Decoupling and tuning the light absorption and scattering resonances in metallic composite nanostructures
Bibliographic record
Abstract
Utilizing the localized surface plasmon resonance (LSPR) effect of metallic nanoparticles enables their usage as contrast agents in a variety of applications for medical diagnostics and treatment. Those applications can use both the very strong absorption and scattering properties of the metallic nanoparticle due to their LSPR effects. There are certain applications where domination of the scattering over absorption or vice versa would be an advantage. However, the scattering and absorption resonance peaks have practically the same spectral location for solid noble metal nanoparticles at a certain domination of one over the other. In this paper we present gold nanoparticles coated with silicon that switches the order between the scattering and the absorption magnitude at the resonance peak by up to 34% in scattering-absorption ratio and tune the plasmon resonance over the spectrum by up to 56nm. This is obtained by modifying the refractive index of the silicon coating of the nanoparticle by illuminating it with a pumping light due to the plasma dispersion effect in silicon.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".