Improving the Surface-Enhanced Raman Scattering Performance of Silver Nanodendritic Substrates with Sprayed-On Graphene-Based Coatings
Bibliographic record
Abstract
This study examines the improvements in surface-enhanced Raman scattering (SERS) performance achieved when silver nanodendritic structures are coated with various graphene-based materials, namely graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs). The tests are performed on our unique SERS-active substrates, prepared on the surface of planar microelectrode chips using an electric field-guided Ag nanoparticle assembly process. The graphene-based materials are introduced into the substrate by means of an in-house spray-coating technique. The SERS enhancement effect of each coating is examined as a function of spray nozzle passes (N) and optimal values are identified for each coating type. The enhancements found for GO, rGO, and GNP (6⁻9 graphene layers thick) coatings are 2.3 (N = 25), 2.5 (N = 5), and 1.6 (N = 1), respectively. Additionally, in comparison with their uncoated counterparts, substrates coated with rGO (N = 5) are shown to enhance the intensity of the methamphetamine (5 ppb) spectrum in artificial saliva by approximately 3-fold. Overall, it can be concluded that the introduction of GO or rGO to the SERS substrate using spray-coating, a simple and also scalable method, can produce substantial SERS performance enhancement.
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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) | 0.001 | 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".