Tuning stoichiometry and its impact on superconductivity of monolayer and multilayer FeSe on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>SrTi</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>
Bibliographic record
Abstract
Synthesis of monolayer FeSe on $\mathrm{SrTi}{\mathrm{O}}_{3}$, with greatly enhanced superconductivity compared to bulk FeSe, remains difficult. Lengthy annealing within a certain temperature window is always required to achieve superconducting samples as reported by different groups around the world, but the mechanism of annealing in inducing superconductivity has not been elucidated. We grow FeSe films on $\mathrm{SrTi}{\mathrm{O}}_{3}$ by molecular beam epitaxy and adjust the stoichiometry by depositing additional small amounts of Fe atoms. The monolayer films become superconducting after the Fe deposition without annealing, and show similar superconducting transition temperatures as those of the annealed films in transport measurements. We also demonstrate on the 5-unit-cell films that the FeSe multilayer films can be reversibly tuned between the nonsuperconducting $\sqrt{5}\ifmmode\times\else\texttimes\fi{}\sqrt{5}$ phase with Fe vacancies and the superconducting 1 \ifmmode\times\else\texttimes\fi{} 1 phase. Our results reveal that the anneal process in essence removes Fe vacancies and the additional Fe deposition serves as a more efficient way to achieve superconductivity. This work highlights the significance of stoichiometry in the superconductivity of FeSe thin films and provides an easy path for superconducting samples.
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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".