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Record W2024415677 · doi:10.1063/1.3677822

Interface magnetism of iron grown on sulfur and hydrogen passivated GaAs(001)

2012· article· en· W2024415677 on OpenAlexafffund
B. Kardasz, S. P. Watkins, Eric Montoya, C. Burrowes, Erol Girt, B. Heinrich

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCondensed matter physicsMagnetismMolecular beam epitaxyFerromagnetismMagnetic anisotropyAnisotropyEpitaxyElectron diffractionSuperlatticeCrystallographyDiffractionMagnetizationChemistryMagnetic fieldNanotechnologyOptoelectronicsOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

Sulfur (S) and hydrogen (H) atom passivated GaAs(001) templates were used for deposition of ultrathin crystalline Fe films using molecular beam epitaxy, where the Fe thickness ranged from 10 to 45 atomic layers. Reflection high-energy electron diffraction patterns showed that the S- and H-passivated surfaces had no and very weak (1 × 2) superlattice reconstructions, respectively. This indicates that these GaAs(001) templates have a square-like symmetry. Magnetic anisotropies were investigated using the in-plane angular dependence of ferromagnetic resonance at 36 GHz. The in-plane cubic and uniaxial anisotropies and perpendicular uniaxial field were described by bulk and interface contributions, indicating that the Fe films have a high lattice coherence. The magnetic properties of the Fe films were compared to those grown on more commonly used GaAs(001) templates having a (4 × 6) reconstruction with an As-rich in-plane uniaxial symmetry. The Fe films grown on S-passivated templates exhibited unique magnetic properties caused by a decreased lattice spacing compared to the bulk Fe.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.608

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 head0.010
GPT teacher head0.215
Teacher spread0.206 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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