Solvent-Modulated Nucleation and Crystallization Kinetics of 12-Hydroxystearic Acid: A Nonisothermal Approach
Bibliographic record
Abstract
The solvent type strongly affects the nucleation behavior of 12HSA and therefore strongly influences the peak nucleation rate, fiber length, spatial distribution of mass, and degree of branching. Using nonisothermal kinetic models, a correlation was established among the activation energy of nucleation, fiber length, and induction time of 12HSA nucleation in different solvents. However, there was no correlation between any measurable parameter and solvent polarity. Activation energies varied from 2.2 kJ/mol in methyl oleate to 15.8 kJ/mol in glycerol. Nucleation behavior and structure were strong functions of the cooling rate, with distinct regions observed above and below 5-7 degrees C/min for fiber length, induction time, rate constant, and peak nucleation rate. The abrupt changes in the rate of nucleation, crystal growth rate constant, and degree of branching around this cooling rate are related to whether the nucleation and crystal growth processes are governed by mass transfer or thermodynamics. Furthermore, the Avrami equation accurately predicted several structural features of the fibrillar network such as fiber length and, to a lesser extent, induction time.
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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".