Conductor-like Screening Model For Real Solvents Research Articles

Towards novel calcium battery electrolytes by efficient computational screening

The development of Ca conducting electrolytes is key to enable functional rechargeable Ca batteries. The here presented screening strategy is initially based on a combined density functional theory (DFT) and conductor-like screening model for real solvents (COSMO-RS) approach, which allows for a rational selection of electrolyte solvent based on a set of physico-chemical and electrochemical properties: solvation power, electrochemical stability window, viscosity, and flash and boiling points. Starting from 81 solvents, N,N-dimethylformamide (DMF) was chosen as solvent for further studies of cation-solvent interactions and subsequent comparisons vs. cation-anion interactions possibly present in electrolytes, based on a limited set of Ca-salts. A Ca2+ first solvation shell of [Ca(DMF)8]2+ was found to be energetically preferred, even as compared to ion-pairs and aggregates, especially for PF6− and TFSI as the anions. Overall, this points to Ca(TFSI)2 and Ca(PF6)2 dissolved in DMF to be a promising base electrolyte for Ca batteries from a physico-chemical point-of-view. While electrochemical assessments certainly are needed to verify this promise, the screening strategy presented is efficient and a useful stepping-stone to reduce the overall R&D effort.

Vapor Pressure and Physicochemical Properties of {LiBr\u2009+\u2009IL-Based Additive\u2009+\u2009Water} Mixtures: Experimental Data and COSMO-RS Predictions

In recent years, many compounds have been proposed as additives to conventional working fluids to improve the performance of the absorption refrigeration system. The main aim of this research is to show the influence of ionic liquid based additives on thermodynamic and physicochemical properties of {LiBr + water} solutions. The following additives: 3-(1-methyl-morpholinium)propane-1-sulfonate, N,N-di(2-hydroxyethyl)-N,N-dimethylammonium bromide, and N,N,N-tri(2-hydroxy-ethyl)-N-methylammonium bromide have been added to aqueous lithium bromide solutions (IL to LiBr mass fraction, w2 = 0.3). The physicochemical and thermodynamic properties of {LiBr (1) + additive (2) + water (3)} and {LiBr + water} systems including (vapor + liquid) phase equilibria (VLE), density (ρ) and dynamic viscosity (η) were determined over wide temperature and composition ranges. The conductor-like screening model for real solvents (COSMO-RS) was used for the VLE data prediction. For the density and dynamic viscosity correlations, empirical equations were applied. A comparison of experimental data for {LiBr + additive + water} with those for {LiBr + water} systems shows the influence of using the additives proposed in this work. The data presented are complementary to the current state of knowledge in this area and provide directions for future research.

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.

Sustainable Synthesis of Omega-3 Fatty Acid Ethyl Esters from Monkfish Liver Oil

The search for economic and sustainable sources of polyunsaturated fatty acids (PUFAs) within the framework of the circular economy is encouraged by their proven beneficial effects on health. The extraction of monkfish liver oil (MLO) for the synthesis of omega-3 ethyl esters was performed to evaluate two blending systems and four green solvents in this work. Moreover, the potential solubility of the MLO in green solvents was studied using the predictive simulation software COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS). The production of ethyl esters was performed by one or two-step reactions. Novozym 435, two resting cells (Aspergillus flavus and Rhizopus oryzae) obtained in our laboratory and a mix of them were used as biocatalysts in a solvent-free system. The yields for Novozym 435, R. oryzae and A. flavus in the one-step esterification were 63, 61 and 46%, respectively. The hydrolysis step in the two-step reaction led to 83, 88 and 93% of free fatty acids (FFA) for Novozym 435, R. oryzae and A. flavus, respectively. However, Novozym 435 showed the highest yield in the esterification step (85%), followed by R. oryzae (65%) and A. flavus (41%). Moreover, selectivity of polyunsaturated fatty acids of R. oryzae lipase was evidenced as it slightly esterified docosahexaenoic acid (DHA) in all the esterification reactions tested.

Role of cation and alkyl chain length on the extraction of phenol from aqueous solution using NTf2-based ionic liquids: Experimental and computational analysis

In this study, the extraction behavior of several ionic liquids (ILs) containing the same anion, bis-(trifluoromethylsulfonyl)imide ([NTf2]) and a range of cations with different alkyl chain length are compared for the removal of phenol from aqueous solution. The [NTf2]− anion was selected due to its hydrophobic nature and low viscosity, which can contribute positively to mass transfer and to the removal efficiency of phenol, in combination with a range of imidazolium, pyrrolidinium and ammonium based cations. To study the effect of alkyl chain length on the extraction of phenol, the alkyl chain length attached to the imidazolium cation was varied from C2 to C12. The effect of process parameters such as IL to water volume ratio (VIL:Vw), phenol concentration, pH and extraction time were investigated. For a given alkyl chain length, the performance of the ILs followed the order: pyrrolidinium>imidazolium>ammonium. Increasing the alkyl chain length of the imidazolium cation resulted in decreasing removal efficiency of phenol. Around 89% removal of phenol was achieved in 3 min, at pH 2 and volume ratio (VIL: Vwater) of 1:3 at room temperature. The extraction of phenol into the ionic liquid phase was confirmed using Fourier Transform Infrared spectroscopy (FTIR). The interactions of phenol with the various studied ionic liquids were modeled using the Conductor like Screening Model for Real Solvents (COSMO-RS). The experimental results were explained based on hydrophobic-hydrophilic interactions between phenol and the ILs as revealed from the σ profiles.

Removal of Siloxanes from Model Biogas by Means of Deep Eutectic Solvents in Absorption Process.

The paper presents the screening of 20 deep eutectic solvents (DESs) composed of tetrapropylammonium bromide (TPABr) and glycols in various molar ratios, and 6 conventional solvents as absorbents for removal of siloxanes from model biogas stream. The screening was achieved using the conductor-like screening model for real solvents (COSMO-RS) based on the comparison of siloxane solubility in DESs. For the DES which was characterized by the highest solubility of siloxanes, studies of physicochemical properties, i.e., viscosity, density, and melting point, were performed. DES composed of tetrapropylammonium bromide (TPABr) and tetraethylene glycol (TEG) in a 1:3 molar ratio was used as an absorbent in experimental studies in which several parameters were optimized, i.e., the temperature, absorbent volume, and model biogas flow rate. The mechanism of siloxanes removal was evaluated by means of an experimental FT-IR analysis as well as by theoretical studies based on σ-profile and σ-potential. On the basis of the obtained results, it can be concluded that TPABr:TEG (1:3) is a very effective absorption solvent for the removal of siloxanes from model biogas, and the main driving force of the absorption process is the formation of the hydrogen bonds between DES and siloxanes.

Quantifying Uncertainties in Solvation Procedures for Modeling Aqueous Phase Reaction Mechanisms.

Computational quantum chemistry provides fundamental chemical and physical insights into solvated reaction mechanisms across many areas of chemistry, especially in homogeneous and heterogeneous renewable energy catalysis. Such reactions may depend on explicit interactions with ions and solvent molecules that are nontrivial to characterize. Rigorously modeling explicit solvent effects with molecular dynamics usually brings steep computational costs while the performance of continuum solvent models such as polarizable continuum model (PCM), charge-asymmetric nonlocally determined local-electric (CANDLE), conductor-like screening model for real solvents (COSMO-RS), and effective screening medium method with the reference interaction site model (ESM-RISM) are less well understood for reaction mechanisms. Here, we revisit a fundamental aqueous hydride transfer reaction-carbon dioxide (CO2) reduction by sodium borohydride (NaBH4)-as a test case to evaluate how different solvent models perform in aqueous phase charge migrations that would be relevant to renewable energy catalysis mechanisms. For this system, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations almost exactly reproduced energy profiles from QM simulations, and the Na+ counterion in the QM/MM simulations plays an insignificant role over ensemble averaged trajectories that describe the reaction pathway. However, solvent models used on static calculations gave much more variability in data depending on whether the system was modeled using explicit solvent shells and/or the counterion. We pinpoint this variability due to unphysical descriptions of charge-separated states in the gas phase (i.e., self-interaction errors), and we show that using more accurate hybrid functionals and/or explicit solvent shells lessens these errors. This work closes with recommended procedures for treating solvation in future computational efforts in studying renewable energy catalysis mechanisms.

Computational studies of ionic liquids as co-catalyst for CO2 electrochemical reduction to produce syngas using COSMO-RS

Transforming carbon dioxide (CO2) into value-added products through electrochemical reduction reaction (CO2ERR) is a promising technique due to its potential advantages using renewable energy. The main challenge is to find a stable catalytic system that could minimize the reaction overpotential with high faradaic efficiency and high current density. Ionic liquids (ILs) as electrolyte in CO2ERR have attracted attention due to the advantages of their unique properties in enhancing catalytic efficiency. For better performance, a systematic understanding of the role of ILs as electrocatalyst is needed. Therefore, this paper aims to correlate the performance of ILs as co-catalyst in (CO2ERR) with the lowest unoccupied molecular orbital (LUMO) energy level and the interaction energy as predicted by quantum chemical calculation using Conductor like Screening Model for Real Solvents (COSMO-RS) and Turbomole. The results show strong linearity (R2=0.98) between hydrogen bond energy (HB) and LUMO values. It is demonstrated that as HB increases, the LUMO value decreases, and the catalytic activity for CO2ERR also increases. This result allows further understanding on the correlation between the molecular structure and the catalytic activity for CO2ERR. It can serve as a priori prediction to aid in the design of new effective catalysts.

COSMO-RS Analysis of CO2 Solubility in N-Methyldiethanolamine, Sulfolane, and 1-Butyl-3-methyl-imidazolium Acetate Activated by 2-Methylpiperazine for Postcombustion Carbon Capture.

Novel aqueous (aq) blends of N-methyldiethanolamine (MDEA), sulfolane (TMSO2), and 1-butyl-3-methyl-imidazolium acetate ([bmim][Ac]) with amine activator 2-methylpiperazine (2-MPZ) are analyzed through conductor-like screening model for real solvents (COSMO-RS) for possible application in the chemisorption of CO2. The molecules associated are analyzed for their ground-state energy, σ potential, and σ surface. Thermodynamic and physicochemical properties have been assessed and paralleled with the experimental data. Vapor pressure of the blended systems and pure component density and viscosity have been compared successfully with the experimental data. Important binary interaction parameters for the aqueous blends over a wide temperature, pressure, and concentration range have been estimated for NRTL, WILSON, and UNIQUAC 4 models. The COSMO-RS theory is further applied in calculating the expected CO2 solubility over a pressure range of 1.0–3.0 bar and temperature range of 303.15–323.15 K. Henry’s constant and free energy of solvation to realize the physical absorption through intermolecular interaction offered by the proposed solvents. Perceptive molecular learning from the behavior of chemical constituents involved indicated that the best suitable solvent is aq (MDEA + 2-MPZ).

Machine learning-based ionic liquids design and process simulation for CO2 separation from flue gas

Rational design of ionic liquids (ILs), which is highly dependent on the accuracy of the model used, has always been crucial for CO2 separation from flue gas. In this study, a support vector machine (SVM) model which is a machine learning approach is established, so as to improve the prediction accuracy and range of IL melting points. Based on IL melting points data with 600 training data and 168 testing data, the estimated average absolute relative deviations (AARD) and squared correlation coefficients (R2) are 3.11%, 0.8820 and 5.12%, 0.8542 for the training set and testing set of the SVM model, respectively. Then, through the melting points model and other rational design processes including conductor-like screening model for real solvents (COSMO-RS) calculation and physical property constraints, cyano-based ILs are obtained, in which tetracyanoborate [TCB]- is often ruled out due to incorrect estimation of melting points model in the literature. Subsequently, by means of process simulation using Aspen Plus, optimal IL are compared with excellent IL reported in the literature. Finally, 1-ethyl-3-methylimidazolium tricyanomethanide [EMIM][TCM] is selected as a most suitable solvent for CO2 separation from flue gas, the process of which leads to 12.9% savings on total annualized cost compared to that of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide [EMIM][Tf2N].

Prediction and verification of heat capacities for pure ionic liquids

The heat capacity of ionic liquids is an important physical property, and experimental measuring is usually used as a common method to obtain them. Owing to the huge number of ionic liquids that can be potentially synthesized, it is desirable to acquire theoretical predictions. In this work, the Conductor-like Screening Model for Real Solvents (COSMO-RS) was used to predict the heat capacity of pure ionic liquids, and an intensive literature survey was conducted for providing a database to verify the prediction of COSMO-RS. The survey shows that the heat capacity is available for 117 ionic liquids at temperatures ranging 77.66–520 K since 2004, and the 4025 data points in total with the values from 76.37 to 1484 J·mol−1·K−1 have been reported. The prediction of heat capacity with COSMO-RS can only be conducted at two temperatures (298 and 323 K). The comparison with the experimental data proves the prediction reliability of COSMO-RS, and the average relative deviation (ARD) is 8.54%. Based on the predictions at two temperatures, a linear equation was obtained for each ionic liquid, and the heat capacities at other temperatures were then estimated via interpolation and extrapolation. The acquired heat capacities at other temperatures were then compared with the experimental data, and the ARD is only 9.50%. This evidences that the heat capacity of a pure ionic liquid follows a linear equation within the temperature range of study, and COSMO-RS can be used to predict the heat capacity of ionic liquids reliably.

Technical note: Estimating aqueous solubilities and activity coefficients of mono- and <i>α</i>,<i>ω</i>-dicarboxylic acids using COSMO<i>therm</i>

Abstract. We have used the COSMOtherm program to estimate activity coefficients and solubilities of mono- and α,ω-dicarboxylic acids and water in binary acid–water systems. The deviation from ideality was found to be larger in the systems containing larger acids than in the systems containing smaller acids. COnductor-like Screening MOdel for Real Solvents (COSMO-RS) underestimates experimental monocarboxylic acid activity coefficients by less than a factor of 2, but experimental water activity coefficients are underestimated more especially at high acid mole fractions. We found a better agreement between COSMOtherm-estimated and experimental activity coefficients of monocarboxylic acids when the water clustering with a carboxylic acid and itself was taken into account using the dimerization, aggregation, and reaction extension (COSMO-RS-DARE) of COSMOtherm. COSMO-RS-DARE is not fully predictive, but fit parameters found here for water–water and acid–water clustering interactions can be used to estimate thermodynamic properties of monocarboxylic acids in other aqueous solvents, such as salt solutions. For the dicarboxylic acids, COSMO-RS is sufficient for predicting aqueous solubility and activity coefficients, and no fitting to experimental values is needed. This is highly beneficial for applications to atmospheric systems, as these data are typically not available for a wide range of mixing states realized in the atmosphere, due to a lack of either feasibility of the experiments or sample availability. Based on effective equilibrium constants of different clustering reactions in the binary solutions, acid dimer formation is more dominant in systems containing larger dicarboxylic acids (C5–C8), while for monocarboxylic acids (C1–C6) and smaller dicarboxylic acids (C2–C4), hydrate formation is more favorable, especially in dilute solutions.

Experimental and theoretical studies on the Sulfamethazine-Urea and Sulfamethizole-Urea solid-liquid equilibria

The miscibility of active pharmaceutical ingredients with excipients is an important aspect in pharmaceutical technology protocols. In this study, the differential scanning calorimetry (DSC) was used for Sulfamethazine-Urea (SI–U) and Sulfamethizole-Urea (SO–U) solid-liquid phase diagrams determination. Both sulfonamides form simple binary eutectics with Urea. The lack of new co-crystal phase formation was confirmed by inspection of the powder x-ray diffractometry (PXRD) and attenuated total reflectance-Fourier-transform infrared spectroscopy (FTIR-ATR) measurements of the co-grinded mixtures. Despite the limited solid-state miscibility, a quantum-chemical calculations along with post-quantum thermodynamic characteristics performed using Conductor-like Screening Model for Realistic Solvents (COSMO-RS), have shown very high affinity of Sulfamethazine and Sulfamethizole to Urea in the liquid phase. However, the most significant contributions to the overall stabilization and solvation comes from self-association of sulfa-drugs. Noteworthy, a different hydrogen patterns are responsible for SI and SO dimerization since in the former case a cyclic C22(8) synthon occurs while in the latter linear hydrogen bonding motif is observed. The phase diagrams were modelled using different approaches including λh-equation, NRTL and Wilson and the corresponding parameters values were determined via non-linear fitting procedure. It was found that λh-equation has the highest predictive potential leading to very accurate reproduction of experimental results.

COSMO-RS-Based Descriptors for the Machine Learning-Enabled Screening of Nucleotide Analogue Drugs against SARS-CoV-2.

Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. We propose novel descriptors based on a COSMO-RS (short for conductor-like screening model for real solvents) σ-profiles for enhanced drug screening enabled by machine learning (ML). The descriptors’ performance is hereby illustrated for nucleotide analogue drugs that inhibit the ribonucleic acid-dependent ribonucleic acid polymerase, key to viral transcription and genome replication. The COSMO-RS-based descriptors account for both chemical reactivity and structure, and are more effective for ML-based screening than fingerprints based on molecular structure and simple physical/chemical properties. The descriptors are evaluated using principal component analysis, an unsupervised ML technique. Our results correlate with the active monophosphate forms of the leading drug remdesivir and the prospective drug EIDD-2801 with nucleotides, followed by other promising drugs, and are superior to those from molecular structure-based descriptors and molecular docking. The COSMO-RS-based descriptors could help accelerate drug discovery for the treatment of emerging diseases.

The role of ionic vs. non-ionic excipients in APIs-based eutectic systems

Aiming to contribute to drug pre-formulation, new eutectic mixtures were developed. Thymol, coumarin, or quaternary ammonium chlorides as excipients, were combined with the active pharmaceutical ingredients (APIs) acetylsalicylic acid, acetaminophen, ibuprofen, ketoprofen, or lidocaine. Their solid-liquid equilibrium (SLE) binary phase diagrams were measured to study eventual phase separation between the compounds, preventing manufacturing problems, and to study the molecular interactions between the APIs and ionic or non-ionic excipients. The Conductor-like Screening Model for Real Solvents (COSMO-RS) capability to predict the SLE of mixtures containing non-ionic excipients was further evaluated. COSMO-RS gives a good quantitative description of the experimental SLE being a tool with great potential in the screening of eutectic systems containing APIs and non-ionic excipients. While thymol presents strong interactions with the APIs, and consequently negative deviations to thermodynamic ideality, systems containing coumarin follow a quasi-ideal behavior. Regarding the ionic excipients, both choline chloride and the tetraalkylammonium chlorides are unable to establish relevant interactions with the APIs, and no significant negative deviations to ideality are observed. The liquefaction of the APIs here studied is favored by using non-ionic excipients, such as thymol, due to the strong interactions it can establish with the APIs.

Eutectic ionic liquids as potential electrolytes in dye-sensitized solar cells: Physicochemical and conductivity studies

Ionic liquids (ILs) have been introduced as the electrolyte in dye-sensitized solar cells (DSSC) to replace organic solvents due to their non-flammability, chemical, and thermal stability, as well as less prone to evaporation and leakage. A series of eutectic ILs mixtures composed of 1-ethyl-3-methylimidazolium iodide, [C2C1im]I, 1-ethylpyridinium iodide [C2py]I, 1-propylpyridinium iodide, [C3py]I and 1-butylpyridinium iodide, [C4py]I was developed. The structures and purity of the compounds were validated by nuclear magnetic resonance and Karl Fischer Titration. Their physicochemical properties, such as viscosities and ionic conductivities were evaluated for their potential use as electrolytes in DSSCs. The melting temperature of pure ionic liquids and the mixtures were determined using differential scanning calorimetry in the temperature range of 123.15 K to 393.15 K. It is found that eutectic mixtures were formed at the molar ratios of {0.75[C2py]I:0.25[C2C1im], {0.25[C2py]I:0.75[C2C1im]I}, {0.50[C2py]I:0.50[C2C1im]I}, {0.25[C2C1im]I:0.75 [C4py]I}, {0.75[C2C1im]I:0.25[C4py]I}, and {0.50[C2C1im]I:0.50[C4py]I} with the latter forming a highly stable liquid with no apparent melting peak in the studied temperature range. To understand the formation of eutectic composition, a correlation between molecular structures and the interaction energies of the cations and anions of ILs was performed using the Conductor-like Screening Model for Real Solvents (COSMO-RS). Dynamic viscosities of the binary mixtures were evaluated in the temperature range of 283.15 K to 353.15 K with 10 K increment. The highest ionic conductivity was exhibited by the eutectic ILs electrolyte {0.50[C2C1im]I:0.50[C4py]I:I2(50 wt%)} at 9.67 mS·cm−1 showing its potential for electrolyte application in DSSC.

High Voltage Electrical Discharges as an Alternative Extraction Process of Phenolic and Volatile Compounds from Wild Thyme (Thymus serpyllum L.): In Silico and Experimental Approaches for Solubility Assessment.

The objective of this study was to evaluate the potential of green solvents for extractions of bioactive compounds (BACs) and essential oils from wild thyme (Thymus serpyllum L.) using theoretical and experimental procedures. Theoretical prediction was assessed by Hansen solubility parameters (HSPs) and conductor-like screening model for realistic solvents (COSMO-RS), to predict the most suitable solvents for extraction of BACs. An experimental procedure was performed by nonthermal technology high voltage electrical discharge (HVED) and it was compared with modified conventional extraction (CE). Obtained extracts were analyzed for chemical and physical changes during the treatment. Theoretical results for solution of BACs in ethanol and water, as green solvents, were confirmed by experimental results, while more accurate data was given by COSMO-RS assessment than HSPs. Results confirmed high potential of HVED for extraction of BACs and volatile compounds from wild thyme, in average, 2.03 times higher yield of extraction in terms of total phenolic content was found compared to CE. The main phenolic compound found in wild thyme extracts was rosmarinic acid, while the predominant volatile compound was carvacrol. Obtained extracts are considered safe and high-quality source reach in BACs that could be further used in functional food production.

ReSCoSS: a flexible quantum chemistry workflow identifying relevant solution conformers of drug-like molecules.

Conformational equilibria are at the heart of drug design, yet their energetic description is often hampered by the insufficient accuracy of low-cost methods. Here we present a flexible and semi-automatic workflow based on quantum chemistry, ReSCoSS, designed to identify relevant conformers and predict their equilibria across different solvent environments in the Conductor-like Screening Model for Real Solvents (COSMO-RS) framework. We demonstrate the utility and accuracy of the workflow through conformational case studies on several drug-like molecules from literature where relevant conformations are known. We further show that including ReSCoSS conformers significantly improves COSMO-RS based predictions of physicochemical properties over single-conformation approaches. ReSCoSS has found broad adoption in the in-house drug discovery and development work streams and has contributed to establishing quantum-chemistry methods as a strategic pillar in ligand discovery.

Study of the toluene absorption capacity and mechanism of ionic liquids using COSMO-RS prediction and experimental verification

As green solvents, ionic liquids (ILs) are quite suitable for the absorption of volatile organic compounds (VOCs) such as benzene and its homologues. However, solvent selection is the key to the VOC absorption process. In the present study, a rapid solvent screening tool, Conductor-like Screening Model for Real Solvents (COSMO-RS), was used to predict the solubility of toluene in 816 ILs. The effects of four structure characters, namely, the type and alkyl chain length of the cations and anions on the solubility of toluene were discussed. The following conclusions were drawn from the results: (1) ILs with pyrrolidinium-based cations showed better solubility than pyridinium- and imidazolium-based ones. (2) The solubility of toluene in PF6-based ILs increased with the increasing alkyl chain length, while its solubility in Ac-based ILs exhibited the opposite trend. (3) Toluene showed greater solubility in Cl-based ILs than those based on other anions. (4) The solubility of toluene increased with the anion alkyl chain length. Ac-based ILs were chosen as the most promising potential solvents, and further studied to determine the relationship between various interaction energy parameters and toluene solubility. The results showed that the misfit energy played a dominant role during the absorption process. Furthermore, several ILs were selected for experimental verification of the predicted solubility behavior using liquid and gaseous toluene. The results demonstrated that COSMO-RS could be used to semi-quantitatively and qualitatively predict the solubility of toluene, and this model had promising prospects in screening ILs for VOCs absorption. In summary, this study provided a fundamental basis and practical data for the control and treatment of VOCs.

Prediction of Potential Ionic Liquids (ILs) for the Solid-Liquid Extraction of Docosahexaenoic Acid (DHA) from Microalgae Using COSMO-RS Screening Model.

This study performs a screening of potential Ionic Liquids (ILs) for the extraction of Docosahexaenoic Acid (DHA) compounds by the calculation of capacity values. For this purpose, a Conductor-Like Screening Model for Real Solvents (COSMO-RS) was employed to study the molecular structures of the ILs, and therefore, predict their extraction potential. The capacity values of 22 anions combined with 16 cations based ILs, were investigated to evaluate the effectiveness of ILs in the extraction of DHA. It was found that among the investigated ILs, a combination of tetramethyl ammonium with SO4 or Cl was the best fit for DHA extraction, followed by pyrrolidinium, imidazolium, pyridinium and piperidinium. Furthermore, it was observed that the extraction capacity and the selectivity of ILs decreased with an increase in alkyl chain length; therefore, ethyl chain-ILs, with the shortest chain lengths, were found to be most suitable for DHA extraction. The predicted results were validated through the experimentally calculated extraction yield of a DHA compound from Nannochloropsis sp. Microalgae. Five selected ILs, namely [EMIM][Cl], [BMIM][Cl], [TMAm][Cl], [EMPyr][Br] and [EMPyrro][Br], were selected from COSMO-RS for empirical extraction purposes, and the validation results pinpointed the good prediction capabilities of COSMO-RS. The findings in this study can simplify the process of selecting suitable ILs for DHA extraction and reduce the number of required empirical evaluations.