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Record W2120764901 · doi:10.1103/physrevb.90.035405

Magneto-optics of general pseudospin-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>s</mml:mi></mml:math>two-dimensional Dirac-Weyl fermions

2014· article· lv· W2120764901 on OpenAlexaff
John D. Malcolm, E. J. Nicol

Bibliographic record

VenuePhysical Review B · 2014
Typearticle
Languagelv
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMagneto opticalGrapheneHamiltonian (control theory)PhysicsDirac (video compression format)Optical conductivityFermionMagnetic fieldDirac fermionField (mathematics)Spectral lineTheoretical physicsQuantum mechanicsCondensed matter physicsMathematical physicsMathematicsPure mathematics

Abstract

fetched live from OpenAlex

The popularity of graphene---a pseudospin-$\frac{1}{2}$ two-dimensional Dirac-Weyl material---has prompted the search for related materials and the characterization of their properties. In this work, the magneto-optical conductivity is calculated for systems that obey the general pseudospin-$s$ two-dimensional Dirac-Weyl Hamiltonian, with particular focus on $s={\frac{1}{2},1,\frac{3}{2},2}$. This generalizes calculations that have been made for $s=\frac{1}{2}$ and follows previous work on the optical response of these systems in zero field. In the presence of a magnetic field, Landau levels condense out of the $2s+1$ energy bands. As the chemical potential in a system is shifted, patterns arise in the appearance and disappearance of certain peaks within the optical spectra. These patterns are markedly different for each case considered, creating unique signatures in the magneto-optics. The general structure of each spectrum and how they compare 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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.297
Teacher spread0.272 · 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 designTheoretical or conceptual
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

Citations45
Published2014
Admission routes1
Has abstractyes

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