Single molecule surface‐enhanced resonance Raman scattering on colloidal silver and Langmuir–Blodgett monolayers coated with silver overlayers
Bibliographic record
Abstract
Abstract Single molecule detection (SMD) of a new perylene dye containing a terminal NH2 group, n‐butylimidoethylenamineperylene (PTCD‐NH2), adsorbed on colloidal silver and silver island films was achieved using surface‐enhanced resonance Raman scattering (SERRS). This work represents the first comparative SERRS study where SMD was achieved using two of the most commonly used SERS substrates, colloidal silver and silver island films. SERRS spectra were obtained in colloidal silver solution with analyte concentrations varying from 10−6 to 10−12 M and also from silver sols dispersed and dried on polysilane‐coated substrates. SERRS on silver islands was obtained by vacuum evaporation of a 6 nm mass thickness silver overlayer on to a Langmuir–Blodgett (LB) monolayer containing 1, 10 and 100 dye molecules per µm2 of surface area. The LB technique is ideally suited to obtain spatially resolved spectra of single molecules dispersed in the matrix of a fatty acid, as is used here to obtain SERRS from a few dye molecules found within the area of illumination. The characteristic fundamental vibrational wavenumbers observed in the single molecule SERRS spectra were unequivocally assigned with the help of their first overtones and combinations. Copyright © 2002 John Wiley & Sons, Ltd.
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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".