Phase Behavior of Non‐Ionic <scp>Surfactant‐Medium</scp> Chain <scp>Triglyceride‐Water</scp> Microemulsion Systems
Bibliographic record
Abstract
Abstract Microemulsion systems have garnered tremendous interest in the pharmaceutical sector for a variety of drug delivery applications. Non‐ionic surfactants are often the preferred surfactant class given their uncharged nature, enhanced oral safety profile, and generally regarded as safe status as compared to other surfactant classes (Myers, Surfactant science and technology, 2005, p. 29), (Malmsten, Handbook of microemulsion science and technology, 1999, p. 755), (Grove & Mullertz, Chapter 5‐liquid self‐microemulsifying drug delivery systems, 2007), (Liu et al., Water‐insoluble drug formulation, 2008), (Hauss, Advanced Drug Delivery Reviews, 2007, 59, pp. 667–676), (Balazs, Solubility, delivery and ADME problems of drugs and drug‐candidates, 2011, p. 68). In this work, the phase behavior and microemulsion formation potential of four commonly used non‐ionic surfactants, PEG‐40 hydrogenated castor oil, Poloxamer 188, Polysorbate 80, and d‐α‐tocopherol polyethylene glycol succinate were studied via ternary phase diagram (TPD) mapping using a medium chain triglyceride, Miglyol 812. Results indicated notable differences in phase behavior despite similarities in hydrophilic–lipophilic balance value (13–15). All surfactants produced Winsor Type I, oil‐in‐water microemulsions at water concentrations above 40% wt/wt. Winsor Type II water‐in‐oil microemulsions were difficult to obtain even at high oil concentrations of ≥70% wt/wt. Winsor III microemulsions, though rare, were generally obtained in the middle regions of the TPD between 10% and 30% wt/wt water while Winsor IV microemulsions dominated at high surfactant concentrations of ≥45% wt/wt. Opaque emulsion areas were particularly notable in wax state surfactants. Polysorbate 80 and PEG‐40 hydrogenated castor oil demonstrated a high degree of synergism as well as the largest oil‐in‐water (o/w) and water‐in‐oil (w/o) microemulsion formation potential rendering them suitable for a number of enteral and parenteral applications.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".