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Record W1975186345 · doi:10.1063/1.2764222

Air gaps in metal stripe waveguides supporting long-range surface plasmon polaritons

2007· article· en· W1975186345 on OpenAlexaff
Pierre Berini

Bibliographic record

VenueJournal of Applied Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSurface plasmon polaritonAir gap (plumbing)Materials scienceSurface plasmonPlasmonOptoelectronicsElectric fieldPolaritonWaveguideFabricationTransverse planeBand gapRange (aeronautics)AttenuationOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

The effects of air gaps in metal stripe waveguides supporting long-range surface plasmon polaritons have been determined theoretically. The study is motivated by a recently adopted fabrication approach based on direct bonding, where various kinds of air gaps may form near the metal stripe due to fabrication imperfections. Specifically, “Air wings,” a top air gap, and side air gaps have been considered as possible perturbations. The main effects of the air gaps on the propagation of the long-range surface plasmon-polariton wave are that its attenuation and confinement decrease as the gaps become more invasive, and that its mode fields become strongly perturbed. Taken together, these effects are deleterious, so air gaps do not appear suitable for range extension. In general, very small air gaps can only be tolerated before confinement is completely lost. A lower-index planarizing layer could be used to help eliminate air gaps but the waveguide design space becomes constrained. The air gaps perturb the long-range mode such that its main transverse electric-field component (Ey) develops a maximum within the gaps and some localization therein. This feature could be interesting in applications where high-intensity fields in nanometric air gaps are sought, but only if coupling and radiation losses are not much of a concern.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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

Citations25
Published2007
Admission routes1
Has abstractyes

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