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Record W2021449011 · doi:10.1364/oe.19.026088

Extremely large extinction efficiency and field enhancement in terahertz resonant dipole nanoantennas

2011· article· en· W2021449011 on OpenAlexaff
Luca Razzari, Andréa Toma, Mostafa Shalaby, Matteo Clerici, Remo Proietti Zaccaria, Carlo Liberale, Sergio Marras, Ibraheem Al‐Naib, Gobind Das, Francesco De Angelis, Marco Peccianti, Andrea Falqui, T. Ozaki, Roberto Morandotti, Enzo Di Fabrizio

Bibliographic record

VenueOptics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsHydro-QuébecInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsTerahertz radiationTerahertz spectroscopy and technologyMetamaterialOpticsDipolePlasmonTerahertz gapMaterials scienceNanostructureNonlinear opticsBiophotonicsOptoelectronicsPhotonicsPhysicsNanotechnologyFar-infrared laserTerahertz metamaterials

Abstract

fetched live from OpenAlex

The distinctive ability of nanometallic structures to manipulate light at the nanoscale has recently promoted their use for a spectacular set of applications in a wide range of areas of research including artificial optical materials, nano-imaging, biosensing, and nonlinear optics. Here we transfer this concept to the terahertz spectral region, demonstrating a metal nanostructure in shape of a dipole nanoantenna, which can efficiently resonate at terahertz frequencies, showing an effective cross section >100 times larger than its geometrical area, and a field enhancement factor of ~280, confined on a lateral section of ~λ/1,000. These results lead to immediate applications in terahertz artificial materials exhibiting giant dichroism, suggest the use of dipole nanoantennas in nanostructure-based terahertz metamaterials, and pave the way for nanoantenna-enhanced terahertz few-molecule spectroscopy and localized terahertz nonlinear optics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.478

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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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

Citations67
Published2011
Admission routes1
Has abstractyes

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