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Record W3022607245 · doi:10.1088/1674-1056/ab8db2

Improvement of valley splitting and valley injection efficiency for graphene/ferromagnet heterostructure*

2020· article· en· W3022607245 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

VenueChinese Physics B · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrapheneHeterojunctionMaterials scienceFermi levelFerromagnetismMonolayerOptoelectronicsCondensed matter physicsNanotechnologyElectronPhysics

Abstract

fetched live from OpenAlex

The valley splitting has been realized in the graphene/Ni heterostructure with the splitting value of 14 meV, and the obtained valley injecting efficiency from the heterostructure into graphene was 6.18% [ Phys. Rev. B 92 115404 (2015)]. In this paper, we report a way to improve the valley splitting and the valley injecting efficiency of the graphene/Ni heterostructure. By intercalating an Au monolayer between the graphene and the Ni, the split can be increased up to 50 meV. However, the valley injecting efficiency is not improved because the splitted valley area of graphene moves away from the Fermi level. Then, we mend the deviation by covering a monolayer of Cu on the graphene. As a result, the valley injecting efficiency of the Cu/graphene/Au/Ni heterostructure reaches 10%, which is more than 60% improvement compared to the simple graphene/Ni heterostructure. Then we theoretically design a valley-injection device based on the Cu/graphene/Au/Ni heterostructure and demonstrate that the valley injection can be easily switched solely by changing the magnetization direction of Ni, which can be used to generate and control the valley-polarized current.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.406

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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