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Record W1978989442 · doi:10.1155/2012/729138

Antenna Design for Directivity-Enhanced Raman Spectroscopy

2012· article· en· W1978989442 on OpenAlexafffund
Aftab Ahmed, Yuanjie Pang, Ghazal Hajisalem, Reuven Gordon

Bibliographic record

VenueInternational Journal of Optics · 2012
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDirectivityOpticsAntenna (radio)PlasmonGround planeMaterials scienceReflector (photography)Raman spectroscopyWaveguideAperture (computer memory)Parabolic reflectorBeam (structure)OptoelectronicsPhysicsComputer scienceTelecommunicationsAcoustics

Abstract

fetched live from OpenAlex

Antenna performance can be described by two fundamental parameters: directivity and radiation efficiency. Here, we demonstrate nanoantenna designs in terms of improved directivity. Performance of the antennas is demonstrated in Raman scattering experiments. The radiated beam is directed out of the plane by using a ground plane reflector for easy integration with commercial microscopes. Parasitic elements and parabolic and waveguide nanoantennas with a ground plane are explored. The nanoantennas were fabricated by a series of electron beam evaporation steps and focused ion beam milling. As we have shown previously, the circular waveguide nanoantenna boosts the measured Raman signal by 5.5x with respect to a dipole antenna over a ground plane; here, we present the design process that led to the development of that circular waveguide nanoantenna. This work also shows that the parabolic nanoantenna produces a further fourfold improvement in the measured Raman signal with respect to a circular waveguide nanoantenna. The present designs are nearly optimal in the sense that almost all the beam power is coupled into the numerical aperture of the microscope. These designs can find applications in microscopy, spectroscopy, light-emitting devices, photovoltaics, single-photon sources, and sensing.

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 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: none
Teacher disagreement score0.478
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.306
Teacher spread0.274 · 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

Citations6
Published2012
Admission routes2
Has abstractyes

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