Tuning Ferulic Acid Solubility in Choline-Chloride- and Betaine-Based Deep Eutectic Solvents: Experimental Determination and Machine Learning Modeling

Abstract

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

Keywords: COSMO-RS; deep eutectic solvents; ferulic acid; green solvents; machine learning; molecular interactions; solubility; support vector regressor.

Figure 1

The solubility curves of ferulic acid (FA) in aqueous DES mixtures involving choline chloride (left panel) and betaine (right panel) and selected polyols at various temperatures, expressed as solvent-composition-related mole fractions. X*_DES stands for mole fractions of solute-free DES in aqueous mixtures. For comparison, the room-temperature solubility of ferulic acid in DMSO is provided.

Figure 2

Schematic representation of ferulic acid conformers with electron density distributions and their relative energies in the bulk state, represented by an infinite conductor. Additionally, in parentheses are the relative values of total energies obtained for the gas phase.

Figure 3

The most representative structures of FA dimers and hetero-molecular pairs formed with choline chloride, betaine, and water.

Figure 3

The most representative structures of FA dimers and hetero-molecular pairs formed with choline chloride, betaine, and water.

Figure 4

The most representative structures of FA with HBD counterparts of studied DESs.

Figure 4

The most representative structures of FA with HBD counterparts of studied DESs.

Figure 5

The correlation between experimental and computed solubility of ferulic acid in neat, binary, and deep eutectic solvents. The gray color denotes the results of COSMO-RS computations, blue indicates the SVR model, and in black, the results obtained from the MLP model are marked.

Figure 6

Characteristics of the best models found by hyper-parameters training for prediction of the ferulic acid solubility. The plots provide the results of the learning curve analysis, which is devoted to testing the consistency of models’ performance using both sub-sampling and cross-validation. The optimal values of each model are provided for reproducibility purposes.

Figure 6

Characteristics of the best models found by hyper-parameters training for prediction of the ferulic acid solubility. The plots provide the results of the learning curve analysis, which is devoted to testing the consistency of models’ performance using both sub-sampling and cross-validation. The optimal values of each model are provided for reproducibility purposes.

Figure 7

The correlation between relative solute-solvent σ-potentials and experimental solubility expressed as a function of σ values. Two series represent correlations computed for subsets including only non-DES solvents (neat solvents and binary mixtures) or only DES systems. Bold symbols define regions used as a set of molecular descriptors, where R² > 0.4 for either subset. The split into three distinct subranges is marked with colorful rectangles.