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Record W2045875768 · doi:10.1039/c3nr04002j

Tuning the 3D plasmon field of nanohole arrays

2013· article· en· W2045875768 on OpenAlexaff
Maxime Couture, Yuzhang Liang, Hugo-Pierre Poirier Richard, Rita Faid, Wei Peng, Jean‐François Masson

Bibliographic record

VenueNanoscale · 2013
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPlasmonMaterials scienceSurface plasmon resonanceSurface plasmonOpticsFinite-difference time-domain methodGratingRaman scatteringSurface plasmon polaritonLocalized surface plasmonOptoelectronicsWavelengthNanophotonicsRaman spectroscopyNanotechnologyNanoparticlePhysics

Abstract

fetched live from OpenAlex

Modern photonics is being revolutionized through the use of nanostructured plasmonic materials, which confine light to sub-diffraction limit resolution providing universal, sensitive, and simple transducers for molecular sensors. Understanding the mechanisms by which light interacts with plasmonic crystals is essential for developing application-focussed devices. The strong influence of grating coupling on electromagnetic field distribution, frequency and degeneracy of plasmon bands has now been characterized using hexagonal nanohole arrays. An equation for nanohole arrays was derived to demonstrate the strong influence of incidence and rotation angle on optical properties of 2D plasmonic crystals such as nanohole arrays. Consequently, we report experimental data that are in strong agreement with finite difference time-domain (FDTD) simulations that clearly demonstrate the influence of the grating coupling conditions on the optical properties (such as plasmon degeneracy and bandwidth), and on the distribution of the plasmon field around nanohole arrays (including tuneable penetration depths and highly localized fields). The tuneable 3D plasmon field allowed for controlled sensing properties and by increasing the angle of incidence to 30 degrees, the resonance wavelength was tuned from 1000 to 600 nm, and the sensitivity was enhanced by nearly 300% for a protein assay using surface plasmon resonance (SPR) and by 40% with surface-enhanced Raman scattering (SERS) sensors.

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 score0.376
Threshold uncertainty score0.632

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)0.0010.000

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 head0.232
Teacher spread0.220 · 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

Citations85
Published2013
Admission routes1
Has abstractyes

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