Electrically tunable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>MoSe</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>WSe</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> heterostructure-based quantum dot
Bibliographic record
Abstract
We describe here a theory of a quantum dot in an electrically tunable ${\mathrm{MoSe}}_{2}/{\mathrm{WSe}}_{2}$ heterostructure. van der Waals heterostructures allow for tuning their electronic properties beyond their monolayer counterparts. We start by determining their electronic structure using density functional theory. We obtain the type-II band alignment and close in energy conduction band minima (valleys) at the $K$ and $Q$ points in the Brillouin zone. The valence band maxima, also energetically close, are located at the $K$ and $\mathrm{\ensuremath{\Gamma}}$ points. By analyzing the Kohn-Sham wave functions, we describe the layer, spin, and orbital contributions. Next, we construct an ab initio--based tight-binding model, which helps us to better understand the complexity of the interlayer interactions. We determine the effect of a vertical electric field, showing that vertical gating enables control of valleys' extrema and their occupancy. Finally, we employ the tight-binding model to investigate laterally gated quantum dots and analyze the influence of a perpendicular electric field on their energy spectrum. Our results demonstrate that tuning the electric field enables control over the valley character of the quantum dot states, selectively localizing them in either the $K$ or $Q$ valleys, as evidenced by their characteristic degeneracies and wave functions.
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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) | 0.009 | 0.001 |
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".