Plasmonic photocatalysis and SERS sensing using ellipsometrically modeled Ag nanoisland substrates
Bibliographic record
Abstract
Silver nanoislands are key platforms for plasmonic photocatalysis, SERS sensing and optical metamaterials due to their localized surface plasmon resonances. The low intrinsic loss in Ag enables high local electromagnetic field enhancements. Solution-based fabrication techniques, while cheap, result in highly non-reproducible plasmonic substrates with wide sample-to-sample variability in geometry, optical resonances and Q-factors. Herein, we present a non-lithographic method of forming silver nanoislands based on sputter deposition of Ag films followed by elevated temperature annealing to induce spontaneous dewetting. The resulting plasmonic substrates show reproducible, well-defined LSPR resonances with high ensemble Q-factors whose optical properties could be modeled using spectroscopic ellipsometry to yield n and k values across the visible range. UV-Vis-NIR, and XRD analyses define the optical and crystallographic characteristics of the Ag nanoisland samples. FESEM was utilized to discern the geometry and architecture of the Ag nanoisland as well as their uniformity and monodispersity. Our vacuum deposited Ag nanoislands demonstrated excellent photocatalytic activity for the transformation of 4-nitrobenzenethiol (4-NBT) and 4-aminothiophenol (PATP) into p,p'-dimercaptoazobenzene (DMAB).
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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".