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Record W2769931936 · doi:10.1039/c7nr06159e

<i>h</i>-BN/graphene van der Waals vertical heterostructure: a fully spin-polarized photocurrent generator

2017· article· en· W2769931936 on OpenAlexaff
Xixi Tao, Lei Zhang, Xiaohong Zheng, Hua Hao, Xianlong Wang, Lingling Song, Zhi Zeng, Hong Guo

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsPhotocurrentGrapheneHeterojunctionvan der Waals forceGenerator (circuit theory)Materials scienceSpin (aerodynamics)Condensed matter physicsOptoelectronicsNanotechnologyPhysicsPower (physics)Quantum mechanicsMoleculeThermodynamics

Abstract

fetched live from OpenAlex

By constructing transport junctions using graphene-based van der Waals (vdW) heterostructures in which a zigzag-edged graphene nanoribbon (ZGNR) is sandwiched between two hexagonal boron-nitride sheets, we computationally demonstrate a new scheme for generating perfect spin-polarized quantum transport in ZGNRs by light irradiation. The mechanism lies in the lift of spin degeneracy of ZGNR induced by the stagger potential it receives from the BN sheets and the subsequent possibility of single spin excitation of electrons from the valence band to the conduction band by properly tuning the photon energy. This scheme is rather robust in that we always achieve desirable results irrespective of whether we decrease or increase the interlayer distance by applying compressive or tensile strain vertically to the sheets or shift the BN sheets in-plane relative to the graphene nanoribbons. More importantly, this scheme overcomes the long-standing difficulties in traditional ways of using solely electrical field or chemical modification for obtaining half-metallic transport in ZGNRs and thus paves a more feasible way for their application in spintronics.

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 categoriesInsufficient payload (model declined to judge)
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 score0.028
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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.

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

Citations57
Published2017
Admission routes1
Has abstractyes

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