Plasmon response evaluation based on image-derived arbitrary nanostructures
Bibliographic record
Abstract
The optical response of realistic 3D plasmonic substrates composed of randomly shaped particles of different size and interparticle distance distributions in addition to nanometer scale surface roughness is intrinsically challenging to simulate due to computational limitations. Here, we present a Finite Element Method (FEM)-based methodology that bridges in-depth theoretical investigations and experimental optical response of plasmonic substrates composed of such silver nanoparticles. Parametrized scanning electron microscopy (SEM) images of surface enhanced Raman spectroscopy (SERS) active substrate and tip-enhanced Raman spectroscopy (TERS) probes are used to simulate the far-and near-field optical response. Far-field calculations are consistent with experimental dark field spectra and charge distribution images reveal for the first time in arbitrary structures the contributions of interparticle hybridized modes such as sub-radiant and super-radiant modes that also locally organize as basic units for Fano resonances. Near-field simulations expose the spatial position-dependent impact of hybridization on field enhancement. Simulations of representative sections of TERS tips are shown to exhibit the same unexpected coupling modes. Near-field simulations suggest that these modes can contribute up to 50% of the amplitude of the plasmon resonance at the tip apex but, interestingly, have a small effect on its frequency in the visible range. The band position is shown to be extremely sensitive to particle nanoscale roughness, highlighting the necessity to preserve detailed information at both the largest and the smallest scales. To the best of our knowledge, no currently available method enables reaching such a detailed description of large scale realistic 3D plasmonic 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 | 0.001 |
| 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 | 0.000 | 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".