A kinematic study of energy barriers for crack formation in graphene tilt boundaries
Bibliographic record
Abstract
Recent experimental studies have observed a surprisingly wide range of strengths in polycrystalline graphene. Previous computational investigations of graphene tilt boundaries have highlighted the role of interfacial topology in determining mechanical properties. However, a rigorous characterization of deformation energy barriers is lacking, which precludes direct comparison to the available experimental data. In the current study, molecular dynamics tensile simulations are performed to quantify kinematic effects on failure initiation in a wide range of graphene tilt boundaries. Specifically, the process of crack formation is investigated to provide a conservative estimate of strength at experimental loading rates. Contrary to previous studies, significant strain rate sensitivity is observed, resulting in reductions of crack formation stresses on the order of 7% to 33%. Energy barriers for crack formation are calculated in the range of 0.58 to 2.07 eV based on an Arrhenius relation that is fit to the collected simulation data. Physically, the magnitude of energy barriers in graphene tilt boundaries is found to be linearly correlated to the pre-stress in the critical bonds. Predictions reported in the present study provide a possible explanation for the wide range of strengths experimentally observed in polycrystalline graphene and greatly improve upon current theoretical estimates.
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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 | 0.001 |
| 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".