Load-Dependent Friction Hysteresis on Graphene
Bibliographic record
Abstract
Nanoscale friction often exhibits hysteresis when load is increased (loading) and then decreased (unloading) and is manifested as larger friction measured during unloading compared to loading for a given load. In this work, the origins of load-dependent friction hysteresis were explored through atomic force microscopy (AFM) experiments of a silicon tip sliding on chemical vapor deposited graphene in air, and molecular dynamics simulations of a model AFM tip on graphene, mimicking both vacuum and humid air environmental conditions. It was found that only simulations with water at the tip-graphene contact reproduced the experimentally observed hysteresis. The mechanisms underlying this friction hysteresis were then investigated in the simulations by varying the graphene-water interaction strength. The size of the water-graphene interface exhibited hysteresis trends consistent with the friction, while measures of other previously proposed mechanisms, such as out-of-plane deformation of the graphene film and irreversible reorganization of the water molecules at the shearing interface, were less correlated to the friction hysteresis. The relationship between the size of the sliding interface and friction observed in the simulations was explained in terms of the varying contact angles in front of and behind the sliding tip, which were larger during loading than unloading.
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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".