Atomic layer deposition of superparamagnetic and ferrimagnetic magnetite thin films
Bibliographic record
Abstract
One of the key challenges in realizing superparamagnetism in magnetic thin films lies in finding a low-energy growth way to create sufficiently small grains and magnetic domains which allow the magnetization to randomly and rapidly reverse. In this work, well-defined superparamagnetic and ferrimagnetic Fe3O4 thin films are successfully prepared using atomic layer deposition technique by finely controlling the growth condition and post-annealing process. As-grown Fe3O4 thin films exhibit a conformal surface and poly-crystalline nature with an average grain size of 7 nm, resulting in a superparamagnetic behavior with a blocking temperature of 210 K. After post-annealing in H2/Ar at 400 °C, the as-grown α−Fe2O3 sample is reduced to Fe3O4 phase, exhibiting a ferrimagnetic ordering and distinct magnetic shape anisotropy. Atomic layer deposition of magnetite thin films with well-controlled morphology and magnetic properties provides great opportunities for integrating with other order parameters to realize magnetic nano-devices with potential applications in spintronics, electronics, and bio-applications.
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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".