Bio-poly(butylene succinate) and Its Composites with Grape Pomace: Mechanical Performance and Thermal Properties
Bibliographic record
Abstract
Abstract Biocomposites from bio-based poly(butylene succinate) (BioPBS) and grape pomace (GP) were made by melt extrusion–injection molding. Grape pomace is a low value byproduct from the wine industry that can be utilized as a filler to increase its value and to decrease the amount of polymer required in a composite blend. Experiments were performed with up to 50% grape pomace by weight. Composites were also compatibilized with in situ manufactured maleic anhydride-grafted BioPBS (MA-g-BioPBS). Flexural and impact strength were improved with the addition of GP up to the addition of 50 wt % GP, suggesting that at this loading the formulation reached threshold performance. The blend of (57:40:3) BioPBS/GP/MA-g-BioPBS showed the best overall performance in terms of a balance of both mechanical properties and thermal properties. The increase in impact resistance confirmed that the GP acted as a reinforcing phase. The addition of 3 wt % MA-g-BioPBS in samples containing 40 wt % of grape pomace resulted in improvements of 28.4 and 59% in flexural and impact strengths, respectively, compared to neat BioPBS. Heat distortion temperature increased with the addition of grape pomace by 14.3% in a blend combination of 57% BioPBS, 40% grape pomace, and 3% MA-g-BioPBS compared to neat BioPBS. Scanning electron microscopy results show improved interfacial adhesion with the addition of MA-g-BioPBS and thermogravimetric analysis results prove that the GP is thermally stable under the processing conditions. This study shows that GP can be successfully incorporated into a BioPBS matrix to create biocomposites with improved thermal and mechanical properties.
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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".