Characterization of polypropylene composites reinforced with flax fibers treated by mechanical and alkali methods
Bibliographic record
Abstract
Abstract Flax is a type of natural fiber widely used as reinforcing materials for polymer composites. The commercially available flax fibers in Canada consist of a significant amount of shive and other impurities, which could act as stress concentration regions to negatively affect the mechanical property of composites. In this study, the shive was manually removed from the commercial flax fibers by screening and combing to obtain different shive contents from 0 to 30 wt%. By contrast, the obtained flax fibers were further treated with alkaline solution. The fibers obtained from mechanical and alkali treatment were compared on their thermal and mechanical properties. As expected, it was found that the thermal stability and mechanical properties of the flax reinforced polypropylene composites increased significantly with the removal of the shive content. However, the alkali treatment on flax fiber did not further improve the composites properties. The possible reason was that the proper mechanical treatment (screening and combing) prior to alkaline treatment effectively loosened the fiber bundles for better single fiber separation in matrix and significantly removed the impurities, thus the effect of alkaline treatment did not become obvious.
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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".