Tuning the Magnetic Anisotropy of Fe<sub>3</sub>O<sub>4</sub>/Pt Heterostructures Fabricated by Atomic Layer Deposition With <inline-formula> <tex-math notation="LaTeX">$In~Situ$ </tex-math> </inline-formula> Magnetic Field
Bibliographic record
Abstract
Research on tuning the magnetic anisotropy of magnetic heterostructures has been receiving intensive attentions due to their intriguing tuning phenomena. In particular, tuning the magnetic anisotropy by in situ magnetic field during the fabrication process allows a remarkable control of magnetism with energy efficiency and shows significant potential applications for compact, fast, and low-power microwave and electronic devices. In this paper, in situ external out-of-plane and in-plane magnetic fields were applied during the deposition of Fe3O4thin films on Pt substrates by atomic layer deposition at different temperatures (350 °C, 400 °C, and 450 °C). The effects of in situ magnetic field with different directions on the surface morphologies and magnetic properties of F3O4/Pt heterostructures were characterized. It was found that both the out-of-plane and in-plane external magnetic fields tuned the magnetic anisotropy of ferromagnetic Fe3O4when deposited at 400 °C with the maximum resonance field changes of 620 and 1930 Oe along in-plane and out-of-plane directions, respectively. This paper achieves tuning the magnetic properties through in situ magnetic field and provides a possibility for spintronic devices.
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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".