An effective method to probe local magnetostatic properties in a nanometric FePd antidot array
Bibliographic record
Abstract
A simple method to quantitatively characterize the local magnetic behaviour of a patterned nanostructure, like a ferromagnetic thin film of antidot arrays, is proposed. The first-order reversal curve (FORC) analysis, coupled with simulations using physically meaningful hysterons, allows us to obtain a quantitative and physically related description of the interaction field and each magnetization reversal process. The hysterons system is built from previously known hypotheses on the magnetic behaviour of the sample. This method was successfully applied to a highly hexagonal ordered FePd antidot array with nanometric dimensions. We achieved a complete characterization of the two different magnetization reversal mechanisms in function of the in-plane applied field angle. For a narrow range of high fields, the magnetization initiates rotating reversibly around the pores, while at lower fields, domain walls are nucleated and propagated. This in-plane magnetization reversal mechanism, partly reversible and partly irreversible, is the only angularly dependent one. While going away from the easy axis, its reversible proportion increases, as well as its switching field distribution. Finally, the results indicate that the high surface roughness between adjacent holes of the antidot thin film induces a parallel interaction field. The proposed method demonstrates its ability also to be applied to characterizing patterned nanostructures with rather complex magnetization reversal processes.
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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 | 0.001 | 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".