Surface‐enhanced Raman scattering from oxazine 720 adsorbed on scratched gold films
Bibliographic record
Abstract
Abstract Surface‐enhanced Raman scattering (SERS) and surface‐enhanced resonance Raman scattering (SERRS) from oxazine 720 (oxa) dye adsorbed on scratched gold films are reported. The SERS‐active surface was prepared by performing a series of scratches in a 100 nm thick gold film deposited in glass. Atomic force microscopic imaging revealed a sub‐structure within the scratches containing a set of parallel gold wires of different sizes and shapes. The 1‐D order imposed by the parallelism between these wires is responsible for an interesting polarization effect observed in forward scattering experiments. It is shown that the maximum enhanced signal is observed when the polarization of the incident field is perpendicular to the direction of the scratches. This polarization discrimination may be useful in the design of SERS applications in chemical sensing and optical switching. Moreover, we also show that these scratched gold surfaces can be used as ordinary SERS substrates for experiments in backscattering using a common Raman microscope in non‐resonance conditions with the excitation energy. This was accomplished by obtaining the electrochemical SERS of oxa in situ (under electrochemical control). The potential dependence of the SERS from oxa adsorbed on scratched Au is compared with previous results obtained with Ag electrodes. Copyright © 2005 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) | 0.001 | 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".