Dynamic Imaging of Multiple SERS Hotspots on Single Nanoparticles
Bibliographic record
Abstract
Signal intensity fluctuations are a ubiquitous characteristic of single-molecule surface-enhanced Raman scattering (SERS). In this work, we observed SERS intensity fluctuations (SIFs) from single nanoparticles fully coated with an adsorbate layer. Fluctuations from dry, fully coated nanoparticles are assigned to a dynamic molecule/metal environment wherein atomic-scale reconstructions support SERS. Using super resolution imaging techniques, we were able to pinpoint the positions of the fluctuations with subparticle precision. We observed that the fluctuation events were separated spatially, temporally, and were unique to different laser excitation wavelengths and polarizations. Dual-wavelength super-resolution SERS imaging with green and red lasers reveal various classes of SIFs that occur either simultaneously or nonsimultaneously and from either the same or different location on a single nanoparticle. Similar results were seen when the particle is excited with different polarizations. This suggests that single molecule responses from several different hotspots in the same nanoparticle were readily probed. Furthermore, each nanoparticle contains multiple unique hotspots of different strengths, and resonance conditions, which are accessible by the different illumination conditions. The plasmon resonances localized by the roughness features at the nanoparticle’s surface play a significant role in the fluctuation events. Our experiments show that SERS hotspots that support single molecules are not a static feature of the nanoparticle. This information should be useful to guide future single-molecule SERS experiments.
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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".