Sustainable PHBV/Cellulose Acetate Blends: Effect of a Chain Extender and a Plasticizer
Bibliographic record
Abstract
-glycidyl methacrylate). To increase the ductility and impact properties of PHBV and to investigate a new biodegradable PHBV-based blend for sustainable packaging, CA was compatibilized with TEC. PHBV and plasticized CA (pCA) blends showed complete immiscibility through separate glass transition and melting peak temperatures in differential scanning calorimetry (DSC), despite the similar Hansen solubility parameters of PHBV, CA, and TEC, indicating partial miscibility. Phase separation between PHBV and pCA was clearly observed by scanning electron microscopy (SEM). PHBV/pCA (70:30) blends had improved impact strength, exceeding that of neat PHBV and pCA, which is attributed to PHBV porosity induced by degradation from the high processing temperature. During processing, the plasticizer migrated from CA to PHBV and partially plasticized it, as evidenced through DSC analysis. The melt temperature of PHBV was reduced, which was confirmed by double melting peaks, representing the formation of secondary crystallites at a lower temperature. Due to processing at high temperatures (210-220 °C), significant porosity was observed in the PHBV/pCA 30:70 blend in SEM analysis. Consequently, the impact strength was improved by 110% as compared to that of virgin PHBV. The addition of CE had no effect on the mechanical properties but did make the PHBV/pCA blends morphologically uniform.
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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".