Optical Interference Effects in the Design of Substrates for Surface-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
This paper presents results showing that the design of substrates used for surface-enhanced Raman spectroscopy (SERS) can impact the apparent enhancement factors (EFs) obtained due to optical interference effects that are distinct from SERS, providing additional enhancement of the Raman intensity. Thus, a combination of SERS and a substrate designed to maximize interference-based enhancement is demonstrated to give additional Raman intensity above that observed for SERS alone. The system explored is 4-nitroazobenzene (NAB) and biphenyl (BP) chemisorbed on a nanostructured silver film obtained by vacuum deposition of Ag on thermally oxidized silicon wafers. The enhancing silver layer is partially transparent, enabling a standing wave to form as a result of the combination of the incident light and light reflected from the underlying Si substrate (i.e., light that passes through the Ag and the intervening dielectric layer of SiO(x)). The Raman intensity is measured as a function of the thickness of the thermal oxide layer in the range from approximately 150 to approximately 400 nm, and despite a lack of morphological variation in the silver films, there is a strong dependence of the Raman intensity on the oxide thickness. The Raman signal for the optimal SiO(x) interlayer thickness is 38 times higher than the intensity obtained when the Ag particles are deposited directly onto Si (with native oxide). To account for the trends observed in the Raman intensity versus thickness data, calculations of the relative mean square electric field (MSEF) at the surface of the SiO(x) are carried out. These calculations are also used to further optimize the experimental setup.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".