Plasmonic nanostructures for enhanced Raman spectroscopy: SERS and TERS of thiolated monolayers
Bibliographic record
Abstract
Although discovered 40 years ago, the interest in surface enhanced Raman spectroscopy (SERS) for a variety of applications in the fields of material and biomaterial has been revived over the past decade mostly due to a better control over the fabrication methods of nanoscale metallic structures. Metallic structures prepared by bottom-up or top-down methods can be tailored for a variety of applications in order to benefit from the best conditions for surface enhancement. SERS platforms made by nanosphere lithography are for example very versatile platforms that show a detection limit in the femtomolar range. Although quantitative measurements are difficult to perform in Raman spectroscopy, the plasmon-mediated enhancement by the metallic nanostructures are of great interest to improve the detection of analytes traces at surfaces. The extension of SERS to tip-enhanced Raman spectroscopy (TERS) is also very valuable to improve spatial resolution of Raman measurements and to yield surface signals, thus making TERS spectroscopy a surface specific technique. Herein we review SERS and TERS measurements of a model molecule (nitrothiophenol) adsorbed onto gold surfaces.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.002 |
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".