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Record W2792093100 · doi:10.1063/1.5012775

Band-offset-induced lateral shift of valley electrons in ferromagnetic MoS2/WS2 planar heterojunctions

2018· article· en· W2792093100 on OpenAlexaff

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsSimon Fraser University
FundersIran Science Elites Federation
KeywordsElectronHeterojunctionFerromagnetismPlanarCore electronBallistic conductionCore charge

Abstract

fetched live from OpenAlex

Low-energy coherent transport and a Goos-Hänchen (GH) lateral shift of valley electrons in planar heterojunctions composed of normal MoS2 and ferromagnetic WS2 monolayers are theoretically investigated. Two types of heterojunctions in the forms of WS2/MoS2/WS2 (type-A) and MoS2/WS2/MoS2 (type-B) with incident electrons in the MoS2 region are considered in which the lateral shift of electrons is induced by band alignments of the two constituent semiconductors. It is shown that the type-A heterojunction can act as an electron waveguide due to electron confinement between the two WS2/MoS2 interfaces which cause the incident electrons with an appropriate incidence angle to propagate along the interfaces. In this case, the spin- and valley-dependent GH shifts of totally reflected electrons from the interface lead to separated electrons with distinct spin-valley indexes after traveling a sufficiently long distance. In the type-B heterojunction, however, transmission resonances occur for incident electron beams passing through the structure, and large spin- and valley-dependent lateral shift values in propagating states can be achieved. Consequently, the transmitted electrons are spatially well-separated into electrons with distinct spin-valley indexes. Our findings reveal that the planar heterojunctions of transition metal dichalcogenides can be utilized as spin-valley beam filters and/or splitters without external gating.

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 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 score0.027
Threshold uncertainty score0.517

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 head0.255
Teacher spread0.237 · 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.

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

Citations13
Published2018
Admission routes1
Has abstractyes

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