A New Approach to Supertough Poly(lactic acid): A High Temperature Reactive Blending
Bibliographic record
Abstract
A novel approach to PLA toughening is proposed in this study. Poly(lactic acid) (PLA) is toughened using poly(ethylene‐ n ‐butylene‐acrylate‐ co ‐glycydyl methacrylate) (EBA‐GMA) as a reactive compatibilizer with the aid of an epoxy‐based chain extender. It is found that the toughening effect of EBA‐GMA in the binary blend investigated is strongly influenced by blending temperature. Blending at high temperatures which are non‐typical for PLA processing (over 250 °C) allows toughness to be increased by an order of magnitude when compared to the toughness of blends prepared at low temperatures (below 200 °C). This effect is attributed to a combination of factors, namely an increasing rate of reactive bonding between PLA and EBA‐GMA at elevated temperatures and enhanced interfacial adhesion between PLA and EBA‐GMA phases. DSC studies show that PLA/EBA‐GMA bonding on the interface acts as an efficient nucleator for PLA. The nucleation ability of the PLA/EBA‐GMA interface strongly depends on blend processing temperature and gradually increases with increasing blending temperature. The PLA/EBA‐GMA interface shows its highest nucleation ability at 250 °C. image
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".