a structurally diverse set of ILs based on thermodynamic properties and Quantitative Structure–Activity Relationship (QSPR) model. In this paper, two datasets of CO2 solubility (taken from the literature) at various operating conditions are used; one model (or dataset) considers critical properties, molecular weight, and acentric factor of pure ILs as the input information, and the second one includes structural descriptors of cations and anions as the input parameters. Among different types of descriptors, the most important input variables (e.g., Homo-Lumo fraction and Disps (cation)) are selected using Genetic Algorithm (GA)-MLR method. The predictive ability of the introduced models is also analyzed using a four-fold external cross-validation procedure. A great match between the predicted values and experimental measurements is attained while using RF and DT techniques developed based on descriptors and thermodynamics properties. The structural descriptors-based models are more accurate and robust than those built on the critical properties. The feature selection approach is also applied to identify the most effective cation and anion descriptors. For both thermodynamic and QSPR modeling approaches, pressure has the maximum relative importance in the estimation of CO2 solubility in ILs. In the absence of temperature and pressure impacts, the critical pressure and Homo-Lumo fraction have the highest contribution to the thermodynamic – based and structural-based tools, respectively.

Prediction of ammonia absorption in ionic liquids based on extreme learning machine modelling and a novel molecular descriptor SEP

The large amounts of ammonia emissions generated from industrial production have caused serious environmental pollution problems, such as soil acidification, eutrophication, the formation of fine particles and changes in the global greenhouse balance, and also greatly endanger human health. At present, effectively reducing ammonia emissions or recovering ammonia is still a huge challenge. Ionic liquids (ILs) as a new class of green solvent have been introduced for ammonia absorption with great potential, but a huge number on combination systems of ILs lead to the difficulty of measuring the ammonia solubility in all ILs by experiments (e.g., danger and cost). Hereby, this study proposed a novel approach for estimating the ammonia solubility in different ILs. A predictive model was developed based on the novel Algorithm - extreme learning machine (ELM) and the molecular descriptors of electrostatic potential surface areas (SEP) as input parameters. Besides, 502 data points of ammonia solubility in 17 ILs were gathered with a wide range of pressure and temperature. For the total set, the determination coefficient (R2) and the average absolute relative deviation (AARD) of the developed model were 0.9937 and 2.95%, respectively. The regression plots revealed good consistency between predictive and experimental data points. Results show the good performance and reliability of the developed model, indicating that the proposed approach can be potentially applied for screening reasonable ILs to absorb ammonia from chemical industry processes.

Study on cloud point pressure of [Emim][Tf2N] in supercritical carbon dioxide microemulsions based on non-ionic surfactant and role of solubilized water

We studied the solubilization effect of the supercritical carbon dioxide (scCO2) microemulsion based on non-ionic surfactants (Ls-45, Ls-54, Ls-36, Dynol-604, and TMN-6) for 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Emim][Tf2N]) ionic liquid (IL) as well as the influencing factors by analyzing the cloud point pressure (CPP) curves, which served as a function of dissolved ILs concentration. The [Emim][Tf2N]/surfactant/scCO2 microemulsion system was characterized by small-angle X-ray scattering (SAXS), which was proved to be a useful method to provide structure and size information of microemulsion. The effect of water on [Emim][Tf2N]-in-scCO2 (ILs/CO2) microemulsions was studied. The results showed that the ILs/CO2 microemulsions could be constructed by five surfactants selected in this paper, and in the Ls-mn system, the Ls-45/[Emim][Tf2N]/scCO2 system had the lowest CPP. However, the microemulsion constructed by Dynol-604 and TMN-6 surfactants had a poor solubilization effect, and the TMN-6 system was more sensitive to temperature. Besides, small amounts of water could reduce the CPP of the system to a certain extent, making the [Emim][Tf2N]/surfactant/scCO2 microemulsion system more stable, which had a positive effect on the solubilization of [Emim][Tf2N], especially the Ls-36 system. Moreover, the size of microemulsion droplets in [Emim][Tf2N]/surfactant/scCO2 systems was obtained by using the method of SAXS experiment, which also confirmed the solubilization of [Emim][Tf2N] in scCO2 microemulsion system. The structure of ILs/CO2 microemulsions constructed with a non-ionic surfactant was speculated. This study can provide a reference for the construction of ILs/CO2 microemulsions, which was helpful in understanding the microstructure and solubilities of ionic liquids in scCO2 microemulsions.

Prediction of CO2 solubility in ionic liquids using machine learning methods

A comprehensive database containing 10,116 CO2 solubility data measured in various ionic liquids (ILs) at different temperatures and pressures is established. Based on this database, the relationship between CO2 solubility and IL structure, temperature and pressure is correlated using group contribution (GC) methods. Two different machine learning algorithms, namely artificial neural network (ANN) and support vector machine (SVM), are employed to develop the GC models. For the 2023 test-set data, the estimated MAE and R2 are 0.0202 and 0.9836, respectively for the ANN-GC model and for the SVM-GC model they are 0.0240 and 0.9783, respectively. The distributions of prediction errors are plotted for both models to provide more comprehensive knowledge on the model performance. The results indicate that both of the models can give reliable predictions on the CO2 solubilities in ILs and the ANN-GC model performs slightly better than the SVM-based model.