Surface-enhanced IR–visible sum frequency generation vibrational spectroscopy
Bibliographic record
Abstract
Surface-enhanced IR-visible sum frequency generation (SFG) was studied using Ag and Au films over nanospheres (AgFON and AuFON), which provided large area of reproducible nanoscale structures with well-defined morphology. SFG vibrational spectra for a self-assembled monolayer of octadecanethiol were investigated on spheres with diameters ranging from 300 nm to 620 nm. With an input wavelength of 532 nm, a sphere diameter near 360 nm produced the maximum enhancement, which was consistent with the localized surface-plasmon resonant wavelength identified by minimum reflectivity. The measured second-order susceptibility for the asymmetric CH3 stretch mode of octadecanethiol was enhanced by up to 27 times on AgFON, which corresponded to an SFG enhancement factor of approximately 730. The SFG enhancement factor for AuFON was a quarter of that from AgFON. The SFG enhancement factor for the symmetric CH3 mode was about 10 times smaller than that for the asymmetric CH3 mode. This difference can be explained by the highly directional electrical-field parallel to the surface, created at the junction between nanospheres. Polarization-dependent studies also indicated that excitations with the electrical field parallel to the surface was the main contribution to the observed surface enhancements, and the electrical field perpendicular to the surface offered little enhancement.
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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".