Injection molding of biodegradable polyester blends filled with mineral and sustainable fillers: Performance evaluation
Bibliographic record
Abstract
Abstract This research focuses on the melt processing of biocomposites from a biodegradable polymer blend mixed with hybrid fillers through injection molding technique. An optimized blend ratio (60/40 wt%) poly(butylene succinate‐co‐butylene adipate) (PBSA) and poly(butylene adipate‐co‐terephthalate) (PBAT) demonstrated promising results after blending with a mixture of walnut shell powder (WSP), corn starch and talc in various proportions for use in rigid packaging. The addition of hybrid fillers (i) 10% WSP with 15% talc and (ii) 5% WSP with 5% starch and 15% talc to the polymer blend (60%PBSA/40%PBAT) improved tensile modulus (160% and 162%, respectively) and flexural modulus (147% and 153%, respectively) because of the dispersion of stiffer talc and WSP. Following the addition of fillers, tensile strength of the composites decreased. However, flexural strength improved significantly after filler introduction because of better stress transfer ability. Rheological analysis of filled composites with starch or WSP (25%) depicted similar characteristics of the polymer blend, indicating lower viscosity than hybrid composites. The abundant hydroxyl groups in starch explained the increased water absorption and decreased contact angle compared with other composites. This research's novelty encompasses utilizing low‐cost biomasses with mineral filler into an under‐researched biodegradable polymer blend suitable for single‐use rigid packaging applications.
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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".