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Record W1996770166 · doi:10.1021/jp806785u

Localized Raman Enhancement from a Double-Hole Nanostructure in a Metal Film

2008· article· en· W1996770166 on OpenAlexaff
Marcos J. L. Santos, Emerson M. Girotto, Alexandre G. Brolo, Reuven Gordon

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2008
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRhodamine 6GMaterials scienceNanostructureRaman scatteringPolarization (electrochemistry)Raman spectroscopyMoleculeOpticsFocused ion beamRhodamineMolecular physicsOptoelectronicsIonNanotechnologyChemistryFluorescencePhysics

Abstract

fetched live from OpenAlex

An isolated double-hole indentation, with concentric rings, in a metal film was used to obtain highly localized surface-enhanced Raman scattering (SERS) from regions much smaller than the optical wavelength. The structure was created by a focused ion beam (FIB) milling partially through the 100 nm thick gold film to a depth of 50 nm. Significant SERS enhancement was observed for both oxazine 720 and rhodamine 6G. The SERS was polarization-dependent because of the biaxial symmetry of the double-hole at the apexes where the indentations overlap; these apexes were responsible for the strong subwavelength focusing. The finite-difference time-domain method was used to calculate the electromagnetic field of the nanostructure, and it showed strong polarization-dependent focusing, in agreement with the experimentally observed SERS enhancement. On the basis of these calculations, it is estimated that the 60% polarization-dependent SERS enhancement is the result of only ∼1300 molecules in the region of the apexes, and it is estimated that the limit of detection is 20 molecules for the best-case configuration. This work is an important step toward single-molecule SERS from tailored nanostructures designed for predictable field enhancement.

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 score0.001
Threshold uncertainty score0.427

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations62
Published2008
Admission routes1
Has abstractyes

Explore more

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