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Record W4200560005 · doi:10.1515/nanoph-2021-0449

The effects of bending on plasmonic modes in nanowires and planar structures

2021· article· en· W4200560005 on OpenAlexafffund
Edson P. Bellido, Isobel C. Bicket, Gianluigi A. Botton

Bibliographic record

VenueNanophotonics · 2021
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCanadian Light Source (Canada)McMaster University Medical CentreMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlasmonNanowireBendingPlanarMaterials scienceNanostructureOptoelectronicsSurface plasmonEnhanced Data Rates for GSM EvolutionOpticsNanotechnologyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this work, we investigate the effects of bends on the surface plasmon resonances in nanowires (NWs) and isolated edges of planar structures using electron energy loss spectroscopy experiments and theoretical calculations. Previous work showed that the sharp bends in NWs do not affect their resonant modes. Here, we study previously overlooked effects and analyze systematically the evolution of resonant modes for several bending angles from 30° to 180°, showing that bending can have a significant effect on the plasmonic response of a nanostructure. In NWs, the modes can experience significant energy shifts that depend on the aspect ratio of the NW and can cause mode intersection and antinode bunching. We establish the relation between NW modes and edge modes and show that bending can even induce antinode splitting in edge modes. This work demonstrates that bends in plasmonic planar nanostructures can have a profound effect on their optical response and this must be accounted for in the design of optical devices.

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 score0.061
Threshold uncertainty score0.430

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 head0.007
GPT teacher head0.221
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

Citations3
Published2021
Admission routes2
Has abstractyes

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