Bibliographic record
Abstract
In this work, the flow-induced crystallization of two polylactides (PLAs) with different microstructures (different l-lactic acid contents) is studied using simple shear, uniaxial extension and capillary flow experiments. In a simple shear and capillary flow, an increase in shear rate and a decrease in temperature were found to enhance the crystallization kinetics particularly for Weissenberg numbers (based on the reptation relaxation time, Wi) greater than 1 (strong flow causing chain stretching). On the other hand, in a uniaxial extensional flow, once a critical Hencky strain is achieved, crystallization starts independently of strain rate and temperature. The amount of mechanical work per unit volume imposed/dissipated onto the polymers during flow to initialize crystallization was also calculated in the simple shear, capillary, and extensional flow. The critical mechanical work for the onset of flow-induced crystallization was found to be independent of temperature and degree of molecular chain stretch (Wi) as Wi becomes greater than 1. The critical mechanical work for the onset of flow-induced crystallization in an extensional flow was found to be much smaller than that in a shear flow. The PLA sample with higher content of PLLA showed slightly higher zero-shear viscosity and a smaller thermodynamic barrier for the onset of crystallization. Finally, the degree of crystallinity increases linearly from 0% at the start of the flow-induced crystallization region and reaches a plateau at Wi equals to around 1.
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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) | 0.000 | 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".