Crystallinity and mechanical properties of polylactide/ether‐amide copolymer blends
Bibliographic record
Abstract
ABSTRACT The present study focuses on the improvement of impact properties and particularly on the interaction between crystallinity development and mechanical properties of impact modified polylactide (PLA). The PLA was toughened by the addition of a random linear ether‐amide copolymer (PEBAX 3533™). A random copolymer of ethylene, methyl‐acrylate, and glycidyl‐methacrylate (LOTADER AX8900™) was also used to reactively compatibilize the ether‐amide copolymer with the PLA matrix. Melt rheology of the blends was investigated in small amplitude oscillatory shear and showed that the impact modifier could significantly influence the viscoelastic response of the material. The Izod impact resistance and tensile properties were measured using standard testing protocols. The blend morphology was also examined using scanning electron microscopy on cryofractured and microtomed surfaces, while the crystalline morphology was assessed by optical microscopy. A sub‐micron dispersion of the impact modifier was achieved in the presence of the reactive compatibilizer. Significantly improved impact strength was found with 10 wt % impact modifier. High crystallinity samples showed the highest impact strength with values reaching 68 kJ/m2, hence a 20‐fold improvement with respect to the neat PLA. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44677.
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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) | 0.002 | 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".