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Record W1991858311 · doi:10.1117/12.2080428

Numerical investigation of plasmonic properties of gold nanoshells

2015· article· en· W1991858311 on OpenAlexaff
Krishnan Sathiyamoorthy, Michael C. Kolios

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPlasmonMaterials scienceRefractive indexNanoshellSurface plasmon resonanceSurface plasmonDielectricOptoelectronicsCore (optical fiber)WavelengthOpticsLocalized surface plasmonNanoparticleNanotechnologyPhysics

Abstract

fetched live from OpenAlex

We numerically investigated plasmonic properties of gold coated 300 nm core shell particles (CS). It is known that the surface plasmon decays into the medium that encompasses the metal nanoparticle. This decay converts changes in the local refractive index into a frequency shift of the SPR. In this work, the core material was polystyrene and the shell was a thin gold layer. We showed that this CS exhibits two plasmonic modes in the visible-near infrared regime. The blue end plasmonic mode was confined at the core-metal dielectric interface and the red end plasmonic mode was attributed to a surface mode that depends on dielectric properties of the surrounding medium. The application of the red end plasmonic mode as a surface plasmon resonance (SPR) sensor revealed that it exhibits wavelength shift of 764±13 nm per refractive index unit change of the surrounding medium (nm/RIU). Potential biomedical applications of these sensors are discussed.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.219
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations4
Published2015
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207