Formation of Graphene p–n Superlattices on Pb Quantum Wedged Islands
Bibliographic record
Abstract
On the basis of first-principles calculations within density functional theory, we report on a novel scheme to create graphene p-n superlattices on Pb wedged islands with quantum stability. Pb(111) wedged islands grown on vicinal Si(111) extend over several Si steps, forming a wedged structure with atomically flat tops. The monolayer thickness variation due to the underlying substrate steps is a sizable fraction of the total thickness of the wedged islands and gives rise to a bilayer oscillation in the work function of Pb(111) due to quantum size effects. Here, we demonstrate that when a graphene sheet is placed on the surface of such a Pb wedged island, the spatial work function oscillation on the Pb wedged island surface caused by the underlying steps results in an oscillatory shift in the graphene Dirac point with respect to the Fermi level. Furthermore, by applying an external electric field of ∼0.5 V/Å in the surface normal direction, the Fermi level of the system can be globally tuned to an appropriate position such that the whole graphene layer becomes a graphene p-n superlattice of seamless junctions, with potentially exotic physical properties and intriguing applications in nanoelectronics.
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) | 0.001 | 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".