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Quantum Hall Effect in Hydrogenated Graphene

2013· article· en· W2325250105 on OpenAlexaff
Jonathan Guillemette, Shadi Sabri, Binxin Wu, Keyan Bennaceur, Peter Gaskell, M. Savard, Pierre L. Lévesque, Farzaneh Mahvash, Abdeladim Guermoune, Mohamed Siaj, Richard Martel, Thomas Szkopek, G. Gervais

Bibliographic record

VenuePhysical Review Letters · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalMcGill University
FundersU.S. Department of EnergyNational Science Foundation
KeywordsGrapheneCondensed matter physicsMagnetoresistanceQuantum Hall effectMagnetic fieldMaterials scienceScatteringImpurityElectronWeak localizationHall effectLandau quantizationPhysicsNanotechnologyQuantum mechanics

Abstract

fetched live from OpenAlex

The quantum Hall effect is observed in a two-dimensional electron gas formed in millimeter-scale hydrogenated graphene, with a mobility less than $10\text{ }\text{ }{\mathrm{cm}}^{2}/\mathrm{V}\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{s}$ and corresponding Ioffe-Regel disorder parameter $({k}_{F}\ensuremath{\lambda}{)}^{\ensuremath{-}1}\ensuremath{\gg}1$. In a zero magnetic field and low temperatures, the hydrogenated graphene is insulating with a two-point resistance of the order of $250h/{e}^{2}$. The application of a strong magnetic field generates a negative colossal magnetoresistance, with the two-point resistance saturating within 0.5% of $h/2{e}^{2}$ at 45 T. Our observations are consistent with the opening of an impurity-induced gap in the density of states of graphene. The interplay between electron localization by defect scattering and magnetic confinement in two-dimensional atomic crystals is discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.303
Teacher spread0.289 · 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 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

Citations32
Published2013
Admission routes1
Has abstractyes

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