Desalted duck egg white nanogels combined with κ‐carrageenan as stabilisers for food‐grade Pickering emulsion
Bibliographic record
Abstract
Summary There was an extensive interest in food‐grade Pickering emulsions formulated by natural stabilisers owing to the gradual growing demand for pursuing green‐label products. Based on this, the desalted duck egg white nanogels (DEWN) combined with κ‐carrageenan (CAR) (CAR/DEWN) were used as stabilisers to prepare environment‐friendly Pickering emulsions. The DEWN and CAR/DEWN as well as the particles‐stabilised emulsions were all characterised. Compared with the DEWN‐ and CAR‐stabilised emulsions, Pickering emulsions based on CAR/DEWN were investigated to illustrate the long‐term stability according to the results of visual appearance, static multiple light scattering and centrifugation experiments. In addition, CAR/DEWN could prepare stable high internal phase emulsions, which may give a new perspective for nutraceutical or drug encapsulation and delivery. Adding CAR to the DEWN not only increased the apparent viscosity of the emulsions to 122.2% but also enhanced the thixotropic recovery rate to 87.7%. In general, CAR/DEWN as stabilisers significantly promoted the Pickering emulsions' properties, and provided a possibility for the application of Pickering emulsion in foods.
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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".