Structure and physical properties of oleogels containing peanut oil and saturated fatty alcohols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examined the capability of fatty alcohols with chain lengths from C 14 OH to C 22 OH to gel peanut oil. The gelation was achieved by crystallizing the samples at 5°C/min or 40°C/min. Results showed that minimum gelling concentration decreased as fatty alcohol chain length increased and it was higher for fast cooled samples than for the corresponding slow cooled ones. More than 7% of C 14 OH was necessary to obtain a self‐standing material highlighting its low capacity as oleogelator. Other oleogels were compared at 5% fatty alcohol concentration in peanut oil and oleogels containing C 16 OH yielded the weakest system, with the lowest ability to retain oil. This was attributed to its higher solubility in oil as compared to other fatty alcohols as well as to the formation of larger crystal aggregates. As the fatty alcohol chain length increased, systems became stronger, displaying smaller crystal aggregates. For all cases, an increase in cooling rate lead to the formation of weaker gels with reduced capacity to entrap oil. Practical applications: A novel strategy to reduce saturated/trans fats in food products involves the use of oleogels. Here we report on the use of even saturated fatty alcohols to gel peanut oil, thus preventing oil separation during storage and handling of products such as nut butters. The structure and physical properties of fatty alcohol oleogels greatly depend on fatty alcohols chain length as well as cooling rate applied during crystallization. Fatty alcohols proved to be a very efficient peanut oil stabilizer. Fatty alcohol chain length and cooling rate greatly affected organogel structure and physical properties.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it