Surface-Enhanced Raman and Resonant Rayleigh Scatterings From Adsorbate Saturated Nanoparticles
Bibliographic record
Abstract
Collective excitation of conduction electrons in metallic nanoparticles (NP) sustains surface plasmon resonance (SPR) resulting in a large and highly localized amplification of the incident electromagnetic field near the metal NP. This in turn enhances scattered radiation from any molecule in this neighborhood and is the enabling mechanism of surface enhanced Raman scattering (SERS). The optical response of the NPs is highly dependent on their composition, size, shape, and dielectric environment as well as on coupling with nearby particles. In this study, the surfaces of isolated gold NPs and NP dimers, trimers, and multimers were saturated with a Raman reporter molecule (4-(mercaptomethyl)benzonitrile), SERS intensity and surface plasmon resonance profiles were measured, and these were correlated with the nanostructure geometry as revealed by atomic force microscopy (AFM). Dark-field optical microscopy aided in the selection of isolated NPs and was used for measurements of their SPR in the form of resonant Rayleigh scattering spectroscopy. Among all of the probed NPs, all aggregates, including dimers, trimers, and other multimers showed intense SERS activity. All but one of the gold monomers that were examined exhibited no detectable SERS activity. The one exception was an individual triangular nanoprism for which a very weak but detectable SERS signature was observed. We have also calculated the spatial electric field distribution about a spherical monomer, a triangular nanoprism and a dimer of spheres to elucidate the observed differences in their SERS response. The combination of high resolution AFM imaging and light scattering spectroscopies in this study highlights the interdependency of the NP structure, its corresponding SPR response and the role they play in forging strong SERS activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".