How to induce superconductivity in epitaxial graphene via remote proximity effect through an intercalated gold layer
Bibliographic record
Abstract
Abstract Graphene holds promises for exploring exotic superconductivity with Dirac-like fermions. Making graphene a superconductor at large scales is however a long-lasting challenge. A possible solution relies on epitaxially-grown graphene, using a superconducting substrate. Such substrates are scarce, and usually destroy the Dirac character of the electronic band structure. Using electron diffraction (reflection high-energy, and low-energy), scanning tunneling microscopy and spectroscopy, atomic force microscopy, angle-resolved photoemission spectroscopy, Raman spectroscopy, and density functional theory calculations, we introduce a strategy to induce superconductivity in epitaxial graphene via a remote proximity effect, from the rhenium substrate through an intercalated gold layer. Weak graphene–Au interaction, contrasting with the strong undesired graphene–Re interaction, is demonstrated by a reduced graphene corrugation, an increased distance between graphene and the underlying metal, a linear electronic dispersion and a characteristic vibrational signature, both latter features revealing also a slight p doping of graphene. We also reveal that the main shortcoming of the intercalation approach to proximity superconductivity is the creation of a high density of point defects in graphene (10 14 cm −2 ). Finally, we demonstrate remote proximity superconductivity in graphene/Au/Re(0001), at low temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".