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Record W4306153523 · doi:10.3390/nano12203586

Spatially-Localized Functionalization on Nanostructured Surfaces for Enhanced Plasmonic Sensing Efficacy

2022· article· en· W4306153523 on OpenAlexafffund
Jean‐François Bryche, Marlo Vega, A. Tempez, Thibault Brulé, Thomas Carlier, Julien Moreau, Marc Chaigneau, Paul G. Charette, Michael Canva

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsSurface modificationThiophenolMaterials sciencePlasmonNanotechnologyRaman spectroscopyPlasmonic nanoparticlesOptoelectronicsOpticsChemical engineeringChemistry

Abstract

fetched live from OpenAlex

This work demonstrates the enhancement in plasmonic sensing efficacy resulting from spatially-localized functionalization on nanostructured surfaces, whereby probe molecules are concentrated in areas of high field concentration. Comparison between SERS measurements on nanostructured surfaces (arrays of nanodisks 110 and 220 nm in diameter) with homogeneous and spatially-localized functionalization with thiophenol demonstrates that the Raman signal originates mainly from areas with high field concentration. TERS measurements with 10 nm spatial resolution confirm the field distribution profiles predicted by the numerical modeling. Though this enhancement in plasmonic sensing efficacy is demonstrated with SERS, results apply equally well to any type of optical/plasmonic sensing on functionalized surfaces with nanostructuring.

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 categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score0.999

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 studies0.0010.000
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 head0.019
GPT teacher head0.252
Teacher spread0.232 · 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.

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

Citations3
Published2022
Admission routes2
Has abstractyes

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