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Bulk and surface sensitivities of surface plasmon waveguides

2008· article· en· W2082382072 on OpenAlexaff
Pierre Berini

Bibliographic record

VenueNew Journal of Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWaveguideInterferometrySurface plasmonPhysicsWavelengthSensitivity (control systems)OpticsAttenuationOptoelectronicsPlasmonPhotonicsThin filmMaterials science

Abstract

fetched live from OpenAlex

The potential of surface plasmon waveguides for bulk and surface (bio)chemical sensing was assessed theoretically, anticipating their use in an integrated optics sensor such as a Mach–Zehnder interferometer (MZI). The performance of a generic MZI implemented with attenuating waveguides was assessed initially, revealing that attenuating waveguides constrain the sensing length to an optimal length equal to the propagation length of the mode used. The MZI sensitivities for bulk and surface sensing were found to be proportional to the ratio of the waveguide sensitivity to its normalized attenuation: H =(∂ n eff /∂ n c )/ k eff for bulk sensing and G =(∂ n eff /∂ a )/ k eff for surface sensing. Maximizing H or G maximizes the corresponding MZI sensitivity and minimizes its detection limit, leading to preferred waveguide designs and operating wavelengths. The propagation constant, the sensitivities, and the H and G parameters were then determined for the surface plasmon in the single interface, the s b mode in the metal–insulator–metal (MIM) waveguide and the s b mode in three variants of the insulator–metal–insulator (IMI) waveguide, as a function of dimensions, for wavelengths spanning 600⩽λ 0 ⩽1600 nm, assuming Au and H 2 O as the materials and adlayers representative of biochemical matter. The principal findings are: (i) the surface sensitivity in the thin MIM can be 100 × larger than in the single interface, whereas that in the thin IMI is up to 5 × smaller; (ii) the bulk sensitivity in the thin MIM can be 3 × larger than in the single interface, whereas that in the IMI is slightly smaller; (iii) G in the thin MIM can be 3 × larger than in the single interface, whereas G in the IMI is about 10 × larger; and (iv) H in the thin MIM can be 10 × smaller than in the single interface, whereas H in the thin IMI is about 10 × larger. The IMI and the MIM both offer an improvement in sensitivity and detection limit for surface sensing over the single interface in an integrated MZI (or Kretschmann–Raether) configuration, despite the fact that they are at opposite ends of the confinement–attenuation trade-off. Preferred wavelengths for surface sensing were found to be near the short wavelength edge of the Drude region, where detection limits of about 0.1 pg mm −2 are predicted. With regard to bulk sensing, only the IMI offers an improvement over the single interface. The results are collected in a form that should be useful for investigating other sensor architectures implemented with these waveguides or variants thereof.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.234
Teacher spread0.208 · 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

Citations185
Published2008
Admission routes1
Has abstractyes

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