Finite-difference time-domain simulation of plasmonic nanoparticles
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".