Extraction and identification of polyphenol from Camellia oleifera leaves using tailor-made deep eutectic solvents based on COSMO-RS design

Camellia oleifera leaf is a rich source of polyphenols. In this study, 50 polyphenolic compounds from C. oleifera leaves was identified by UHPLC-Q-TOF-MS/MS. Accordingly, COSMO-RS was used in the design of deep eutectic solvents (DESs) to extract those polyphenols. 17 types of choline chloride (ChCl)-based DESs molecules (ChCl-acid, ChCl-sugar, ChCl-alcohol, ChCl-amine and amide) were synthetized into virtual cluster molecules with Materials Studio software. They were used to determine the activity coefficients with the standard compounds. The results showed that the amine and amide-based DESs exhibited outstanding dissolution effects. Additionally, ChCl-acetamide was selected as the solvent in response surface methodology to optimize the ultrasound-assisted DES extraction process parameters, including ultrasonic power, ultrasonic time, and liquid-solid ratio, resulting in an improved total phenolic content of 131.63 ± 0.85 mg GAE/g. This study developed a system utilizing UHPLC-Q-TOF-MS/MS to acquire specific substances required for COSMO-RS calculations.

A review on advancement of cocrystallization approach and a brief on screening, formulation and characterization of the same

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.

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.

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.

Computational study on organochlorine insecticides extraction using ionic liquids

Insecticides pose hazardous environmental effects and can enter the food chain and contaminate water resources. Ionic liquids (ILs) have recently drawn much interest as environmentally friendly solvents and have been an efficient choice for extracting pesticides because of their outstanding thermophysical characteristics and tunable nature. In this study, ILs were screened using COSMO-RS (Conductor-like Screening Model for Real Solvents) to extract organochlorine insecticides from water at 289 K. A total of 165 ILs, a combination of 33 cations with five anions, were screened by COSMO-RS to predict the selectivity and capacity of the organochlorine insecticides at infinite dilution. The Organochlorine insecticide compounds, such as benzene hexachloride (BHC), Heptachlor, Aldrin, Gamma-Chlordane (γ-Chlordane), Endrin, and Methoxychlor are selected for this study. Charge density profiles show that Endrin and Methoxychlor compounds are strong H-bond acceptors and weak H-bond donors, while the rest of the compounds are H-bond donors with no H-bond acceptor potential. Moreover, it has been shown that ILs composed of halides and heteroatomic anions in conjunction with cations have enhanced selectivity and capacity for insecticides. Moreover, the hydrophobic phosphonium-based ILs have enhanced selectivity and capacity for insecticides. In BHC extraction, the selectivity of 1,3-dimethyl-imidazolium chloride was found to be the highest at 1074.06, whereas 2-hydroxyethyl trimethyl ammonium chloride exhibited the highest capacity being 84.0.1,3-dimethyl-imidazolium chloride exhibits the highest performance index, which is 57064.77. In addition, the ILs that have been chosen are well-recognized as environmentally friendly and very effective solvents to extract insecticides from water. As a result, this study evaluated that ILs could be promising solvents that may be further developed for the extraction of insecticides from contaminated water.

COSMO-RS-based assessment of thermodynamic tools in predicting the polar and non-polar solvents efficiency in vegetable oil extraction

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.

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.

Deep eutectic solvents based in situ isolation technique for extractive deterpenation of essential oils

Essential oils, intricate blends of volatile compounds obtained from a variety of sources, play a crucial role in numerous industries. To elevate product quality, deterpenation becomes an indispensable step. This study proposes an in situ isolation technique based on deep eutectic solvents (DESs) for the deterpenation of essential oil. Salient features of relevant compounds were obtained using conductor-like screening model for real solvents (COSMO-RS) and density functional theory (DFT) methods to predict deterpenation performance. Tetrabutylammonium chloride (TBAC) was chosen based on the results of theoretical analysis and experiment to extract hydroxy-terpenoids. COSMO-RS was employed to evaluate the extraction performance at different molar ratios, and then combined with experimental analysis to determine the optimal conditions. The σ-profiles of organic solvents and their interactions with terpene revealed n-hexane to be the best solvent for purifying DES. TBAC and terpenoids were obtained through the re-extraction procedure, with a recovery of 81.8-84.4%.

Structural, optimization of and mechanistic insights into a new thiosemicarbazone derivative as a highly sensitive and selective chemosensor for Hg2+ recognition using DFT, COSMO-RS, RSM, and molecular dynamics simulation approaches

A new thiosemicarbazone derivative, N-(2-hydroxyphenyl)-2-[1-(pyridin-4-yl)ethylidene]hydrazinecarbothioamide (HPEH), has been synthesized, characterized, and further developed as a highly selective and sensitive colorimetric chemosensor for Hg2+ recognition in environmental water samples. Structural conformers of HPEH were successfully identified using a combination of the potential energy surface (PES) and time-dependent density functional theory (TD-DFT) methods. The synthesized HPEH was successfully characterized further and analyzed based on its harmonic vibrational frequencies, NMR spectra, and electronic transitions using the DFT approach. Sigma profiles were generated using the COSMO-RS approach to identify a compatible medium for HPEH to act as a chemosensor. The conditions for the highly sensitive and selective detection of Hg2+ by HPEH were successfully optimized using the statistical response surface methodology approach. The optimum sensing of HPEH occurred in an 8:2 v/v DMSO/pH 7.8 solution at a 20:60 μM HPEH/Hg2+ concentration and after a reaction time of 18 min, with statistically significant independent variables (p < 0.05) for all parameters. Under optimal conditions, the lowest Hg2+ concentration detected by HPEH was 3.56 µM, indicating that HPEH can serve as an alternative and comparable probe for the detection of Hg2+ in aqueous systems. Using the optimized results, the interaction between HPEH and Hg2+ in the chemosensor system was successfully modeled, and the model was subsequently used with the TD-DFT, non-covalent interaction-reduced density gradient (NCI-RDG), and molecular dynamics approaches to gain mechanistic insights into the interaction. The results showed that the newly synthesized HPEH, in addition to being cost-effective, could serve as a suitable alternative and comparable chemosensor for Hg2+ recognition in water samples, with the advantages of being efficient, portable, and eco-friendly, and offering rapid analysis without the need of specialized training.

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.

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.

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.

COSMO-RS solute partition ratios for solvent mixtures of unknown composition: Henry's law constants as descriptors for mixture sigma profiles

Henry's law constants (H) for selected probe molecules have been used as descriptors to estimate the COSMO-RS sigma profiles of solvents and solvent mixtures. Henry's law constants were calculated with COSMOtherm for small sets of probe molecules in 155 organic solvents (training set), and these constants subsequently used as descriptors to model the solvent sigma profiles with 61 multiple linear regression (MLR) equations. Subsequent input into COSMOtherm of weighted basis molecule solvent mixtures whose sigma profiles closely matched those modelled for the training set solvents allowed estimation of air-solvent and water-solvent partition ratios for solutes in solvents and solvent mixtures without input of the solvent or solvent mixture identity. The best performing model had 16 descriptors and gave both a training and test set average root-mean square error (RMSE) of 0.008 and an average relative square error (RSE) of 0.07. Partition ratios (K) were then generated for a test set of 251 additional organic solute molecules in solvent/water media where solvents were test set compounds and H constants for the same probe molecules were used as descriptors. The best performing sigma profile model yielded log K RMSE values ranging from 0.17 to 0.92. Finally, this approach was applied to several mixtures ranging from simple binary mixtures to two mixtures considered to be of unknown or variable composition, complex reaction productions or biological materials (UVCBs), namely gasoline and an essential oil mixture. Mixture/water partition ratios were estimated for 251 solutes giving log K RMSE values ranging from 0.24 to 0.88.

Expanding the applicability domain of QSPRs for predicting water solubility and vapor pressure of PFAS

This work aimed to verify whether it is possible to extend the applicability domain (AD) of existing QSPR (Quantitative Structure-Property Relationship) models by employing a strategy involving additional quantum-chemical calculations. We selected two published QSPR models: for water solubility, logSW, and vapor pressure, logVP of PFAS as case studies. We aimed to enlarge set of compounds used to build the model by applying factorial planning to plan the augmentation of the set of these compounds based on their structural features (descriptors). Next, we used the COSMO-RS model to calculate the logSW and logVP for selected chemicals. This allowed filling gaps in the experimental data for further training QSPR models. We improved the published models by significantly extending number of compounds for which theoretical predictions are reliable (i.e., extending the AD). Additionally, we performed external validation that had not been carried out in original models. To test effectiveness of the AD extension, we screened 4519 PFAS from NORMAN Database. The number of compounds outside the domain was reduced comparing the original model for both properties. Our work shows that combining physics-based methods with data-driven models can significantly improve the performance of predictions of phys-chem properties relevant for the chemical risk assessment.

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.

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.

Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures

Dapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS: 112-57-2), and diethylene glycol bis(3-aminopropyl) ether (CAS: 4246-51-9). Furthermore, the study proposes the use of intermolecular interactions as molecular descriptors to predict the solubility of dapsone in neat solvents and binary mixtures using machine learning models. An ensemble of regressors was used, including support vector machines, random forests, gradient boosting, and neural networks. Affinities of dapsone to solvent molecules were calculated using COSMO-RS and used as input for model training. Due to the polymorphic nature of dapsone, fusion data are not available, which prohibits the direct use of COSMO-RS for solubility calculations. Therefore, a consonance solvent approach was tested, which allows an indirect estimation of the fusion properties. Unfortunately, the resulting accuracy is unsatisfactory. In contrast, the developed regressors showed high predictive potential. This work documents that intermolecular interactions characterized by solute-solvent contacts can be considered valuable molecular descriptors for solubility modeling and that the wealth of encoded information is sufficient for solubility predictions for new systems, including those for which experimental measurements of thermodynamic properties are unavailable.

Ionic liquid-based dispersive liquid-liquid microextraction of succinic acid from aqueous streams: COSMO-RS screening and experimental verification

AbstractIn 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.

A Comparison between High-Performance Countercurrent Chromatography and Fast-Centrifugal Partition Chromatography for a One-Step Isolation of Flavonoids from Peanut Hulls Supported by a Conductor like Screening Model for Real Solvents

Peanut hulls (Arachis hypogaea, Leguminosae), which are a side stream of global peanut processing, are rich in bioactive flavonoids such as luteolin, eriodictyol, and 5,7-dihydroxychromone. This study aimed to isolate these flavonoid derivatives by liquid-liquid chromatography with as few steps as possible. To this end, luteolin, eriodictyol and 5,7-dihydroxychromone were isolated from peanut hulls using two different techniques, high-performance countercurrent chromatography (HPCCC) and fast-centrifugal partition chromatography (FCPC). The suitability of the biphasic solvent system composed of n-hexane/ethyl acetate/methanol/water (1.0/1.0/1.0/1.5; v/v/v/v) was determined by the Conductor like Screening Model for Real Solvents (COSMO-RS), which allowed the partition ratio K_D-values of the three main flavonoids to be calculated. After a one-step HPCCC separation of ~1000 mg of an ethanolic peanut hull extract, 15 mg of luteolin and 8 mg of eriodictyol were isolated with purities over 96%. Furthermore, 3 mg of 5,7-dihydroxychromone could be isolated after purification by semi-preparative reversed-phase liquid chromatography (semi-prep. HPLC) in purity of over 99%. The compounds were identified by electrospray ionization mass spectrometry (ESI-MS) and nuclear magnetic resonance spectroscopy (NMR).

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.

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.

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.

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.

An achieved strategy for magnetic biochar for removal of tetracyclines and fluoroquinolones: Adsorption and mechanism studies

In this study, poplar wood biochar modified with Fe₃O₄ (MPBC) was prepared using poplar wood as carbon source applied to remove tetracyclines and fluoroquinolones. The adsorption behavior was investigated by batch experiments, and a series of characterization techniques were used to study the corresponding mechanism. Characterizations indicated that pore filling, electrostatic interactions, π-π interaction, surface complexation, and hydrogen bond contributed to the adsorption of antibiotics on MPBC. Most importantly, the thermodynamic experiment results showed that the adsorption capacity of MPBC for tetracyclines (70.28-89.58 mg⋅g^-1) was significantly higher than fluoroquinolones (35.54-60.31 mg⋅g^-1), which was further explained by hydrogen bond interactions calculated from Conductor-like screening model for real solvents (COSMO-RS). In addition, the adsorption between MPBC and antibiotics was favorable at lower ionic strengths and neutral conditions. Conclusively, this study could provide a promising approach to controlling the pollution of tetracyclines and fluoroquinolones.

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 x_E = 0.158 (c_E = 302.96 mg/mL) and x_E = 0.105 (c_E = 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 x_E = 0.193 (c_E = 459.69 mg/mL) and x_E = 0.145 (c_E = 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).

Screening of ionic liquids as green entrainers for ethanol water separation by extractive distillation: COSMO-RS prediction and aspen plus simulation

Ionic liquids (ILs) have been demonstrated as promising alternatives to conventional entrainers in separation of azeotropic mixtures mostly investigating phase equilibrium and process design scenarios. However, proper selection of ILs for a specific task always remains challenging. Hence a simulation tool, i.e. conductor like screening model for real solvents (COSMO-RS) was applied to address this challenge. Furthermore, screened ILs were simulated as entrainers for ethanol water separation by extractive distillation. The current study also aims to demonstrate a systematic approach to retrofit existing processes, by employing ILs as green entrainers. Screening of twenty-five (25) ILs was carried out using COSMO-RS to select suitable ILs as green entrainers based on activity coefficient, capacity and selectivity. Results illustrated that tetramethylammonium chloride ([TMAm][Cl]) due to its strong hydrogen bonding ability was found to be the best ILs entrainer. Moreover, in order to reduce the operating costs without compromising desired product purity (ethanol purity ≥99.5% in top product), the selected ILs (8 kg/h) in a mixture with ethylene glycol (72 kg/h) were simulated using Aspen plus v.11. The simulation results revealed that by combining tetramethylammonium chloride (2 kg/h) with ethylene glycol (78 kg/h) reduced 7.26 tons of CO2 emissions/year through heat integration by saving 1.49*108 kJ/year energy besides minimizing operating costs. In conclusion, the systematic selection of ILs as green entrainers in combination with ethylene glycol and then the appropriate simulation of the whole system will ultimately reduce the cost of the separation process and reduce the emission of greenhouse gases as well utilization of toxic conventional entrainers.

Quantitative assessment of the contribution of soil organic matter functional groups and heteroatoms to PAHs adsorption based on the COSMO-RS model

Soil organic matter (SOM) is considered as a pivotal factor influencing the adsorption of pollutants. However, few prior quantitative investigations of the SOM functional group distribution to the contaminants' fate have been conducted. In this paper, the SOM cluster method based on COSMO-RS theory has been conducted to illustrate the chemical composition variables of SOM that affect the polycyclic aromatic hydrocarbons (PAHs) fate in quantitative terms. In the theoretical simulations, the contributions of carbonyl, carboxyl, aromatic, oxyalkyl and aliphatic groups in SOM to phenanthrene (Phe) and pyrene (Pyr) adsorption are evaluated by calculating the partition coefficients (LogP). The results show that the increase in oxyalkyl content leads to a decrease in LogP. Inversely, carbonyl and carboxyl groups of SOMs positively associated with Phe adsorption. The changes in aromatic and alkyl components have a similar magnitude of influence on LogP. Moreover, the effect of non-carbon-based functional groups in SOM on the Phe partitioning has been examined for the first time. The increase of sulfur and nitrogen content in SOM hinder Phe adsorption, while the rise of phosphorus content promotes the adsorption. In soil adsorption experiments, four natural soils, characterized by X-ray photoelectron spectroscopy (XPS) and Diffuse reflectance infrared Fourier transform (DRIFT), are selected to verify the influence of SOM functional group distribution. Comparing the experimental SOM-water partition coefficient (LogK_oc) with the simulation predicted LogP suggests that the COSMO-RS based SOM cluster method can predict PAHs adsorption ability in SOM.

Acid Rain and Flue Gas: Quantum Chemical Hydrolysis of NO2

Despite decades of efforts, much is still unknown about the hydrolysis of nitrogen dioxide (NO₂ ), a reaction associated with the formation of acid rain. From the experimental point of view, quantitative analyses are hard, and without pH control the products decompose to some reagents. We resort to high-level quantum chemistry to compute Gibbs energies for a network of reactions relevant to the hydrolysis of NO₂ . With COSMO-RS solvation corrections we calculate temperature dependent thermodynamic data in liquid water. Using the computed reaction energies, we determine equilibrium concentrations for a gas-liquid system at controlled pH. For different temperatures and initial concentrations of the different species, we observe that nitrogen dioxide should be fully converted to nitric and nitrous acid. The thermodynamic data in this work can have a potential major impact for several industries with regards to the understanding of atmospheric chemistry and in the reduction of anthropomorphic pollution.

Purely Predicting the Pharmaceutical Solubility: What to Expect from PC-SAFT and COSMO-RS?

A pair of popular thermodynamic models for pharmaceutical applications, namely, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state and the conductor-like screening model for real solvents (COSMO-RS) are thoroughly benchmarked for their performance in predicting the solubility of active pharmaceutical ingredients (APIs) in pure solvents. The ultimate goal is to provide an illustration of what to expect from these progressive frameworks when applied to the thermodynamic solubility of APIs based on activity coefficients in a purely predictive regime without specific experimental solubility data (the fusion properties of pure APIs were taken from experiments). While this kind of prediction represents the typical modus operandi of the first-principles-aided COSMO-RS, PC-SAFT is a relatively highly parametrized model that relies on experimental data, against which its pure-substance and binary interaction parameters (k_ij) are fitted. Therefore, to make this benchmark as fair as possible, we omitted any binary parameters of PC-SAFT (i.e., k_ij = 0 in all cases) and preferred pure-substance parameter sets for APIs not trained to experimental solubility data. This computational approach, together with a detailed assessment of the obtained solubility predictions against a large experimental data set, revealed that COSMO-RS convincingly outperformed PC-SAFT both qualitatively (i.e., COSMO-RS was better in solvent ranking) and quantitatively, even though the former is independent of both substance- and mixture-specific experimental data. Regarding quantitative comparison, COSMO-RS outperformed PC-SAFT for 9 of the 10 APIs and for 63% of the API-solvent systems, with root-mean-square deviations of the predicted data from the entire experimental data set being 0.82 and 1.44 log units, respectively. The results were further analyzed to expand the picture of the performance of both models with respect to the individual APIs and solvents. Interestingly, in many cases, both models were found to qualitatively incorrectly predict the direction of deviations from ideality. Furthermore, we examined how the solubility predictions from both models are sensitive to different API parametrizations.

Quantification of Caffeine Interactions in Choline Chloride Natural Deep Eutectic Solvents: Solubility Measurements and COSMO-RS-DARE Interpretation

Solubility of active pharmaceutical ingredients is an important aspect of drug processing and formulation. Although caffeine was a subject of many studies aiming to quantify saturated solutions, many applied solvents suffer from not being environmentally friendly. This work fills this gap by presenting the results of solubility measurements in choline chloride natural deep eutectic solvents, ccNADES, comprising one of seven of the following polyalcohols: glycerol, sorbitol, xylitol, glucose, sucrose, maltose and fructose. The ratio of ccNADES components was optimized for maximizing caffeine solubility at room temperature. Additionally, temperature dependent solubility was measured for the first four systems exhibiting the highest solubility potential, both in their neat forms and in mixtures with water. Results were used for intermolecular interactions assessments using the COSMO-RS-DARE approach, which led to a perfect match between experimental and computed solubility values. An important methodological discussion was provided for an appropriate definition of the systems. Surprising linear trends were observed between the values of fitting parameters and water-ccNADES composition. In addition, comments on selection of the values of the fusion thermodynamic parameters were provided, which led to the conclusion that COSMO-RS-DARE solubility computations can effectively compensate for the inaccuracies of these important physicochemical properties.

Prediction of pH Value of Aqueous Acidic and Basic Deep Eutectic Solvent Using COSMO-RS σ Profiles' Molecular Descriptors

The aim of this work was to develop a simple and easy-to-apply model to predict the pH values of deep eutectic solvents (DESs) over a wide range of pH values that can be used in daily work. For this purpose, the pH values of 38 different DESs were measured (ranging from 0.36 to 9.31) and mathematically interpreted. To develop mathematical models, DESs were first numerically described using σ profiles generated with the COSMOtherm software. After the DESs’ description, the following models were used: (i) multiple linear regression (MLR), (ii) piecewise linear regression (PLR), and (iii) artificial neural networks (ANNs) to link the experimental values with the descriptors. Both PLR and ANN were found to be applicable to predict the pH values of DESs with a very high goodness of fit (R2independent validation > 0.8600). Due to the good mathematical correlation of the experimental and predicted values, the σ profile generated with COSMOtherm could be used as a DES molecular descriptor for the prediction of their pH values.

Prediction of acid pKa values in the solvent acetone based on COSMO-RS

In this contribution we extent the use of the conductor-like screening model for realistic solvation (COSMO-RS) to the prediction of pK_a values in acetone, a commonly used dipolar aprotic solvent. For this, we calculated the Gibbs free energy of dissociation of 120 organic acids (nine acrylic acids, 87 benzoic acids, nine phenols, and 15 benzenesulfonamides) using COSMO-RS at the two levels BP-TZVP and BP-TZVPD-FINE and determined the parameters for a linear free energy relation for the p K a prediction by performing linear fits to experimental values. Our results suggest that the data set dissects into two groups, with the phenols being different from the other three subsets. The acrylic and benzoic acids and the sulfonamides can be treated together and yield an excellent linear correlation ( r 2 > 0.95 ) with an RMSD of only ~0.3. The slope is found to be significantly smaller than the theoretical value ( 1 / RT ln 10 ), only 45% of it, which is in accordance with findings in the literature. The phenols, however, while similarly well correlated in their own subset with an RMSD of 1.7-1.9, exhibit a slope larger than one. We discuss both a higher uncertainty in the reference values as well as physical origins as possible reasons.

Design of Ionic Liquids for Fluorinated Gas Absorption: COSMO-RS Selection and Solubility Experiments

In recent years, the fight against climate change and the mitigation of the impact of fluorinated gases (F-gases) on the atmosphere is a global concern. Development of technologies that help to efficiently separate and recycle hydrofluorocarbons (HFCs) at the end of the refrigeration and air conditioning equipment life is a priority. The technological development is important to stimulate the F-gas capture, specifically difluoromethane (R-32) and 1,1,1,2-tetrafluoroethane (R-134a), due to their high global warming potential. In this work, the COSMO-RS method is used to analyze the solute-solvent interactions and to determine Henry's constants of R-32 and R-134a in more than 600 ionic liquids. The three most performant ionic liquids were selected on the basis of COSMO-RS calculations, and F-gas absorption equilibrium isotherms were measured using gravimetric and volumetric methods. Experimental results are in good agreement with COSMO-RS predictions, with the ionic liquid tributyl(ethyl)phosphonium diethyl phosphate, [P₂₄₄₄][C₂C₂PO₄], being the salt presenting the highest absorption capacities in molar and mass units compared to salts previously tested. The other two ionic liquids selected, trihexyltetradecylphosphonium glycinate, [P₆₆₆₁₄][C₂NO₂], and trihexyl(tetradecyl)phosphonium 2-cyano-pyrrole, [P₆₆₆₁₄][CNPyr], may be competitive as far as their absorption capacities are concerned. Future works will be guided on evaluating the performance of these ionic liquids at an industrial scale by means of process simulations, in order to elucidate the role in process efficiency of other relevant absorbent properties such as viscosity, molar weight, or specific heat.

Liquefying Flavonoids with Terpenoids through Deep Eutectic Solvent Formation

The formation of deep eutectic solvents (DES) is tied to negative deviations to ideality caused by the establishment of stronger interactions in the mixture than in the pure DES precursors. This work tested thymol and menthol as hydrogen bond donors when combined with different flavonoids. Negative deviations from ideality were observed upon mixing thymol with either flavone or flavanone, two parent flavonoids that only have hydrogen bond acceptor (HBA) groups, thus forming non-ionic DES (Type V). On the other hand, the menthol systems with the same compounds generally showed positive deviations from ideality. That was also the case with the mixtures containing the more complex hydroxylated flavonoid, hesperetin, which resulted in positive deviations when mixed with either thymol or menthol. COSMO-RS successfully predicted the behavior of the solid-liquid phase diagram of the studied systems, allowing for evaluation of the impact of the different contributions to the intermolecular interactions, and proving to be a good tool for the design of DES.

Application of the Solute-Solvent Intermolecular Interactions as Indicator of Caffeine Solubility in Aqueous Binary Aprotic and Proton Acceptor Solvents: Measurements and Quantum Chemistry Computations

The solubility of caffeine in aqueous binary mixtures was measured in five aprotic proton acceptor solvents (APAS) including dimethyl sulfoxide, dimethylformamide, 1,4-dioxane, acetonitrile, and acetone. The whole range of concentrations was studied in four temperatures between 25 °C and 40 °C. All systems exhibit a strong cosolvency effect resulting in non-monotonous solubility trends with changes of the mixture composition and showing the highest solubility at unimolar proportions of organic solvent and water. The observed solubility trends were interpreted based on the values of caffeine affinities toward homo- and hetero-molecular pairs formation, determined on an advanced quantum chemistry level including electron correlation and correction for vibrational zero-point energy. It was found that caffeine can act as a donor in pairs formation with all considered aprotic solvents using the hydrogen atom attached to the carbon in the imidazole ring. The computed values of Gibbs free energies of intermolecular pairs formation were further utilized for exploring the possibility of using them as potential solubility prognostics. A semi-quantitative relationship (R² = 0.78) between caffeine affinities and the measured solubility values was found, which was used for screening for new greener solvents. Based on the values of the environmental index (EI), four morpholine analogs were considered and corresponding caffeine affinities were computed. It was found that the same solute–solvent structural motif stabilizes hetero-molecular pairs suggesting their potential applicability as greener replacers of traditional aprotic proton acceptor solvents. This hypothesis was confirmed by additional caffeine solubility measurements in 4-formylmorpholine. This solvent happened to be even more efficient compared to DMSO and the obtained solubility profile follows the cosolvency pattern observed for other aprotic proton acceptor solvents.

Multitask Quantum Study of the Curcumin-Based Complex Physicochemical and Biological Properties

Density functional theory (DFT), time-dependent density functional theory (TDDFT), quantum theory of atoms in molecules (QTAIM), and extended transition state natural orbitals for chemical valence (ETS-NOCV) have all been used to investigate the physicochemical and biological properties of curcumin and three complexes, i.e., Cur-M (M = Ni, Cu, and Mg). Based on DFT calculations, the enolic form (Cur-Enol) is more stable than the anti-diketone form (Cur-Anti diketone) favored for complexation. This enolic form stability was explained by the presence of three intramolecular hydrogen bonds according to the QTAIM analysis. Furthermore, the ETS-NOCV technique revealed that the enolic form had more significant antioxidant activity compared with the anti-diketone form. The calculations from the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) showed that the dimethyl sulfoxide (DMSO) solvent could dissolve all the curcumin tautomers Cur-Enol, Cur-Anti-diketone and Cur-Cu, Cur-Mg, and Cur-Ni complexes in contrast to benzene, acetone, octanol, ethanol, methanol, and water. Furthermore, except for Cur-Mg, which had a relatively low solubility (14 g/L), all complexes were insoluble in water. Cur-Anti-diketone was considerably more soluble than Cur-Enol in the examined solvents.

First-principles prediction of critical micellar concentrations for ionic and nonionic surfactants

The concentration of surfactant in solution for which micelles start to form, also known as critical micelle concentration is a key property in formulation design. The critical micelle concentration can be determined experimentally with a tensiometer by measuring the surface tension of a concentration series. In analogy with experiments, in-silico predictions can be achieved through interfacial tension calculations. We present a newly developed method, which employs first principles-based interfacial tension calculations rooted in COSMO-RS theory, for the prediction of the critical micelle concentration of a set of nonionic, cationic, anionic, and zwitterionic surfactants in water. Our approach consists of a combination of two prediction strategies for modelling two different phenomena involving the removal of the surfactant hydrophobic tail from contact with water. The two strategies are based on regular micelle formation and thermodynamic phase separation of the surfactant from water and both are required to take into account a wide range of polarity in the hydrophilic headgroup. Our method yields accurate predictions for the critical micellar concentration, within one log unit from experiments, for a wide range of surfactant types and introduces possibilities for first-principles based prediction of formulation properties for more complex compositions.

Salts of 2-hydroxybenzylamine with improvements on solubility and stability: Virtual and experimental screening

2-Hydroxybenzylamine (2-HOBA) is a drug used to effectively treat oxidative stress. To improve its aqueous solubility and thermal stability, salt screening and synthesis was carried out. The conductor-like screening model for the real solvents model (COSMO-RS) was applied to virtual screening of coformers among 200 commonly used candidates for salification of 2-HOBA. As a result, 40 hit compounds were subjected to experimental liquid-assisted grinding (LAG) with 2-HOBA, then 21 systems were characterized as new solid phases by PXRD. Nine multicomponent single crystals of 2-HOBA with succinic acid, p-aminobenzoic acid, p-nitrobenzoic acid, o-nitrobenzoic acid, p-toluic acid, 2,3-dihydroxybenzoic acid, 3,4-dihydroxybenzoic acid, p-nitrophenol, and 5-hydroxyisophthalic acid were obtained and characterized by single-crystal X-ray diffraction, powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis. All of them were salts and exhibited higher decomposition temperatures compared with pure 2-HOBA. The apparent aqueous solubility of three new salts, i.e., those with succinic acid, p-aminobenzoic acid, and p-nitrophenol were higher than the equilibrium solubility of 2-HOBA. The accelerated stability test indicated that all salts show excellent stability under conditions (40 °C and 75% RH) for 4 weeks. Overall, this work introduced a protocol that combined the virtual screening tool based on the COSMO-RS model and the experimental LAG method to screen new salts for a target compound. The feasibility of this protocol was confirmed in the case of 2-HOBA whose new salts were successfully obtained and represented an improvement for aqueous solubility and thermal stability.

Carvone and its eutectic mixtures as novel, biodegradable, and tunable solvents to extract hydrophobic compounds in substitution for volatile toxic solvents

Finding safe solvents with low viscosities has been in great demand in extraction processes. Herein, R-(-)-carvone, a natural monoterpenoid rich in spearmint, was mixed with naturally occurring fatty acids and terpenes. Most eutectic mixtures presented a wide liquid window in the solid-liquid equilibrium phase diagrams. Carvone mixtures at the ideal eutectic points were characterized for physicochemical properties. Despite varying properties, all the tested solvents were immiscible with water and displayed low viscosity with reasonable biodegradability. Sigma potentials of the mixtures were applied to machine learning algorithms, suggesting carvone mixtures as substitutes for polar protic and dipolar aprotic solvents. Carvone mixtures could be successfully applied to liquid-liquid extraction of a red algae called laver, which is rich in natural hydrophobic and hydrophilic pigments of high value. This study proposes carvone as a new bio-based source of hydrophobic solvents and the eutectic mixtures as biodegradable and tunable solvents to extract hydrophobic compounds.

Experimental and Theoretical Screening for Green Solvents Improving Sulfamethizole Solubility

Solubility enhancement of poorly soluble active pharmaceutical ingredients is of crucial importance for drug development and processing. Extensive experimental screening is limited due to the vast number of potential solvent combinations. Hence, theoretical models can offer valuable hints for guiding experiments aimed at providing solubility data. In this paper, we explore the possibility of applying quantum-chemistry-derived molecular descriptors, adequate for development of an ensemble of neural networks model (ENNM), for solubility computations of sulfamethizole (SMT) in neat and aqueous binary solvent mixtures. The machine learning procedure utilized information encoded in σ-potential profiles computed using the COSMO-RS approach. The resulting nonlinear model is accurate in backcomputing SMT solubility and allowed for extensive screening of green solvents. Since the experimental characteristics of SMT solubility are limited, the data pool was extended by new solubility measurements in water, five neat organic solvents (acetonitrile, N,N-dimethylformamide, dimethyl sulfoxide, 1,4-dioxane, and methanol), and their aqueous binary mixtures at 298.15, 303.15, 308.15, and 313.15 K. Experimentally determined order of decreasing SMT solubility in neat solvents is the following: N,N-dimethylformamide > dimethyl sulfoxide > methanol > acetonitrile > 1,4dioxane >> water, in all studied temperatures. Similar trends are observed for aqueous binary mixtures. Since N,N-dimethylformamide is not considered as a green solvent, the more acceptable replacers were searched for using the developed model. This step led to the conclusion that 4-formylmorpholine is a real alternative to N,N-dimethylformamide, fulfilling all requirements of both high dissolution potential and environmental friendliness.

Direct measurements and modeling of congener group specific vapor pressure for chlorinated paraffins

Chlorinated Paraffins (CPs) are a complex group of manmade chemicals detected widely in the environment. To predict their environmental fate and effects, it is important to understand their physical-chemical properties including vapor pressure. In this study, the first direct measurements of the vapor pressure for CP congener groups (C_10-16Cl_4-11) are presented. Vapor pressure was measured above three industrial CP mixtures with different congener distributions between 20 and 50 °C using a gas saturation method. The measured saturated vapor pressure (P^∗) decreased with increasing carbon chain length and Cl content. ΔH_vap ranged between 73 and 122 kJ mol^-1, consistent with data from the literature and model prediction. The experimental log P^∗ at 25 °C agreed well with predictions from an empirical regression model in the literature (R² = 0.97; RSME = 0.25) and with those predicted from the COSMO-RS-trained fragment contribution model (R² = 0.95; RSME = 0.35). A new empirical model was calibrated with the P^∗ data for 35 congener groups measured in this study. Predicted log P^∗ values correlate well with field-measured gas/particle partition coefficients and may therefore be used for estimating the environmental fate and pathways of a broad range of CPs in the environment.

Guidance on the Application of Polyurethane Foam Disk Passive Air Samplers for Measuring Nonane and Short-Chain Chlorinated Paraffins in Air: Results from a Screening Study in Urban Air

This study provides guidance on using polyurethane foam-based passive air samplers (PUF-PASs) for atmospheric nonane chlorinated paraffins (C₉-CPs) and short-chain CPs (SCCPs) and reports SCCP concentrations in air in the Greater Toronto Area (GTA), Canada. We estimated the partition coefficients between PUF and air (K_PUF-A) and between octanol and air (K_OA) for C₉-CP and SCCP congeners using the COSMO-RS method, so that PUF disk uptake profiles for each formula group could be calculated. We then measured SCCP concentrations in PUF disk samples collected from distinct source sectors in urban air across the GTA. Concentrations in samplers were used to calculate C₉-CP and SCCP concentrations in air and the PUF disk uptake profiles revealed that time-weighted linear phase sampling was possible for congeners having log K_OA values greater than 8.5. The highest SCCP concentrations, with an annual average concentration of 35.3 ng/m³, were measured at the industrial site, whereas lower but comparable SCCP concentrations were found in residential and background sites, with annual averages of 7.73 and 10.5 ng/m³, respectively. No consistent seasonal variation in SCCP concentrations was found in the six distinct source sectors. Direct measurements of K_PUF-A and K_OA values as a function of temperature could be used to increase accuracy in future studies.

Experimental and Theoretical Study on Theobromine Solubility Enhancement in Binary Aqueous Solutions and Ternary Designed Solvents

The solubility of theobromine was studied both experimentally and theoretically. The solubility was determined spectrophotometrically at 25 °C in neat organic solvents, aqueous binary mixtures, Natural Deep Eutectic Solvents (NADES) and ternary NADES mixtures with water. It was found that addition of water in unimolar proportions with some organic solvents increases theobromine solubility compared to neat solvents. Additionally, using NADES results in a solubility increase of the studied compound not only in relation to water but also DMSO. The addition of water (0.2 molar fraction) to NADES is responsible for an even larger increase of solubility. The measured solubilities were interpreted in terms of three theoretical frameworks. The first one-belonging to the set of data reduction techniques-proved to be very efficient in quantitative back-computations of excess solubility of theobromine in all studied systems. The default approach utilizing the well-recognized COSMO-RS (Conductor-like Screening Model for Real Solvents) framework offered at most a qualitative solubility description. The extended search for possible contacts provided evidence for the existence of many intermolecular complexes that alter the electron density of the solute molecule, thus influencing solubility computations. Taking into account such intermolecular contacts by using the COSMO-RS-DARE (Conductor-like Screening Model for Realistic Solvation-Dimerization, Aggregation, and Reaction Extension) framework seriously increased the accuracy of solubility computations.

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.

Thermodynamics and Intermolecular Interactions of Nicotinamide in Neat and Binary Solutions: Experimental Measurements and COSMO-RS Concentration Dependent Reactions Investigations

In this study, the temperature-dependent solubility of nicotinamide (niacin) was measured in six neat solvents and five aqueous-organic binary mixtures (methanol, 1,4-dioxane, acetonitrile, DMSO and DMF). It was discovered that the selected set of organic solvents offer all sorts of solvent effects, including co-solvent, synergistic, and anti-solvent features, enabling flexible tuning of niacin solubility. In addition, differential scanning calorimetry was used to characterize the fusion thermodynamics of nicotinamide. In particular, the heat capacity change upon melting was measured. The experimental data were interpreted by means of COSMO-RS-DARE (conductor-like screening model for realistic solvation-dimerization, aggregation, and reaction extension) for concentration dependent reactions. The solute-solute and solute-solvent intermolecular interactions were found to be significant in all of the studied systems, which was proven by the computed mutual affinity of the components at the saturated conditions. The values of the Gibbs free energies of pair formation were derived at an advanced level of theory (MP2), including corrections for electron correlation and zero point vibrational energy (ZPE). In all of the studied systems the self-association of nicotinamide was found to be a predominant intermolecular complex, irrespective of the temperature and composition of the binary system. The application of the COSMO-RS-DARE approach led to a perfect match between the computed and measured solubility data, by optimizing the parameter of intermolecular interactions.

COSMO-RS predictions of logP in the SAMPL7 blind challenge

We applied the COSMO-RS method to predict the partition coefficient logP between water and 1-octanol for 22 small drug like molecules within the framework of the SAMPL7 blind challenge. We carefully collected a set of thermodynamically meaningful microstates, including tautomeric forms of the neutral species, and calculated the logP using the current COSMOtherm implementation on the most accurate level. With this approach, COSMO-RS was ranked as the 6st most accurate method (Measured by the mean absolute error (MAE) of 0.57) over all 17 ranked submissions. We achieved a root mean square deviation (RMSD) of 0.78. The largest deviations from experimental values are exhibited by five SAMPL molecules (SM), which seem to be shifted in most SAMPL7 contributions. In context with previous SAMPL challenges, COSMO-RS demonstrates a wide range of applicability and one of the best in class reliability and accuracy among the physical methods.

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

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

Enrichment of surface oxygen functionalities on activated carbon for adsorptive removal of sevoflurane

Activated carbons have been reported to be useful for adsorptive removal of the volatile anaesthetic sevoflurane from a vapour stream. The surface functionalities on activated carbons could be modified through aqueous oxidation using oxidising solutions to enhance the sevoflurane adsorption. In this study, an attempt to oxidise the surface of a commercial activated carbon to improve its adsorption capacity for sevoflurane was conducted using 6 mol/L nitric acid, 2 mol/L ammonium persulfate, and 30 wt per cent (wt%) of hydrogen peroxide (H₂O₂). The adsorption tests at fixed conditions (bed depth: 10 cm, inlet concentration: 528 mg/L, and flow rate: 3 L/min) revealed that H₂O₂ oxidation gave desirable sevoflurane adsorption (0.510 ± 0.005 mg/m²). A parametric study was conducted with H₂O₂ to investigate the effect of oxidation conditions to the changes in surface oxygen functionalities by varying the concentration, oxidation duration, and temperature, and the Conductor-like Screening Model for Real Solvents (COSMO-RS) was applied to predict the interactions between oxygen functionalities and sevoflurane. The H₂O₂ oxidation incorporated varying degrees of both surface oxygen functionalities with hydrogen bond (HB) acceptor and HB donor characters under the studied conditions. Oxidised samples with enriched oxygen functionalities with HB acceptor character and fewer HB donor character exhibited better adsorption capacity for sevoflurane. The presence of a high amount of oxygen functional groups with HB donor character adversely affected the sevoflurane adsorption despite the enrichment of oxygen functional groups with HB acceptor character that have a higher tendency to adsorb sevoflurane.