Understanding the Independent and Interdependent Role of Water and Oxidation on the Tribology of Ultrathin Molybdenum Disulfide (MoS<sub>2</sub>)
Bibliographic record
Abstract
Abstract In this work, the tribological behavior of ultrathin MoS2 is investigated to understand the independent roles of water and oxidation. Water adsorption is identified as the primary interfacial mechanism for both SiO2/pristine‐MoS2 and SiO2/graphene interfaces, however, tribological behavior of pristine‐MoS2 is observed to be more sensitive to presence of water due to stronger MoS2–water interaction. Comparison of pristine‐MoS2 and oxidized‐MoS2 reveals that the oxidation of MoS2 significantly increases its friction and sensitivity to water by playing a more detrimental role. The specific effect of oxygen on friction via chemical interactions is studied in isolation through density functional theory simulations of a tip sliding on MoS2 basal planes and over edges before and after oxidation. The maximum change in energy, or energy barrier correlating with friction, as the tip moves across the surface, increases after oxidation by up to 66% for the basal plane and by 25% at the edge. Charge density analysis suggests that the more localized and nonuniform interfacial charge distribution on oxygen‐rich surfaces, as compared to pristine surfaces, leads to higher resistance to sliding. This confirms that oxygen presence alone increases friction and when coupled with the presence of water, both effects are additive in increasing friction.
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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 | 0.001 |
| 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".