Effects of Sorbitan Monostearate and Stearyl Alcohol on the Physicochemical Parameters of Sunflower-Wax-Based Oleogels
Bibliographic record
Abstract
A rising health concern with saturated fatty acids allowed researchers to look into the science of replacing these fats with unsaturated fatty acids. Oleogelation is a technique to structure edible oil using gelators. The present study looked for the effect of solid emulsifiers; namely, sorbitan monostearate (SP) and stearyl alcohol (SA), on the physicochemical parameters of oleogels. All the oleogels were formulated using 5% sunflower wax (SW) in sunflower oil (SO). The formulated oleogels displayed irregular-shaped wax crystals on their surface. The bright-field and polarized microscopy showed the fiber/needle network of wax crystals. Formulations consisting of 10 mg (0.05% w/w) of both the emulsifiers (SA10 and SP10) in 20 g of oleogels displayed the appearance of a dense wax crystal network. The SP and SA underwent co-crystallization with wax molecules, which enhanced crystal growth and increased the density and size of the wax crystals. The XRD and FTIR studies suggested the presence of a similar β’ polymorph to that of the triacylglycerols’ arrangement. The incorporation of SA and SP in wax crystal packing might have resulted in a lower crystallization rate in SA10 and SP10. Evaluation of the thermal properties of oleogels through DSC showed better gel recurrence of high melting enthalpy. These formulations also displayed a sustained release of curcumin. Despite the variations in several properties (e.g., microstructures, crystallite size, thermal properties, and nutrient release), the emulsifiers did not affect the mechanical properties of the oleogel. The meager amounts of both the emulsifiers were able to modulate the nutrient release from the oleogels without affecting their mechanical properties in comparison to the control sample.
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) | 0.001 | 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".