Linear and nonlinear rheological behavior of fat crystal networks
Bibliographic record
Abstract
Fats are ubiquitous in biological membranes, foods, and many other commercial products. In these, they play essential roles in biological, nutritional, and physical functions. In this review, we focus on physical mechanical functions. The rheology of fats arises from the crystal network, which displays hierarchical structural levels from the molecular to the mesoscopic. Under linear deformations, the crystal network behaves as a viscoelastic solid with elasticity dictated by particle concentration and microstructural features as represented in fractal rheo-mechanical models. Under nonlinear deformations, the crystal network yields, showing a variety of nonlinear phenomena, i.e., softening, stiffening, thixotropy. These features largely contribute to functionality or performance as essentially all processing and end-uses of fatty materials involve large nonlinear deformations. Early work on rheology of fats gave hints of their nonlinear mechanical behavior, although in many cases the measured properties were empirical. In contrast, recent efforts from our group measured fundamental rheological functions using large amplitude oscillatory shear rheology. We demonstrate the ability of this technique to discern among the bulk functionality of bakery fats (all-purpose and lamination shortenings) based on well-defined rheological signatures that also relate to the fat structure. This technique has the potential to provide similar insights on other fatty systems and novel ideas for reformulation and design of alternative lipid-structuring materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".