Tribological properties of ultra‐high molecular weight polyethylene (UHMWPE) filled with copper micro‐powder
Bibliographic record
Abstract
This paper investigates the tribological properties of ultra‐high molecular weight polyethylene (UHMWPE) filled with copper micro‐powder (CMP). The fabrication and testing procedures implemented to characterize strength and wear properties of the composite are discussed. The effect of copper micro‐powder concentration on tensile strength, elongation at break, impact resistance, coefficient of friction, and wear resistance of the composite is investigated. Results show that copper micro‐powder concentration of 1 wt% yields the optimal combination of wear resistance and tensile strength of the composite. A morphological analysis based on scanning electron microscope (SEM) images of the copper micro‐powder‐ultra‐high molecular weight polyethylene specimens is also discussed. The presence of ridges and plaques on the specimens, analyzed after the sliding wear tests, is attributed to fatigue and adhesion mechanisms. Investigations performed by using an electron probe micro‐analyzer provide evidence that the detachment of copper micro‐powder particles from the matrix during sliding wear tests creates a lubricating layer that drastically decreases the coefficient of friction of the composite and improves its wear resistance 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 | 0.001 |
| 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".