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Ballistic transport through graphene nanostructures of velocity and potential barriers

2011· article· en· W2140775223 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Physics Condensed Matter · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneSuperlatticeFermi energyCondensed matter physicsPhysicsBallistic conductionPeriodic potentialFermi levelConductanceNanostructureModulation (music)ElectronQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate the electronic properties of graphene nanostructures when the Fermi velocity and the electrostatic potential vary in space. First, we consider the transmission T and conductance G through single and double barriers. We show that G for velocity barriers differs markedly from that for potential barriers for energies below the height of the latter and it exhibits periodic oscillations as a function of the energy for strong velocity modulation. Special attention is given to superlattices (SLs). It is shown that an applied bias can efficiently widen or shrink the allowed minibands of velocity-modulated SLs. The spectrum in the Kronig-Penney limit is periodic in the strength of the barriers. Collimation of an electron beam incident on an SL with velocity and potential barriers is present but it disappears when the potential barriers are absent. The number of additional Dirac points may change considerably if barriers and wells have sufficiently different Fermi velocities.

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.

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 score0.222
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 studies0.0000.000
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 head0.018
GPT teacher head0.244
Teacher spread0.225 · 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