Chemical adsorption-induced distinct friction behaviors of supported atomically thin nanofilm
Bibliographic record
Abstract
Graphene, with its excellent mechanical properties and friction-reducing capabilities, functions as a solid lubricant and protective coating. However, environmental contamination, consisting of various compounds, elements, and molecules, can degrade these properties and is challenging to characterize. We address this difficulty to unravel the impact of contamination on graphene's tribological performance by adsorbing six different chemical reagents on graphene supported by silicon substrates. Through friction experiments, six distinct frictional behaviors were observed on these contaminated graphene samples. Specifically, benzyl alcohol, toluene, and ethanol all increased the surface friction, adhesion, and friction coefficient of graphene to varying degrees, resulting in positive frictional hysteresis. In contrast, acetone, 1-pentanol, and 1-pentane had the opposite effect to different extents. Notably, 1-pentane significantly reduced the friction coefficient of graphene, achieving superlubricity, while benzyl alcohol damaged thin layers of graphene, causing them to completely disappear. Finally, through MD simulations, we demonstrated that hydrogen bonds formed by hydroxyl groups and the carbon chain structure of the chemical contaminants cause variations in the contact area and stress/strain distribution within it, thus leading to varied surface friction. The evolution of these factors during the loading-unloading process was the primary reason behind these six distinct hysteretic friction behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".