Structure‐Dependent Wear and Shear Mechanics of Nanostructured MoS<sub>2</sub> Coatings
Bibliographic record
Abstract
Abstract Sputter‐deposited molybdenum disulfide coatings are one of the most common lubricants for extreme environments. However, their performance predictability remains limited by the complexity of van der Waals wear and shear mechanics in bulk materials resulting in unexpected premature failure. In the present study, two nanostructured MoS2 coatings of similar macroscopic properties are shown to exhibit entirely different wear and shear mechanics due to their nanostructure. Friction force microscopy with steel‐beaded cantilevers is used to measure the per‐cycle evolution of friction, wear, and topography in situ over the lubricant lifetime under an inert nitrogen environment. Molecular dynamics simulations confirm the subsurface structural failure mechanisms of the coatings under shear stress, and atomic force microscope phase imaging and Raman spectroscopy are used to identify tribofilm formation mechanics. The nanocrystal–amorphous composite structure shows improved wear resistance but at the cost of limited stress relaxation which creates high‐stress failure and fracture‐dominated wear. The purely nanocrystalline coating exhibits lower shear resistance but consistent stress relaxation by van der Waals cleavage and triple junction fracture which results in higher wear rates with predictable abrasion‐dominated failure. The contrast in nanoscale performance of the coatings allows for the lubricant nanostructure to be tuned for ideal applications for extreme environments.
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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".