Single molecule analysis by surfaced-enhanced Raman scattering
Bibliographic record
Abstract
Our main objective in this tutorial review is to provide insight into some of the questions surrounding single molecule detection (SMD) using surface-enhanced Raman scattering (SERS) and surface-enhanced resonance Raman scattering (SERRS). Discovered thirty years ago, SERS is now a powerful analytical tool, strongly tied to plasmonics, a field that encompasses and profits from the optical enhancement found in nanostructures that support localized plasmon excitations. The spectrum of the single molecule carries the quantum fingerprints of the system modulated by the molecule-nanostructure interactions and the electronic resonances that may result under laser excitation. This information is embedded in vibrational band parameters. The dynamics and the molecular environment will affect the bandwidth of the observed Raman bands. In addition, the localized surface plasmon resonances (LSPR) empower the nanostructure with a number of optical properties that will also leave their mark on the observed inelastic scattering process. Therefore, controlling size, shape and the formation of the aggregation state (or fractality) of certain metallic nanostructures becomes a main task for experimental SERS/SERRS. This molecule-nanostructure coupling may, inevitably, lead to spectral fluctuations, increase photobleaching or photochemistry. An attempt is made here to guide the interpretation of this wealth of information when approaching the single molecule regime.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".