Natural Deep Eutectic Systems (NADES) have emerged as environmentally friendly alternatives to traditional organic solvents, with potential applications spanning from pharmaceuticals to food processing. A recent study has delved deep, employing Hansen Solubility Parameters (HSPs) to differentiate between empirical and semi-empirical models used for predicting the solubility properties of these innovative solvents.
Researchers led by C.C. Fernandes and A. Paiva sought to address the discrepancies among existing theoretical models, which, though primarily conceived for simple molecular solvents, are increasingly applied to the more complex, multi-component structure of NADES. The motivation behind this study stems from the urgent need to optimize solvent selection processes, especially considering the significant role of solubility behavior for enhancing the extraction efficiency of valuable compounds.
While empirical models—firmly established methodologies based on group contribution principles—demonstrated consistent reliability, the semi-empirical models, which derive parameters from correlational equations, revealed notable limitations. The findings indicated discrepancies particularly with the hydrogen bond solubility parameter (δh). According to the researchers, “Although there is a large discrepancy between the estimated values of the hydrogen bond parameter, especially for systems with higher polar character, there is still good similarity for the other parameters.”
This substantial reliance on the hydrogen bond interactions among NADES components highlights the complex chemistry involved. Unlike typical solvents, NADES are composed of natural organic compounds leading to unique solvation properties, which are heavily influenced by their molecular structure and intermolecular forces.
To conduct the research, the team employed both empirical (EM) and semi-empirical models (SEM), initially testing them on conventional solvents whose solubility parameters are thoroughly documented. The empirical models were found to offer superior accuracy, particularly for calculating commonly used parameters such as dispersion (δd) and polar (δp) contributions to solubility.
The pivotal role of the hydrogen bonding parameter emerged strongly, illustrating how the existing models might not be suitably refined to predict behaviors accurately within complex mixtures. This discovery is particularly relevant for industries making use of NADES, which are recognized for their capability to dissolve diverse organic compounds, making them ideal for varied applications from medicinal to cosmetic.
Yet, the researchers revealed possible paths to improve predictive modeling: “It was concluded ... when combining the semi-empirical models, it was possible to obtain ... values more similar to the empirical ones.” Through such combinations, the discrepancies might narrow, making these models more interoperable with empirical approaches for future research.
Considering the increasing focus on sustainability}, it's clear there is much potential for NADES to revolutionize solvent use, enhancing the extraction processes and yield effectiveness. With plans to expand the parameters and include additional natural solvent candidates, the research indicates significant opportunities for refining HSPs estimation methods to aid industries transitioning to greener alternatives.
While the conclusive findings of this study present promising results for NADES applications, the work also underlines the necessity for continued exploration and improvements to existing solubility parameter models. The authors advocate for broader datasets and experimental validations to fully capture the interactions and behaviors of these mixtures for future developments.
These findings, set to be published on February 1, 2025, position the study as a significant contribution to solubility research, particularly within the scope of green chemistry, indicating the potential evolution of modeling practices as the scientific community embraces these sustainable solvents.