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Record W2094410289 · doi:10.1021/nl048818w

Nanohole-Enhanced Raman Scattering

2004· article· en· W2094410289 on OpenAlexaff
Alexandre G. Brolo, Erin Arctander, Reuven Gordon, B. Leathem, K. L. Kavanagh

Bibliographic record

VenueNano Letters · 2004
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of VictoriaSimon Fraser University
Fundersnot available
KeywordsRaman scatteringRaman spectroscopyMaterials sciencePlasmonOpticsSurface plasmonOptoelectronicsWavelengthLaserCoherent anti-Stokes Raman spectroscopyScatteringPhysics

Abstract

fetched live from OpenAlex

Periodic arrays of sub-wavelength apertures (nanoholes) in ultrathin Au films were used as substrates for enhanced-Raman spectroscopy in the optical range. Nanohole-enhanced (resonance) Raman scattering from oxazine 720 (oxa) adsorbed on arrays of different periodicities (distance between the center of the holes) was obtained. The overall Raman intensity of the adsorbed molecule was dependent on the periodicity of these arrays. The enhancement factor reached a maximum for the array that presented the largest transmission at the excitation wavelength of the laser. This shows that the enhancement of the Raman signal is provided by surface plasmon (SP) modes excited at the array of nanoholes. SP excitations lead to spatial localization of the electromagnetic fields in nanometric regions close to the surface. This field localization, allied to the unique vibrational signature of the Raman scattering and the simplified optical arrangement from the transmission optics, suggests that arrays of nanoholes should be useful for the fabrication of dense biochips for the detection of Raman-labeled analytes with high sensitivity and selectivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.011
GPT teacher head0.222
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 source (direct Gemma or distilled Codex), 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

Citations430
Published2004
Admission routes1
Has abstractyes

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