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Record W2019140122 · doi:10.1063/1.4917503

Microwave meta-atom enhanced spintronic rectification

2015· article· en· W2019140122 on OpenAlexaff
Peng Gou, Fuchun Xi, Qinbai Qian, Jie Xu, Y. S. Gui, C.‐M. Hu, Zhenghua An

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsSpintronicsMicrowaveMagnetismFerromagnetismMaterials scienceRectificationOptoelectronicsSpin (aerodynamics)Condensed matter physicsPhysicsVoltage

Abstract

fetched live from OpenAlex

An artificial meta-atom (MA), or alternatively, a plasmonic antenna, has been demonstrated to significantly enhance the microwave spin rectifying photovoltage by more than two orders in magnitude (∼280) in the ferromagnetic resonance regime. The large enhancement is attributed to the unique structure of the MA which magnifies both microwave electric (∼5) and magnetic (∼56) fields in the same near-field spatial region. Our work develops the interdisciplinary direction with artificial and natural magnetism and may find promising applications in high-frequency or opto-spintronic devices and wireless microwave energy harvesting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.060
GPT teacher head0.267
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2015
Admission routes1
Has abstractyes

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