Double Salt Ionic Liquids Containing the Trihexyl(tetradecyl)phosphonium Cation: The Ability to Tune the Solubility of Aromatics, Ethers, and Lipophilic Compounds
Bibliographic record
Abstract
Ionic liquids (ILs) certainly have some interesting properties and this has led to tremendous growth in interest, knowledge, and applications of these materials in a diverse array of science and technology. However, the field is still dominated by study of the simplest binary salts and an over reliance on ‘magic’ solvent properties which currently limits their creative use for new technologies. Lately, the scientific community has been attracted towards “IL/IL mixtures” prepared by mixing two or more ILs, and (as was the case with the early studies of the binary salts), a focus on specific physicochemical properties. Interestingly, crystalline salts dissolved in ILs are seen as a different class of liquids (e.g., high ionicity ILs). We have found that the physical, chemical, and biological (i.e., the materials) properties of IL/IL mixtures (or Double Salt Ionic Liquids, DSILs, as we like to call them) seem to be mainly controlled by the intrinsic ion interactions which are influenced by the exact ion composition. Perhaps, a new way of thinking about these multi-ion fluids is needed in order to direct research toward an understanding of the complex and varied types of interionic forces possible in a liquid. We have proposed the DSIL concept to help guide our work towards obtaining this deeper understanding. In this nomenclature, any homogeneous liquid containing three or more ions (no matter how they are prepared) are described as DSILs and are defined by the specific ion combinations present. In this presentation, these concepts will be illustrated by discussion of the chemical, physical, and biological properties of several DSIL systems chosen to explore different interactions between the ions (i.e., Coulombic, hydrogen bonding, dispersion, etc.). While we will present physical properties as a function of composition, we will focus more on chemical properties illustrated by the solubilities of pharmaceuticals and on biological properties illustrated by changes in membrane transport, both as a function of ion composition. Spectroscopic studies will be presented to provide insight into specific ion-ion interactions. For example, the solubility of active pharmaceutical ingredients with different acidity and lipophilicity are finely tunable by varying the ratio of the ions (Figure 1). Such studies support the concept that each DSIL is a unique ionic fluid that is able to provide tunable property sets derived from the ions present and that these properties can be controlled by choosing the nature and the abundance of the ions. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".