Mapping single‐molecule SERRS from Langmuir–Blodgett monolayers on nanostructured silver island films
Bibliographic record
Abstract
Abstract To explore the breakdown of ensemble averaging as the single molecule regime is approached, spatial mapping of surface‐enhanced resonance Raman scattering (SERRS) intensities was employed in the study of mixed dye–fatty acid Langmuir–Blodgett (LB) monolayers deposited on nanostructured Ag island films. By variation of the ratio of the two components in the films, the effects of dye concentration, on both SERRS spectra and LB monolayer architecture, were explored down to the single‐molecule level. Insight was gained into the nature of areas of intense local electromagnetic field strength (i.e. ‘hot spots’) that provide enormous enhancement of Raman signals, and their wavenumber of occurrence on nanostructured Ag island films. These enhancing films were further characterized by UV–visible surface plasmon absorbance and atomic force microscopy. The target analyte employed in this work, n‐pentyl‐5‐salicylimidoperylene, was dispersed in monolayers of arachidic acid on Ag nanostructured films for single point and 2D mapping SERRS experiments. The optical characterization of this dye was completed with solution‐phase molecular absorption and fluorescence, in addition to monolayer resonance Raman scattering (RRS). Single‐molecule SERRS mapping results suggest that electromagnetic hot spots on Ag island films are in fact highly localized on the nanoscale, corresponding to relatively few molecular sites, and hence also stress the rarity of coincidence between hot spots and single isolated molecules as a key consideration in ultrasensitive SERRS measurements of this type. Copyright © 2005 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
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