Optical properties of gated bilayer graphene quantum dots with trigonal warping
Bibliographic record
Abstract
We determine the optical properties of gated bilayer graphene quantum dots with trigonal warping (TW) of single-particle energy spectra. The lateral structure of metallic gates confines electrons and holes in a quantum dot (QD) electrostatically. The gated bilayer graphene energy spectrum is characterized by two $K$ valleys surrounded by three minivalleys with energies depending on the applied vertical electric field. Employing an atomistic tight-binding model, we compute the single-particle QD states and analyze the influence of TW on the energy spectrum as the lateral confining potential depth varies. We find a regime where the QD levels are dominated by the presence of three minivalleys around each $K$ valley. Next, we compute dipole matrix elements and analyze the oscillator strengths and optical selection rules for optical valence to conduction band transitions. We then include electron-electron interactions by first computing the microscopic Coulomb matrix elements and electron self-energy and solving the Bethe-Salpeter equation to obtain the excitonic spectrum. Finally, we obtain the absorption spectrum for a shallow confining potential depth, which further amplifies the effects of TW on the optical properties. Our results predict the existence of two degenerate bright exciton states, each built of the three minivalley states that do not exist in the deep confinement regime, where the effects of TW are negligible.
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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".