Liquid Solubility Research Articles

Prediction of CO2 solubility in glymes and ionic liquids using modified generalized BWR EoS

Glymes or ionic liquids are considered excellent solvents for carbon capture because they are insoluble in the gas phase during CO2 recovery. Hence, it is beneficial to predict the solubility of CO2 in these solvents to plan appropriate experiments for achieving carbon neutrality. In this study, Joback's simple group contribution method (Joback and Reid, Chem. Eng. Commun. 57 (1987) 233–243) was used to predict the effectivity of ionic liquids or glymes toward CO2 dissolution. Using this method, the fundamental and critical properties, such as critical temperature, critical pressure, critical molar volume, and acentric factor, were predicted. A binary interaction parameter, mij, which is necessary for calculating the solubility of CO2 in glyme or an ionic liquid, was also predicted using the critical volume ratio, Vc,i/Vc,CO2. Using the modified generalized BWR EoS, the value was best fitted at approximately 300 and 370 K. In general, the method proposed was effective in predicting the amount of CO2 that will dissolve in an unknown ionic liquid, which is essential for achieving carbon neutrality.

Tungsten(II) chloride hydrates with high solubility in chloroaluminate ionic liquids for the electrodeposition of Al–W alloy films

• Tungsten(II) chloride hydrates showed high solubility in AlCl 3 -based ionic liquids. • Al–W alloy films with high W contents were obtained at high current densities. • Current efficiencies close to 100% were achieved even in the baths with hydrates. • Spectroscopic techniques were used to identify the chemical species in the bath. The electrodeposition of Al alloys using chloroaluminate ionic liquids (ILs), such as 1-ethyl-3-methylimidazolium chloride (EMIC)–AlCl 3 , has been of significant interest as a surface coating technology. To minimize the contamination of the ILs with water, which can inhibit Al deposition, anhydrous metal chlorides were generally used as metal precursors in the previous studies on Al alloy electrodeposition. However, many anhydrous transition metal chlorides have limited solubility in ILs, making it difficult to obtain Al-based alloy films with a high alloy content at a high current density. This article describes the electrodeposition of Al–W alloy films using W(II) chloride hydrates, W 6 Cl 12 ·2H 2 O and (H 3 O) 2 [W 6 Cl 14 ]·7H 2 O, as W precursors. These hydrated salts were found to have much higher solubilities in EMIC–AlCl 3 ILs than anhydrous W 6 Cl 12 . Therefore, owing to the high solubility of the hydrated salts, Al–W alloy films with a high W content could be electrodeposited even at high current densities. Furthermore, the use of hydrated salts had no negative effect on the quality of the electrodeposited films or the current efficiency. A chemical speciation of the Al and W ions in the ILs was carried out using Raman spectroscopy, Fourier-transform infrared spectroscopy, extended X-ray absorption fine structure spectroscopy, and UV-visible spectroscopy. The spectral data conclusively indicated that the dissolved species derived from the hydrated salts were different compared to those derived from the anhydrous salt. The high solubility of the hydrated W salts and the success of Al–W alloy electrodeposition encourage the use of hydrated salts in chloroaluminate ILs.

Impact of an engineered micro-patterned interface on chitosan/glycerol membranes for wound healing

Biomimetic materials are of great significance in wide applications. As a bioactive polysaccharide, chitosan (CS) has attracted much attention in multiple fields. Inspired by the natural shark skin interface, herein, we proposed a methodology to obtain serial biomimetic CS/glycerol membranes via a facile mold casting and solvent evaporation process under mild aqueous conditions. The surface morphology, chemical structures, and physical characteristics of the developed membranes were determined by scanning electron microscopy, Fourier transformed infrared spectroscopy, tensile measurement, water vapor transmission rate, liquid uptake behavior, solubility, and dynamic water contact angle tests. Furthermore, in vitro and in vivo biological assays were performed, including antibacterial ability, hemostatic performance, hemolytic reaction, cytotoxicity evaluation, and contaminated wound healing assessment. Compared with the flat membranes, the micro-patterned CS/glycerol membranes show the higher water-absorbing ability (up to ca. 26-fold@30 min), moderate tensile strength (8.5–14.7 MPa), superior bacteriostasis (84.9% to 98.6% against S. aureus, 84.3% to 99.6% against E. coli), decent hemostat efficacy (within 75.7 s), low hemolysis rates (less than 0.5%), and benign cytotoxicity (84.2% to 98.4% of viability), which favor promoting contaminated wound healing. Thus, the biomimetic micro-patterned membranes improve bioactive performance and show the merits of simple operation, low cost, and easy scale-up. All these properties indicate that the hybrid micro-patterned membrane could be used as a suitable candidate for dermal wound dressings and open up the insights for developing other bionic interfaces in future potential applications.

Reversible absorption of NF3 with high solubility in Lewis acidic ionic liquids

It is urgent to find a way to capture Nitrogen trifluoride (NF3) with the increasing emissions during the expansion of the electronic industry. Due to the strong electron − withdrawing of F atom, the interactions between NF3 and most compounds are quite weak, making the capture of NF3 full of challenges. Herein, a pioneering attempt was made to capture NF3 in Lewis acidic ionic liquids (LAILs). Impressively, under the condition of 313.2 K and 1.0 bar, [Omim][Fe2Cl7] LAILs exhibited high NF3 solubility (0.1801 mol/mol), which is 64 and 180 times higher than that of [Emim][Ac] and [Bmim][PF6] respectively. The interaction mechanism between LAILs and NF3 was proposed with its reversible regeneration, which has been confirmed by FT − IR, Raman, and theoretical calculations. This work reveals a novel insight into industrial prospects and applications of NF3 capture.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Application of PC-SAFT EoS for calculating gas solubility and viscosity of ammonium-based ionic liquids

Application of PC-SAFT EoS for calculating gas solubility and viscosity of ammonium-based ionic liquids

Potentiometric properties of the electrochemical cells equipped with ionic liquid salt bridge and its application to determine the solubility of the ionic liquid and the mean activity coefficients of the chloride salt of the ionic liquid-constituent cation in water

Potentiometry of electrochemical cells equipped with ionic liquid salt bridge (ILSB) has been studied and applied for determination of the solubility in water of a sparingly soluble ionic liquid (IL) employed for the ILSB consisting of B + and A − , and also the mean activity coefficient of the chloride salt of the IL-constituting cation, BCl. In the case of an electrochemical cell with ILSB made of BA sandwiched by two electrolyte solutions, one containing only hydrophilic ions and the other containing BCl, the cell voltage, E cell , involves the term of the solubility of the IL, enabling the potentiometric determination of the solubility of BA in water based on the Debye-Hückel limiting law. Experimentally, from measured E cell values the solubility of tributyl(2-methoxyethyl) phosphonium bis(pentafluoroethanesulfonyl) amide ([TBMOEP + ][C 2 C 2 N − ]) is determined to be 0.15 mmol dm −3 at 25 °C. The value obtained is of thermodynamic nature because it does not involve the activity coefficient terms. An estimated value of the mean activity coefficient of tributyl(2-methoxyethyl) phosphonium chloride (TBMOEPCl) in aqueous 0.0093 M TBMOEPCl, 0.88 ± 0.01 at 25 °C, agrees with 0.87 ± 0.02 in aqueous 0.01 M TBMOEPCl obtained by the cell in which the ILSB is sandwiched by two aqueous TBMOEPCl solutions, suggesting the consistency in the present potentiometric approach.

Toward predicting SO2 solubility in ionic liquids utilizing soft computing approaches and equations of state

BackgroundThe use of novel and green solvents like ionic liquids (ILs) for the capture of air pollutant gases has gained extensive attention in recent years. However, getting reliable and fast predictions of gases solubility in ILs is complex. MethodsFour soft computing methods including deep belief network (DBN), group method of data handling (GMDH), genetic programming (GP), and K-nearest neighbor (KNN) were utilized for estimating the solubility of sulfur dioxide (SO2) in ILs. A total of 374 experimental data points of SO2 solubility in 15 types of ILs were collected and used for model development. Moreover, Valderrama-Patel-Teja (VPT), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), Redlich-Kwong (RK), and Soave-Redlich-Kwong (SRK) equations of state (EOSs) were applied for the solubility predictions in the SO2 + ILs systems. Significant findingsThe results illustrated that DBN model is the most reliable predictive tool for the SO2 solubility in ILs by having an average absolute percent relative error (AAPRE) of 3.56%. Furthermore, the proposed simple to use GMDH mathematical correlation also provides good estimations with an AAPRE of 8.05%. Despite the weaker performance of the EOSs than the intelligent models, the PR EOS presented better estimations among other EOSs for the SO2 solubility in ILs.

Vapor Absorption and Marangoni Flows in Evaporating Drops.

We experimentally and analytically studied vapor-driven solutal Marangoni flow by varying volatile liquid sources on top of the water droplet. We checked and compared the effects of solubility and vapor pressures of volatile liquids on the internal flow pattern using particle image velocimetry (PIV) and the droplet shape using shadowgraphy experiments. To explain the internal flow, we explored the absorption and evaporation mechanism of the vapors and we found that Henry's constant of the volatile liquid is the primary factor. Based on the scaling arguments, we developed theoretical models to explain how much vapor is absorbed into the water droplet, and how the flow pattern occurs and evolves. The scaling models show that there is a good agreement with the experimental results. We believe that understanding this phenomenon is useful for microfluidics applications and fundamental liquid-gas interface problems where vapors can be absorbed into another liquid.

Solubility measurement and thermodynamic properties of Nintedanib Esylate Hemihydrate in pure solvents

The thermodynamic property of NE crystallization in 10 pure solvents was studied, and the solid–liquid equilibrium solubility of NE in these ten solvents was determined by laser dynamic method. The result showed that the solubility was proportional to the temperature. Five models including the modified Apleblat model, λh model, Polynomial model, Van't Hoff model, and Yaws model were adopted to fit the solubility of the solute in a single solvent, and the result showed that the Polynomial model had a high fitting degree for the solubility of NE in the single solvent chosen. The activity coefficients, as well as thermodynamic properties of the mixing, were obtained and studied. The effect of solvents themselves on the solubility order of Nintedanib Esylate was also studied by correlating to the solubility of Nintedanib Esylate through the KAT-LSER model. What is more, by adopting Molecular Dynamic simulation, it was proved that the Nintedanib Esylate solubility in different solvents could be largely influenced by the interaction of the solute molecules and solvent molecules.

Accelerate the Electrolyte Perturbed-Chain Statistical Associating Fluid Theory-Density Functional Theory Calculation With the Chebyshev Pseudo-Spectral Collocation Method. Part II. Spherical Geometry and Anderson Mixing.

To improve the efficiency of electrolyte perturbed-chain statistical associating fluid theory–density functional theory (ePC-SAFT-DFT) calculation of the confined system, in this work, first, the Chebyshev pseudo-spectral collocation method was extended to the spherical pores. Second, it was combined with the Anderson mixing algorithm to accelerate the iterative process. The results show that the Anderson mixing algorithm can reduce the computation time significantly. Finally, based on the accelerated ePC-SAFT-DFT program, a systematic study of the effects of the temperature, pressure, pore size, and pore shape on the CO2 solubilities in the ionic liquids (ILs) confined inside the silica nanopores was conducted. Based on the simulation results, to obtain high CO2 solubilities in the ILs confined in silica, a better option is to use the silica material with a narrow spherical pore, and the IL-anion should be selected specifically considering that it has a more significant impact on the absorption enhancement effect.

Hydrogen Solubility in Liquid and Solid Pure Aluminum—Critical Review of Measurement Methodologies and Reported Values

Reliable information on the solubility of hydrogen in aluminum and its alloys is critical to the effort of the aluminum industry to control and ameliorate the usually deleterious effects of hydrogen on the properties and performance of pure aluminum and aluminum alloy products. Unfortunately, there is a significant disparity between published values of hydrogen solubility in pure aluminum and aluminum alloys. This is because the measurement of the extremely low soluble hydrogen concentration in aluminum and its alloys is experimentally difficult. Also, the reproducibility, accuracy, and reliability of the hydrogen solubility values are very sensitive to the measurement techniques, test conditions, chemical composition, and state of the aluminum sample. Thus, no serious discussion of the reliability of reported values of hydrogen solubility in aluminum and its alloys can be undertaken without a critical assessment of the fundamental principles of the experimental techniques used in the determination of the reported values. In this article, a critical review of the fundamental principles of the experimental techniques used in the measurement of hydrogen solubility in liquid and solid pure aluminum and aluminum alloys is presented. In addition, the reliability and possible accuracy of reported values of hydrogen solubility in solid and liquid pure aluminum are critically assessed. Empirical equations for calculating hydrogen solubility in liquid and solid pure aluminum as a function of temperature and pressure, derived from the most reliable sets of data are recommended. At 101.3 kPa (1 atm.) hydrogen partial pressure, the most reliable values of hydrogen solubility at the melting point (833 K) of pure aluminum are 0.71 cm3/100g (i.e., 6.32 × 10-5 wt.% H) and 0.043 cm3/100g (i.e., 3.81 × 10-6 wt.% H), in the liquid and solid state, respectively. So, the partition coefficient of hydrogen in pure aluminum is 0.061.

Microstructure, mechanical and wear properties of mechanically alloyed and conventionally sintered Nb-W and Nb-Mo alloys

Nb-W and Nb-Mo exhibit unrestrained solubility in liquid and solid phase, leading to influence the mechanical properties of Nb. In the present work 90Nb-10 W (alloy A) and 90Nb-10Mo (alloy B) (in weight%) are synthesized by mechanical alloying for 10 h and consolidated by conventional pressureless sintering in argon atmosphere at 1450 °C with 2 h of soaking time. The effect of compaction pressure (375 MPa, 500 MPa, 625 MPa) on densification before sintering and densification, hardness after sintering of the alloys is also investigated. Plastic-assisted deformation is more in alloy B against alloy A which enhances the densification during sintering. Elemental mapping and line scanning result of sintered alloy A show the incidence of W in Nb phase, suggesting profound inter-diffusion of W/Nb. Maximum % relative sintered density of 68.89% is achieved in alloy B at 500 MPa pressure. Alloy A exhibits maximum hardness of 3.11 GPa at 625 MPa compared to 2.55 GPa hardness for alloy B compacted at identical compaction pressure. The wear resistance of alloy B is comparatively higher than alloy A, both compacted at 500 MPa pressure.

Solubility of carbon dioxide in some imidazolium and pyridinium-based ionic liquids and correlation with NRTL model

In this study, the solubility of carbon dioxide gas in a series of 1-alkyl-4-methyl pyridinium and 1-alkyl-3-methylimidazolium-based ionic liquids with various anions, viz. thiocyanate ([SCN]−), chloride ([Cl]−) and bromide ([Br]−) was investigated using a quartz crystal microbalance at 298.15 K and pressures up to 0.4 MPa. CO2 solubility in the ionic liquids correlates well with the non-random two-liquid (NRTL) model. The results indicate that the cation alkyl chain length and the type of anion have the main effects on the solubility of carbon dioxide in ionic liquids. CO2 solubility in both 1-alkyl-4-methyl pyridinium and 1-alkyl-3-methylimidazolium-based ionic liquids increased with increasing alkyl chain length of the cation. Also, CO2 solubility was strongly dependent on the selection of the anion. CO2 solubility in both 1-alkyl-4-methyl pyridinium and 1-alkyl-3-methylimidazolium-based ionic liquids increased as follows: [SCN]− > [Cl]− > [Br]−.

Thermodynamic and Artificial Intelligence Approaches of H2S Solubility in Some Imidazolium-Based Ionic Liquids

Thermodynamic and Artificial Intelligence Approaches of H2S Solubility in Some Imidazolium-Based Ionic Liquids

Synthesis and fungicidal activity of alkyltrimethylammonium salicylate ionic liquids

Three alkyltrimethylammonium salicylates (CnTASal) with different alkyl side chains were synthesized and characterized. Solubility, surface activity, and biological activity of the ionic liquids were determined. The results of solubility and surface activity showed that the synthesized ILs are amphiphilic surface-active compounds. Five common agricultural fungi, Valsa mali, Rhizoctonia solani, Fusarium graminearum, Phytophthora capsica and Alternaria solani were tested to identify the most sensitive strain and Valsa mali was selected for the structure-activity relationship (SAR) study. The results of the study revealed that the antifungal activity of the ILs is positively related to the alkyl chains length.

Carbon Dioxide Solubility in Guanidinium-Based Ionic Liquids at High Pressure

Carbon Dioxide Solubility in Guanidinium-Based Ionic Liquids at High Pressure

Modeling of H2S solubility in ionic liquids using deep learning: A chemical structure-based approach

Hydrogen sulfide (H2S) is a toxic, flammable, corrosive, and acidic gas that can have harmful impacts on the environment. Ionic liquids (ILs) are relatively new solutions that have desirable characteristics such as high thermal and electrochemical stabilities, negligible vapor pressure, non-combustibility, and high solubility, allowing them to be an appropriate choice of liquid solvents in gas removal processes. In this study, deep learning (DL) approaches were utilized to predict the H2S solubility in ionic liquids (ILs). The proposed models include convolutional neural network (CNN), recurrent neural networks (RNNs), deep belief networks (DBN), and deep neural network initialization with decision trees (DJINN). To this end, a large databank in a broad range of pressure and temperature encompassing 1516 data points of H2S solubility in 37 various ILs was used for developing models. In these models, the chemical structure of ILs, temperature, and pressure were considered as the model’s inputs, while H2S solubility was the model’s output. The results reveal that the CNN model provides more accurate, rapid, flexible, and inexpensive estimations for H2S solubility in ILs with an average absolute percent relative error (AAPRE) of 2.92% and a determination coefficient (R2) of 0.99. The results were then compared to those of previously proposed techniques in the literature. According to the results of the sensitivity analysis, it was found that pressure and OH (as substructure) impose the highest positive and the highest negative impacts on the solubility of H2S in ILs, respectively. Also, based on sensitivity analysis, temperature has a reverse effect on the solubility of H2S and with increasing temperature, the H2S solubility decreases. The Taylor diagram illustrated that the CNN approach is very efficient, accurate, and realistic for predicting the solubility of H2S in diverse ILs based on the chemical structure, pressure, and temperature. Finally, trend analysis revealed that the trends of CNN predictions are in great agreement with the measured data of H2S solubility in ILs as a function of pressure. The findings of this work may be used not only to overcome the difficulties of calculating H2S solubility in ILs, but also to develop novel and accurate forecasting algorithms for a large dataset that cover a broad range of temperatures and pressures.

Estimation of solubility of acid gases in ionic liquids using different machine learning methods

Ionic liquids (ILs) can capture acid gases that damaged the environment. Due to the properties of low-cost and non-toxic, machine learning can be used to screen ILs for gas absorption. To find the most suitable machine learning method for estimating gas absorption in ILs, 12 different machine learning algorithms are used to train models to estimate CO2 and H2S solubility in different ILs. Temperature (T), pressure (P), molecular weight (Mw), critical temperature (Tc), and critical pressure (Pc) of solutions are used as the input variables; Solubility is used as the output variable in the model training. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R2) are used to evaluate the models. Stacking algorithm has the most accurate model in IL- CO2 system, with MSE, RMSE, MAE, and R2 of 0.001, 0.025, 0.018, and 0.969 respectively on average. Voting algorithm performs best in IL-H2S system; the four averaged metrics are 0.002, 0.032, 0.024, and 0.934 accordingly. By combining different algorithms, Voting and Stacking algorithms can balance out each model's weakness and produce a more accurate model. Stacking and Voting algorithms can be considered as a promising candidate for the estimation of acid gases solubility in ionic liquids.

Potassium carbonate-based ternary transition temperature mixture (deep eutectic analogues) for CO2 absorption: Characterizations and DFT analysis

Is it possible to improve CO2 solubility in potassium carbonate (K2CO3)-based transition temperature mixtures (TTMs)? To assess this possibility, a ternary transition-temperature mixture (TTTM) was prepared by using a hindered amine, 2-amino-2-methyl-1,3-propanediol (AMPD). Fourier transform infrared spectroscopy (FT-IR) was employed to detect the functional groups including hydroxyl, amine, carbonate ion, and aliphatic functional groups in the prepared solvents. From thermogravimetric analysis (TGA), it was found that the addition of AMPD to the binary mixture can increase the thermal stability of TTTM. The viscosity findings showed that TTTM has a higher viscosity than TTM while their difference was decreased by increasing temperature. In addition, Eyring’s absolute rate theory was used to compute the activation parameters (∆G*, ∆H*, and ∆S*). The CO2 solubility in liquids was measured at a temperature of 303.15 K and pressures up to 1.8 MPa. The results disclosed that the CO2 solubility of TTTM was improved by the addition of AMPD. At the pressure of about 1.8 MPa, the CO2 mole fractions of TTM and TTTM were 0.1697 and 0.2022, respectively. To confirm the experimental data, density functional theory (DFT) was employed. From the DFT analysis, it was found that the TTTM + CO2 system has higher interaction energy (|∆E|) than the TTM + CO2 system indicating the higher CO2 affinity of the former system. This study might help scientists to better understand and to improve CO2 solubility in these types of solvents by choosing a suitable amine as HBD and finding the best combination of HBA and HBD.