Distinguishing Individual Vibrational Fingerprints: Single-Molecule Surface-Enhanced Resonance Raman Scattering from One-to-One Binary Mixtures in Langmuir−Blodgett Monolayers
Bibliographic record
Abstract
Here, it is demonstrated that similar chemical species within a multicomponent sample can be distinguished, down to the single-molecule level, by means of their surface-enhanced vibrational fingerprints. Surface-enhanced resonance Raman scattering spectra and 2D spatial intensity maps are recorded from thin Ag nanoparticle films coated with fatty acid Langmuir-Blodgett monolayers containing one-to-one binary mixtures, at varying concentrations, of two dye molecules of similar absorption and scattering cross section (n-pentyl-5-salicylimidoperylene and octadecylrhodamine B). The results reveal the change in the distribution of the two dyes within the monolayer, and the breakdown of ensemble spectral averaging, which occur as the single-molecule regime is approached. It is found that the unimolecular level is reached when 1-10 molecules of each dye occupy the 1-microm2 scattering areas probed by the laser. These signals are attributed to the rare spatial coincidence of isolated target analyte molecules and localized electromagnetic hot spots in the nanostructured metal film. The bianalyte nature of the samples provides strong corroborative support for the attribution of spectra to single molecules at high dilution, while the effect of domain formation/aggregation is found to be important at higher concentrations.
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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 | 0.001 | 0.001 |
| 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".