Unveiling CO2 capture in tailorable green neoteric solvents: An ensemble learning approach informed by quantum chemistry.

In the relentless battle against the impending climate crisis, deep eutectic solvents (DESs) have emerged as beacons of hope in the realm of green chemistry, igniting a resurgence of scientific exploration. These versatile compounds hold the promise of revolutionizing carbon capture, effectively countering the rising tide of carbon dioxide (CO2) emissions responsible for global warming and climate instability. Their adaptability offers a tantalizing prospect, as they can be finely tailored for a multitude of applications, thereby encompassing the uncharted territory of potential DESs. Navigating this unexplored terrain underscores the vital need for predictive computational methods, which serve as our guiding compass in the expansive landscape of DESs. Thermodynamic modeling and solubility prognostications stand as our unwavering navigational aides on this treacherous odyssey. In this direction, the COSMO-RS model intertwined with the captivating Stochastic Gradient Boosting (SGB) algorithm. Together, they unveil the elusive truths pertaining to CO2 solubility in DESs, forging a path toward a sustainable future. Our quest is substantiated by two exhaustive datasets, a repository of knowledge encompassing 1973 and 2327 CO2 solubility data points spanning 132 and 150 distinct DESs respectively, encapsulating a spectrum of conditions. The SGB models, incorporating features derived from COSMO-RS, as well as accounting for pressure and temperature variables, furnishes predictions that harmonize seamlessly with experimental CO2 solubility values, boasting an impressive Average Absolute Relative Deviation (AARD) of a mere 0.85% and 2.30% respectively. When juxtaposed with literature-reported methodologies like different EoS, as well as Computational Solvation, and machine learning (ML) models, our SGB model emerges as the epitome of reliability, offering robust forecasts of CO2 solubility in DESs. It emerges as a potent tool for the design and selection of DESs for CO2 capture and utilization, heralding a sustainable and environmentally conscientious future in the battle against climate change.

Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen.

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.

Exploring the Efficiency of Algerian Kaolinite Clay in the Adsorption of Cr(III) from Aqueous Solutions: Experimental and Computational Insights.

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.

Experimental and Theoretical Insights into the Intermolecular Interactions in Saturated Systems of Dapsone in Conventional and Deep Eutectic Solvents.

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.

Investigating the synergistic effects of apple vinegar and deep eutectic solvent as natural antibiotics: an experimental and COSMO-RS analysis.

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.

Predicting sulfanilamide solubility in the binary mixtures using a reference solvent approach.

BACKGROUND: Solubility is a fundamental physicochemical property of active pharmaceutical ingredients. The optimization of a dissolution medium aims not only to increase solubility and other aspects are to be included such as environmental impact, toxicity degree, availability, and costs. Obtaining comprehensive solubility characteristics of chemical compounds is a non-trivial and demanding process. Therefore, support from theoretical approaches is of practical importance. OBJECTIVES: This study aims to examine the accuracy of the reference solubility approach in the case of sulfanilamide dissolution in a variety of binary solvents. This pharmaceutically active substance has been extensively studied, and a substantial amount of solubility data is available. Unfortunately, using this set of data directly for theoretical modeling is impeded by noticeable inconsistencies in the published solubility data. Hence, this aspect is addressed by data curation using theoretical and experimental confirmations. MATERIAL AND METHODS: In the experimental part of our study, the popular shake-flask method combined with ultraviolet (UV) spectrophotometric measurements was applied for solubility determination. The computational phase utilized the conductor-like screening model for real solvents (COSMO-RS) approach. RESULTS: The analysis of the results of solubility calculations for sulfonamide in binary solvents revealed abnormally high error values for acetone-ethyl acetate mixtures, which were further confirmed with experimental measurements. Additional confirmation was obtained by extending the solubility measurements to a series of homologous acetate esters. CONCLUSIONS: Our study addresses the crucial issue of coherence of solubility data used for many theoretical inquiries, including parameter fitting of semi-empirical models, in-depth thermodynamic interpretations and application of machine learning protocols. The effectiveness of the proposed methodology for dataset curation was demonstrated for sulfanilamide solubility in binary mixtures. This approach enabled not only the formulation of a consistent dataset of sulfanilamide solubility binary solvent mixtures, but also its implementation as a qualitative tool guiding rationale solvent selection for experimental solubility screening.

COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge.

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.

Sustainable recovery of phenolic antioxidants from real olive vegetation water with natural hydrophobic eutectic solvents and terpenoids.

Olive oil production leads to the generation of olive mill wastewater (OMWW). Due to the presence of phenolic compounds, they are difficult to process, but they represent a source of high-added value chemicals since they have antioxidant and therapeutic properties. This work has studied the extraction of phenolic compounds from a type of OMWW, olive vegetation water, which presents these compounds in a more diluted dosage than in other studied to date, to revalue this waste stream. A real olive vegetation water from a Spanish olive oil producer was used, and liquid-liquid extraction was applied. Terpenoids and terpene-based hydrophobic eutectic solvents were systematically used to extract phenolic compounds following the concentrations of tyrosol, catechol, caffeic acid, and total phenolic content. By molecular simulation with the COSMO-RS method, 4 terpenoids, and 2 eutectic solvents were selected and compared with 2 conventional solvents. The Solvent/Feed ratio in the extraction of phenolic compounds was studied, showing that the solvents with the highest extraction results were geraniol, eucalyptol, and eutectic solvent menthol + camphor, which outperformed conventional solvents methyl isobutyl ketone and diisopropyl ether. Menthol + camphor gave total phenol extraction yields of 88.73% at a Solvent/Feed ratio in volume of 0.50, surpassing all solvents tested. A solvent reuse and regeneration process was applied by back-extraction of the 4 solvents: FTIR results showed the stability of the solvents while maintaining yields in the solvent reuse process. The phenolic compounds could be concentrated in the alkaline phase to factors up to 49.3 to the initial concentration in olive vegetation water. The alkaline phases were neutralized to obtain a precipitate with a caffeic acid content of up to 26 % wt%, and a tyrosol-rich supernatant with a concentration of up to 6.54 g/L. This work proposes a process using natural solvents to extract phenolic compounds from olive vegetation water.

COSMO-RS guided screening of ionic liquids for the separation of fluorinated greenhouse gases R-410A: Delving into anion, cation effects, and hydrogen bond dynamics.

Reclamation of high-GWP near-azeotropic refrigerant R-410A (50 wt% R-32 (difluoromethane) + 50 wt% R-125 (pentafluoroethane)) can be an effective way to mitigate the greenhouse effect and achieve a circular economy. Efficient ionic liquids (ILs) as extractants needed to be found for the extractive distillation (ED) separation process of R-410A. Given the numerous combinations of cations and anions in ILs, the discovery of an efficient IL via experimental methods proves to be an exceedingly complex task. In this work, the solubilities of R-32, and R-125 in 840 conventional ILs (comprised of 20 cations and 42 anions) were analyzed based on infinite dilution activity coefficient. The absorption mechanisms of R-32 and R-125 in ILs were elucidated by analyzing excess enthalpy (HE), excess Gibbs free energy (GE)) and surface charge density distribution through COSMO-RS (Conductor-like Screening Model for Real Solvents). Results revealed that HE and GE of the mixture formed by R-125 and most ILs surpassed those of R-32, resulting in higher solubility of R-32 in most ILs compared to R-125. Structural changes of anions and cations had a greater effect on the solubility of R-125 in ILs. It is found for the first time that the existence of a strong hydrogen bond donor region in cations/anions generated intense repulsion with the hydrogen atom in R-125. Furthermore, a large area of weak polarity on the surface of cations/anions was difficult to form an effective charge shield with fluorine atoms in R-125, thus inhibiting the dissolution of R-125. Finally based on the identified interaction sites, combined with melting point and viscosity, some novel functional ILs with high selectivity for R-32 + R-125 were designed and determined for actual separation process. These findings significantly enrich the understanding of the solubility mechanism and provide theoretical guidance for designing new ILs for R-410A reclamation.

Evaluating ionic liquids for its potential as eco-friendly solvents for naproxen removal from water sources using COSMO-RS: Computational and experimental validation.

An emerging contaminant of concern in aqueous streams is naproxen. Due to its poor solubility, non-biodegradability, and pharmaceutically active nature, the separation is challenging. Conventional solvents employed for naproxen are toxic and harmful. Ionic liquids (ILs) have attracted great attention as greener solubilizing and separating agent for various pharmaceuticals. ILs have found extensive usage as solvents in nanotechnological processes involving enzymatic reactions and whole cells. The employment of ILs can enhance the effectiveness and productivity of such bioprocesses. To avoid cumbersome experimental screening, in this study, conductor like screening model for real solvents (COSMO-RS) was used to screen ILs. Thirty anions and eight cations from various families were chosen. Activity coefficient at infinite dilution, capacity, selectivity, performance index, molecular interactions using σ-profiles and interaction energies were used to make predictions about solubility. According to the findings, quaternary ammonium cations, highly electronegative, and food-grade anions will form excellent ionic liquid combinations for solubilizing naproxen and hence will be better separating agents. This research will contribute easy designing of ionic liquid-based separation technologies for naproxen. In different separation technologies, ionic liquids can be employed as extractants, carriers, adsorbents, and absorbents.

Ionic Liquid-Based Green Emulsion Liquid Membrane for the Extraction of the Poorly Soluble Drug Ibuprofen.

Ibuprofen (Ibf) is a biologically active drug (BADs) and an emerging contaminant of concern (CECs) in aqueous streams. Due to its adverse effects upon aquatic organisms and humans, the removal and recovery of Ibf are essential. Usually, conventional solvents are employed for the separation and recovery of ibuprofen. Due to environmental limitations, alternative green extracting agents need to be explored. Ionic liquids (ILs), emerging and greener alternatives, can also serve this purpose. It is essential to explore ILs that are effective for recovering ibuprofen, among millions of ILs. The conductor-like screening model for real solvents (COSMO-RS) is an efficient tool that can be used to screen ILs specifically for ibuprofen extraction. The main objective of this work was to identify the best IL for the extraction of ibuprofen. A total of 152 different cation-anion combinations consisting of eight aromatic and non-aromatic cations and nineteen anions were screened. The evaluation was based upon activity coefficients, capacity, and selectivity values. Furthermore, the effect of alkyl chain length was studied. The results suggest that quaternary ammonium (cation) and sulfate (anion) have better extraction ability for ibuprofen than the other combinations tested. An ionic liquid-based green emulsion liquid membrane (ILGELM) was developed using the selected ionic liquid as the extractant, sunflower oil as the diluent, Span 80 as the surfactant, and NaOH as the stripping agent. Experimental verification was carried out using the ILGELM. The experimental results indicated that the predicted COSMO-RS and the experimental results were in good agreement. The proposed IL-based GELM is highly effective for the removal and recovery of ibuprofen.

Studies on the Prediction and Extraction of Methanol and Dimethyl Carbonate by Hydroxyl Ammonium Ionic Liquids.

The separation of dimethyl carbonate (DMC) and methanol is of great significance in industry. In this study, ionic liquids (ILs) were employed as extractants for the efficient separation of methanol from DMC. Using the COSMO-RS model, the extraction performance of ILs consisting of 22 anions and 15 cations was calculated, and the results showed that the extraction performance of ILs with hydroxylamine as the cation was much better. The extraction mechanism of these functionalized ILs was analyzed by molecular interaction and the σ-profile method. The results showed that the hydrogen bonding energy dominated the interaction force between the IL and methanol, and the molecular interaction between the IL and DMC was mainly Van der Waals force. The molecular interaction changes with the type of anion and cation, which in turn affects the extraction performance of ILs. Five hydroxyl ammonium ILs were screened and synthesized for extraction experiments to verify the reliability of the COSMO-RS model. The results showed that the order of selectivity of ILs predicted by the COSMO-RS model was consistent with the experimental results, and ethanolamine acetate ([MEA][Ac]) had the best extraction performance. After four regeneration and reuse cycles, the extraction performance of [MEA][Ac] was not notably reduced, and it is expected to have industrial applications in the separation of methanol and DMC.

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines.

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.

Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study.

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.

Finding the Right Solvent: A Novel Screening Protocol for Identifying Environmentally Friendly and Cost-Effective Options for Benzenesulfonamide.

This study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly developed Python code. To evaluate the models, a novel scoring function was formulated, considering not only the accuracy but also the bias-variance tradeoff through a learning curve analysis. An ensemble approach was adopted by selecting the top-performing regression models for test and validation subsets. The obtained model accurately back-calculated the experimental data and was used to predict the solubility of BSA in 2067 potential solvents. The analysis of the entire solvent space focused on the identification of solvents with high solubility, a low environmental impact, and affordability, leading to a refined list of potential candidates that meet all three requirements. The proposed procedure has general applicability and can significantly improve the quality and speed of experimental solvent screening.

Theoretical Study on Fluorinated Derivatives of Sulfolane, Cyclopentanone, and Gamma-Butyrolactone.

In this paper, fluorinated compounds based on sulfolane, cyclopentanone, and gamma-butyrolactone are studied computationally, focusing on their applicability in electrochemical devices and acid-base-related studies. Candidates for solvents with (1) high polarity, (2) good electrochemical stability, and (3) low basicity were searched for. Some of the compounds are studied here for the first time. Electrochemical stabilities, dielectric constants, boiling points, basicities, and lipophilicities were estimated using DFT and COSMO-RS methods with empirical corrections. The effect of fluorination on these properties as well as the bond parameters was studied. The possible synthesis routes of the proposed compounds are outlined. Some molecules display a combination of estimated properties favorable for a solvent, although none of the studied compounds are expected to surpass acetonitrile and propylene carbonate by the width of the electrochemical stability window.

Extraction of Pyrrole from Its Mixture with n-Hexadecane Using Ionic Liquids and Their Binary Mixtures.

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.

Step-by-Step Replacement of Cyano Groups by Tricyanovinyls-The Influence on the Acidity.

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.

Intermolecular Interactions of Edaravone in Aqueous Solutions of Ethaline and Glyceline Inferred from Experiments and Quantum Chemistry Computations.

Edaravone, acting as a cerebral protective agent, is administered to treat acute brain infarction. Its poor solubility is addressed here by means of optimizing the composition of the aqueous choline chloride (ChCl)-based eutectic solvents prepared with ethylene glycol (EG) or glycerol (GL) in the three different designed solvents compositions. The slurry method was used for spectroscopic solubility determination in temperatures between 298.15 K and 313.15 K. Measurements confirmed that ethaline (ETA = ChCl:EG = 1:2) and glyceline (GLE = ChCl:GL = 1:2) are very effective solvents for edaravone. The solubility at 298.15 K in the optimal compositions was found to be equal xE = 0.158 (cE = 302.96 mg/mL) and xE = 0.105 (cE = 191.06 mg/mL) for glyceline and ethaline, respectively. In addition, it was documented that wetting of neat eutectic mixtures increases edaravone solubility which is a fortunate circumstance not only from the perspective of a solubility advantage but also addresses high hygroscopicity of eutectic mixtures. The aqueous mixture with 0.6 mole fraction of the optimal composition yielded solubility values at 298.15 K equal to xE = 0.193 (cE = 459.69 mg/mL) and xE = 0.145 (cE = 344.22 mg/mL) for glyceline and ethaline, respectively. Since GLE is a pharmaceutically acceptable solvent, it is possible to consider this as a potential new liquid form of this drug with a tunable dosage. In fact, the recommended amount of edaravone administered to patients can be easily achieved using the studied systems. The observed high solubility is interpreted in terms of intermolecular interactions computed using the Conductor-like Screening Model for Real Solvents (COSMO-RS) approach and corrected for accounting of electron correlation, zero-point vibrational energy and basis set superposition errors. Extensive conformational search allowed for identifying the most probable contacts, the thermodynamic and geometric features of which were collected and discussed. It was documented that edaravone can form stable dimers stabilized via stacking interactions between five-membered heterocyclic rings. In addition, edaravone can act as a hydrogen bond acceptor with all components of the studied systems with the highest affinities to ion pairs of ETA and GLE. Finally, the linear regression model was formulated, which can accurately estimate edaravone solubility utilizing molecular descriptors obtained from COSMO-RS computations. This enables the screening of new eutectic solvents for finding greener replacers of designed solvents. The theoretical analysis of tautomeric equilibria confirmed that keto-isomer edaravone is predominant in the bulk liquid phase of all considered deep eutectic solvents (DES).

Solid-Liquid Equilibrium in Co-Amorphous Systems: Experiment and Prediction.

In this work, the solid-liquid equilibrium (SLE) of four binary systems combining two active pharmaceutical ingredients (APIs) capable of forming co-amorphous systems (CAMs) was investigated. The binary systems studied were naproxen-indomethacin, naproxen-ibuprofen, naproxen-probucol, and indomethacin-paracetamol. The SLE was experimentally determined by differential scanning calorimetry. The thermograms obtained revealed that all binary mixtures investigated form eutectic systems. Melting of the initial binary crystalline mixtures and subsequent quenching lead to the formation of CAM for all binary systems and most of the compositions studied. The experimentally obtained liquidus and eutectic temperatures were compared to theoretical predictions using the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state and conductor-like screening model for real solvents (COSMO-RS), as implemented in the Amsterdam Modeling Suite (COSMO-RS-AMS). On the basis of the obtained results, the ability of these models to predict the phase diagrams for the investigated API-API binary systems was evaluated. Furthermore, the glass transition temperature (Tg) of naproxen (NAP), a compound with a high tendency to recrystallize, whose literature values are considerably scattered, was newly determined by measuring and modeling the Tg values of binary mixtures in which amorphous NAP was stabilized. Based on this analysis, erroneous literature values were identified.