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Doping-Induced Second-Harmonic Generation in Centrosymmetric Graphene from Quadrupole Response

2019· article· en· W2908198701 on OpenAlexafffund
Yü Zhang, Di Huang, Yuwei Shan, Tao Jiang, Zhihong Zhang, Kaihui Liu, Lei Shi, Jinluo Cheng, J. E. Sipe, Wei-Tao Liu, Shiwei Wu

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaScience and Technology Commission of Shanghai MunicipalityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsGrapheneSecond-harmonic generationQuadrupolePoint reflectionCondensed matter physicsDipoleHigh harmonic generationDopingPhysicsDirac fermionWave vectorNonlinear opticsMaterials scienceAtomic physicsOpticsNonlinear systemQuantum mechanics

Abstract

fetched live from OpenAlex

For centrosymmetric materials such as monolayer graphene, no optical second-harmonic generation (SHG) is generally expected, because it is forbidden under the electric-dipole approximation. Yet we observe a strong, doping-induced SHG from graphene, with its highest strength comparable to the electric-dipole-allowed SHG in noncentrosymmetric 2D materials. This novel SHG has the nature of an electric-quadrupole response, arising from the effective breaking of inversion symmetry by optical dressing with an in-plane photon wave vector. More remarkably, the SHG is widely tuned by carrier doping or chemical potential, being sharply enhanced at Fermi-edge resonances but vanishing at the charge neutral point that manifests the electron-hole symmetry of massless Dirac fermions. This striking behavior in graphene, which should also arise in graphenelike Dirac materials, expands the scope of nonlinear optics and holds the promise of novel optoelectronic and photonic applications.

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)0.001
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 score0.055
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.033
GPT teacher head0.317
Teacher spread0.283 · 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.

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

Citations95
Published2019
Admission routes2
Has abstractyes

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