Oscillatory motion in layered materials: graphene, boron nitride, and molybdenum disulfide
Bibliographic record
Abstract
Offset-driven self-retraction and oscillatory motion of bilayer graphene has been observed experimentally and is potentially relevant for nanoscale technological applications. In a previous article, we showed that friction between laterally offset graphene layers is controlled by roughness and proposed a simple reduced-order model based on density-functional theory (DFT) and molecular dynamics (MD) data, with which predictions on the experimental size-scale could be made. In this article, we extend our study to other layered materials, with emphasis on boron nitride (BN) and molybdenum disulfide (MoS2). Using MD and DFT simulations of these systems and a generalized version of the reduced-order model, we predict that BN will exhibit behavior similar to graphene (heavily-damped oscillation with a decay rate that increases with roughness) and that MoS2 shows no oscillatory behavior even in the absence of roughness. This is attributed to the higher energy barrier for sliding in MoS2 as well as the surface structure. Our generalized reduced-order model provides a guide to predicting and tuning experimental oscillation behavior using a few parameters that can be derived from simulation data.
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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) | 0.000 | 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 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".