Bamboo Particles-Polyvinyl Chloride Composites: Analysis of Particles Size Distribution and Composites Performance
Bibliographic record
Abstract
Analysis of particles size distribution of Malaysian bamboo species (Bambusa vulgaris and Schizostachyum brachycladum) for polyvinyl chloride (PVC) composites production was conducted using dynamic image analysis (DIA). A wide distribution of bamboo particles length was recorded, varying from almost 0 to 1500-µm for both species. Inadequate amount of actual particles length distribution from each sieve size (75-µm and 1-mm) was also recorded. DIA observed an increase of aspect ratio from small to large particles, and fine particles were recorded to be slightly elongated than the large ones. However, the effects of bamboo particles size on the finished PVC composites performance were uncertain, considerable of numerous other factors that influence the performance. Only impact and water uptake properties of composites have been obviously affected by different particles size. Greater modulus value is observed in composites with high particles loading, though low impact strength and water resistance were recorded. The incorporation of high concentration of selected processing lubricants in the composites formulation helped to improve the impact and water resistance of the composites. Malaysian bamboo particles-PVC composites performance between different species was equivalent, demonstrated that both species displayed identical behaviour for composites production.
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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 | 0.001 | 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".