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Record W2584130247 · doi:10.1088/2053-1583/aa598d

Perfect spin and valley polarized quantum transport in twisted SiC nanoribbons

2017· article· en· W2584130247 on OpenAlexafffund
Xiaohong Zheng, Xiaobin Chen, Lei Zhang, Liantuan Xiao, Suotang Jia, Zhi Zeng, Hong Guo

Bibliographic record

Venue2D Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsZigzagCondensed matter physicsRibbonGraphene nanoribbonsSpin (aerodynamics)QuantumPhysicsMaterials scienceNanotechnologyQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

Abstract The edge magnetism of zigzag SiC nanoribbons in its ground state is half-metallic, but is competed by a non-half-metal state that is energetically extremely close. In this work, we propose and theoretically analyze two-probe transport junctions that overcome this difficulty so that perfect half-metal quantum transport is realized. When two zigzag SiC nanoribbons are connected by a C–Si–C–Si tetramer where the right ribbon is turned by 180° around the transport direction, 100% spin polarization with nearly perfect transmission is obtained due to perfect momentum k -matching across the transport junction. We show such a transport to be independent of the magnetic configurations of the two ribbons. A concomitant property is that 100% valley polarized charge transport is achieved. In principle, the structure of the proposed transport junction turns SiC nanoribbons into a promising, robust, and essentially perfect spin and valley filter.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0010.000
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 head0.030
GPT teacher head0.312
Teacher spread0.282 · 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 designSimulation or modeling
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

Citations38
Published2017
Admission routes2
Has abstractyes

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