SERS Mapping in Langmuir–Blodgett Films and Single-Molecule Detection
Bibliographic record
Abstract
Plasmon-enhanced spectroscopic techniques have expanded single-molecule detection (SMD) and are revolutionizing areas such as bio-imaging and single-cell manipulation. Surface-enhanced (resonance) Raman scattering (SERS or SERRS) combines high sensitivity with molecular-fingerprint information at the single-molecule level. Spectra originating from single-molecule SERS experiments are rare events, which occur only if a single molecule is located in a hot-spot zone. In this spot, the molecule is selectively exposed to a significant enhancement associated with a high, local electromagnetic field in the plasmonic substrate. Here, we report an SMD study with an electrostatic approach in which a Langmuir film of a phospholipid with anionic polar head groups (PO4(-)) was doped with cationic methylene blue (MB), creating a homogeneous, two-dimensional distribution of dyes in the monolayer. The number of dyes in the probed area of the Langmuir-Blodgett (LB) film coating the Ag nanostructures established a regime in which single-molecule events were observed, with the identification based on direct matching of the observed spectrum at each point of the mapping with a reference spectrum for the MB molecule. In addition, advanced fitting techniques were tested with the data obtained from micro-Raman mapping, thus achieving real-time processing to extract the MB single-molecule spectra.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".