Oleogels Based on Fatty Acids and Fatty Alcohols
Bibliographic record
Abstract
Fatty acids and fatty alcohols are both Low Molecular Weight Gelators (LMWGs). They lead to the gelation of vegetable oils forming so-called oleogels, which can be used in food, pharmaceutical or cosmetic domains. A synergistic effect for specific weight ratio (R) in oleogels obtained from mixtures of fatty alcohols and fatty acids with the same chain lengths is obtained. Mixed crystals of fatty alcohol/fatty acid are observed for the optimal weight ratio of the two fatty components between 7:3 (alcohol:acid) and 8:2, which correspond to molar ratios around 3. For these R values, co-crystallisation occurs forming mixed crystals of small size in high quantity. Co-crystallization is an easy way to tune the quantity of crystals and their size, while keeping the platelet-shape in oleogel systems, improving oleogel stability as well as their mechanical properties. The effect of R has also been studied for oil foams, which correspond to gas bubbles stabilized by the crystalline particles contained in the oleogel. Oleogel properties such as hardness and stability against oil loss are correlated with their resulting foaming properties in terms of foamability, foam firmness and foam stability. There is a direct link between the type of crystals, crystalline particle size, solid fat content, foamability and foam firmness in this mixed oleogel system. Pure fatty alcohol or fatty acids are less efficient in terms of oil foam properties than mixed crystals obtained for the optimal weight ratio. Co-crystallisation appears to be a promising route to enhance the properties of oil foams.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".