Multiwavelength Surface‐Enhanced Raman Spectroscopy Using Rainbow Trapping in Width‐Graded Plasmonic Gratings
Bibliographic record
Abstract
Abstract Plasmonic gratings of rectangular groove profile with gradient in the groove width perform as unique surface‐enhanced Raman spectroscopy (SERS) substrates by simultaneously confining multiple laser wavelengths proximally and inside their rectangular nanotrenches. These gratings consist of a metal–insulator–metal (MIM) groove of 40 nm width at the center, surrounded by grooves with widths increasing in 10 nm steps to a maximum of 180 nm. It is experimentally shown and theoretically confirmed that upon illumination a maximally enhanced electromagnetic field is generated at the center of these gratings as a result of plasmonic light trapping as well as waveguiding produced by the surrounding grooves. SERS enhancement factors of 106–107 are demonstrated for 20 μL min−1 flow of 1 × 10−3m aqueous phospholipid solution using 532, 638, and 785 nm laser illumination of the gratings integrated within microfluidic devices. These robust multiwavelength SERS substrates offer highly reproducible plasmonic field enhancement that can be tuned to cover broad wavelength ranges within the visible and near‐infrared regime and are ideal for static and dynamic characterization of low concentration species. Further, the multispectral characteristic of these gratings facilitates multiplexing through various laser wavelengths thereby making it possible to readily access weak or silent Raman modes.
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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".