Ferromagnetism in LaFeO3/LaNiO3 superlattices with high Curie temperature
Bibliographic record
Abstract
Interfacing complex oxides in atomically engineered layered structures can give rise to a wealth of exceptional electronic and magnetic properties that surpass those of the individual building blocks. Herein, we demonstrate a ferromagnetic spin order with a high Curie temperature of 608 K in superlattices consisting of otherwise paramagnetic perovskite LaNiO3 (LNO) and antiferromagnetic LaFeO3 (LFO). The ferromagnetism likely results from the covalent exchange due to interfacial charge transfer from Fe to Ni cations. By deliberately controlling the thickness of the LNO sublayers thus the amount of charge transfer, a robust ferromagnetism of 4 uB is realized for a stacking periodicity consisting of one single unit cell of both LNO and LFO, an emergent double perovskite phase of La2FeNiO6 with B-site layered ordering configurations. The ferromagnetic LFO/LNO superlattices offer great potential for the search of emergent magnetodielectric and/or multiferroic properties as well as applications in spintronics and electrocatalysts. Perovskite oxides, when combined into heterostructures and superlattices can yield emergent properties not present in the constituent components. Here, by combining LaNiO3, a Pauli paramagnetic material, with LaFeO3, an antiferromagnetic insulator, Zhou et al create a strongly ferromagnetic superlattice with a Curie temperature of over 600 K.
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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) | 0.002 | 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".