A hybrid approach to oil structuring – combining wax oleogels and capillary suspensions
Bibliographic record
Abstract
There is continuing interest in finding new approaches to gel liquid oil for processed food applications. Here, we combined oleogels and capillary suspensions to generate model oil-continuous networks consisting of a wax oleogel and a water-bridged, glass particle network. The composition map tested comprised 30 vol% polar or non-polar glass beads dispersed in a 70 vol% non-particle phase consisting of water (≤9 vol%) as well as 2 wt% hexatriacontane as oleogelator in canola oil. While the hexatriacontane wax alone gelled the oil, presence of the glass beads (but no water) prevented oleogelation. Self-supporting capillary networks formed with polar particles and 1 vol% water or non-polar glass beads and 3 vol% water in canola oil. The capillary suspensions demonstrated significant differences in rheological behaviour as the polar particles yielded much higher elastic moduli than their non-polar particle counterparts. Polar hybrids were weakened by inclusion of the wax whereas the non-polar particle hybrid network displayed elastic moduli greater than the respective contributions of both capillary and wax gel networks. This hybrid method of oleogelation can be applied to virtually any food particles and uses minimal water and wax.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".