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Record W2519374811 · doi:10.1038/srep33349

Engineering optical mode ferromagnetic resonance in FeCoB films with ultrathin Ru insertion

2016· article· en· W2519374811 on OpenAlexafffund
Shandong Li, Cuiling Wang, Xian‐Ming Chu, Guo‐Xing Miao, Qian Xue, Wenqin Zou, Meimei Liu, Jie Xu, Qiang Li, Youyong Dai, Shishen Yan, Shishou Kang, Yun‐Ze Long, Yueguang Lü

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Shandong ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsFerromagnetic resonanceMaterials scienceFerromagnetismAnisotropyCondensed matter physicsMicrowaveResonance (particle physics)Mode (computer interface)Magnetic anisotropyMagnetic fieldOptoelectronicsNuclear magnetic resonanceOpticsTelecommunicationsPhysicsMagnetizationComputer science

Abstract

fetched live from OpenAlex

Ferromagnetic resonance (FMR) in soft magnetic films (SMFs) to a large extent determines the maximum working frequency of magnetic devices. The FMR frequency (fr) in an optical mode is usually much higher than that in the corresponding acoustic mode for exchange coupled ferromagnet/nonmagnet/ferromagnet (FM/NM/FM) trilayers. In this study, we prepared a 50 nm FeCoB film with uniaxial magnetic anisotropy (UMA), showing a high acoustic mode fr of 4.17 GHz. When an ultrathin Ru spacer was inserted in the very middle of the UMA-FeCoB film, the zero-field FMR was abruptly switched from an acoustic mode to an optical one with fr dramatically enhanced from 4.17 GHz to 11.32 GHz. Furthermore, the FMR mode can be readily tuned to optical mode only, acoustic mode only, or double mode by simply varying the applied filed, which provides a flexible way to design multi-band microwave devices.

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 score0.151
Threshold uncertainty score0.788

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)0.0010.000

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 head0.006
GPT teacher head0.197
Teacher spread0.190 · 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

Citations46
Published2016
Admission routes2
Has abstractyes

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