Nature of Weak Magnetism in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>SrTiO</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>LaAlO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math>Multilayers
Bibliographic record
Abstract
We report the observation of weak magnetism in superlattices of ${\mathrm{LaAlO}}_{3}/{\mathrm{SrTiO}}_{3}$ using $\ensuremath{\beta}$-detected nuclear magnetic resonance. The spin lattice relaxation rate of $^{8}\mathrm{Li}$ in superlattices with a spacer layers of 8 and 6 unit cells of ${\mathrm{LaAlO}}_{3}$ exhibits a strong peak near $\ensuremath{\sim}35\text{ }\text{ }\mathrm{K}$, whereas no such peak is observed in a superlattice with spacer layer thickness of 3 unit cells. We attribute the observed temperature dependence to slowing down of weakly coupled electronic moments at the ${\mathrm{LaAlO}}_{3}/{\mathrm{SrTiO}}_{3}$ interface. These results show that the magnetism at the interface depends strongly on the thickness of the spacer layer, and that a minimal thickness of $\ensuremath{\sim}4--6$ unit cells is required for the appearance of magnetism. A simple model is used to determine that the observed relaxation is due to small fluctuating moments ($\ensuremath{\sim}0.002{\ensuremath{\mu}}_{B}$) in the two samples with a larger ${\mathrm{LaAlO}}_{3}$ spacer thickness.
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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".