Tuning Surface-Enhanced Raman Scattering (SERS) via Filling Fraction and Period in Gold-Coated Bullseye Gratings
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) is a highly sensitive analytical technique capable of single-molecule detection, owing to its exceptional chemical specificity, sensitivity, and reproducibility. In this study, we developed a robust SERS platform based on long-range ordered bullseye plasmonic nano-gratings, fabricated via a combination of electron beam lithography and reactive ion etching. The resulting nanostructured arrays—comprising concentric bullseye patterns with tunable period and filling fraction—were uniformly coated with a thin gold film to support strong surface plasmon resonances, generating intense electromagnetic field enhancements across the substrate. Using this platform, we demonstrated quantitative detection of small molecules such as Rhodamine 6G at low concentrations, achieving enhancement factors on the order of 105. Interestingly, we found that the geometric configuration yielding the strongest local electric field did not correspond to the highest SERS enhancement, which we attribute to a mismatch between the field orientation and molecular polarizability alignment. This study provides insights for optimizing plasmonic substrates for sensitive molecular detection.
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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) | 0.000 | 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".