Enhanced SERS Detection of Toxic Dyes Using Gold–Silver Core‐Shell Nanoparticles
Bibliographic record
Abstract
Abstract Sensitive detection of organic pollutants in aqueous medium is crucial for environmental safety. In this regard, we demonstrate the surface‐enhancement Raman spectroscopy (SERS)‐based detection of organic dyes using Au–Ag core‐shell nanoparticles (NPs) prepared via a single‐step seed‐mediated method. This facile method allows straightforward optimization of Ag shell thickness on Au core by simply adjusting the concentration of silver nitrate in the growth solution. Subsequently, these NPs were employed for the SERS‐based trace detection of various organic dye pollutants, namely, crystal violet (CV), methylene blue (MB), and methyl orange (MO). The core‐shell NPs exhibited excellent sensitivity and enhancement of the Raman signal during organic dye detection. The SERS signal intensity was seen to depend sensitively on the Ag shell thickness and under optimal conditions, an experimental enhancement factor (EF) of 1.49 × 10 5 was obtained. Furthermore, detection limits of 10 −9 , 10 −7 , and 10 −6 M for CV, MB, and MO, respectively, were achieved. The SERS signal was also stable for 30 days owing to the combined effect of core‐shell structure formation and cetyltrimethylammonium bromide (CTAB) bilayer on NPs. To evaluate the potential of these core‐shell NPs in real‐world environmental applications, MB and MO dyes were detected in lake water samples.
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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".