Auto-hybridization of Polyethylene/Maple Composites: The Effect of Fiber Size and Concentration
Bibliographic record
Abstract
This work investigated the effect of maple fiber size and content on the auto-hybridization of linear low density polyethylene (LLDPE) composites. The samples were compounded by twin-screw extrusion and molded by compression molding. Different fiber loadings (5 to 20% wt.), fiber sizes (0-425 microns) and size ratios (30/70, 50/50, 70/30 of short, medium, and long fibers) were used to prepare the auto-hybrid composites with 3% of coupling agent (maleated polyethylene). Micrographs and impact strength results showed that the fracture in auto-hybrid composites is mostly dependent on the longer fibers. At 10% wt. the optimum ratio was 30/70 of shorter/longer fibers, which improved tensile strength (20%), tensile modulus (20%), and impact strength (13%) compared with composites with a single fiber size. But at 20% wt., tensile modulus increased by 30% and torsion modulus by 40% above the rule of hybrid mixtures (RoHM) at a 70/30 ratio of shorter/longer fibers.
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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".