Influence of Emulsifier Concentration on Nanoemulsion Gelation
Bibliographic record
Abstract
Nanoemulsion gels are a new class of soft materials that manifest stronger elasticity even at lower dispersed phase volume fraction. In this work, gelation in 40 wt % canola oil-in-water nanoemulsions was investigated as a function of emulsifier type (anionic sodium dodecyl sulfate (SDS) or nonionic Tween 20) and concentration. It was observed that the liquid nanoemulsions transformed into viscoelastic gels at a specific concentration range of SDS, whereas no gelation was observed for Tween 20. The apparent viscosity, yield stress, and storage modulus of the nanogels increased with SDS concentration until 15 times critical micelle concentration (CMC), thereafter decreased steadily as the gelation weakened beginning 20 CMC. Three regimes of colloidal interactions in the presence of emulsifier were proposed. (1) Repulsive gelation: at low SDS concentration (0.5-2 times CMC) the repulsive charge cloud around the nanodroplets acted as interfacial shell layer that significantly increased the effective volume fraction of the dispersed phase (ϕ(eff)). When ϕ(eff) became comparable to the volume fraction required for maximal random jamming, nanoemulsions formed elastic gels. (2) Attractive gelation: as the SDS concentration increased to 5-15 times CMC, ϕ(eff) dropped due to charge screening by more counterions from SDS, but depletion attractions generated by micelles in the continuous phase led to extensive droplet aggregation which immobilized the continuous phase leading to stronger gel formation. (3) Decline in gelation due to oscillatory structural forces (OSF): at very high SDS concentration (20-30 time CMC), structural forces were manifested due to the layered-structuring of excess micelles in the interdroplet regions resulting in loss of droplet aggregation. Tween 20 nanoemulsions, on the other hand, did not show repulsive gelation due to lack of charge cloud, while weak depletion attraction and early commencement of OSF regime leading to liquid-like behavior at all concentrations. The nanogels possess great potential for use in low-fat foods, pharmaceuticals and cosmetic products.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".