Tribological and mechanical properties of copper matrix composites reinforced with carbon nanotube and alumina nanoparticles
Bibliographic record
Abstract
Copper is widely used as electrical contact materials due to its excellent thermal and electrical conductivity. However, low strength and poor wear resistance restrict its practical applications. Herein, we report a high-performance copper matrix composite reinforced with carbon nanotubes (CNT) and alumina (Al 2 O 3 ) nanoparticles prepared by powder metallurgy route. The microstructure, density, hardness, tensile strength and tribological properties were studied. CNTs and Al 2 O 3 were successfully mixed with copper powders by acid treatment and mechanical milling. After sintering, CNTs and Al 2 O 3 were uniformly distributed around the grain boundaries and limited the grain growth. Furthermore, all copper matrix composites showed decreased density, but increased hardness and tensile strength compared with the copper matrix. More importantly, the incorporation of CNTs and Al 2 O 3 significantly improved the tribological properties of copper matrix. This is because Al 2 O 3 nanoparticles with high strength enhanced the wear resistance by dispersion strengthening, while CNTs served as solid lubricant greatly improving the anti-friction properties. Besides, the friction coefficient as well as wear rate increased with higher load and sliding speed. The Cu-1.5CNTs-0.5Al 2 O 3 composite had the optimal hardness, tensile strength, anti-friction, and 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 | 0.000 |
| 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".