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Record W2889503608 · doi:10.1103/physrevb.98.075429

Electrically controlled dc and ac transport in bilayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>WSe</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>

2018· article· lv· W2889503608 on OpenAlexafffund
M. Tahir, P. M. Krstajić, P. Vasilopoulos

Bibliographic record

VenuePhysical review. B./Physical review. B · 2018
Typearticle
Languagelv
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaColorado State University
KeywordsCondensed matter physicsValence (chemistry)SpintronicsPhysicsBand gapCoupling (piping)BilayerHall effectElectrical resistivity and conductivityMaterials scienceFerromagnetismChemistryQuantum mechanicsMembrane

Abstract

fetched live from OpenAlex

We investigate quantum transport in a bilayer ${\mathrm{WSe}}_{2}$ in the presence of an external potential $V$ and interlayer coupling. The dc and ac conductivities are evaluated in the framework of linear response theory. As functions of the Fermi energy, the dc ones exhibit a nonmonotonic behavior with a flat region in the band gap. A deep local minimum is visible in spin-Hall conductivity near the top of the valence band as a consequence of a large valence band splitting. On the other hand, the ac spin- and valley-Hall conductivities, as functions of the frequency, show two opposite peaks near the top of the valence band. The spin-Hall conductivity exhibits a minimum value at $V=0$ whereas the valley-Hall conductivity decreases monotonically upon varying $V$ from negative to positive values. Furthermore, we evaluate the power absorption spectrum and assess its dependence on the spin and valley degrees of freedom. The results are pertinent to the development of electrically controlled spintronic and valleytronic devices based on bilayer ${\mathrm{WSe}}_{2}$ and other group VI semiconductors.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.006

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 head0.019
GPT teacher head0.295
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2018
Admission routes2
Has abstractyes

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