Impact of cooling rate and shear flow on crystallization and mechanical properties of wax-crystal networks
Bibliographic record
Abstract
Wax oleogels are one of the most promising strategies to produce trans-fat free and low-saturate functional fats. Under quiescent isothermal conditions, waxes form strong space-filling networks where oil is embedded. Nevertheless, in industrial processes, crystallization conditions deviate significantly from being isothermal and quiescent, yet these far from equilibrium conditions have received limited attention in the literature. Cooling and shear rate gradients during crystallization can promote molecular alignment, crystal growth, and crystal network reorganization that hold the potential to tune the mechanical properties of oleogels. Therefore, this study aimed to investigate the impact of different controlled cooling and shear rates during the crystallization process of beeswax oleogels. An analysis of both small and large amplitude oscillatory shear was conducted to understand the linear and nonlinear mechanical properties of oleogels. Additionally, microscopic/macroscopic analyses, including oil-binding capacity, were performed. The results indicate that sheared oleogels display plastic-like behavior, lower linear elastic moduli, and a higher perfect plastic dissipation ratio than oleogels cooled under quiescent conditions, which displayed stiff, brittle-like characteristics. In addition, these oleogels displayed a microstructure with smaller crystals than oleogels cooled under quiescent conditions. This phenomenon can be attributed to a transition of oleogels from a strong, yet brittle interconnected particle network, to a dispersion of jammed crystal particles that align more easily along the direction of flow, resulting in minimal additional contribution from viscous stress after yielding. Therefore, a controlled cooling and shear rate application is an effective method to tune the mechanical properties of wax oleogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".