The effect of the target size on the optical response of ultrafine metallic spherical particles arranged in a two-dimensional array
Bibliographic record
Abstract
Discrete Dipole Approximation (DDA) is a computational technique to simulate the optical properties of nanostructures of different shapes, sizes, and compositions. The influence of the target size on the optical response of 5 nm-diameter nanoparticles arranged in a monolayer hexagonal array is investigated by using DDA at various incident angles of the incident light on the target considered for silver (Ag), gold (Au) and copper (Cu) nanoparticles. In our study, the target size is controlled by the number of the spherical nanoparticles used to generate the two dimensional arrays. The interparticle distance is kept constant in all the simulations. The anisotropic response of noble-metal nanoparticles is generally characterized by the excitation of the high-energy (transverse) surface plasmon (SP) mode and the low-energy (longitudinal) SP mode. Results of the simulations for the three chosen metals show an exponential dependency of the absorption efficiency of the SP modes with respect to the target size. As the target size is increased, the energy of Ag-longitudinal SP mode is red-shifted and it displays an exponential decay while the band position of the transverse mode is blue-shifted. They however overlap when the smallest target size is considered. Although, the optical response of Au and Cu nanoparticle arrays shows the same dependency on the target size as observed in the case of Ag, the positions of their respective longitudinal and transverse modes are very close, making these almost indistinguishable. The dependency of the absorption efficiency of SP modes on the incident angle is fitted linearly for Cu, Au and longitudinal- Ag modes to the target size, while the transverse-Ag mode shows an exponential fitting. No change in the Ag-SP band position is observed when the incident angle is changed, but the SP bands for both Au and Cu exhibit exponential variation behavior.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".