Nanoscale Adhesive Properties of Graphene: The Effect of Sliding History
Bibliographic record
Abstract
Single‐asperity adhesion between nanoscale silicon tips and few‐layer graphene (FLG) sheets, as well as graphite, was measured using atomic force microscopy (AFM). The adhesion mechanism was understood through experiments and finite element method (FEM) simulations by comparing conventional pull‐forces measurements (contact and separation, without sliding) to those obtained after the tip was slid along the surface before separation (“pre‐sliding”). Without pre‐sliding, no variation in the pull‐off force was measured between consecutive measurements, and there was no observable dependence of the mean pull‐off force value on the number of FLG layers. However, when the tip was pre‐slid over a local area, the first pull‐off force was enhanced by 12–17%; subsequent pull‐off forces then relaxed to a lower, constant value. This occurred regardless of the number of layers, and occurred for aged graphite samples as well. Our analysis indicates that this is due to sliding‐induced changes of graphene's interfacial geometry, whereby local delamination of the top graphene layer occurs, provided there is sufficient atmospheric exposure of the surface after cleaving. This effect provides another unique feature of the nanotribological behavior of atomically‐thin sheets and is consequential for designing graphene‐based devices and coatings where adhesive interactions are important.
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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 | 0.000 | 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) | 0.002 | 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".