Modeling the effects of type and concentration of organic modifiers, column type and chemical structure of analytes on the retention in reversed phase liquid chromatography using a single model

A previously proposed model for representing the retention factor (k) of an analyte in mixed solvent mobile phases was extended to calculate the k of different analytes with respect to the nature of analyte, organic modifier, its concentration and type of the stationary phase. The accuracy of the proposed method was evaluated by calculating mean percentage deviation (MPD) as accuracy criterion. The predicted vs. observed plots were also provided as goodness of fit criteria. The developed model prediction capability compared with a number of previous models (i.e. LSER, general LSER and Oscik equation) through MPD and fitting plots. The proposed method provided acceptable predictions with the advantage of modeling the effects of organic modifiers, mobile phase compositions, columns and analytes using a single equation. The accuracy of developed model was checked using the one column and one analyte out cross validation analyses and the results showed that the developed model was able to predict the unknown analyte retention and the analytes retentions on unknown column accurately.     

Solubility prediction of anthracene in mixed solvents using a minimum number of experimental data

Numerical methods to predict the solubility of anthracene in mixed solvents have been proposed. A minimum number of 3 solubility data points in sub-binary solvents has been employed to calculate the solvent-solute interaction terms of a well established colsolvency model, i.e. the combined nearly ideal binary solvent/Redlich-Kister model. The calculated interaction terms were used to predict the solubility in binary and ternary solvent systems. The predicted solubilities have been compared with experimental solubility data and the absolute percentage mean deviation (APMD) has been computed as a criterion of prediction capability. The overall APMD for 25 anthracene data sets in binary solvents is 0.40%. In order to provide a predictive method, which is based fully on theoretical calculations, the quantitative relationships between sub-binary interaction terms and physicochemical properties of the solvents have been presented. The overall APMD value for 41 binary data sets is 9.19%. The estimated binary interaction terms using a minimum number of data points and the quantitative relationships have then been used to predict anthracene solubility data in 30 ternary solvent systems. The produced APMD values are 3.72 and 15.79%, respectively. To provide an accurate correlation for solubility in ternary solvent systems, an extension to the combined nearly ideal multicomponenet solvent/Redlich-Kister (CNIMS/R-K) model was proposed and the corresponding overall AMPD is 0.38%.     

Predicting solubility of anthracene in non-aqueous solvent mixtures using a combination of Jouyban-Acree and Abraham models

Quantitative structure property relationships were proposed to calculate the binary interaction terms of the Jouyban-Acree model using coefficients of Abraham solvational models. The applicability of the proposed methods for reproducing solubility data of anthracene in binary solvents has been evaluated using 56 solubility data sets collected from the literature. The mean percentage deviation (MPD) of experimental and calculated solubilities, using predicted mole fraction solubility of anthracene in solvents 1 and 2, has been computed as a measure of accuracy and the MPD of the proposed methods were 5.5 and 4.2%. The accuracy of the method was compared with that of a previously reported method where the MPD was 14.4% and the mean differences between proposed and previous methods was statistically significant. To provide a predictive model, solubility of anthracene was computed using Abraham solvational models and employed to predict the solubility in binary solvents using derived model constants of Jouyban-Acree model and the obtained MPDs were 37.9 and 22.2%, respectively.     

Solubility characteristics of poly(3‐hexylthiophene)

Solubility data for poly(3‐hexylthiophene) (P3HT) in 29 pure solvents are presented and discussed in detail. Functional solubility parameter (FSP) and convex solubility parameter (CSP) computations are performed and the CSP and FSP results are compared to previously reported Hansen solubility parameters (HSPs) and to the parameters calculated using additive functional group contribution methods. The empirical data reveals experimental solubility parameters with substantial polar (δ_P) and hydrogen‐bonding (δ_H) components, which are not intrinsic to the structure of the P3HT polymer. Despite these apparent irregularities, it is shown that the predictor method based on the solubility function, f, does provide a reliable way to quantitatively evaluate the solubility of P3HT in other solvents in terms of a given set of empirical solubility data. The solubility behavior is further investigated using linear solvation energy relationship (LSER) modeling and COSMO‐RS computations of the activity coefficients of P3HT. The LSER model reveals that (1) the cavity term, δ_T, is the dominant factor governing the solubility behavior of P3HT and (2) the solvent characteristics that dictate the structural order (crystallinity) of P3HT aggregates do not similarly influence the overall solubility behavior of the polymer. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017 , 55, 1075–1087     

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.     

A general treatment of solubility. 2. QSPR prediction of free energies of solvation of specified solutes in ranges of solvents

As part of our general QSPR treatment of solubility (started in the preceding paper), we now present quantitative relationships between solvent structures and the solvation free energies of individual solutes. Solvation free energies of 80 diverse organic solutes are each modeled in a range from 15 to 82 solvents using our CODESSA PRO software. Significant correlations (in terms of squared correlation coefficient) are found for all the 80 solutes: the best fit is obtained for n-propylamine (R(2) = 0.996); the lowest R(2) corresponds to toluene (0.604).     

Models to predict solubility in ternary solvents based on sub-binary experimental data

The capability of the extended forms, of two well established cosolvency models, i.e. the combined nearly ideal binary solvent/Redlich-Kister equation and the modified Wilson model, used to predict the solute solubility in non-aqueous ternary solvent mixtures is presented. These predictions are based on the measured solubilities of anthracene in binary solvent mixtures. As a result the values of average percent deviations were less than 2% for the anthracene solubility in ternary mixtures. This work was also extended to other cosolvency models, ie. the extended Hildebrand solubility approach and the mixture response surface method, which are also commonly used for correlating solubility data in ternary solvents. The accuracy of the models is compared with each other and also with a published solubility model for ternary mixtures. The results illustrate that all models produced comparable accuracy.     

Solubilities of families of heterocyclic polynuclear aromatics in organic solvents and their mixtures

The solubilities of anthracene, acridine, xanthene, thioxanthene, carbazole, dibenzofuran, and dibenzothiophene have been experimentally determined in benzene, cyclohexane, thiophene, and pyridine from ambient temperature to approximately 440 K. The results have been correlated using the classical equation for solid-liquid solubility to obtain the experimental activity coefficient of the solute in the solvent. These experimental activity coefficients have been regressed, using three common solution models, to find the binary interaction parameters needed in those models. The solubilities of biphenyl, dibenzofuran, and dibenzothiophene have been experimentally determined in five binary mixtures of the solvents. The experimental activity coefficients have been found and compared to the values predicted by the four solution models, using the binary interaction parameters obtained from the solubilities in the pure solvents and solventsolvent binary interaction parameters obtained from literature vaporliquid equilibria data. The effect of substituting various heteroatoms into the ring structure has been discussed.     

Prediction of infinite dilution activity coefficients of sulfur dioxide in organic solvents

Infinite dilution activity coefficients of sulfur dioxide in various organic solvents were correlated with two basicity scales: the solvent Gutmann donor number and Arnett heat of hydrogen bonding. Linear correlations were observed for both basicity scales, and the accuracy of activity coefficient prediction is estimated to be ±20 to 25%. Infinite dilution activity coefficients of sulfur dioxide in over 80 organic solvents were estimated from the correlations.     

Mixture-designed electrolytes for the MEKC separation of natural and synthetic steroids

In this work, the separation of 11 natural and synthetic steroids was studied using MEKC electrolytes modified by property-selected organic solvents: ethanol, ACN, and THF. The interplay between electrophoretic behavior and structural features was disclosed and the effects of organic modifiers to modulate retention and to alter selectivity were discussed in terms of system linear solvation energy relationships (LSER). The LSER indicated the total organic solvent percentage in the electrolyte as a major parameter to control retention and as a minor contribution, the hydrogen bond acidity. By evaluating the electropherograms obtained from mixture-designed electrolytes, a favorable separation condition for all solutes was achieved in ca. 25 min with an electrolyte composed of 20 mmol/L sodium tetraborate at pH 9.4, 20 mmol/L SDS and 20% EtOH (0.80% CV for migration time and 2.5% CV for peak area, n = five consecutive injections). The applicability of the proposed separation condition was demonstrated by the inspection of estrogens in urine sample (puberty stage).     

Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents

Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including ‘mixtures-out’- and ‘compounds-out’-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.