Toughened Sustainable Green Composites from Poly(3-hydroxybutyrate-<i>co</i>-3-hydroxyvalerate) Based Ternary Blends and Miscanthus Biofiber
Bibliographic record
Abstract
Abstract Novel green composites with an excellent balance of properties were successfully fabricated from poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) based renewable ternary blends and miscanthus through a cost-efficient reactive extrusion process. The ternary blend of PHBV with poly(butylene adipate-co-terephthalate) (PBAT) and epoxidized natural rubber (ENR) was engineered as a high toughening matrix for the natural fiber composites using dicumyl peroxide (DCP) as the reactive compatibilizer. The addition of miscanthus fibers into the matrix significantly enhanced its stiffness and thermal resistance while still keeping a good toughness. A high value of impact strength up to 240.5 J/m was still achieved even with 20 wt % miscathus added. The mechanical modulus of the composites were also analyzed using mathematical models including rule of mixtures (ROM), inverse rule of mixtures (IROM), and the Tsai–Pagano equations. In the multiphase blends and composites, ENR played unique dual roles as an effective coupling agent and impact modifier in the presence of DCP. Scanning electron microscopy (SEM) results indicated good interfacial adhesion among the different phases in the composites, which played a vital role in improving the strength and toughness of the materials. At the same time, balanced melt viscosity and density were also achieved for the composites, which are important for wide application.
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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".