Polylactide-Based Renewable Green Composites from Agricultural Residues and Their Hybrids
Bibliographic record
Abstract
Agricultural natural fibers like jute, kenaf, sisal, flax, and industrial hemp have been extensively studied in green composites. The continuous supply of biofibers in high volumes to automotive part makers has raised concerns. Because extrusion followed by injection molding drastically reduces the aspect ratio of biofibers, the mechanical performance of injection molded agricultural residue and agricultural fiber-based composites are comparable. Here, the use of inexpensive agricultural residues and their hybrids that are 8-10 times cheaper than agricultural fibers is demonstrated to be a better way of getting sustainable materials with better performance. Green renewable composites from polylactide (PLA), agricultural residues (wheat straw, corn stover, soy stalks, and their hybrids) were successfully prepared through twin-screw extrusion, followed by injection molding. The effect on mechanical properties of varying the wheat straw amount from 10 to 40 wt % in PLA-wheat straw composites was studied. Tensile moduli were compared with theoretical calculations from the rule of mixture (ROM). Combination of agricultural residues as hybrids is proved to reduce the supply chain concerns for injection molded green composites. Densities of the green composites were found to be lower than those of conventional glass fiber composites.
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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".