SERRS for Single-Molecule Detection of Dye-Labeled Phospholipids in Langmuir−Blodgett Monolayers
Bibliographic record
Abstract
The coupling of molecular excitations to localized surface plasmon resonances (LSPR) in silver or gold nanostructures is at the center of single-molecule detection (SMD) using surface-enhanced Raman scattering (SERS). The effect is attributed to the enhanced scattering power caused by coupling with the surface plasmons of the metal. The most efficient coupling is attained when the excitation is in resonance with the molecule and the nanostructure, the case of surface-enhanced resonance Raman scattering (SERRS). This incredible effect has the potential to be a powerful optical tool when used in conjunction with vibrationally differentiable chromophores. Here we present a unique study where the targeted system is a phospholipid that is tagged with a xanthene dye (the SERRS probe), a chromophore that dominates the Raman signal when the laser is in resonance with its absorption. The labeled phospholipid was incorporated into a single fatty acid Langmuir monolayer at varying concentrations and transferred onto a silver nanoparticle film to form Langmuir-Blodgett (LB) monolayers. Because the xanthene dye is tagged to a much larger molecule, the chances of dye aggregation (formation of dimers or higher aggregates) is negligible. Single-molecule detection of the dye tag (SERRS probe monomer) is readily achieved and demonstrated through the use of doped LB monolayers, Raman microscopy, spectral mapping, and efficient coupling of the laser line into the dye absorption band and plasmon resonances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".