Self‐Assembled Au Nanoparticles as Substrates for Surface‐Enhanced Vibrational Spectroscopy: Optimization and Electrochemical Stability
Bibliographic record
Abstract
Three-dimensional nanostructured metallic substrates for enhanced vibrational spectroscopy are fabricated by self-assembly. Nanostructures consisting of one to 20 depositions of 13 nm-diameter Au nanoparticles (NPs) on Au films are prepared and characterized by means of AFM and UV/Vis reflection-absorption spectroscopy. Surface-enhanced polarization modulation infrared reflection-absorption spectroscopy (PM-IRRAS) is observed from Au NPs modified by the probe molecule 4-hydroxythiophenol. The limitation of this kind of substrate for surface-enhanced PM-IRRAS is discussed. The surface-enhanced Raman scattering (SERS) from the same probe molecule is also observed and the effect of the number of Au-NP depositions on the SERS efficiency is studied. The SERS signal from the probe molecule maximizes after 11 Au-NP depositions, and the absolute SERS intensities from different batches are reproducible within 20%. In situ electrochemical SERS measurements show that these substrates are stable within the potential window between -800 and +200 mV (vs. Ag/AgCl/sat. Cl(-)).
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".