Directional Light Scattering In Mie‐Resonant Si Particles With Ultra‐Thin Au Shells
Bibliographic record
Abstract
Abstract Metamaterial research has sought to create nanostructures with strong directional optical scattering to control light propagation at the nanoscale. Core–shell architectures comprised of both resonant cores and resonant shells are suggested as candidate particles in which the spectral overlap of the electric and magnetic dipoles is controlled to create strong directional scattering. In this study, Au‐decorated Si core–shell (Si@Au) particles are presented, studying the role of the architecture (particulate, discontinuous shells vs continuous) and dimensions of the shell. The core–shell particles are synthesized by first creating Si particles, through the thermal disproportionation of hydrogen silsesquioxane (HSQ), which are then decorated with ≈4 nm diameter Au nanoparticles. The resonant behavior of the core–shell particles is characterized using electron energy‐loss spectroscopy mapping and optical single‐particle scatter spectroscopy. These observations are supported by T‐matrix simulations and Mie‐theory calculations of the scattering spectra, which show that, compared to Si, Si@Au particles demonstrate a dampened magnetic dipole resonance for smaller Si core diameters (100–130 nm) and an enhanced magnetic dipole resonance for larger Si core sizes (150–200 nm). The study indicates that the previously reported hybridized modes do not exist in particulate Au shells around a Si core and can only exist in continuous plasmonic shells. Thus, it is shown here how important it is to be as precise as possible regarding the nanomaterial architecture used in simulations. No configuration of Si@Au core–shell particles with a particulate shell could be found that strongly enhanced directional scattering, and a continuous shell may do so only modestly. However, the simulations show that the synthesis of thin, continuous Ag shells might represent an alternative route towards achieving good directional scattering properties.
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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".