Surface‐Enhanced Raman Spectroscopy as a Probe of the Surface Chemistry of Nanostructured Materials
Bibliographic record
Abstract
Surface-enhanced Raman spectroscopy (SERS) is now widely used as a rapid and inexpensive tool for chemical/biochemical analysis. The method can give enormous increases in the intensities of the Raman signals of low-concentration molecular targets if they are adsorbed on suitable enhancing substrates, which are typically composed of nanostructured Ag or Au. However, the features of SERS that allow it to be used as a chemical sensor also mean that it can be used as a powerful probe of the surface chemistry of any nanostructured material that can provide SERS enhancement. This is important because it is the surface chemistry that controls how these materials interact with their local environment and, in real applications, this interaction can be more important than more commonly measured properties such as morphology or plasmonic absorption. Here, the opportunity that this approach to SERS provides is illustrated with examples where the surface chemistry is both characterized and controlled in order to create functional nanomaterials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".