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Record W2116839378 · doi:10.1109/aps.2006.1710633

Finite-difference time-domain simulation of plasmonic nanoparticles

2006· article· en· W2116839378 on OpenAlex
Yaxun Liu, Costas D. Sarris, George V. Eleftheriades

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2006 IEEE Antennas and Propagation Society International Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinite-difference time-domain methodMetamaterialPlasmonNanoparticleMaterials scienceDielectricCapacitorCapacitive sensingRefractive indexTransmission lineOptoelectronicsNanotechnologyOpticsComputer sciencePhysicsVoltageTelecommunications

Abstract

fetched live from OpenAlex

Recently noble metal nanoparticles have attracted strong interest due to their potential as nano-scale waveguides and lumped elements in the optical frequency range. Engheta et. al., (2005) showed that a non-metal nanoparticle can serve as a capacitor whereas a metal nanoparticle can serve as an inductor. This idea is interesting for the design of optical metamaterials, if the proposed nanoparticles are employed to implement the inductive/capacitive blocks in 2-D/3-D negative-refractive-index transmission line (NRI-TL) based metamaterials. Due to the difficulty of adjusting to the inherent numerical intricacies of this problem, simulating plasmonic nanoparticles becomes a challenge for commercial packages. Full-wave analysis through the finite-difference time-domain (FDTD) can provide an alternative to them. In this paper, we present the FDTD analysis of a silver nano sphere and compare its field pattern to that of a dielectric nano sphere. These results are also validated against data obtained from quasi-static theory.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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