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Record W2080923527 · doi:10.1063/1.3040719

Correlating exchange bias with magnetic anisotropy in ion-beam bombarded NiFe/Mn-oxide bilayers

2008· article· en· W2080923527 on OpenAlexaff
Ko‐Wei Lin, J.‐Y. Guo, T.-J. Chen, Hao Ouyang, E. Vass, J. van Lierop

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsExchange biasBilayerAntiferromagnetismFerrimagnetismMaterials scienceOxideAnalytical Chemistry (journal)Tetragonal crystal systemFerromagnetismTransmission electron microscopyMicrostructurePermalloyCondensed matter physicsMagnetic anisotropyChemistryCrystallographyMagnetizationMagnetic fieldNanotechnologyMetallurgyCrystal structureMembrane

Abstract

fetched live from OpenAlex

The exchange bias field dependence on the Mn-oxide and its microstructure in NiFe/Mn-oxide bilayers was investigated. Transmission electron microscopy results have shown that the bilayer bottom consisted of either α-Mn, rocksalt MnO, or a composite of tetragonal Mn3O4+MnO, depending on the ratio of O2/Ar used during dual ion-beam deposition. Magnetometry results at 5 K indicate that the exchange bias field (Hex∼−300 Oe) is largest in a NiFe/Mn (0%O2/Ar) bilayer. The MnO formation by in situ Mn oxidation results in a decrease in Hex in a NiFe/Mn-oxide (21%O2/Ar) bilayer. In contrast, a further increase in the O2/Ar ratio during deposition results in larger Hex and Hc. This is attributed to the oxidation of MnO into a harder ferrimagnet, Mn3O4. Our results indicate that the antiferromagnetic Mn enabled stronger coupling with NiFe than MnO. In addition, we find that the MnO–Mn3O4 coupling dominates the exchange bias effects at high oxygen concentrations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.206
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2008
Admission routes1
Has abstractyes

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