Sustainable Biocomposites from Pyrolyzed Grass and Toughened Polypropylene: Structure–Property Relationships
Bibliographic record
Abstract
A novel class of injection-molded, toughened biocomposites was engineered from pyrolyzed miscanthus-based biocarbon, poly(octene ethylene) elastomer, and polypropylene (PP). The elastomer and biocarbon were added to the PP matrix at 30 and 20 wt %, respectively. The particle size of the biocarbon varied within two main ranges: <20 and 106-125 μm. The morphology and adhesion between the filler and the matrix were controlled by the addition of maleic anhydride grafted PP (MAPP). The composites were melt-blended and then injection-molded to tensile, flexural, and impact bars. The results showed that although the morphology of the composite is almost independent of particle size it is greatly dependent on the addition of MAPP. Two completely different morphologies, separate dispersion and encapsulated filler particles, were obtained in the presence and absence of MAPP, which was verified by atomic force and scanning electron microscopies. Model calculations based on a modified Kerner equation showed that the encapsulated filler content decreased from 64 to 8% by the addition of MAPP, which caused a major improvement in the stiffness and strength of the composites. Despite having a different morphology caused by the compatibilizer, composites with smaller particles exhibited better strength and modulus and lower impact toughness compared to those with a larger particle size. Results suggest that the failure mechanisms are mainly controlled by the local fracturing of biocarbon particles, which was more pronounced when the particle size was larger.
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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) | 0.000 | 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".