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Record W2078262927 · doi:10.1063/1.3369440

Theoretical proposal for a biosensing approach based on a linear array of immobilized gold nanoparticles

2010· article· en· W2078262927 on OpenAlexafffund
Seyed Mohammad Hashemi Rafsanjani, T. Cheng, Silvia Mittler, Chitra Rangan

Bibliographic record

VenueJournal of Applied Physics · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsWestern UniversityUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCompute CanadaOntario Innovation Trust
KeywordsBiosensorDielectricPolarization (electrochemistry)NanoparticleMaterials scienceSurface plasmonColloidal goldAnalytePlasmonDipoleDiscrete dipole approximationCoatingSurface modificationOpticsNanotechnologyOptoelectronicsChemistryPhysics

Abstract

fetched live from OpenAlex

We propose a sensing mechanism for detection of analytes that can specifically recognized. The sensor is based on closely-spaced chains of functionalized gold nanoparticles (NPs) immobilized on a waveguide surface, with the signal detected by evanescent waveguide absorption spectroscopy. The localized surface plasmon spectrum of a linear array of closely-spaced, hemispherical gold NPs is calculated using the discrete dipole approximation. The plasmon band is found to broaden to a nanowirelike spectrum when a dielectric coating is put on the particles, and the light polarization is along the NP chain. The origin of this broadening is shown to be the polarization-dependent overlap of the evanescent fields of adjacent NPs upon application of the dielectric coating. These features suggests a mechanism for biosensing with an improved sensitivity compared with traditional NP biosensor methods.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations17
Published2010
Admission routes2
Has abstractyes

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