Engineering nanostructures for single-molecule surface-enhanced Raman spectroscopy
Bibliographic record
Abstract
Surface enhanced Raman spectroscopy (SERS), an effect discovered in the 1970s and studied systematically in the 1980s, received a significant "second wind" with the report (primarily by Nie and by Kneipp) of enhancements large enough to allow the Raman spectrum of single molecules to be obtained. It is now understood that this occurs as a result of the extremely high electromagnetic fields that can exist at appropriately configured gaps and interstices between nanoparticles and other nanostructures composed of suitable materials (such as silver). With this insight one is now in a position to fabricate structures that will dependably and repeatably produce single-molecule SERS. We describe three such strategies: using molecular linkers to self assemble silver clusters possessing the correct geometry; fabricating nanowire rafts in which the gap between nanowires are "hot"; and structuring the interior of nanopores so as to produce finely-architectured nanostructured arrays.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".