A critical study of sustainable biocomposites developed from rheologically distinct poly(butylene adipate‐co‐terephthalate) ( <scp>PBAT</scp> ) reinforced with biocarbon from coconut ( <i>Cocos nucifera</i> ) for rigid applications
Bibliographic record
Abstract
Abstract This study investigates the impact of rheological behaviour on the development of highly filled biocomposites for rigid applications using two grades of poly(butylene adipate‐co‐terephthalate) (PBAT). PBAT, a fully biodegradable polymer, has garnered significant attention as an alternative to non‐biodegradable plastics in flexible packaging applications. However, increasing filler content in PBAT can enhance its stiffness, thereby expanding its potential for rigid applications. Filler incorporation is critically influenced by the polymer's flow behaviour, and excessive filler loading in a highly viscous matrix can lead to a decline in material's ease of processing and performance. This research is focused on the processing‐performance evaluation of low melt flow (MFI) and high MFI PBAT filled biocarbon composites. While PBAT 1 supports up to 30 wt.% biocarbon, PBAT 2 can incorporate 50 wt.% biocarbon. Overall, at maximum filler loading, the mechanical and thermal performance of PBAT 2 ‐based composites were superior as compared to those of PBAT 1 composites. The tensile and flexural moduli of PBAT 2 composites increased by 122% and 171%, respectively. Additionally, the thermal stability showed a 38% improvement as compared to PBAT 1 composites. This study underscores the effect of the rheological properties on composites development and provides valuable insights for selecting optimal polymer matrices for high‐filler, rigid 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".