Modeling of the surface plasmon resonance tunability of silver/gold core–shell nanostructures
Bibliographic record
Abstract
the modulation of the surface plasmon resonance wavelength as a function of the ambient refractive index is one of the important criteria to be understood clearly. Among various noble metals, gold and silver nanoparticles have the strongest surface enhancement factors for the Raman signal and their tunability for many practical applications has been experimentally demonstrated. We present a comprehensive numerical investigation by means of a finite element analysis on Ag/Au core-shell nanospheres including agglomerated and non-agglomerated dimers. Tunability as a function of shell thickness, total nanosphere radius and fraction of overlap between the dimer is discussed. Our studies show that tunability is considerably affected by the nanosphere radius rather than the shell thickness. These findings may be helpful in the synthesis of nanoplasmonic structures, especially related to an optimized use of gold as the shell material for the targeted application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".