Formation, structure, and rheological properties of ricinelaidic acid‐vegetable oil organogels
Bibliographic record
Abstract
Abstract Vegetable oil‐based organogels were formed using ricinelaidic acid (12‐hydroxy‐9‐trans‐octadecenoic acid, REA). Gelation kinetics, gel structure, and stability were studied. Gelation occurred with as little as 0.5% (w/w) REA, depending on temperature and oil composition. Phase diagrams were constructed using canola, sesame, and DAG oils. Lower gelation tendencies were correlated with the presence of potential hydrogen‐bonding moieties in the oils. REA concentration had a significant influence on gelation kinetics and gel rheology. At 5°C, the 0.5% canola oil gel behaved like a weak, viscoelastic network composed of entangled strands. Between 1.0 and 5.0% REA, solid‐like, viscoelastic gels were formed. The 24‐hour G′LVR (storage modulus in the linear viscoelastic region) was highly dependent on concentration and less so on temperature. Values for gelation time indicated a change in behavior below 2% REA and above 20°C. Polarized light microscopy revealed that the gels were formed through the interactions of long, thin, and birefringent fibers. Structural analysis using X‐ray diffractometry (XRD) indicated the presence of repeating REA dimers and increasing order with concentration and gel storage time. Increases in gel opacity, birefringence, XRD scattering, and fiber clustering were observed during storage.
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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) | 0.000 | 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".