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Record W2564997090 · doi:10.1103/physrevb.91.205407

Optimizing third-harmonic generation at terahertz frequencies in graphene

2015· article· en· W2564997090 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

VenuePhysical Review B · 2015
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAmplitudeScatteringTerahertz radiationHigh harmonic generationCondensed matter physicsGrapheneHarmonicFermi energyScattering amplitudeFermi levelOpticsQuantum mechanicsElectron

Abstract

fetched live from OpenAlex

We model third-harmonic generation in doped monolayer graphene at terahertz frequencies by employing a nearest-neighbor tight-binding model in the length gauge. We show that for a given incident-field amplitude there is an optimum Fermi level that maximizes the emitted third-harmonic field. The optimum Fermi level depends very strongly on the incident-field amplitude as well as on the scattering time and increasing either enhances the third-harmonic response. We consider the general case of Fermi-level-independent scattering as well as three different scattering mechanisms that are Fermi-level dependent: phonon, long-range impurity, and short-range impurity scattering. For each case, we determine the optimal Fermi level as well as the amplitude of the optimized third-harmonic response for single-cycle incident fields with central frequencies of 1 THz and amplitudes in the range of 25--75 kV/cm. We find that although nonlinear processes beyond third order suppress third-harmonic generation, we still obtain third-harmonic amplitudes as large as 1.6% of the fundamental of the transmitted 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.381

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)0.0000.000

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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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