The effect of emulsifier type on the formation and stability of nanoemulsion gels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Liquid nanoemulsions are shown to transform into viscoelastic gels by reducing droplet size, increasing interfacial repulsive barrier between the nanodroplets and therefore increasing the effective oil volume fraction. The repulsive gelation in nanoemulsions can be achieved at a significantly lower oil volume fraction compared to conventional emulsion gels, making the nanoemulsion gel an attractive material for various low‐fat food applications. Gelation in nanoemulsions stabilized by anionic small molecule emulsifier and polymeric protein are compared in terms of gel strength, average droplet size, effective oil volume fraction, and long‐term gel stability. It is expected that higher stability and large surface area of nanoscale droplet size can further extend the application of nanoemulsion gels in the field of functional foods, cosmetics and pharmaceuticals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it