Multiscale models of plasmonic structural colors with nanoscale surface roughness
Bibliographic record
Abstract
Plasmonic coloration arises from resonant interaction between visible light and metallic nanostructures, which causes wavelength-selective absorption or scattering of light. This effect is sensitive to surface roughness that can perturb these resonant interactions and cause observed coloration to deviate from coloration predicted by simulations. We present a computational visualization approach that incorporates electrodynamic simulations and physically based rendering (PBR) to investigate the effect of nanoscale roughness on the structural coloration from thin, planar silver films decorated with nanohole arrays. Nanoscale roughness is modeled mathematically by a surface correlation function and parameterized in terms of roughness that is either out of or into the plane of the film. Our results provide photorealistic visualization of the influence of nanoscale roughness on the coloration from silver nanohole arrays in both reflectance and transmittance. Out-of-plane roughness has a significantly greater effect on coloration than in-plane roughness. The methodology introduced in this work is useful for modeling artificial coloration phenomena.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".