Nanoparticle-Containing Structures as a Substrate for Surface-Enhanced Raman Scattering
Bibliographic record
Abstract
Metallic nanostructures were prepared through the alternate immersion of derivatized glass slides in solutions of gold nanoparticles (NPs) and a propanedithiol linker molecule. Nanostructures consisting of 1-17 depositions of gold NPs were synthesized, and these substrates were characterized using UV-vis spectroscopy and atomic force microscopy. Subsequently, the surface-enhanced Raman scattering (SERS) of oxazine 720 was obtained at two excitation wavelengths (632 and 785 nm) from all substrates. Maximum SERS enhancement was observed for 9 and 13 NP depositions for 632 and 785 nm excitations, respectively. The difference in the number of NP depositions required for maximum enhancement is attributed to different wavelengths which can excite distinct aggregate structures within the metallic substrate. Therefore, these NP-containing structures can be "tuned" to yield maximum SERS enhancement for the excitation source being used by varying the number of NP depositions.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".