Optimization of the formulation of water in oil emulsions entrapping polysaccharide by increasing the amount of water and the stability
Bibliographic record
Abstract
Water in oil emulsions entrapping a polysaccharide have been formulated and the purpose of this study is first to increase the amount of dispersed water and the amount of polysaccharide. The second part of the study is devoted to check the stability of the obtained emulsion over 2 years. Encapsulation of polysaccharide was realized by introducing the aqueous phase (containing the required polysaccharide and glycerol) into a stirred oil phase (wherein the polyglycerol polyricinoleate (PGPR) as the surfactant has been previously dissolved). Emulsion stability tests were carried out immediately after preparation and after ageing tests. For that purpose the emulsions were submitted to cooling and heating cycles performed in a calorimeter in order to detect the freezing and melting temperatures of the dispersed water. Due to nucleation phenomena, the delay between freezing and melting is correlated to the way the water is dispersed within the emulsion and therefore gives information about the stability of the emulsions. To complete these tests other ones were performed such as bottle test, laser diffraction granulometry and rheometry. It was shown that it is possible to get emulsions containing 75% of water, showing the required stability and flow ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".