Investigating the Impact of Thermal Oxidative Aging on the Frictional Properties of Ultra-High Molecular Weight Polyethylene and the Modulating Effects of Lubricating Media
Bibliographic record
Abstract
This study thoroughly investigates the influence of thermal oxidative aging on the frictional properties of ultra-high molecular weight polyethylene (UHMWPE) and comparatively analyzes the regulatory effects of four lubricating media on the material's tribological behavior.Experimental results reveal a significant positive correlation between the duration of thermal oxidative aging and the friction coefficient of UHMWPE, with a 157.14% increase in the friction coefficient observed in samples aged for 20 days.Simultaneously, the wear volume of the material continuously increases with aging, and the wear mechanism exhibits a phased evolution: initially dominated by abrasive wear (0-5 days), transitioning to adhesive-fatigue composite wear (5-15 days), and ultimately evolving into a brittle failure mode (15-20 days).In different lubricating environments, molybdenum disulfide (MoS2) demonstrates the most excellent frictional stability, with its friction coefficient fluctuation range significantly reduced compared to dry conditions; meanwhile, white oil exhibits the strongest friction-reducing effect, reducing the friction coefficient of UHMWPE by 45.89% compared to dry friction.The study confirms that the appropriate selection of lubricants can effectively mitigate material aging damage, with MoS2 offering greater advantages in maintaining long-term stable friction, while white oil is more suitable for operational environments where minimizing friction resistance to the greatest extent is required.
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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) | 0.000 | 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".