Valley-dependent tunneling through electrostatically created quantum dots in heterostructures of graphene with hexagonal boron nitride
Bibliographic record
Abstract
Kelvin probe force microscopy (KPFM) has been employed to probe charge carriers in a graphene/hexagonal boron nitride (hBN) heterostructure [Nano Lett. 21, 5013 (2021)]. We propose an approach for operating valley filtering based on the KPFM-induced potential ${U}_{0}$ instead of using external or induced pseudomagnetic fields in strained graphene. Employing a tight-binding model, we investigate the parameters and rules leading to valley filtering in the presence of a graphene quantum dot (GQD) created by the KPFM tip. This model leads to a resolution of different transport channels in reciprocal space, where the electron transmission probability at each Dirac cone (${\mathbf{K}}_{\mathbf{1}}=\ensuremath{-}\mathbf{K}$ and ${\mathbf{K}}_{\mathbf{2}}=+\mathbf{K}$) is evaluated separately. The results show that ${U}_{0}$ and the Fermi energy ${E}_{F}$ control (or invert) the valley polarization, if electrons are allowed to flow through a given valley. The resulting valley filtering is allowed only if the signs of ${E}_{F}$ and ${U}_{0}$ are the same. If they are different, the valley filtering is destroyed and might occur only at some resonant states affected by ${U}_{0}$. Additionally, there are independent valley modes characterizing the conductance oscillations near the vicinity of the resonances, whose strength increases with ${U}_{0}$ and are similar to those occurring in resonant tunneling in quantum antidots and to the Fabry-Perot oscillations. Using KPFM, to probe the charge carriers, and graphene-based structures to control valley transport, provides an efficient way for attaining valley filtering without involving external or pseudomagnetic fields as in previous proposals.
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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".