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Record W2604285384 · doi:10.1021/acsphotonics.6b00923

Improvement of Sensing and Trapping Efficiency of Double Nanohole Apertures via Enhancing the Wedge Plasmon Polariton Modes with Tapered Cusps

2017· article· en· W2604285384 on OpenAlexaff
Mostafa Ghorbanzadeh, Steven Jones, Mohammad Kazem Moravvej‐Farshi, Reuven Gordon

Bibliographic record

VenueACS Photonics · 2017
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaterials sciencePlasmonTrappingSurface plasmon polaritonWedge (geometry)OpticsOptoelectronicsNumerical apertureAperture (computer memory)FabricationNanophotonicsDielectricPolaritonOptical tweezersSurface plasmon resonanceSurface plasmonWavelengthNanoparticleNanotechnology

Abstract

fetched live from OpenAlex

In the past few years, double nanohole (DNH) apertures in a gold film have been used extensively to trap and sense biological and artificial dielectric nanoparticles. Using numerical simulations we show that the conical shape of a DNH, milled by a focused ion beam into a thin gold layer, which is an inherent property of the fabrication process, plays a critical role in the sensitivity of the DNHs, and is beneficial to the optical sensing and trapping applications. The slope of the metallic wedges in an appropriately designed DNH leads to 2D nanofocusing of gap surface plasmons (GSPs) and couples them to the wedge plasmon polaritons (WPPs), creating “hot spots” required for trapping. The transmission variations due to the trapping polystyrene nanoparticles of radii 11 ± 1 nm by particularly designed DNHs, measured at the wavelength near the corresponding wedge mode resonance, are shown to be in good agreements with numerical results using conically modeled DNHs. This observation highlights the extreme sensitivity of aperture assisted trapping, specifically with regard to the DNH structure. These findings open up new routes toward the design and optimization of efficient aperture structures for trapping and sensing 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)0.000
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 score0.307
Threshold uncertainty score0.523

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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

Citations64
Published2017
Admission routes1
Has abstractyes

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