Super enhancement of rheological properties of amorphous PLA through generation of a fiberlike oriented crystal network
Bibliographic record
Abstract
Blends containing 85 wt. % of an amorphous polylactide with 15 wt. % of three different semicrystalline PLA (cPLA) grades with different crystallizabilty were separately blended via a twin-screw extruder below the melting temperature of the cPLAs. The extrudates were either directly pelletized or pelletized after being drawn at a draw ratio of 10. The small amplitude oscillatory shear behavior of the samples revealed that while the rheological properties of the undrawn samples were enhanced, those of the drawn samples were much more dramatically increased. In undrawn samples, the enhancements were due to the presence of unmelted crystal clusters, which could form a solid network structure in the blend. The much more pronounced increases in drawn samples, however, were due to the transformation of the crystal clusters into the fiberlike oriented crystal network, which formed a stronger solid network. This reinforcing behavior in both undrawn and drawn samples was even more pronounced when cPLA with a higher degree of crystallinity and a higher melting temperature was used. In drawn samples, the stress growth experiments confirmed the formation of such oriented crystal structure during which the primary overshoot caused by the crystal network structure could be formed again after molecular relaxation. This was while, in undrawn samples, stress overshoots were barely visible.
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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".