Achieving Ultra-Low Friction with Diamond/Metal Systems in Extreme Environments
Bibliographic record
Abstract
In the search for achieving ultra-low friction for applications in extreme environments, we evaluate the interfacial processes of diamond/tungsten sliding contacts using an on-line macro-tribometer and a micro-tribometer in an ultra-high vacuum. The coefficient of friction for the tests with the on-line tribometer remained considerably low for unlubricated sliding of tungsten, which correlated well with the relatively low wear rates and low roughness on the wear track throughout the sliding. Ex situ analysis was performed by means of XPS and SEM-FIB in order to better understand the underlying mechanisms of low friction and low-wear sliding. The analysis did not reveal any evidence of tribofilm or transferfilm formation on the counterface, indicating the absence of significant bonding between the diamond and tungsten surfaces, which correlated well with the low-friction values. The minimal adhesive interaction and material transfer can possibly be explained by the low initial roughness values as well as high cohesive bonding energies of the two materials. The appearance of the wear track as well as the relatively higher roughness perpendicular to the sliding indicated that abrasion was the main wear mechanism. In order to elucidate the low friction of this tribocouple, we performed micro-tribological experiments in ultra-high vacuum conditions. The results show that the friction coefficient was reduced significantly in UHV. In addition, subsequently to baking the chamber, the coefficient of friction approached ultra-low values. Based on the results obtained in this study, the diamond/tungsten tribocouple seems promising for tribological interfaces in spacecraft systems, which can improve the durability of the components.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".