High-Speed Fluctuations in Surface-Enhanced Raman Scattering Intensities from Various Nanostructures
Bibliographic record
Abstract
The observation of single molecule events using surface-enhanced Raman scattering (SERS) is a well-established phenomenon. These events are characterized by strong fluctuations in SERS intensities. High-speed SERS intensity fluctuations (in the microsecond time scale) have been reported for experiments involving single metallic particles. In this work, the high-speed SERS behavior of six different types of nanostructured metal systems (Ag nanoshells, Ag nanostars, Ag aggregated spheres, Au aggregated spheres, particle-on-mirror, and Ag deposited on microspheres) was investigated. All systems demonstrated high-speed SERS intensity fluctuations. Statistical analysis of the duration of the SERS fluctuations yielded tailed distributions with average event durations around 100 μs. Although the characteristics of the fluctuations seem to be random, the results suggest interesting differences between the system that might be associated with the strength distribution and density of the localized SERS hotspots. For instance, systems with more localized fields, such as nanostars, present faster fluctuation bursts compared to metallic aggregates that support spread-out fields. The results presented here appear to confirm that high-speed SERS intensity fluctuations are a fundamental characteristic of the SERS effect.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".