Effects of Silica Ceramic Particle Sizes on the Properties of Recycled Polyethylene Composites
Bibliographic record
Abstract
Particulate filled polymeric composites are becoming attractive because of their wide applications and lower production cost. To evaluate the possibility of using waste silica based ceramic materials as reinforcing filler in recycled polyethylene (PE) composite, the effect of ceramic (PC) particle sizes on the mechanical, wear and thermal behaviours of polyethylene (PE) composites were investigated at (2wt% filler) and grain sizes (40µm-150µm). The wear behaviour was characterized using analysis of variance (ANOVA) and linear regression to determine the main and interactive effects of the process parameters such as speed, load and time on the wear behaviour of the fabricated PE-PC composites. Test results show that the decreasing the ceramic particles 150µm-40µm improved the mechanical, wear and thermal properties of the recycled polyethylene (PE) composites. Factorial design of the experiment can be successfully employed to describe the wear behavior of the samples and developed linear equation for predicting wear rate with in selected experimental conditions.
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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 | 0.001 |
| 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".