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Record W2089057886 · doi:10.1063/1.3697405

Tailoring interfacial exchange coupling with low-energy ion beam bombardment: Tuning the interface roughness

2012· article· en· W2089057886 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Physics Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNon-blocking I/OMaterials scienceSurface finishSurface roughnessCoupling (piping)Ion beamCoercivityTexture (cosmology)Ion beam-assisted depositionCondensed matter physicsBeam (structure)Composite materialOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

By ascertaining NiO surface roughness in a Ni80Fe20/NiO film system, we were able to correlate the effects of altered interface roughness from low-energy ion-beam bombardment of the NiO layer and the different thermal instabilities in the NiO nanocrystallites. From experiment and by modelling the temperature dependence of the exchange bias field and coercivity, we have found that reducing the interface roughness and changing the interface texture from an irregular to striped conformation enhanced the exchange coupling strength. Our results were in good agreement with recent simulations using the domain state model that incorporated interface mixing.

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.

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 score0.535
Threshold uncertainty score0.865

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 integrity0.0000.000
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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