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Record W2050842014 · doi:10.1039/c2nr31885g

Fine tuning of plasmonic properties of monolayers of weakly interacting silver nanocubes on thin silicon films

2012· article· en· W2050842014 on OpenAlexafffund
Adam Bottomley, Daniel Prezgot, Alyssa Staff, Anatoli Ianoul

Bibliographic record

VenueNanoscale · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceRhodamine 6GPlasmonMonolayerSiliconThin filmRefractive indexSubstrate (aquarium)Raman spectroscopyOptoelectronicsSurface plasmon resonanceQuenching (fluorescence)NanotechnologyFluorescenceOpticsNanoparticle

Abstract

fetched live from OpenAlex

Plasmonic properties, such as refractive index sensitivity (RIS), surface enhancement of the Raman signal (SERS), fluorescence quenching, and photocatalytic activity, of monolayers of weakly interacting monodisperse silver nanocubes were qualitatively modified in a very well controlled manner by supporting them on thin silicon films with varying thickness. Such fine tunability is made possible by the strong dependence of the nanocube dipolar (D) and quadrupolar (Q) plasmon mode hybridization on the refractive index of the supporting substrate. By increasing the Si film thickness from zero to ~25 nm we were able to "shift" the D resonance mode by up to 200 nm for ~80 nm cubes without significantly affecting the Q mode. The silicon supported nanocubes showed a significant improvement in RIS via the Q mode with a figure of merit greater than 6.5 and about an order of magnitude enhancement of the SERS signal due to the stronger electric field created by the D mode. Such substrates also showed a ~10 times decrease in rhodamine 6G fluorescence as well as the rates of amorphous carbon formation. The study proposes a new way to design and engineer plasmonic nanostructures.

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 score0.004
Threshold uncertainty score0.308

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 head0.029
GPT teacher head0.243
Teacher spread0.215 · 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

Citations23
Published2012
Admission routes2
Has abstractyes

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