Large electropositive cations as surfactants for the growth of polar epitaxial films
Bibliographic record
Abstract
Using density functional theory (DFT) we demonstrate that the adsorption of large cations such as potassium or cesium facilitates the epitaxial growth of polar ${\mathrm{LaAlO}}_{3}\phantom{\rule{0.28em}{0ex}}(\mathrm{LAO})$ on ${\mathrm{SrTiO}}_{3}\phantom{\rule{0.28em}{0ex}}(\mathrm{STO})$. The low ionization potential of K favors efficient electron transfer to the STO conduction band and results in a two-dimensional electron gas which exactly compensates for the diverging potential with increasing layer thickness. For large cations like K or Cs, DFT total energy considerations show that they remain adsorbed on the LAO surface and do not enter substitutionally into LAO. These results suggest a scheme for growing clean LAO/STO interface systems, and polar systems in general, by performing the growth process in the presence of large, low ionization potential alkali-metal ions.
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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 | 0.001 |
| 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".