Appearance of SERS activity in single silver nanoparticles by laser-induced reshaping
Bibliographic record
Abstract
We report simultaneous plasmonic scattering and Raman spectroscopic observations of single citrate capped silver nanoparticles (AgNPs) which exhibit surface enhanced Raman scattering (SERS) upon meeting specific conditions induced by laser (532 nm) exposure. We show that nanoparticles which are not initially SERS active become SERS active by laser-induced reshaping/reorientation. A set-up developed for these observations enabled in situ high speed time-lapse characterization using plasmonic and Raman spectroscopies in conjunction with dark-field microscopy (DFM). Changes in the AgNPs were confirmed by monitoring plasmonic scattering spectra and DFM images. Time-lapse observations have shown that laser-induced changes in the plasmonic properties of AgNPs resulted in the appearance of SERS. Spectral matching between plasmon resonance and downward molecular vibronic transitions for molecules adsorbed on the surface of plasmonic nanomaterials is attributed to the nanoparticle SERS. We have further shown that the release of silver ions by silver nanoparticles can be the probable reason for their plasmonic changes. Gold nanoparticles inert to such mild (850 μW, 532 nm) laser-induced changes do not exhibit the appearance of SERS.
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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 | 0.000 |
| 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) | 0.001 | 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".