Sample induced intensity variations of localized surface plasmon resonance in tip-enhanced Raman spectroscopy
Bibliographic record
Abstract
Tip-enhanced spectroscopy techniques, in particular tip-enhanced Raman spectroscopy (TERS), rely on a localized surface plasmon resonance (LSPR). This LSPR depends on the near field antenna, its material and shape, and the surrounding medium with respect to its relative permittivity and the volume fraction of the optical near field occupied by the sample. Here, we investigate the effects of the surface composition and topography on the change of the LSPR intensity in tip-enhanced spectroscopy on SrTiO3 nanoislands by monitoring the LSPR enhanced luminescence of gold tips. Our experimental results and analytical estimates indicate that by affecting the effective permittivity of the dielectric environment at the tip apex, the material composition as well as topography of the studied sample induce a change in LSPR intensity. This result significantly helps the understanding of the evolution or origin of the LSPR intensity during a typical TERS measurement, which in turn leads to a more accurate assessment of the relative intensity of different Raman modes in TERS.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".