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Record W2340216057 · doi:10.1103/physrevb.84.235301

Biased bilayer graphene as a helical quantum Hall ferromagnet

2011· article· en· W2340216057 on OpenAlexaff
R. Côté, Jérémie P. Fouquet, Wenchen Luo

Bibliographic record

VenuePhysical Review B · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCondensed matter physicsPhysicsBilayer grapheneElectronGround stateCoherence (philosophical gambling strategy)Hamiltonian (control theory)Landau quantizationDipoleGrapheneQuantum mechanics

Abstract

fetched live from OpenAlex

The two-dimensional electron gas in a graphene bilayer in the Bernal stacking supports a variety of uniform broken-symmetry ground states in Landau level $N=0$ at integer filling factors $\ensuremath{\nu}\ensuremath{\in}[\ensuremath{-}3,4].$ When an electric potential difference (or bias) is applied between the layers at filling factors $\ensuremath{\nu}=1,3$, the ground state evolves from an interlayer coherent state at small bias to a state with orbital coherence at higher bias, where electric dipoles associated with the orbital pseudospins order spontaneously in the plane of the layers. In this paper, we show that, by further increasing the bias at these two filling factors, the two-dimensional electron gas goes first through an electron crystal with an orbital pseudospin texture at each site and then into a helical state where the pseudospins rotate in space. The pseudospin textures in the electron crystal and the helical state are due to the presence of a Dzyaloshinskii-Moriya interaction in the effective pseudospin Hamiltonian when orbital coherence is present in the ground state. We study in detail the electronic structure of the helical and electron crystal states as well as their collective excitations and then compute their electromagnetic absorption.

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 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 score0.364
Threshold uncertainty score0.999

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)0.0020.006

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 head0.080
GPT teacher head0.352
Teacher spread0.271 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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