Impact of Fiber Morphology and Content on the Thermal Stability, and Mechanical Performance of Maple Wood Fiber‐Polypropylene Composites
Bibliographic record
Abstract
ABSTRACT In recent years, natural‐fiber‐reinforced polymers have gained significant attention due to their sustainability and enhanced properties. This study used Canadian maple wood fiber as reinforcement in a polypropylene (PP). Fiber length and fiber content are known as key factors affecting the properties of the composites. To investigate the effects of fiber length and fiber content on thermal, mechanical, and morphological properties, samples with 5%, 10%, 15%, and 20% fiber content and fiber lengths of 50, 75, and 100 μm were prepared. The DSC results indicated that adding fibers altered the exothermic and endothermic peaks, increased melting points, and decreased crystallization points, with fiber content having a more significant impact than fiber length. TGA showed that fiber reinforcement led to lower thermal stability at lower temperatures but higher stability at elevated temperatures. The tensile tests revealed that increasing fiber content up to 15% improved strength, while a further increase to 20% resulted in a decrease in strength. These results were repeated for the impact test, and the composites containing 20% by volume of fiber showed the lowest impact strength. SEM analysis showed that beyond a certain fiber content, fiber accumulation occurred, negatively affecting mechanical properties.
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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".