Modulation doping of the FeSe monolayer on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>SrTiO</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:msub></mml:math>
Bibliographic record
Abstract
The discovery of higher-temperature superconductivity in FeSe monolayers on ${\mathrm{SrTiO}}_{3}$ substrates has sparked a surge of interest in the interface superconductivity. One point of agreement reached to date is that modulation doping by impurities in the substrate is critical for the enhanced superconductivity. Remarkably, the universal doping about 0.1 electrons per Fe, i.e., so-called ``magic'' doping, has been observed on a range of Ti oxide substrates, which concludes that there likely is some important interaction limiting the FeSe doping. Our study discovers that the polarization change at the interface Se because of the close proximity to the substrate from that in the free-standing FeSe film significantly amplifies the total potential difference at the interface above and beyond the work function difference for charge transfer. Additionally, the titanate substrate with a large number of free electrons basically serves as an ``infinite'' charge reservoir, which leads to the saturated FeSe doping with the complete removal of the interface potential gradient. Our work has developed the theory for modulation doping in the van der Waals materials/oxides heterostructure, providing a solution to the puzzle of magic doping in FeSe monolayers on titanates. The information also presents experimental pathways to accommodate a variable carrier density of FeSe monolayers via modulation doping.
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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) | 0.002 | 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".