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Record W2070493691 · doi:10.1109/tap.2013.2260517

Modeling Graphene in the Finite-Difference Time-Domain Method Using a Surface Boundary Condition

2013· article· en· W2070493691 on OpenAlexaff
Vahid Nayyeri, Mohammad Soleimani, Omar M. Ramahi

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFinite-difference time-domain methodGrapheneBoundary value problemSurface (topology)Finite difference methodStability (learning theory)Boundary (topology)ConductivitySurface conductivityTime domainMathematical analysisMaterials scienceComputer scienceMathematicsPhysicsOpticsGeometryNanotechnology

Abstract

fetched live from OpenAlex

An effective approach for finite-difference time-domain modeling of graphene as a conducting sheet is proposed. First, we present a new technique for implementing a conducting surface boundary condition in the FDTD method; then, the dispersive surface conductivity of graphene is imposed. Numerical examples are presented to show the stability, accuracy, applicability, and advantages of the proposed approach. Validation is achieved by comparison with existing analytic methods.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.274
Teacher spread0.252 · 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
GenreMethods

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

Citations155
Published2013
Admission routes1
Has abstractyes

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