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The melting points of ionic liquids (ILs) reported since 2020 were surveyed, collected, and reviewed, which were further combined with the previous data to provide a database with 3129 ILs ranging from 177.15 to 645.9 K in melting points. In addition, the factors that affect the melting point of ILs from macro, micro, and thermodynamic perspectives were summarized and analyzed. Then the development of the quantitative structure-property relationship (QSPR), group contribution method (GCM), and conductor-like screening model for realistic solvents (COSMO-RS) for predicting the melting points of ILs were reviewed and further analyzed. Combined with the evaluation together with the preliminary study conducted in this work, it shows that COSMO-RS is more promising and possible to further improve its performance, and a framework was thus proposed. [ABSTRACT FROM AUTHOR]

Viscosity is one of the most important fundamental properties of fluids. However, accurate acquisition of viscosity for ionic liquids (ILs) remains a critical challenge. In this study, an approach integrating prior physical knowledge into the machine learning (ML) model was proposed to predict the viscosity reliably. The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size, structure, and interactions of the ILs. Three strategies based on the residuals of the COSMO-RS model were created as the output of ML, where the strategy directly using experimental data was also studied for comparison. The performance of six ML algorithms was compared in all strategies, and the CatBoost model was identified as the optimal one. The strategies employing the relative deviations were superior to that using the absolute deviation, and the relative ratio revealed the systematic prediction error of the COSMO-RS model. The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set (R² = 0.9999, MAE = 0.0325), reducing the average absolute relative deviation (AARD) in modeling from 52.45% to 1.54%. Features importance analysis indicated the average energy correction, solvation-free energy, and polarity moment were the key influencing the systematic deviation. [ABSTRACT FROM AUTHOR]

As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO2 and N2, where a conductor-like screening model for real solvents (COSMO-RS) was used as the basis. First, experimental data on the solubility of CO2 and N2 in ILs were collected. Then, the solubility of CO2 and N2 in ILs was predicted using COSMO-RS based on the structures of cations, anions, and gases. To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. The results show that the COSMO-RS with correction can significantly improve the prediction of CO2 solubility, and the corresponding average absolute relative deviation (AARD) is decreased from 43.4% to 11.9%. In contrast, such an option cannot improve that of the N2 dataset. Instead, the results obtained from coupling machine learning algorithms with the COSMO-RS model agree well with the experimental results, with an AARD of 0.94% for the solubility of CO2 and an average absolute deviation (AAD) of 0.15% for the solubility of N2. [ABSTRACT FROM AUTHOR]

Recycling hydrofluoric acid (HF) is a significant issue within the field of organofluoride chemistry. A key obstacle in this endeavor lies in the separation of azeotropic mixtures containing HF, such as the azeotropic mixture of HF and 1,1,1,3,3-pentafluoropropane (HFC-245fa), which exhibit similar boiling points. This study investigates the application of extractant distillation (ED) with ionic liquids (ILs) to achieve the separation of HF/HFC-245fa at the molecular scale, cell dimension, and systematic level (including the optimization and analysis of ED). COSMO-RS was employed to identify the suitable IL, with [Tf2N]-based ILs emerging as the most optimal extractants. Four representative [Tf2N]-based ILs were chosen based on their physical properties and thermodynamic behavior, and were applied in a continuous ED process simulation for the separation of HF/HFC-245fa using Aspen Plus, resulting in the production of 99.5 mol% HFC-245fa and 99.5 mol% HF. The determination of the optimal operational parameters was carried out through sequential quadratic programming, considering the purity requirements. An assessment of energy consumption analysis indicated that [C1Py][Tf2N] stands out as the most suitable IL for separation of HF/HFC-245fa, with heat and cooling duties of 467.82 kW and 304.80 kW, respectively. Economic analysis for the process with [C1Py][Tf2N] indicated that the annual operating costs and equipment costs are $ 5.58 × 104 and $ 3.75 × 106, respectively, of which the costs of IL are 93%, the total annual cost was comparable to that of the pressure-swing distillation process, suggesting that this ED process utilizing ILs for separating azeotropes is economically viable in terms of industrial application. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles. [ABSTRACT FROM AUTHOR]

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The phase transition temperature of single fatty acids is generally high and prone to leakage， which cannot meet the demand for using them as phase change materials for regulating the energy consumption of building air conditioning in summer.Based on conductor-like screening model for real solvents （COSMO-RS），136 binary low eutectic fatty acids of 7 medium chain fatty acids and 10 long chain fatty acids were designed and calculated by using COSMOthermX software，and the eutectic temperature and molar ratio of binary low eutectic fatty acids were predicted.Furthermore， the optimal combination was used as the core material， and melamine-urea-formaldehyde （MUF） resin was used as the wall material to prepare phase change microcapsules through in-situ polymerization. The effects of different process conditions （core-to-wall ratio， reaction temperature， reaction time， reaction speed，etc.） on the thermal and physical properties of the microcapsules were systematically discussed. The results show that the COSMO-RS model can judge the relationship between hydrogen bond donor （HBD） and hydrogen bond acceptor （HBA） more intuitively. The eutectic temperature （33.25 ℃） in theory is 98.08% similar to the experimental temperature （33.1 ℃） of lauric acid （LA）-myristic acid （MA） （LM） with the optimal combination of LA and MA with a molar ratio of 0.66 to 0.34. Under the conditions of a core-to-wall ratio of 2∶1， reaction time of 3 h， reaction temperature of 80 ℃， and stirring speed of 200 r/min， the encapsulation efficiency of MUF on the core material LM is 61.37%， which effectively solves the leakage problem and has potential application value in reducing the energy consumption of building air conditioning refrigeration.

The removal of nitrogen compounds from fuel via the conventional method, which is hydrodenitrogenation, is costly and involves catalysts and energy-intensive conditions (600 K and 300 atm). Recently, ionic liquids (ILs) have emerged as a promising alternative solvent for the denitrogenation of fuel oil. However, certain ILs are expensive and challenging to synthesize, prompting the exploration of protic ionic liquid (PIL) substitutes, which offer similar advantages to ILs. This study utilized the conductor-like screening model for real solvents (COSMO-RS) to predict the phase equilibria for three PILs—triethylammonium p-toluenesulfonate (TEA-TSA), triethylammonium salicylate (TEA-SA) and triethylammonium benzoate (TEA-BZ)—which were subsequently validated through experimental investigations. Liquid–liquid extraction experiments were conducted at 298 K and 1 atm, with pyrrole (serving as the model nitrogen compound) concentrations in n-hexadecane (representing the model fuel) ranging from 10 to 50 wt%. Additionally, the NRTL model effectively correlated the experimental tie lines. The obtained data indicated that TEA-TSA exhibited superior selectivity and distribution ratio compared to TEA-SA and TEA-BZ. All the ternary systems tested displayed positive slopes, suggesting a higher affinity of nitrogen compounds for the PIL. Supporting this observation, interaction energy (ΔE) and excess enthalpy (HE) were employed. The predicted outcomes revealed that TEA-TSA had high ΔE, and all PILs exhibited negative values of HE. The HE calculation underscored the significance of strong hydrogen bond interactions between pyrrole and the PIL for successful extraction. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the C 2 8 type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems. [ABSTRACT FROM AUTHOR]

Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: (S)-alcohol dehydrogenase from Rhodococcus ruber (ADH-A), (R)-alcohol dehydrogenase from Lactobacillus kefir (Lk-ADH) and glucose dehydrogenase from Bacillus megaterium (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R² > 0.75) for in silico screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and in silico approach for the rational design of DES tailored to stabilize enzymes. [ABSTRACT FROM AUTHOR]

In the present investigation, a total of 108 combinations of ionic liquids (ILs) were screened using the conductor-like screening model for real solvents (COSMO-RS) with the aid of six cations and eighteen anions for the extraction of succinic acid (SA) from aqueous streams through dispersive liquid–liquid microextraction (DLLME). Using the screened ILs, an ionic liquid-based DLLME (IL-DLLME) was developed to extract SA and the role of different reaction parameters in the effectiveness of IL-DLLME approach was investigated. COSMO-RS results suggested that, quaternary ammonium and choline cations form effective IL combinations with [OH¯], [F¯], and [SO42¯] anions due to hydrogen bonding. In view of these results, one of the screened ILs, tetramethylammonium hydroxide [TMAm][OH] was chosen as the extractant in IL-DLLME process and acetonitrile was adopted as the dispersive solvent. The highest SA removal efficiency of 97.8% was achieved using 25 μL of IL [TMAm][OH] as a carrier and 500 μL of acetonitrile as dispersive solvent. The highest amount of SA was extracted with a stir time of 20 min at 300 rpm, followed by centrifugation for 5 min at 4500 rpm. Overall, the findings showed that IL-DLLME is efficient in extracting succinic acid from aqueous environments while adhering to the first-order kinetics. [ABSTRACT FROM AUTHOR]

The present investigation examines the antimicrobial and antifungal characteristics of natural deep eutectic solvents (NADES) and apple vinegar in relation to a diverse array of bacterial and fungal strains. The clinical bacterial strains, including gram-negative and gram-positive, and the fungal pathogen Candida albicans, were subjected to solid medium diffusion to determine the inhibitory effects of these compounds. The results show that NADES has superior antimicrobial and antifungal action compared to apple vinegar. The observed inhibitory zones for apple vinegar and NADES varied in length from 16.5 to 24.2 and 16 to 52.5 mm, respectively. The results obtained indicate that no synergy is observed for this mixture (50% AV + 50% NADES). The range of values for bactericidal concentrations (MBC) and minimal inhibitory concentrations (MIC) was 0.0125 to 0.2 and 0.0125 to 0.4 µl/ml, respectively. Antibacterial and antifungal chemicals may be found in apple vinegar and NADES, with NADES offering environmentally safe substitutes for traditional antibiotics. Additional investigation is suggested to refine these compounds for a wide range of bacteria, which could create antimicrobial solutions that are both highly effective and specifically targeted, thereby offering extensive potential in medicine and the environment. [ABSTRACT FROM AUTHOR]

Highly concentrated electrolytes show promise in enhancing lithium-sulfur (Li-S) battery performance by mitigating polysulfide (PS) solubility. The role of the salt anion for the performance improvement(s) is however not well understood. Here a systematic characterization using (concentrated) electrolytes based on three different salts: LiTFSI, LiTf, and LiTDI, in a common DOL:DME solvent mixture is reported for a wide range of physicochemical and electrochemical properties: ionic conductivity, density, viscosity, speciation, and PS solubility. While increased salt concentration in general improves Li-S battery performance, the role of the salt anion introduces complexity. The 2 m LiTDI-based electrolyte, with a slightly higher viscosity and lower PS solubility, outperforms the LiTFSI-based counterpart in terms of accessible reversible capacity. Conversely, the 2 m LiTf-based electrolyte exhibits subpar performance due to the formation of ionic aggregates that renders more free solvent and, therefore higher PS solubility, which, however can be improved by using a 5 m concentrated electrolyte. Hence, using electrolyte salt concentration as a rational design route demands an understanding of the local molecular structure, largely determined/affected by the choice of anion, as well as how it connects to the global properties and in the end improved Li-S battery performance. [ABSTRACT FROM AUTHOR]

Carbon dioxide electrochemical reduction (CO2ER) presents numerous advantages in mitigating greenhouse gas emissions by converting CO2 into value-added chemicals and can be integrated with renewable energy sources such as solar and wind. Nevertheless, establishing an electrochemically stable catalytic system that can effectively decrease the overpotential while maintaining high current density and faradaic efficiency is challenging. The precise mechanisms causing the reactions and the specific functions of the electrode with electrolytes are still not fully understood. Hence, a significant increase in attention has been paid to using ionic liquids (ILs) as electrolytes for CO2ER. This phenomenon is attributed to the unique capabilities of ILs to reduce overpotential, increase current density, and improve electrochemical stability. Therefore, this study evaluated the effect of incorporating ILs into electrolytes to comprehend the cation and anion influences on CO2ER reactions. Linear sweep voltammetry (LSV) and chronoamperometry (CA) were employed to examine the reduction peaks and current density values for different electrolytes, respectively. Consequently, a 0.1 M NBu4PF6 acetonitrile solution containing 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM][BF4] demonstrated a significantly lower onset potential of the reduction by 320 mV. A reduced CO2ER efficiency involving ILs with long alkyl chains was also observed. Meanwhile, a novel hypothesis concerning molecular orbitals for the CO2ER reaction mechanism was discussed. Overall, various performance variables (reduction stability, applied potential, and current density) of CO2ER were improved using cations with short alkyl chains, anions with high highest occupied molecular orbital (HOMO) levels, and appropriate solvation media. These findings can serve as selection criteria to aid in choosing appropriate ionic liquids for CO2 electrochemical reduction (CO2ER). [ABSTRACT FROM AUTHOR]

This study presents a facile method for assessing the effect of anion functional group substitution on the water affinity of phosphonium-acetate ionic liquids. By combining the same cation with three different α-substituted carboxylate anions, the effect of anion functional group substitution on the IL miscibility with water and hygroscopicity was experimentally evaluated and rationalized using COSMO-RS sigma profile analysis. Lower critical solution temperature (LCST) phase separation experiments and water vapor partial pressure measurements revealed that there is a stark α-substitution dependency on the affinity of water for acetate [AcO], pivalate [PivO], and trifluoroacetate [TFA] derived tributyl(octyl)phosphonium [P4448] ILs. It was found that the IL water affinities varied as: [P4448][AcO] > [P4448][PivO] > [P4448][TFA]. With the lowest water partial pressure and no LCST driven phase separation, [P4448][AcO] exhibited a high affinity for water. On the other hand, [P4448][PivO] and [P4448][TFA] exhibited LCST-driven phase separation and room temperature immiscibility, respectively, with higher water partial pressures, as a result of relatively weak water affinities. COSMO-RS sigma profile analysis revealed that the aliphatic carboxylate anions should incorporate apolar interactions commensurate with or greater than the polar hydrogen bond acceptor interactions for phase immiscibility of the corresponding IL with water. It was found that the relative magnitudes of apolar and polar interactions, assessed using the anion sigma profile peak intensity ratio (Ir), are directly correlated with the IL water affinity, and inversely correlated with the solution water activity coefficient. Therefore, it was concluded that Ir forms a quantitative link between the structural changes at the molecular level and the bulk water affinity exhibited by the ionic liquid as a whole.

As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO2 and N2, where a conductor-like screening model for real solvents (COSMO-RS) was used as the basis. First, experimental data on the solubility of CO2 and N2 in ILs were collected. Then, the solubility of CO2 and N2 in ILs was predicted using COSMO-RS based on the structures of cations, anions, and gases. To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. The results show that the COSMO-RS with correction can significantly improve the prediction of CO2 solubility, and the corresponding average absolute relative deviation (AARD) is decreased from 43.4% to 11.9%. In contrast, such an option cannot improve that of the N2 dataset. Instead, the results obtained from coupling machine learning algorithms with the COSMO-RS model agree well with the experimental results, with an AARD of 0.94% for the solubility of CO2 and an average absolute deviation (AAD) of 0.15% for the solubility of N2.

To develop an environmentally sustainable and efficient extraction method for flavonoids from Moringa oleifera Lam. (M. oleifera) leaves, natural deep eutectic solvents (NADES) with ultrasound-assisted extraction was utilized in this study. After optimization of extraction parameters of NADES, including ultrasonic power, ultrasonic time, and liquid–solid ratio, the extraction yield of ultrasound-assisted NADES (UAN) composed of betaine and urea (Bet-Urea) reached 54.69 ± 0.19 mg RE/g DW, which made a 1.7-fold increase compared to traditional ultrasound-assisted traditional solvent (UATS). UPLC-Q Exactive/MS analysis revealed that M. oleifera leaves flavonoids (MOLF) was mainly composed of Quercetin 3-β-D-glucoside, Rutin, Kaempferol-3-O-glucoside, Vitexin and Quercetin. Furthermore, the COSMO-RS model was employed to verify the optimal compatibility of solubility and activity coefficient between Bet-Urea and the five primary flavonoids in MOLF. In vitro antioxidant assays verified that MOLF extracted by UAN exhibited superior antioxidant activity compared to MOLF extracted by UATS. Overall, the devised process not only augmented the extraction yield of MOLF but also effectively preserved the bioactive compounds, thus promoting the utilization of green extraction solvents in the food industry.

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications. [ABSTRACT FROM AUTHOR]

Over the past few years, eutectic solvents (ESs) have drawn the scientific community's attention because they are usually more environmentally friendly than traditional organic solvents. One of the applications of ESs is in the gas capture field, where they are considered promising absorbers to replace amine- (MEA, DEA, or MDEA processes), methanol- (Rectisol process), dimethyl ethers of polyethylene glycol- (Selexol process), N-methyl-2-pyrrolidone- (Purisol process), propylene carbonate- (Fluor solvent process), or morpholine-based (Morphysorb process) solvents on CO2 capture from the atmosphere. Although several studies have reported experimental gas solubility data in ESs, especially for CO2, only a few existing options are covered. In fact, resorting to experimental methods to obtain the solubility data seems unfeasible considering the vast number of possible eutectic mixtures. Therewith, theoretical predictions of gas solubility in ESs are valuable for the fast pre-screening of prospective solvents. In this work, the ability of the thermodynamic model COSMO-RS to represent solubility data of CO2, CH4, and H2S in 17 choline chloride-based (ChCl) ESs was evaluated. The experimental data were collected from the literature at different molar ratios, at 298.15 K or 313.15 K, and in the pressure range from 1 to 125 bar. COSMO-RS offers a qualitative description of these gases' solubility, which was expected due to the model's fully predictive character. To improve the CO2 and CH4 solubility data description, a temperature–pressure-dependent correction was applied to the COSMO-RS predictions for these gases, offering a global average relative deviation of 15%. [ABSTRACT FROM AUTHOR]

The current study comprehensively investigates the adsorption behavior of chromium (Cr(III)) in wastewater using Algerian kaolinite clay. The structural and textural properties of the kaolinite clay are extensively characterized through a range of analytical methods, including XRD, FTIR, SEM-EDS, XPS, laser granulometry, N2 adsorption isotherm, and TGA–DTA. The point of zero charge and zeta potential are also assessed. Chromium adsorption reached equilibrium within five minutes, achieving a maximum removal rate of 99% at pH 5. Adsorption equilibrium is modeled using the Langmuir, Freundlich, Temkin, Elovich, and Dubinin–Radushkevitch equations, with the Langmuir isotherm accurately describing the adsorption process and yielding a maximum adsorption capacity of 8.422 mg/g for Cr(III). Thermodynamic parameters suggest the spontaneous and endothermic nature of Cr(III) sorption, with an activation energy of 26.665 kJ/mol, indicating the importance of diffusion in the sorption process. Furthermore, advanced DFT computations, including COSMO-RS, molecular orbitals, IGM, RDG, and QTAIM analyses, are conducted to elucidate the nature of adsorption, revealing strong binding interactions between Cr(III) ions and the kaolinite surface. The integration of theoretical and experimental data not only enhances the understanding of Cr(III) removal using kaolinite but also demonstrates the effectiveness of this clay adsorbent for wastewater treatment. Furthermore, this study highlights the synergistic application of empirical research and computational modeling in elucidating complex adsorption processes. [ABSTRACT FROM AUTHOR]

As a functional extraction medium, natural deep eutectic solvents (NADESs) can dissolve various plant metabolites. Those solvents provide untapped potential for creating novel green extracts with distinctive phytochemical signatures and unique biological activities. This is particularly relevant given the rising need for eco-friendly and sustainable skin care products. The main aim of this work was to optimize the most efficient natural deep eutectic solvents (NADESs) for extracting bioactives from 18 medicinal herbs applicable to the cosmetic industry. Selection of the most potent herbs involved assessing their conventional extracts for tyrosinase inhibition, antioxidant activity, and keratinocyte cytotoxicity. Moreover, we analyzed the phenolic profile using ultra-high-pressure liquid chromatography/mass spectrometry (UHPLC/MS) and spectrophotometric assays such as total phenolic (TPC) and flavonoid content (TFC). Using the COSMO-RS method, we modeled the solubility of 12 phenolics in 64 virtual NADESs and selected the 7 most promising ones for further experimental validation. NADESs, including betaine-urea, betaine-proline, and betaine-lysine, were computationally chosen and demonstrated the highest levels of TPC and antioxidative capacity, as confirmed by in vitro assays. The proposed combination of NADES herbal extracts represents a promising natural constituent for the cosmetic industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Amine‐based deep eutectic mixtures composed of choline chloride and a DEA‐based hydrogen‐bond donor (HBD) can be used in various molar ratios such as 1:2, 1:3, 1:4, 1:5, 1:6 and 1:7 for isolation of cardanol from cashew nut shell liquid (CNSL). RESULTS: In this study, green techniques were created to extract cardanol from CNSL by utilizing a deep eutectic solvent (DES) based on diethanolamine with various molar ratios [hydrogen‐bond acceptor (HBA): HBD] of 1:2, 1:3,1:4,1:5, 1:6 and 1:7. To enhance cardanol's solubility in DES, promote phase separation, and increase cardanol yield, ethyl acetate (EA) and isobutyl methyl ketone (IBMK) could be utilized as co‐solvents. At temperatures ranging from 293 to 343.15 K and 1 atm, the density of various molar ratios of DES was determined. Additionally, the prepared DES pH behaviour as well as the relationship between pH and temperature were investigated. All investigated DES temperature increases caused a decrease in pH value. By using thermal gravimetric analysis (TGA) and differential scanning calorimetry (DSC), similar molar ratios of amine‐based DESs were studied for their thermal stability in terms of decomposition temperature, mass loss and melting temperature. The melting temperature and thermal stability were both increased when the HBD increased. The thermal stability of DES, which mostly depends on intermolecular interactions, is greatly influenced by HBD. Finally, at T = 298.15 K and 1 atm, cardanol was extracted from 50 mL CNSL using different volumes of various molar ratios of DES (100 mL, 75 mL, 50 mL and 25 mL) together with 25 mL IBMK and 25 mL EA. The extracted cardanol was evaluated using Fourier transform infrared spectroscopy and gas chromatography–mass spectrometry. CONCLUSION: It was observed that the yields of cardanol from each prepared molar ratios ratio of 100 mL DES were 96.57%, 95.38% and 92.87%, respectively. In this study, the sigma potential and sigma profile of chosen HBA and HBD were determined using conductor‐like screening model for real solvents (COSMO‐RS), and the interactions between the molecules during the extraction process were analyzed. The findings demonstrated the viability of using the chosen DESs for the polyphenol extraction process. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

Solubility is not only a crucial physicochemical property for laboratory practice but also provides valuable insight into the mechanism of saturated system organization, as a measure of the interplay between various intermolecular interactions. The importance of these data cannot be overstated, particularly when dealing with active pharmaceutical ingredients (APIs), such as dapsone. It is a commonly used anti-inflammatory and antimicrobial agent. However, its low solubility hampers its efficient applications. In this project, deep eutectic solvents (DESs) were used as solubilizing agents for dapsone as an alternative to traditional solvents. DESs were composed of choline chloride and one of six polyols. Additionally, water–DES mixtures were studied as a type of ternary solvents. The solubility of dapsone in these systems was determined spectrophotometrically. This study also analyzed the intermolecular interactions, not only in the studied eutectic systems, but also in a wide range of systems found in the literature, determined using the COSMO-RS framework. The intermolecular interactions were quantified as affinity values, which correspond to the Gibbs free energy of pair formation of dapsone molecules with constituents of regular solvents and choline chloride-based deep eutectic solvents. The patterns of solute–solute, solute–solvent, and solvent–solvent interactions that affect solubility were recognized using Orange data mining software (version 3.36.2). Finally, the computed affinity values were used to provide useful descriptors for machine learning purposes. The impact of intermolecular interactions on dapsone solubility in neat solvents, binary organic solvent mixtures, and deep eutectic solvents was analyzed and highlighted, underscoring the crucial role of dapsone self-association and providing valuable insights into complex solubility phenomena. Also the importance of solvent–solvent diversity was highlighted as a factor determining dapsone solubility. The Non-Linear Support Vector Regression (NuSVR) model, in conjunction with unique molecular descriptors, revealed exceptional predictive accuracy. Overall, this study underscores the potency of computed molecular characteristics and machine learning models in unraveling complex molecular interactions, thereby advancing our understanding of solubility phenomena within the scientific community. [ABSTRACT FROM AUTHOR]

Estrogens are emergent pollutants found in low levels in water bodies, and for this reason they require concentration for quantification. In this study, aqueous two‐phase systems (ATPSs) were used as an alternative strategy to improve hormone detection and quantification from real water samples. In addition, a predictive computational method (COSMO‐RS) was used to understand better the migration of hormones between the two phases at the molecular level. Water samples previously analyzed as hormone‐free were added to the 250‐fold concentrated ATPS, where it was possible to detect and quantify concentrations of these micropollutants in the range of 9.3–29.19 ng L−1. [ABSTRACT FROM AUTHOR]

To reduce the environmental pollution caused by organic solvents and improve the extraction efficiency, a green and high-effective natural deep eutectic solvent (NADES) was screened out and prepared for the extraction of flavonoids from Fructus aurantii based on the COMOS-RS and experimental verification. The results showed the results of theoretical screening and experimental verification were consistent, and Ch-Lea was the best extraction solvent, which further proved the COMOS-RS was an efficient tool for the solvent screening. Then, the extraction conditions were investigated. The optimum extraction conditions were as follows: molar ratio (1:2), water content (30%), solid–liquid ratio (1/60 g/g) and extraction time (15 min), yet the extraction temperature and power did not significantly affect the extraction yield. Under the optimal extraction condition, the extraction yield of flavonoids was 122.68 mg/g. Compared with the traditional extraction solvents (methanol and ethanol), Ch-Lea has a higher extraction yield. And the green assessment was conducted based on Eco-Scale methodology, which results indicated the Ch-Lea was more environmentally friendly than methanol and ethanol. Meanwhile, the Ch-Lea has good reusability, and the extraction yields from four reuses were 122.68 mg/g, 125.13 mg/g, 122.56 mg/g and 105.63 mg/g, respectively. Moreover, the extraction mechanism was delved into using molecular dynamics (MD) simulation and reduced density gradient (RDG) analysis. The hydrogen bond interaction, particularly the O-H bonds, between Ch-Lea and the naringin molecule emerged as the primary force driving the extraction process. In summary, Ch-Lea is a green, economical, and highly effective solvent, making it a promising candidate for flavonoid extraction from Fructus aurantii. Its potential as an alternative to traditional extraction solvents further underscores its value.

Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: (S)-alcohol dehydrogenase from Rhodococcus ruber (ADH-A), (R)-alcohol dehydrogenase from Lactobacillus kefir (Lk-ADH) and glucose dehydrogenase from Bacillus megaterium (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R2 > 0.75) for in silico screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and in silico approach for the rational design of DES tailored to stabilize enzymes.

When considering the usage of ionic liquids (ILs) for reactions and separations involving non-polar or weak-polar hydrocarbons, the knowledge of the mutual solubility behaviors of ILs and hydrocarbons is of the utmost importance. In this work, taking four typical C6-hydrocarbons namely benzene, cyclohexene, cyclohexane, and hexane as representatives, the mutual solubility of ILs and non-polar or weak-polar hydrocarbons are systematically studied based on the COSMO-RS model. The reliability of COSMO-RS for these systems is first evaluated by comparing experimental and predicted hydrocarbon-in-IL activity coefficient at infinite dilution and binary/ternary liquid-liquid equilibria of related systems. Then, the mutual solubility of the four hydrocarbons and 13,650 ILs (composed by 210 cations and 65 anions) are predicted. The effect of different IL structural characteristics including alkyl chain length, cation family/symmetry/functional group, and anion on the IL-hydrocarbon mutual solubility behaviors are further analyzed by the analyses of interaction energy and screen charge distribution. The mutual solubility databases and the structural effects identified thereon could provide useful guidance for IL selection in related applications.

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles.

As a volatile organic compound (VOC), styrene can pose significant risks to human health and environment if released into the air without proper treatment. Absorption based on ionic liquids (ILs) has been widely regarded as a promising VOC treatment approach, whereas there is limited research on capturing styrene by ILs. In this work, a multilevel framework was proposed to screen IL absorbents from the largest number of cation-anion combinations in the latest COSMObase. The COSMO-RS model is applied to estimate the absorption and desorption potentials of all IL candidates for styrene capture based on solubility prediction, while important physical properties of them namely melting point, viscosity, and toxicity are estimated by a recently-reported deep learning model. A parallel screening specific to the commercially available ILs in the initial candidate database was also performed. The top-ranked three ILs, identified in both scenarios with and without limiting the commercially availability, were further evaluated in a simulated absorption-desorption process in Aspen Plus. The most promising ILs in each scenario were figured out, which vividly demonstrates the value of discovering novel ILs. [Display omitted] • Propose a multilevel framework for screening ionic liquid (IL) absorbents. • Present two parallel scenarios with and without limiting commercial availability. • Find top 3 ILs from 347406 hypothetical and 108 available candidates, respectively. • Demonstrate the value of discovering novel ILs guided by large-scale screening. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) is an effective way to convert and remove hydrogen sulfide, H 2 S from natural gas streams. Amine-based ILs have recently been discovered due to their efficiency of removing H 2 S due to their excellent characteristics. However, the selection of potential amine-based ILs from a wide range combination of cations and amine-based anions are rather challenging if performed experimentally. Conductor-like Screening Model for Real Solvents (COSMO-RS) was used for the prediction of liquids thermodynamics such as activity coefficient at infinite dilution, selectivity, capacity and performance index (PI) values to evaluate the performance of each amine-based ILs and determine the most efficient ILs for H 2 S absorption without requiring any experimental data. A total of 42 previously unexplored ionic liquids, amine-based ILs from a combination of 7 amine cations and 6 anions were screened by COSMO-RS model. The findings from COSMO-RS prediction results revealed that ILs without aromatic rings displayed higher efficiencies compared to ILs with aromatic rings. Moreover, N,N,N′,N′-tetramethyl-1,3-propanediamine chloride, [TMPDA][Cl] cation-anion combinations displayed the highest PI value of 52.55 among the screened ILs thus, predicted as the most efficient ILs for H 2 S absorption. The experimental results obtained validated the COSMO-RS predictions with an approximation of ± 4.51% discrepancies between predicted and experimental data. Hence, it is concluded that COSMO-RS can be used for a reliable pre-experimental prediction and amine-based ILs approach has a huge potential for effective H 2 S absorption in industrial applications. [Display omitted] • Successfully screened an effective amine-based ILs for H 2 S absorption using COSMO-RS approach. • The synthesized ABILs possess desirable physical properties for H 2 S absorption. • The predicted results obtained were in agreement with the experimental data. • ILs of [TMPDA][Cl] displayed the highest removal efficiency for H 2 S absorption. [ABSTRACT FROM AUTHOR]

Context: One of the prevalent methods for evaluating separation performance is to predict the interactions of solvent and solute molecules. The infinite dilution activity coefficient, Gibbs free energy, and sigma profiles provided insights into the solubilization of a solute and revealed the intensity of the solution's molecular interactions. The effective thermodynamic tools (infinite dilution activity coefficient, Gibbs free energy) were evaluated for predicting the efficiency of 18 polar and non-polar organic solvents in rubber seed oil (RSO) extraction. An infinite dilution activity coefficient was computed to evaluate the solubility of the rubber seed oil model compound (linoleic acid) in the organic solvents. Gibbs free energy was evaluated to show the energy change associated with the molecules mixing process and forecast the miscibility of linoleic acid molecules in the solvents. Moreover, the study examined the sigma profiles and sigma surfaces of organic solvents and linoleic acid to acquire a deeper insight into their similarities and how they interact molecularly. According to the computational prediction and experimental verification, the thermodynamic properties of Gibbs free energy and activity coefficient proved to be highly effective tools for screening polar and moderately polar solvents, predicting the molecular interactions with solute. Whereas the sigma profile and sigma surface were found to be the most efficient tools for evaluating the efficacy of non-polar solvents. Solvents with moderate polarity, such as tetrahydrofuran and diethyl ether, as well as non-polar solvents like pentane, heptane, and n-hexane, proved to be effective and favorable for oil extraction, resulting in the highest oil yields of approximately 27.0%. Overall, the COSMO-RS method demonstrates its utility in estimating the solubility of RSO in organic solvents, enabling early identification of the most effective solvent. Methods: The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions. [ABSTRACT FROM AUTHOR]

BACKGROUND: The tetrahydrofuran–ethanol azeotrope cannot be separated by common distillation, but can be effectively separated by extractive distillation. Extractive distillation can be simulated by Aspen Plus software. The minimum annual cost of the process can be calculated to assess ionic liquid separation ability. The interaction energy can be used to explain the separation mechanism. RESULTS: The non‐random two‐liquid model parameters of ternary mixtures for tetrahydrofuran and ethanol containing tributylmethylammonium acetate or 1‐octyl‐3‐methylimidazolium acetate were obtained. Thermodynamic property databases of tributylmethylammonium acetate, 1‐hexyl‐2,3‐dimethylimidazolium acetate and 1‐octyl‐3‐methylimidazolium acetate were established. Based on the thermodynamic model parameters and properties, the sequential iterative method was used to determine the best operating conditions and minimum total annual cost the of extractive distillation process. The interaction energy was calculated to explain the separation mechanism. CONCLUSIONS: The separation performance of tributylmethylammonium acetate is better than that of 1‐hexyl‐2,3‐dimethylimidazolium acetate or 1‐octyl‐3‐methylimidazolium acetate. The selection and design of the ionic liquids can be conducted in terms of the interaction energy. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

To predict the size (m, σ) and energy (ε) parameters of the perturbed‐chain statistical associating fluid theory (PC‐SAFT) based on the conductor‐like screening model for realistic solvents (COSMO‐RS), in this work, the PC‐SAFT model parameters of 304 substances were reviewed and summarized, their COSMO‐RS results linked to the size and energy were predicted, and the relationships between the PC‐SAFT parameters and COSMO‐RS results were obtained in 3 cases (Case 1: relationships based on n‐alkanes, Case 2: category‐specific relationships, Case 3: generic relationships for 138 substances). Case 2 (including Case 1) shows the lowest average absolute relative deviations (AARDs) of 7.63% (m), 3.05% (σ), and 9.05% (ε), while Case 3 provides the prediction capacity with AARDs of 10.00% (m), 4.05% (σ), and 13.21% (ε), which is better than those developed by others. Finally, the new PC‐SAFT parameters were used to predict the densities and vapor pressures of 8 representative substances and compared with the results using the original PC‐SAFT parameters, showing that the parameters in Case 3 can be used to reproduce densities and the parameters in Case 2 (including Case 1) can be used to reproduce both densities and vapor pressures. [ABSTRACT FROM AUTHOR]

The solid-liquid equilibrium and thermodynamic properties of FOX-7 in three binary mixed solvents (N-methyl pyrrolidone-water, N,N-dimethylacetamide-water and 1,4-Butyrolactone-water) are studied by focused beam reflectance measurement (FBRM) dynamic detection method and the solubility is predicted by conductor-like screening model for real solvents (COSMO-RS) under atmospheric pressure from 298.15 to 343.15 K. The results show that the solubility of FOX-7 increase with the increasing temperature and the mole fraction of W-methyl pyrrolidone, N,N-dimethylacetamide and 1,4-Butyrolactone. The solubility data can be well correlated by Apelblat, van't Hoff and Yaws equation. The data of hybrid thermodynamic parameters (including the hybrid enthalpy, entropy, and Gibbs energy) show that the dissolution process is endothermic, non-spontaneous and enthalpy driven. In addition, COSMO-RS can accurately predict the increasing trend of solubility with increasing of temperature and mole fraction of solvent. However, the COSMO-RS overestimates solubility in three binary solvents except for xNMP = 0.1575, the average mean squared quadratic error becomes larger with an increasing mole fraction of water. Solubility data and thermodynamic parameters can lay a foundation for the crystallization and spheroidization modification of FOX-7. [ABSTRACT FROM AUTHOR]

The Abraham solvation parameter model, known alternatively as the Linear Solvation-Energy Relationships (LSER) model, is critically examined for its capacity to predict the hydration free-energy for a variety of solutes. The very linearity of the LSER approach is reconsidered as regards the hydrogen-bonding contribution to solvation free energy. This is done by combining the equation-of-state solvation thermodynamics with the statistical thermodynamics of hydrogen bonding. Thus, this hydrogen-bonding contribution is placed on a firm thermodynamic basis and the predictive calculations are now possible with known acidity and basicity, A and B, molecular descriptors. The LFER coefficients are now expressed in terms of the A and B descriptors. The methodology for the derivation of the new linear equations for the hydrogen-bonding contribution to solvation free energy is presented along with examples of calculations. The implication for the exchange of information on intermolecular interactions between diverse Quantitative Structure–Property Relationships (QSPR) and other approaches is discussed. The proposed changes and descriptor adjustments augments the LSER capacity for solvent screening and use in numerous applications in the broader chemical, biochemical and environmental sector. A critical discussion of perspectives and the challenging issues is also presented. [ABSTRACT FROM AUTHOR]

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The objective of this review is, to discuss recent advancements in screening methods for co-formers, evaluation cum confirmation methods and co-crystallization with examples.Co-crystals are considered as a new form of an old drug entity. Co-crystals improve the stability, hygroscopicity, solubility, dissolution, and physicochemical properties of pure drugs without altering chemical and pharmacological properties.Advancement in co-crystal formulation methods like electrospray and laser-irradiation methods are showing potential for solvent-free co-crystallization and tends to give better yield and lesser loss of materials. Screening methods are also transformed from trial and error to in-silico methods, which facilitate the selection process by reducing the time of screening and increasing the number of co-formers to be screened. Advanced evaluation methods like Raman and solid-state NMR spectroscopy provide a better understanding of crystal lattice by pinpointing the interaction between drug/co-former molecules. The same evaluation methods can also differentiate between the formation of salt and co-crystals.Co-crystals are helping open a new door in pharmaceutical industries in the field of formulation for the improvement of physicochemical properties in existing old molecules and several new molecules. With a motto of “making a good drug better”, co-crystals show scope for vast research and give researchers an ocean of opportunities to make the impossible, possible.

The removal of nitrogen compounds from fuel via the conventional method, which is hydrodenitrogenation, is costly and involves catalysts and energy-intensive conditions (600 K and 300 atm). Recently, ionic liquids (ILs) have emerged as a promising alternative solvent for the denitrogenation of fuel oil. However, certain ILs are expensive and challenging to synthesize, prompting the exploration of protic ionic liquid (PIL) substitutes, which offer similar advantages to ILs. This study utilized the conductor-like screening model for real solvents (COSMO-RS) to predict the phase equilibria for three PILs—triethylammonium p-toluenesulfonate (TEA-TSA), triethylammonium salicylate (TEA-SA) and triethylammonium benzoate (TEA-BZ)—which were subsequently validated through experimental investigations. Liquid–liquid extraction experiments were conducted at 298 K and 1 atm, with pyrrole (serving as the model nitrogen compound) concentrations in n-hexadecane (representing the model fuel) ranging from 10 to 50 wt%. Additionally, the NRTL model effectively correlated the experimental tie lines. The obtained data indicated that TEA-TSA exhibited superior selectivity and distribution ratio compared to TEA-SA and TEA-BZ. All the ternary systems tested displayed positive slopes, suggesting a higher affinity of nitrogen compounds for the PIL. Supporting this observation, interaction energy (ΔE) and excess enthalpy (HE) were employed. The predicted outcomes revealed that TEA-TSA had high ΔE, and all PILs exhibited negative values of HE. The HE calculation underscored the significance of strong hydrogen bond interactions between pyrrole and the PIL for successful extraction.

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the C28 type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems.

Acid-base properties are the simplest expression of compounds' coordinating ability. In the present work, we studied in silico how the gas-phase Brønsted acidity (GA) of several polycyano-substituted compounds change when cyano (CN) groups are replaced by 1,2,2-tricyanovinyl (TCNV) groups in (iso)cyanic acid, dicyanoamine, cyanoform, and hydrogen tetracyanoborate. Different tautomers and conformers/isomers are included in this study. Gas-phase acidity values are compared with the acidities of various acids, including percyanated protonated monocarba-closo-dodecaborate (carborane acid) and dodecaborate, as well as hydrogen cyanide and 1,2,2-tricyanoethene. An estimation of acetonitrile (MeCN), dimethylsufoxide (DMSO), and 1,2-dichloroethane (DCE) acidities is presented using the COSMO-RS method and correlation analysis. The strongest acid with four TCNV groups shows remarkable acidic properties. [ABSTRACT FROM AUTHOR]

The conventional hydrodenitrogenation method is expensive and involves the use of catalysts and harsh procedures. In the last few years, ionic liquids (ILs) have gained attention as a promising alternative solvent for fuel oil extractive denitrogenation. In this work, the Conductor-like Screening Model for Real Solvents (COSMO-RS) was used to screen 173 potential ILs as solvents for fuel oil. Two ILs (1-ethyl-3-methylimidazolium dicyanamide ([EMIM][N(CN)2]) and 1-ethyl-3-methylimidazolium methanesulfonate ([EMIM][MeSO3])) were selected for experimental investigation. The experimental liquid–liquid extraction of pyrrole (taken as the model nitrogen compound) from n-hexadecane (the model fuel) was conducted at 298 K and 1 atm with feed concentrations of pyrrole ranging from 10 to 50 wt%, using either the two pure ILs or their mixtures with dimethylformamide or ethylene glycol. Moreover, the NRTL model was effectively used to correlate the experimental tie lines. This work shows that the use of a binary mixture of ILs with a conventional solvent results in good selectivity, but has a low capacity for extracting pyrrole compounds. On the other hand, using an IL-IL mixture exhibits good results for both capacity and selectivity. All the ternary systems tested showed positive slopes, indicating that the nitrogen compounds had a higher affinity for the IL and binary mixture extract phase. In fact, the extraction efficiency for all the systems shows promising results. This characteristic is advantageous, as it requires less solvent to remove nitrogen compounds. [ABSTRACT FROM AUTHOR]

Solubility prediction and intermolecular interaction of four explosives (1,3,5-Trinitroperhydro-1,3,5-triazine,ε‑2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaiso-wurtzitane, 3-Nitro-1,2,4-triazolin-5-one，3,4-dinitro-1 H-pyrazole) are researched by the Conductor-like Screening Model – Realistic solvents (COSMO-RS). The results show that the study of σ-surface and σ-profile can qualitatively analyze the potential mechanism of the dissolution behavior of four explosives in the studied solvents. COSMO-RS can accurately predict the solubility of four explosives in the studied solvents, but some of the predicted solubility is different from the experimental solubility, and the deviation between the predicted solubility and the experimental solubility was analyzed. The molecular interaction energy between explosive molecules and solvent partially reveals the internal mechanism of the dissolution of explosives, however, the solvation of the four explosives is a complex process that requires the synthesis of various molecular interactions. This has a great effect on the study of solvation of explosives, and it is of great significance for solvent screening of other explosives molecules. [ABSTRACT FROM AUTHOR]

Using ionic liquids to capture CO to produce high-purity hydrogen (H 2) is a promising method. However, there is a lack of theoretical screening for CO capture, which can reduce the number of experiments and costs. In addition, ionic liquids need to reduce their viscosity and maintain an excellent CO capture effect. Therefore, The COSMO-RS was used to screen 300 ionic liquids using CO/H 2 selectivity and viscosity at 293.15 K as indicators. The results showed that the target ionic liquid screened was [EimH][CuCl 2 ]. The addition of ethylene glycol (EG) reduced the viscosity of [EimH][CuCl 2 ] and improved the CO capture ability. After adding 25 wt% EG, its separation factor of CO/H 2 could reach 180 at 293.15 K and 2.1 MPa, which was 59% higher compared to the pure ionic liquid. Furthermore, EG inhibits foaming, and the mixed solvent could maintain high stability after recycling. [Display omitted] • Solvent screening from 300 ionic liquids by COSMO-RS. • Mechanistic analysis by σ -profiles and Infrared spectroscopy. • Solvent improvement by addition of ethylene glycol to protic ionic liquid. • Experiments show the CO/H 2 separation factor using mixed solvent reach 180. [ABSTRACT FROM AUTHOR]

This study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of edaravone solubility data. Hence, new measurements were performed to collect edaravone solubility values in polar non-protic and diprotic media. Such an extended set of data was used in the machine learning process for tuning the parameters of regressor models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring high predictive power and accurate solubility computations for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria, were included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are better suited for edaravone dissolution than those described in this manuscript. The theoretical framework presented in this study provides a precise guideline for conducting experiments, as well as saving time and resources in the pursuit of new findings. [ABSTRACT FROM AUTHOR]

The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, opacity, and gloss) to materials and various end-use products. Since few methods exist to accurately estimate this property, we have designed a graph machine model (GMM) capable of predicting the RI of liquid organic compounds containing up to 16 different types of atoms and effective in discriminating between stereoisomers. Using 8267 carefully checked RI values from the literature and the corresponding 2D organic structures, the GMM provides a training root mean square relative error of less than 0.5%, i.e., an RMSE of 0.004 for the estimation of the refractive index of the 8267 compounds. The GMM predictive ability is also compared to that obtained by several fragment-based approaches. Finally, a Docker-based tool is proposed to predict the RI of organic compounds solely from their SMILES code. The GMM developed is easy to apply, as shown by the video tutorials provided on YouTube. [ABSTRACT FROM AUTHOR]

Solid–liquid equilibria (SLE) of binary mixtures of several n-alkanes (n-octadecane, n-eicosane, n-tetracosane, n-pentacosane, n-triacontane) and dibenzofuran covering the whole composition range were measured by differential scanning calorimetry (DSC) in a temperature range of 301–356 K. The dibenzofuran and the n-alkanes are completely miscible in the liquid state but non-miscible in the solid state, and in the context of this work, they seem to exhibit eutectic behavior. A linear trend is obtained for the eutectic temperature and eutectic composition versus the number of carbon atoms of n-alkane. The experimental data were compared to predictions made by using the ideal solution model, the DISQUAC model and several versions of the UNIFAC model, including the classical UNIFAC, the modified versions of Lyngby and Dortmund, and the recently proposed modified UNIFAC (NIST) model, to account for non-ideality in the liquid phase. Moreover, the experimental data were also compared with COSMO-RS predictions that utilize quantum chemical calculations and statistical thermodynamics to interpret the behavior of molecular interactions in the binary mixture. A good agreement was obtained between the predicted and the experimental temperatures. [ABSTRACT FROM AUTHOR]

Pistacia lentiscus L. fruits are an excellent source of phenolic compounds endowed with antioxidant activities. Ethanol–water mixtures are in focus for plant extracts preparation due to their acceptability for human consumption. In this study, the effect of ethanol concentration in ethanol/water (EtOH/H2O) extraction solvent on the solubility of target phenolic compounds was firstly predicted using COnductor-like Screening MOdel for Real Solvents (COSMO-RS). Subsequently, the computational results were confirmed experimentally based on extraction yield, phenolic composition, antioxidant activity and RP-HPLC analysis. Extractions were achieved using bead milling by varying EtOH concentrations in order to prepare a suitable extract that could be safely used for food purposes. The obtained results highlighted a good correlation between COSMO-RS simulations and experiments. The highest polyphenol and flavonoid contents (292.04 mg GAE/g DR and 11.07 mg CE/g DR, respectively) were achieved with 70% EtOH, which is in correlation with RP-HPLC quantitative analysis, while the highest anthocyanin content (94.00 mg C3OG/100 g DR) was obtained with 80% EtOH. Concerning antioxidant activity, the best DPPH· radical scavenging activity (IC50 = 2.39 µg/mL) was obtained with 70% EtOH. Reverse Phase High Performance Liquid Chromatography (RP-HPLC) analysis showed that gallic acid was the major compound of Pistacia lentiscus fruits in all EtOH/H2O mixtures. These findings showed that the EtOH/H2O (70/30) mixture is the most suitable to obtain P. lentiscus fruit extracts with the highest phenolic compounds content and antioxidant activity. [ABSTRACT FROM AUTHOR]

Biobased molecules constitute sustainable alternatives for fossil‐based chemicals. However, to allow further valorization, the complex product mixtures resulting from biomass processing typically require downstream separation. Although many (semi)empirical thermodynamic models, like NRTL, are available for separation process design, their application for biobased molecules is hampered due to the lack of the necessary data to determine the binary interaction parameters used in these models. Therefore, in this work a case study comprising a set of furfural derivates has been used to showcase a methodology to efficiently determine these parameters. Based on first principles, COSMO‐RS is used to generate a dataset of thermodynamic activities, which is subsequently used to regress the NRTL model against. Comparison of NRTL‐HOC simulated vapor–liquid equilibria (VLE's) with experimental VLE's showed excellent agreement, confirming that COSMO‐RS can be used to extend the use of (semi)‐empirical models toward applications involving biobased molecules of which limited experimental data is available. [ABSTRACT FROM AUTHOR]