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Record W2049138266 · doi:10.1103/physrevb.88.085434

Magneto-optical conductivity of silicene and other buckled honeycomb lattices

2013· article· en· W2049138266 on OpenAlexaff
C. J. Tabert, E. J. Nicol

Bibliographic record

VenuePhysical Review B · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSiliceneCondensed matter physicsPhysicsElectric fieldMagnetic fieldTopological insulatorSemiclassical physicsOptical conductivityBand gapSpin (aerodynamics)QuantumGrapheneQuantum mechanics

Abstract

fetched live from OpenAlex

The magneto-optical longitudinal, transverse Hall, and circularly polarized responses of silicene and other materials described by a Kane-Mele Hamiltonian are calculated. Particular attention is paid to the effects of an external electric field and finite charge doping. The energy of interband transitions can be tuned by varying the electric field. The onset frequency of the absorptive peaks moves differently between the topological insulator and band insulator regimes. This may be used to verify experimentally the existence of the two insulating phases as well as provide a measure of the spin-orbit band gap. The zeroth Landau level splits between four spin and valley distinct energies resulting in valley-spin-polarized levels in the density of states. With charge doping, transitions between these levels allow for a spin- and valley-polarized response in the conductivity whereby charge carriers of specific spin and valley index can be isolated by tuning the incident photon frequency. Increasing the chemical potential is shown to redistribute spectral weight from interband transitions to a strong low-energy intraband response. For large chemical potential, this intraband feature is associated with the semiclassical cyclotron resonance frequency which is shown to linearly increase with magnetic field.

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 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 score0.157
Threshold uncertainty score0.752

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)0.0010.001

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 head0.032
GPT teacher head0.338
Teacher spread0.306 · 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 teacher head, 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

Citations142
Published2013
Admission routes1
Has abstractyes

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