Electronic properties and electron–electron interactions in graphene quantum dots
Bibliographic record
Abstract
magnified image We review the electronic properties of graphene quantum dots (GQD) with emphasis on the role of electron–electron interactions. We describe the electronic properties using a combination of tight binding, Hartree–Fock (HF), density functional theory and configuration interaction methods applied to interacting electrons on pz orbitals of carbon atoms. The electron–electron interactions are computed using Slater orbitals and screened by the environment and sigma electrons. We show that the electronic properties of graphene can be tuned by the lateral size, shape, character of edge, number of layers and screening. In particular, the energy gap can be tuned from THz to UV by varying the size of graphene quantum dot. The dependence of the gap on the size can be understood in terms of confined Dirac fermions. The effect of edges and edge reconstruction is discussed using ab‐initio techniques. The role of screening is investigated using the HF approach. HF ground states corresponding to semiconductor, Mott‐insulator, and spin‐polarized phases are obtained as a function of the strength of the screened Coulomb interactions. For GQDs in the semiconductor phase, the role of correlations in ground and excited states is computed perturbatively and shown to result in size dependent band gap renormalization. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)
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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".