Surface‐enhanced Raman scattering from polystyrene on gold clusters
Bibliographic record
Abstract
Abstract Surface‐enhanced Raman scattering (SERS) constitutes a spectroscopic method of rapidly growing importance, and polystyrene is a widely used compound of great industrial importance. In this work, SERS data were obtained from polystyrene samples prepared by vapor deposition of gold and plasma‐induced polymerization of styrene gas. A thorough examination of this data is presented. The relationships between sample preparation parameters, gold‐cluster morphology, and SERS intensity were elucidated. Using Wilson's notation, vibrations were assigned to all bands between 250 and 1750 cm−1 in the ordinary Raman and SERS spectra of polystyrene. The correct assignment of these bands would be a significant achievement because they have been controversial in the literature for ∼30 years. Our assignments were made by reviewing the literature and comparing the assignments found there to spectral data acquired during this study; they were confirmed using density functional theory (DFT) calculations performed on the styrene monomer. The orientation of polystyrene's phenyl ring, relative to the gold surface, was determined. It has been suggested that reactions involving silver catalyze polystyrene degradation during SERS, but we found that silver is not necessary for the degradation to occur. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".