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, yet its performance strongly depends on the underlying plasmonic architecture. In this study, we developed a robust SERS platform based on long-range-ordered bullseye plasmonic nano-gratings with tunable period and filling fraction, fabricated via electron beam lithography and reactive ion etching and uniformly coated with a thin gold film. These concentric nanostructures support efficient surface plasmon resonance and radial SPP focusing, enabling intense electromagnetic field enhancement across the substrate. Using this platform, we achieved quantitative detection of Rhodamine 6G with enhancement factors of 105. Notably, our results reveal a previously unrecognized mechanistic insight: the geometric configuration producing the strongest local electric fields does not yield the highest SERS enhancement, due to misalignment between the dominant field orientation and the molecular polarizability tensor. This finding explains the non-monotonic dependence of SERS performance on grating geometry and introduces a new design principle in which both field strength and field-molecule alignment must be co-optimized. Overall, this work provides a mechanistic framework for rationally engineering plasmonic substrates for sensitive and quantitative 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".