Stability and Tunability of O/W Nanoemulsions Prepared by Phase Inversion Composition
Bibliographic record
Abstract
We report on an analysis of the parameters that control both the stability and tunability of O/W nanoemulsions prepared by the phase inversion composition (PIC). These nanoemulsions are prepared with Tween 80 and Span 80, two nonionic surfactants, that can be mixed to adjust the hydrophilic lipophilic balance (HLB). We used a process mixture design method, which combines mixture and process design with phase diagrams, to describe the cross-link between parameters like composition, temperature of preparation, and HLB. Nanoemulsions, stable for several days, are obtained by this method, and they remain unchanged even at high concentration. We have identified the different critical distances of interactions that control the degree of freedom in the motion of the oil droplets. The diameter of these oil droplets could be adjusted between 50 and 300 nm. Different parameters, among them the temperature of preparation, the surfactant over oil ratio (S/O), and the HLB, allow control the final size of the nanoemulsions. As these parameters can exhibit opposite effects on the oil droplet size, the process mixture design method allowed us to illustrate these cross-interactions.
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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 | 0.001 |
| 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) | 0.000 | 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".