Magnetization Process in Vortex-imprinted Ni<sub>80</sub>Fe<sub>20</sub>/Ir<sub>20</sub>Mn<sub>80</sub>Square Elements
Bibliographic record
Abstract
The vortex-driven magnetization process of micron-sized, exchange-coupled square elements with composition of $Ni_{80}Fe_{20}$ (12 nm)/ $Ir_{20}Mn_{80}$ (5 nm) is investigated. The exchange-bias is introduced by field-cooling through the blocking temperature (TB) of the system, whereby Landau-shaped vortex states of the $Ni_{80}Fe_{20}$ layer are imprinted into the $Ir_{20}Mn_{80}$ . In the case of zero-field cooling, the exchange-coupling at the ferromagnetic/antiferromagnetic interface significantly enhances the vortex stability by increasing the nucleation and annihilation fields, while reducing coercivity and remanence. For the field-cooled elements, the hysteresis loops are shifted along the cooling field axis. The loop shift is attributed to the imprinting of displaced vortex state of $Ni_{80}Fe_{20}$ into $Ir_{20}Mn_{80}$ , which leads to asymmetric effective local pinning fields at the interface. The asymmetry of the hysteresis loop and the strength of the exchange-bias field can be tuned by varying the strength of cooling field. Micromagnetic modeling reproduces the experimentally observed vortex-driven magnetization process if the local pinning fields induced by exchange-coupling of the ferromagnetic and antiferromagnetic layers are taken into account.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".