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Record W3080374263 · doi:10.1364/osac.401743

Tweezing and manipulating the distribution of gold nanorods (GNRs) on a tapered optical fiber to develop a plasmonic structure

2020· article· en· W3080374263 on OpenAlexafffund
Navneet Kaur, Joshua O. Trevisanutto, Gautam Das

Bibliographic record

VenueOSA Continuum · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsNanorodMaterials sciencePlasmonFiberOptical fiberRaman spectroscopyOptoelectronicsSurface-enhanced Raman spectroscopyNanotechnologyMulti-mode optical fiberSurface plasmonOpticsRaman scatteringComposite material

Abstract

fetched live from OpenAlex

A fiber-based plasmonic structure, called a fiber probe, was developed using gold nanorods (GNRs). The distribution of gold nanorods was manipulated using different wavelengths by the phenomenon called optical tweezing. The GNRs are deposited on a tapered fiber surface, which was prepared by etching a multimode fiber. We investigated the physical characteristics of the tapered fiber on the distribution of GNRs. The experimental results based on the developed plasmonic structure as a surface-enhanced raman spectroscopy (SERS) substrate for the detection of graphite and R6G has been reported. The plasmonic structure was also characterized optically.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.019
GPT teacher head0.229
Teacher spread0.209 · 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 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

Citations6
Published2020
Admission routes2
Has abstractyes

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