Influence of zigzag edges on the Ruderman-Kittel-Kasuya-Yosida interaction between magnetic impurities in graphene nanoribbons
Bibliographic record
Abstract
Localized magnetic impurity centres in graphene can interact through the π-electrons, leading to an effective Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction. A RKKY-type study is presented for graphene ribbons with zigzag edges. Specifically investigations of how the edges modify the interaction between two localized magnetic moments are made by using a tight-binding Hamiltonian to describe the hopping of the π-electrons between adjacent sites and a contact term for interactions with the localized moments. In terms of a Green's function formalism for the excitation spectrum, which comprises modified bulk modes and two different types of localized edge modes, explicit analytical expressions are obtained for the RKKY interaction for any two magnetic sites on the graphene ribbon. The results enable us to determine the RKKY contributions that arise individually from the bulk-like modes and from the two types of edge modes in the zigzag geometry. The importance of these contributions varies depending on the proximity of the magnetic impurities to each other and to an edge.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".