A comprehensive review of superconductivity in heterostructures and superlattices comprising 2D materials
Bibliographic record
Abstract
2D materials are materials that only exist in two dimensions, which means that their thickness is only one atom or several atoms. Since the first kind of 2D material was found, which is graphene, much research, not only including the findings of the new kind of 2D materials such as hBN but also including many complicated heterostructures formed by these 2D materials and many amazing properties caused by them, are done recently. This paper will begin with a review of the widely applied ways to produce graphene and the special properties and applications of graphene. Furthermore, the Moiré/Super-Moiré patterns and heterostructure formed by the graphene and hBN, either the hBN-graphene case or the hBN-graphene-hBN case, along with the necessary theoretical and experimental approaches to characterize them, would be reviewed in detail. Finally, the superconductivity found in the heterostructure of 2D materials with twisted angles, specifically the twisted bilayer graphene and trilayer graphene case would be reviewed along with many experimental outcomes. This paper will give a systematic review of some basic experimental and theoretical developments and findings of the heterostructure and superlattice of 2D materials, providing a comprehensive review of the current status and possible directions in the future for people who want to do further research on the superconductivity of the 2D materials’ heterostructure and superlattice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".