Cyclic Wear Reliability of 2D Monolayers
Bibliographic record
Abstract
Understanding wear, a critical factor impacting the reliability of mechanical systems, is vital for nano-, meso-, and macroscale applications. Due to the complex nature of nanoscale wear, the behavior of nanomaterials such as two-dimensional materials under cyclic wear and their surface damage mechanism is yet unexplored. In this study, we used atomic force microscopy coupled with molecular dynamic simulations to statistically examine the cyclic wear behavior of monolayer graphene, MoS 2, and WSe 2 . We show that graphene displays exceptional durability and lasts over 3000 cycles at 85% of the applied critical normal load before failure, while MoS 2 and WSe 2 last only 500 cycles on average. Moreover, graphene undergoes catastrophic failure as a result of stress concentration induced by local out-of-plane deformation. In contrast, MoS 2 and WSe 2 exhibit intermittent failure, characterized by damage initiation at the edge of the wear track and subsequent propagation throughout the entire contact area. In addition to direct implications for MEMS and NEMS industries, this work can also enable the optimization of the use of 2D materials as lubricant additives on a macroscopic level.
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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".