Can surface-enhanced Raman scattering serve as a channel for strong optical pumping?
Bibliographic record
Abstract
The surface-enhanced Raman scattering spectra of a number of dye and colorless molecules adsorbed on deposited coloidal silver films were systematically studied as a function of power and position using a Raman microscope. The anti-Stokes portions of the spectra of the dyes reproducibly show line intensities much greater than what is expected on the basis of the equilibrium population of the excited vibrational states, even at the lowest incident light intensities used. This behavior was observed previously and attributed to optical pumping of vibrationally excited states of the molecules by unusually intense surface-enhanced Raman transitions, [Phys. Rev. Lett. 76, 2444 (1996)] suggesting either uncommonly large Raman cross-sections or very intense local field strengths exceeding those encountered in the most powerful currently available lasers. Based on this work, however, we ascribe the apparently large anti-Stokes intensities primarily to a difference in the Stokes and anti-Stokes Raman cross-sections resulting from resonance or pre-resonance Raman processes in the adsorbate–surface complex rather than to strongly nonequilibrium populations in the molecular vibrational states. Finally, we observed no significant inhomogeneity in the Raman enhancement in the images of the deposited silver coloid samples down to spatial resolutions of ∼1 μm.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".