Sublattice engineering and voltage control of magnetism in triangular single and bi‐layer graphene quantum dots
Bibliographic record
Abstract
magnified image When a Dirac electron is confined to a triangular graphene quantum dot with zigzag edges, its low‐energy spectrum collapses to a shell of degenerate states at the Fermi level leading to a magnetized edge. The shell degeneracy and the total magnetization are proportional to the edge size and can be made macroscopic. In this review, we start with a general discussion of magnetic properties of graphene structures and its relation to broken sublattice symmetry. Then, we discuss single electronic properties of single and bilayer triangular graphene quantum dots, focusing on the nature of edge states. Finally, we investigate the role of electronic correlations in determining the nature of ground state and excitation spectra of triangular graphene quantum dots as a function of dot size and filling fraction of the shell of zero‐energy states. The interactions are treated by a combination of tight‐binding, Hartree–Fock and configuration interaction methods. We show that the spin polarization of the triangular graphene quantum dots can be controlled through gating, i.e., by adding or removing electrons. In bilayer graphene dots, the relative filling of edge states in each layer and the magnetization can be tuned down to single localized spin using an external vertical electrical field. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".