Modification of the Avrami model for application to the kinetics of the melt crystallization of lipids
Bibliographic record
Abstract
Abstract The Avrami model was developed to model the kinetics of crystallization and growth of a simple metal system. The original assumptions of the model do not apply for high‐volume‐fraction crystallizing lipids, although it is incorrectly and frequently applied. A modified form of the Avrami model, wellsuited to complex lipid crystallization kinetics, is developed. It produces excellent fits to experimental data and allows the prediction of physically meaningful parameters, such as changes in nucleation rate and type, growth rate, morphology, and dimensionality. Morphological changes highlighted by time‐resolved temperature‐controlled polarized light microscopy support its application to crystallizing lipids. The kinetics of crystallization for six separate lipid samples were monitored by pulsed NMR, and fits were performed using the classical and modified Avrami model. In all cases, the modified model provided superior fits to the data compared with that of the classical model. The modified model supports the theory that lipids crystallize and grow into networks via very specific growth modes. Furthermore, the case is made that it is useful for interpreting crystallization kinetics of other systems such as polymer melts, which have nonconstant growth rates, dimensionalities, and nucleation conditions, and whose growth become diffusion‐limited within specific regimes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".