Non-Isothermal Nucleation and Crystallization of 12-Hydroxystearic Acid in Vegetable Oils
Bibliographic record
Abstract
The non-isothermal nucleation rate of a self-assembling fibrillar network of 12-hydroxystearic acid in canola oil was found to be inversely proportional to the undercooling time exposure of the system. Moreover, fiber growth kinetics could be adequately modeled using the Avrami equation. Both nucleation and fiber growth kinetics displayed two distinct regimes, above and below 5 °C/min. The abrupt change in the rate of nucleation, crystal growth rate constant, and the degree of branching at this cooling rate are related to whether the nucleation and crystal growth processes are governed by mass transfer or thermodynamics. At rapid cooling rates, above 5 °C/min, the driving force for both nucleation and crystal growth is the time-dependent chemical potential difference between the molten solid in solution and the crystallized solid (the degree of undercooling or supersaturation). At low cooling rates, however, the rate of crystallization is no longer determined by this dynamic chemical potential difference, but rather only limited by time. In the slow cooling regime, we show that there are no noncrystallographic mismatches which would lead to fiber branching. Using the models developed and adapted in this work, we could accurately predict the fiber length, rate of nucleation, rate of crystal growth, induction time of nucleation, and the degree of branching of a 12HSA SAFIN.
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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 | 0.001 |
| 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 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".