Robust long-range magnetic correlation across antiphase domain boundaries in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">Sr</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">CrReO</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math>
Bibliographic record
Abstract
We report a resonant elastic x-ray scattering study of a thin-film sample of ${\mathrm{Sr}}_{2}{\mathrm{CrReO}}_{6}$, which has one of the highest ferrimagnetic transition temperatures among ordered double perovskites. We found resonantly enhanced magnetic Bragg peaks at $\mathbf{Q}=$ (odd, odd, odd) at both the rhenium ${L}_{2}$ and ${L}_{3}$ edges, which coincide with the structural Bragg peaks of ${\mathrm{Sr}}_{2}{\mathrm{CrReO}}_{6}$. By analyzing the widths of these Bragg peaks, we extracted very different structural and magnetic correlation lengths. The former is about 15 nm, while the latter is constrained by the instrumental resolution to be at least 90 nm. We argue that a finite structural correlation length is consistent with the existence of antiphase nanodomains in our sample. On the other hand, a much larger magnetic correlation length indicates that the magnetic correlation extends far beyond the boundaries of individual domains and is consistent with strong antiferromagnetic coupling between different antiphase domains. Last, from the azimuthal dependence of the magnetic intensity, we show that the magnetic moments lie perpendicular to the $c$ axis, which explains the earlier bulk magnetization data.
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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".