On the Nature of Interlayer Interactions in a System of Two Graphene Fragments
Bibliographic record
Abstract
With the help of the quantum chemistry methods, we have investigated the nature of interlayer interactions between graphene fragments in different stacking arrangements (AA and AB). We found that the AB stacking pattern, as the ground state of the system, is characterized by the effective interband orbital interactions which are barely present in the AA. Their vanishing induces electronic decoupling between the graphene layers, so that the bonding interaction Δ E oi between the flakes is drastically reduced from −0.482 to −0.087 eV as the stacking pattern is changed from AB to AA. The effective way to improve the bonding interaction between layers preserving the same AA lattice order is to induce rotation of the layer. As the flake is rotated, the bonding interactions are improved mostly due to suppression of the Pauli repulsion which in turn increases the interlayer orbital interactions, while the interband part of those remain negligible on the whole range of the rotation angle. The Pauli repulsion is also found to be the main force that moves the two fragments apart as the stacking pattern is changed from AA to AB. This enhances the equilibrium interlayer distance, which for the AA staking is larger than the established value for the AB stacking (3.4 Å).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".