Exploiting graded triangular gratings for optimal nano-focusing: A novel approach to enhance SERS efficiency
Bibliographic record
Abstract
Plasmonic graded nano-gratings enable rainbow trapping of multiple resonant modes over a wide wavelength spectrum, useful for multi-channel Surface Enhanced Raman Spectroscopy (SERS) of molecular species. However, rectangular nano-gratings have limitations in achieving efficient rainbow trapping and localizing a wide spectrum of plasmonic modes due to their stepwise geometry, which induces high dissipation of surface plasmon polaritons into the substrate. An alternative platform of graded triangular nano-gratings enables increased localization and more efficient adiabatic transformation between neighboring grooves. Varying groove angles, depths, and periods in the tapered geometry allow for smooth adjustment of the surface plasmon polariton propagation constant, reducing losses and maximizing nano-focusing inside the groove tips. To overcome the limitation of low aspect ratio in wet-etching silicon, we employed a multi-step process of reactive ion etching of a SiO2 barrier layer to generate aperture width, followed by anisotropic wet-etching. The resulting graded triangular nano-gratings showed excellent SERS enhancement along three laser wavelength excitations. The enhancement factors of 638 and 785 nm wavelengths are 8.5 × 109 and 9 × 108, respectively, for the detection of 1 µM Rhodamine 6G. In addition, graded triangular nano-gratings show similar enhancement factors for other species, specifically the lipid DPEE-PEG, at the 532 nm laser excitation wavelength with an excellent SERS enhancement factor of 1.5 × 109. Owing to the ability of the graded triangular gratings to elicit pronounced SERS responses across three distinct laser excitations, they unequivocally qualify as “rainbow trapping” structures. Wider apertures, lower ohmic losses, and the ability to tune the groove angle beyond conventional etching methods bode well for graded triangular gratings as a superior platform for miniature sensors.
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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) | 0.001 | 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".