Effect of a chain extender on the rheological and mechanical properties of biodegradable poly(lactic acid)/poly[(butylene succinate)‐<i>co</i>‐adipate] blends
Bibliographic record
Abstract
Abstract Biodegradable polymer blends based on poly(lactic acid) (PLA) and poly[(butylene succinate)‐ co ‐adipate] (PBSA) were prepared with a laboratory internal mixer. An epoxy‐based, multifunctional chain extender was used to enhance the melt strength of the blends. The morphology of the blends was observed with field emission scanning electron microscopy. The elongational viscosities of the blends, with and without chain extender, were measured with a Sentmanat extensional rheometer universal testing platform. The blends with chain extender exhibited strong strain‐hardening behavior, whereas the blends without chain extender exhibited only weak strain‐hardening behavior. Measurements of the linear viscoelastic properties of the melts suggested that the chain extender promoted the development of chain branching. The results show that PBSA contributed to significant improvements in the ductility of the PLA/PBSA blends, whereas the chain extender did not have a significant effect on the elastic modulus and strain at break of the blends. The combined blending of PLA with PBSA and the incorporation of the chain extender imparted both ductility and melt strength to the system. Thus, such an approach yields a system with enhanced performance and processability. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
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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 | 0.000 |
| 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".