Surface hydrogenation regulated wrinkling and torque capability of hydrogenated graphene annulus under circular shearing
Bibliographic record
Abstract
Wrinkles as intrinsic topological feature have been expected to affect the electrical and mechanical properties of atomically thin graphene. Molecular dynamics simulations are adopted to investigate the wrinkling characteristics in hydrogenated graphene annulus under circular shearing at the inner edge. The amplitude of wrinkles induced by in-plane rotation around the inner edge is sensitive to hydrogenation, and increases quadratically with hydrogen coverage. The effect of hydrogenation on mechanical properties is investigated by calculating the torque capability of annular graphene with varying hydrogen coverage and inner radius. Hydrogenation-enhanced wrinkles cause the aggregation of carbon atoms towards the inner edge and contribute to the critical torque strength of annulus. Based on detailed stress distribution contours, a shear-to-tension conversion mechanism is proposed for the contribution of wrinkles on torque capacity. As a result, the graphane annulus anomalously has similar torque capacity to pristine graphene annulus. The competition between hydrogenation caused bond strength deterioration and wrinkling induced local stress state conversion leads to a U-shaped evolution of torque strength relative to the increase of hydrogen coverage from 0 to 100%. Such hydrogenation tailored topological and mechanical characteristics provides an innovative mean to develop novel graphene-based devices.
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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".