Unified Model for Plasmon Coupling in Metal Nanoparticles and Effect of the Polymer Shell
Bibliographic record
Abstract
Plasmon resonance and its coupling in metal nanoparticles enable the modulation of the resonance spectrum with particle size, shape, and near/far-field interactions. The plasmon coupling of the resonance spectrum is blue-shifted when the nanoparticles are separated by a distance in the range of lattice spacings but is red-shifted for larger separations (much more greater than the lattice spacing). The spectral behavior in these two regimes is driven by charge transfer and plasmon hybridization and has not been explained simultaneously for an assembly of nanoparticles with existing models. Herein, we developed a model by considering a virtual nanobridge for lattice parameter spacing to account for the charge transfer between nanoparticles in the dimer, trimer, tetramer, and pentamer assemblies, while for larger separations, the coupling adheres to hybridization. The interparticle separation-dependent spectral shift in both regimes was established well with the proposed unified plasmon coupling model, which is consistent with previous experiments and theories and is applicable to assemblies of any number of nanoparticles. We also showed the effect of the polymer shell on monomer/dimer plasmons and examined the polarization-dependent plasmon coupling in multinanoparticle assemblies, providing insight on tunable plasmon enhancement toward advanced quantum devices.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".