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Record W2529398822 · doi:10.1088/2053-1591/3/10/105004

Towards the reality of spin field effect transistor utilizing a graphene channel with spin-splitting

2016· article· en· W2529398822 on OpenAlexafffund
Yihang Yang, Lin Li, Ying Liu, Guo‐Xing Miao

Bibliographic record

VenueMaterials Research Express · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCondensed matter physicsGrapheneSpin (aerodynamics)PhysicsSpin Hall effectMagnetic fieldFerromagnetismElectronExchange interactionSpin transistorDirac fermionSpin polarizationMaterials scienceQuantum mechanics

Abstract

fetched live from OpenAlex

We propose a spin field effect transistor using a graphene nanoribbon as the channel. The control and manipulation of the electron spin in the channel modulate the spin-polarized current. The modulation is carried out by the magnetic exchange field which arises from the electron exchange interaction with ferromagnetic gate and quantum confinement effect. Numerical estimation indicates that a substantial magnetic exchange field can generate a phase difference on the order of π within a timeframe far below the spin lifetime and timescale between successive collisions, which ensures both the spin coherence and ballistic conduction during the electron transport. A graphene ribbon with armchair boundaries has the desired Dirac point and metallic character. This Dirac-like dispersion relation, together with negligible spin–orbit coupling, makes large on-off ratio feasible even in the presence of thermal dispersion.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.082
GPT teacher head0.373
Teacher spread0.291 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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