Work of Adhesion Measurements of MoS<sub>2</sub> Dry Lubricated 440C Stainless Steel Tribological Contacts
Bibliographic record
Abstract
The tribological behavior of dry lubricants depends on their mechanical and physicochemical environment, making it difficult to predict in practice. Discrete Element Method‐based modeling has been one successful approach to provide valuable insight into the tribology of dry lubricated contacts. However, it requires well‐defined interactions between discrete elements, in particular between those simulating different materials. Measuring the properties governing those interactions, such as the work of adhesion (W), is therefore critical. The present work describes a method for measuring the W between AISI440C steel and MoS2‐based coatings used in spacecraft. Using Atomic Force Microscopy local asperity and adhesion measurements, the W between steel microbeads and MoS2 coatings is determined at different stages in its wear life. The distributions of W values in the worn coatings and pristine coatings agree well with earlier Time‐of‐Flight Secondary Ion Mass Spectroscopy studies on the physicochemistry of the samples, as well as contact angle measurements. Additional measurements between the same materials on a ball bearing from a real life‐test unit of a spacecraft instrument also show a similar W distribution, suggesting that the approach used here provides relevant data for use in numerical simulations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".