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Record W2535144615 · doi:10.1109/jstqe.2016.2617619

Graphene-Integrated Plasmonic Structure for Optical Third Harmonic Generation

2016· article· en· W2535144615 on OpenAlexafffund
Behrooz Semnani, S. Mohsen Raeis-Zadeh, Arash Rohani, Amir Hamed Majedi, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlasmonGrapheneOptoelectronicsMaterials scienceHigh harmonic generationNonlinear opticsOpticsNanotechnologyPhysicsLaser

Abstract

fetched live from OpenAlex

In this paper, a general recipe is proposed to design an efficient graphene-integrated plasmonic structure for third harmonic generation. Specifically, the design procedure for an integrated graphene-based ultraviolet light generator is presented. In order to enhance the field intensity at the graphene layer, two distinct mechanisms are utilized. A multilayer Bragg structure is used as a perfect magnetic conductor to make a constructive interference between incident and reflected field at the graphene layer. A periodic array of shaped resonant gold nanoparticles is placed on top of the graphene sheet to enhance the field intensity due to the plasmonic resonance at the fundamental frequency. A hybrid and fast numerical method based on the scattering matrix of Floquet modes and the Generalized Multipole Technique is also proposed to analyze the periodic structure. This numerical method is used to optimize the dimensions of the multilayer structure and boost the nonlinear conversion efficiency by more than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> times.

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 score0.326
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.253
Teacher spread0.234 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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