Scaling Behavior of the Elastic Modulus in Colloidal Networks of Fat Crystals
Bibliographic record
Abstract
The scaling relationship between the shear elastic modulus and the solid fat content (SFC) was determined for anhydrous milk fat (AMF), palm oil (PO), and cocoa butter (CB). The fats were diluted with canola oil to achieve specific SFCs and crystallized at 5 °C for 24 h. SFC decreased linearly by increasing the canola oil mass fraction as determined by pulsed NMR. Log−log plots of the shear storage modulus ( G ‘) versus the solids' volume fraction (Φ = SFC/100) of the diluted fats were used to determine the fractal dimension ( D ) of the networks. Three different linear regions were identified for the range of dilutions studied. The scaling relationship of the stress at the limit of linearity (σ o ) to Φ indicated that the fats were in the weak-link rheological regime in all three regions. These results suggested that three different types of weak-link rheological regimes could be present in the same material depending on the SFC. Polarized light microscopy showed that varying the solid fat content (SFC) modified the microstructure of these fats. In general, at low SFCs, large crystal clusters were observed, while at high SFCs, only a fine crystal mass was detected. Crystallite morphology and size distribution was also affected by dilution. The onset of crystallization temperature ( T c ) and the peak melting temperature decreased with decreasing SFC in the three fats; however, plots of T c versus SFC demonstrated the existence of distinct linear regions that were similar to those identified in the rheological data. Moreover, Hildebrand plots also demonstrated the existence of distinct linear regions, of characteristic solution behavior, which agreed closely with crystallization and rheological results. We propose that, upon dilution, changes in crystal phase behavior lead to changes in the crystallization kinetics of the fats. This in turn translated into alterations in the microstructure of the fat, which ultimately affected its mechanical 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 | 0.000 | 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) | 0.001 | 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".