Plasmonic enhancement of SERS measured on molecules in carbon nanotubes
Bibliographic record
Abstract
We isolated the plasmonic contribution to surface-enhanced Raman scattering (SERS) and found it to be much stronger than expected. Organic dyes encapsulated in single-walled carbon nanotubes are ideal probes for quantifying plasmonic enhancement in a Raman experiment. The molecules are chemically protected through the nanotube wall and spatially isolated from the metal, which prevents enhancement by chemical means and through surface roughness. The tubes carry molecules into SERS hotspots, thereby defining molecular position and making it accessible for structural characterization with atomic-force and electron microscopy. We measured a SERS enhancement factor of 10 6 on α-sexithiophene (6T) molecules in the gap of a plasmonic nanodimer. This is two orders of magnitude stronger than predicted by the electromagnetic enhancement theory (10 4 ). We discuss various phenomena that may explain the discrepancy (including hybridization, static and dynamic charge transfer, surface roughness, uncertainties in molecular position and orientation), but found all of them lacking in enhancement for our probe system. We suggest that plasmonic enhancement in SERS is, in fact, much stronger than currently anticipated. We discuss novel approaches for treating SERS quantum mechanically that appear promising for predicting correct enhancement factors. Our findings have important consequences on the understanding of SERS as well as for designing and optimizing plasmonic substrates.
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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) | 0.001 | 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".