Perspective: Margarine as an emulsion-filled colloidal oleogel
Bibliographic record
Abstract
Common margarine is a semi-solid water-in-oil emulsion, where the continuous phase is a plastic polycrystalline colloidal oleogel composed of aggregated nanoplatelets of crystallized triacylglycerols (TAGs). TAG nanocrystal platelets nucleate, grow, and aggregate into clusters, which then form a fractal colloidal network of polycrystalline particles, trapping liquid TAGs in this fat crystal network. This hierarchical structure is very sensitive to external temperature and shear fields, which affect everything from crystalline nanoplatelet (CNP) size to the aggregation of such CNPs into larger clusters. Ultimately, structure affects the mechanical properties, oil binding, and plasticity of such industrially relevant materials. Here, we review some of the history of margarine, its quality characteristics, and its uses. We also describe the structure of margarine and relate it to macroscopic functionality, which is given mainly by the structure of the fat crystal network in the continuous oil phase. The concept of the yield stress of a fat as the most global indicator of functionality is discussed in light of quantitative physical models developed, which allow for the exact calculation of the yield stress. By understanding the effects of crystal size, strength of intermolecular interactions, amount of solid crystalline mass present, and the spatial distribution of such crystalline mass within the network, it is possible to engineer functionality and optimize performance. The structural complexity of such common food highlights the need for an in-depth understanding of the physics of such soft materials for manufacturing optimization as well as for engineering new structures with novel macroscopic properties.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".