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Record W1975294680 · doi:10.1103/physrevb.77.205114

Left-handed behavior of split-ring resonators: Optical measurements and numerical analysis

2008· article· en· W1975294680 on OpenAlexafffund
Jing Yang, Jungseek Hwang, T. Timusk

Bibliographic record

VenuePhysical Review B · 2008
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsCanadian Institute for Advanced ResearchMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsMetamaterialSplit-ring resonatorNegative refractionPermittivityResonatorRing (chemistry)OpticsNegative index metamaterialsMaterials sciencePhysicsRealization (probability)Refractive indexResonance (particle physics)Condensed matter physicsOptoelectronicsDielectricAtomic physics

Abstract

fetched live from OpenAlex

The periodic double-ring split-ring resonator (SRR) array was one of the first proposed magnetic metamaterials for the realization of a negative index of refraction. We experimentally and numerically study double-ring SRRs on silicon substrates in the midinfrared frequency regime. For light at normal incidence, we observe that an electric resonance in the outer ring and a magnetic resonance in the inner ring exist at similar frequencies in our sample, which suggests that the double-ring SRR array could simultaneously have a negative permittivity and a negative permeability, or a left-handed behavior. Our conjecture are confirmed by numerical simulations. We also propose a left-handed metamaterial composed of two single-ring SRRs in each unit cell. The left-handed behaviors in the two metamaterials studied here originate only from the SRR itself. Thus, there are no metallic continuous wires involved in the devices compared to conventional left-handed SRR metamaterials.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.079
GPT teacher head0.345
Teacher spread0.266 · 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 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

Citations7
Published2008
Admission routes2
Has abstractyes

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