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Record W2023481117 · doi:10.7567/jjap.53.06jb03

Effect of ion-beam bombardment on microstructural and magnetic properties of Ni<sub>80</sub>Fe<sub>20</sub>/α-Fe<sub>2</sub>O<sub>3</sub>thin films

2014· article· en· W2023481117 on OpenAlexaff
Chao Zheng, Tien-Chi Lan, Chin Shueh, R. D. Desautels, J. van Lierop, Ko‐Wei Lin, Philip W. T. Pong

Bibliographic record

VenueJapanese Journal of Applied Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceCoercivityFerromagnetismIon beamBilayerMagnetic anisotropyIon beam-assisted depositionMicrostructureThin filmAnisotropyAntiferromagnetismIonCondensed matter physicsNuclear magnetic resonanceMagnetizationChemistryComposite materialNanotechnologyMagnetic fieldOptics

Abstract

fetched live from OpenAlex

Ion-beam bombardment has been established as an effective way to tune the microstructure and thus modify the magnetic anisotropy of thin film materials, leading to certain remarkable magnetic properties. In this work, we investigated a Ni80Fe20/α-Fe2O3 bilayer deposited with a dual ion-beam deposition technique. Low-energy argon ion-beam bombardment during the α-Fe2O3 deposition led to a decline of crystallinity and interfacial roughness of the bilayer, whereas the grain size distribution remained essentially unchanged. At low temperature, the coercivity exhibited a pronounced decrease after the bombardment, indicating that the effective uniaxial anisotropy in the ferromagnetic layer was dramatically reduced. Such reduction in uniaxial anisotropy was likely attributed to the irreversible transition in the α-Fe2O3 grains caused by the ion-beam bombardment, which subsequently modified the anisotropy in the Ni80Fe20 layer. The bombarded bilayer also exhibited a larger ΔMFC–ZFC compared to the un-bombarded bilayer, which indicated a stronger exchange coupling between the ferromagnetic layer and the antiferromagnetic layer.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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.

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

Citations7
Published2014
Admission routes1
Has abstractyes

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