Electrochemically Deposited Silver Nanostructures on Reduced Graphene Oxide Aerogels as Sensitive SERS Substrates
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Surface-enhanced Raman scattering (SERS) pushes the boundaries of Raman spectroscopy as an analytical technique, allowing improved sensitivity and high discriminatory ability in analyte detection. Here, we introduce a SERS substrate using reduced graphene oxide aerogels as scaffolds. Reduced graphene oxide aerogels are hydrophobic, electrically conductive, and easily formable, providing a versatile platform for silver dendritic nanostructure growth via electrochemical deposition. We show that the electrochemical growth conditions (applied voltage, reduction time) have a significant effect on both the morphology and coverage of the silver nanostructures, which in turn have a strong effect on the SERS performance of the substrate. The importance of Ag dendrite morphology to the SERS substrate’s performance is also confirmed by finite-difference time-domain simulations. Under silver growth conditions of 10 V applied voltage at 10 Hz for 120 min, we obtained a limit of detection of 3.16 × 10 –5 ppm for thiram, which is lower than the testing requirements set by food and environmental regulatory agencies. Moreover, the substrates showed high silver coverage (85.6%), reproducibility (relative standard deviation ∼6% for substrates produced under the same conditions), and relative stability (∼20% change) of the obtained signal over one month. In view of their SERS capabilities and relative ease of preparation, we consider this new class of substrates a strong candidate for meeting detection and quantification challenges for a broad spectrum of analytes.
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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".