Tuning friction behaviors of supported nanofilms via multiscale roughness of underlying substrate
Bibliographic record
Abstract
Substrate roughness plays a critical role in governing the interfacial friction of supported nanofilms, yet the underlying mechanism remains unclear. Here, we systematically investigate how roughness affects the friction and hysteresis behavior of graphene and MoS 2 films using atomic force microscopy (AFM) and molecular dynamics (MD) simulations. Experiments reveal that smoother substrates lead to lower friction and a distinct transition from positive to negative hysteresis. Phase imaging and contact stiffness measurements indicate that this transition corresponds to a sudden increase in nanofilm–substrate conformity. Simulations further show that in rough systems, two key factors contribute to enhanced positive hysteresis: persistently low interface conformity and a gradual, irreversible increase in nanofilm roughness during repeated sliding. In contrast, smooth substrates enable stable, highly conformal interfaces, resulting in negative hysteresis. Additionally, thicker nanofilms exhibit reduced conformability under the same roughness. These findings highlight roughness-governed conformity and morphological evolution as the dual mechanisms controlling nanoscale frictional behavior.
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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".