Effect of protein type, concentration and oil droplet size on the formation of repulsively jammed elastic nanoemulsion gels
Bibliographic record
Abstract
Rheology of sodium caseinate (SC) and whey protein isolate (WPI)-stabilized nanoemulsions (NEs) was investigated as a function of protein (1-5 wt%) and oil (30 and 40 wt%) concentration and storage time. For SC NEs, gel strength increased with an increase in protein and oil concentration and a decrease in droplet size and below a critical size transformed into a strong elastic gel that did not flow under gravity. Surprisingly, WPI NEs, although stable and had similar droplet size to SC NEs, did not form elastic gels. The stability of the NEs was studied for 3 months, and no significant change was observed. Considerable higher storage modulus (G') of SC NEs compared to WPI NEs was attributed to an increased effective droplet volume fraction (φeff) due to a thicker steric barrier of SC compared to WPI. The DLVO interdroplet potential was used to calculate the thickness of the charge cloud at an overall repulsive interaction of 1 kBT, which was added to the steric barrier to calculate the effective droplet size and φeff. At the highest φeff (0.79) for 5% SC NEs with 40% oil, the nanodroplets and associated repulsive barrier randomly jammed, leading to the formation of a strong elastic gel. For WPI NEs, maximum φeff was 0.57, leading to a lack of jamming and viscous fluid-like behaviour. Re-plotting G' with φeff for SC NEs with different protein concentration showed a linear trend followed by a rapid increase in G' at a critical φeff, confirming the transition from weak glassy region to strong randomly jammed structure. SC-stabilized repulsively jammed NE-gels could be used as a novel soft material where a lower oil volume fraction and long-term stability is required.
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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) | 0.001 | 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".