Effective Medium Properties of Arbitrary Nanoparticle Shapes in a Localized Surface Plasmon Resonance Sensing Layer
Bibliographic record
Abstract
Inhomogeneous nanoparticle layers are often modeled as effective homogeneous layers in order to simplify optical device design. Maxwell–Garnett (MG) theory is often used to find the effective medium properties of localized surface plasmon resonance (LSPR) sensing layers. However, MG theory is only applicable for small spherical particles with low filling fractions, thus limiting its applicability. In this paper, an extraction method is used to determine the effective medium properties of an LSPR sensing layer consisting of metal nanoparticles of arbitrary shape. Complex reflection and transmission coefficients ( S parameters) are found using CST Microwave Studio (CST MWS), a commercial software package. Effective index of refraction ( n eff ) and impedance ( z eff ) are calculated from the simulated S parameters. This method is extended to account for substrate effects on the effective medium properties. Thus, this method allows for more accurate homogenization of LSPR sensor layers made of any nanoparticle shape, enabling improved LSPR device design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".