Enhancing solubilization in microemulsions—State of the art and current trends
Bibliographic record
Abstract
Abstract Along a formulation scan, solubilization is maximal when a bicontinuous microemulsion is in equilibrium with both oil and water excess phases in a so‐called Winsor III system. The logical way to enhance solubilization is to increase the interaction of the surfactant for both the oil and water phases, which can be easily attained by increasing the size of both the head and tail groups. However, this approach is limited by solubility constraints. Additional solubilization enhancement can be attained by introducing a molecule(s) that bridge the bulk phase and the adsorbed surfactant layer; this can be accomplished by using the so‐called lipophilic and hydrophilic “linker effect” or by using block copolymer additives. In either case, the goal is to modify an extended zone in the oil and water domains close to their boundary. The intramolecular grafting of a linker group between the hydrophilic and lipophilic moieties in a surfactant results in a so‐called “extended” surfactant structure, which produces enhanced solubilization, as does the surfactant/linker combination, but with the added benefit that the self‐contained extended surfactant structure does not undergo selective partitioning. We conclude that an improvement in solubilization is directly related to the presence of a smooth, blurred, and expanded transition across the interfacial region from polar to apolar bulk phases.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".