Plasmonic Bullseye Nanocavities for Broadband Light Localization and Multi‐Wavelength SERS
Bibliographic record
Abstract
Abstract Plasmonic nanostructures capable of broadband light trapping and field enhancement have promising applications in a wide range of fields. This study presents a platform for broadband, polarization‐independent field enhancement in the visible regime through the use of width‐graded nanocavities in a bullseye configuration. The fabrication procedure utilizes electron beam lithography (EBL) to achieve fine control over the nanocavity geometry and template stripping to enable rapid and low‐cost production. The utility of these devices as substrates for multi‐wavelength surface enhanced Raman spectroscopy (SERS) is demonstrated through molecular detection in a 10 μM solution at two excitation wavelengths. The impact of bullseye geometry on both the broadband spectral response and multi‐wavelength SERS performance is examined. The measured SERS enhancement factor (EF) is shown to depend primarily on the plasmonically active surface area of the device, regardless of the local electromagnetic field strength within the nanocavities. These results highlight not only the utility of the width‐graded bullseye as a broadband platform for SERS and other applications but also provide design guidelines to optimize the enhancement factor and broadband performance of similar devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".