Electromagnetic response of nanoparticles with a metallic core and a semiconductor shell
Bibliographic record
Abstract
Abstract We study the interplay between localized surface plasmon resonances from metallic cores and electromagnetic resonances from semiconducting shells in core@shell nanoparticles in the optical and near-infrared regions. To this end, we consider silver (Ag) spheres as plasmonically active nanoparticles with radii 20 nm, covered with shells of silicon (Si) up to 160 nm in thickness. We use the classical Lorenz-Mie theory to calculate the response of the core@shell nanoparticles to an external electromagnetic field that reveals a high degree of tunability of the Ag surface plasmons with a varying Si shell thickness, and a consequent merging of their Mie resonances. In contrast with pure metallic systems, the use of a low-bandgap semiconducting shell allows for a unique interrelation between its strong characteristic magnetic dipole mode and the localized surface plasmon resonance of the metallic core. This allows control over the forward and backward scattering efficiencies in the near-infrared in accordance with the predictions based on the Kerker conditions. Employing several other core@shell materials (Al@Si, Au@Si and Ag@Ge), we show that this approach to tailoring the absorption and scattering efficiencies, based on Kerker’s conditions, can be further generalized to other similar core@shell systems.
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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".