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Record W4403175878 · doi:10.1088/1361-6528/ad83d7

Engineering Fano resonances in plasmonic metasurfaces for colorimetric sensing and structural colors

2024· article· en· W4403175878 on OpenAlexaff
Reza Kohandani, Simarjeet S. Saini

Bibliographic record

VenueNanotechnology · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePlasmonFano resonanceRefractive indexNanowireSurface plasmon resonanceStructural colorationFabricationOptoelectronicsOpticsLattice (music)Surface plasmonNanotechnologyPhotonic crystalNanoparticlePhysics

Abstract

fetched live from OpenAlex

) nanowire arrays integrated with plasmonic layers. The structure is engineered to produce Fano resonances within the visible spectrum, resulting from the coupling of localized surface plasmon resonances, lattice modes, and nanowire's optical modes. Experimentally, we show that by tuning the geometrical features of the metasurface, such as the length, diameter, and period of the nanowires, a high-quality factor single peak can be achieved in the reflection spectra, resulting in vivid structural colors in bright field. To our knowledge, this is the first demonstration of such vivid colors with nanowire arrays in bright field reflections. When characterized by refractive index fluids around the refractive index of water, the plasmonic metasurface also showed great potential for biochemical colorimetric sensing. The best design demonstrated a bulk sensitivity of 183 nm/RIU with high Q resonance features and linear changes in color values using image processing.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 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

Citations6
Published2024
Admission routes1
Has abstractyes

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