Plasmon Tunability and Field Enhancement of Gold Nanostar
Bibliographic record
Abstract
Objective: The localized surface plasmon resonance (LSPR) and field enhancement of Gold nanosphere and nanostar were evaluated. Method: FDTD solutions, a product of Lumerical solutions Inc., Vancouver, Canada [17], was used to perform the electromagnetic simulations in this work. The impact of particle size and spike number on peak wavelength was studied quantitatively. Result: By altering the particle size and amount of spikes, we were able to detect a hot zone around nanostar. For Au nanostar, the peak wavelength for nanostar varies from visible to near-infrared. When compared to a nanosphere of the same dimension, the shift seen in nanostar is substantially higher, making it more suitable for biosensing applications. When the refractive index of the surrounding medium is increased, a red shift in peak wavelength is noticed, forming the basis for a plasmonic refractive index sensor. Aside from having a higher sensitivity, nanostar has a twofold hot spot system due to their unique surfaces. There is no evidence of spike aggregation in the near field pattern. As a result, it is thought to be a better nanostructure for biosensing applications. Conclusion: The LSPR and field enhancement for Au nanosphere and Nanostar were investigated using the FDTD method. The nanosphere's peak wavelength is in visible region, whereas the nanostar's range extends from visible to near-infrared, depending on the size and number of spikes. At 517 nm, the enhancement factor for a nanosphere was 102, but at 1282 nm, the enhancement factor for a nanostar with six spikes was 108.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".