Surface-Enhanced Raman Spectroscopy Amplification with Film over Etched Nanospheres
Bibliographic record
Abstract
Polymer nanosphere lithography (NSL) masks were etched in oxygen plasma prior to metal deposition, which tunes the localized surface plasmon resonance (LSPR) and the interparticle distance resulting in greater Raman amplification than conventional film over nanosphere (FON). Various nanosphere masks were investigated, using nanosphere sizes of 220, 360, 450, 520, and 650 nm etched from 0 (conventional FON) to 10 min. Thereby, the film over etched nanospheres (FOEN) amplifies by up to a factor of 4 the Raman response of 4-nitrobenzenethiol (4-NBT) on Ag as compared to unetched FON. The LSPR response of FOEN and SEM analysis of the gap distance revealed that the optimal amplification results from a combination of tuning the gap (a gap/diameter of less than 1 improves the Raman response), matching the laser excitation wavelength (633 and 785 nm both investigated), and an increasing roughness of FOEN. Metal multilayers of Ag and Au were also deposited to investigate the effect on the Raman and LSPR response. While the LSPR response remains relatively invariable, the Raman signal from 4-NBT decreased significantly by increasing the number of layers when Ag is used as the outer layer. However, a bimetallic film composed of Ag underlayer with Au as an outerlayer further increased the Raman response by a factor of 1.6. Overall, the most intense Raman response for FOEN was obtained with 360 nm nanospheres, etched for 1.5 min to create a gap of 66 nm with an overall bimetallic film thickness of 175 nm, which was composed of an Ag underlayer of 87.5 nm and an Au outerlayer of 87.5 nm. Thus, by performing these simple modifications to FON, the Raman response can be increased by nearly 1 order of magnitude.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".