The Influence of Biomimetic Textures in Synergy with Nanoparticles on the Anti-Friction and Wear Resistance Properties of Titanium Alloys
Bibliographic record
Abstract
As a material with excellent properties, titanium alloy is widely used in aerospace, vehicle manufacturing, medical equipment and other industries due to its low density, high strength, and excellent corrosion resistance.However, the non-wear-resistant surface of the titanium alloy makes its application in the drilling environment very difficult.In order to solve this problem, in this paper, C3N4 nanoparticles were prepared by calcination, C3N4@MoS2 nanoparticles were prepared by hydrothermal method, and titanium alloy (TC4) drill pipe samples with bionic banana leaf texture patterns were processed by laser engraving technology.The MWF-02 friction tester was selected as the main test instrument.Friction and wear tests were conducted under the lubrication of calcium-based bentonite water-based drilling fluid by forming a friction pair with TC4 titanium alloy samples and silicon nitride ceramic balls.The friction coefficient and wear rate were used as evaluation indicators of tribological properties.Through the analysis of the appearance and morphology of the wear scar, the influence of the synergy of bionic texture and nanoparticles on the tribological properties of titanium alloy at 50℃ was explored.The conclusions are as follows: (1) After adding C3N4 and C3N4@MoS2 nanoparticles, the friction and wear performance of TC4 titanium alloy drill pipes was effectively improved, among which the improvement effect of C3N4@MoS2 composite particles was better, reducing the average friction coefficient of the original sample by 62.4% and the wear rate by 54.0%.(2) With the introduction of the banana leaf texture, the synergy of nanoparticles and texture further improved the tribological properties of titanium alloy.At 50℃, the average friction coefficient was reduced by 71.6% and the wear rate by 72.3% compared with the original sample.
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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) | 0.001 | 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".