Interface mixing and its impact on exchange coupling in exchange biased systems
Bibliographic record
Abstract
Abstract Exchange bias and interlayer exchange coupling are interface driven phenomena. Since an ideal interface is very challenging to achieve, a clear understanding of the chemical and magnetic natures of interfaces is pivotal to identify their influence on the magnetism. We have chosen Ni 80 Fe 20 /CoO( t CoO )/Co trilayers as a model system, and identified non-stoichiometric Ni-ferrite and Co-ferrite at the surface and interface, respectively. These ferrites, being ferrimagnets typically, should influence the exchange coupling. However, in our trilayers the interface ferrites were found not to be ferro- or ferri-magnetic; thus having no observable influence on the exchange coupling. Our analysis also revealed that (i) interlayer exchange coupling was present between Ni 80 Fe 20 and Co even though the interlayer thickness was significantly larger than expected for this phenomenon to happen, and (ii) the majority of the CoO layer (except some portion near the interface) did not contribute to the observed exchange bias. We also identified that the interlayer exchange coupling and the exchange bias properties were not interdependent.
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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 | 0.001 |
| 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 | 0.001 | 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".