Plasmonic Ag Nanoparticles: Correlating Nanofabrication and Aggregation for SERS Detection of Thiabendazole Pesticide
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The level of aggregation and aggregate morphology of metallic nanoparticles are factors that influence the SERS signal (surface-enhanced Raman scattering), affecting reproducibility and sensitivity. This study presents a systematic evaluation of the colloidal aggregation on the SERS signal by combining transmission electron microscopy and UV–vis extinction spectroscopy. It focuses on the effect of two methods of sample preparation (“external standard method-ESM” and “standard addition method-SAM”) on the SERS signal using the fungicide thiabendazole (TBZ) in Ag colloid as a probe molecule. The TBZ critical concentration (concentration for which SERS reaches the maximum intensity) was 6.0 × 10 –6 mol/L for ESM and 1.5 × 10 –6 mol/L for SAM. Besides, TBZ exhibited a sigmoid-type isotherm for ESM, indicating formation of a TBZ first layer on Ag nanoparticles at lower concentrations (Ag aggregates more compact; size <500 nm) and TBZ multilayers at higher concentrations (Ag aggregates more branched; >2 μm). For SAM, the TBZ first layer formation was also observed at lower concentrations (Ag aggregates more branched; <2 μm). However, at higher concentrations, the Ag colloid degradation/precipitation was observed (Ag aggregates more compact; >2 μm). The Ag aggregation mechanisms align with reaction-limited colloidal aggregation at lower concentrations and diffusion-limited colloidal aggregation at higher concentrations. We believe these results contribute to the SERS research field despite all of the work already done over its 50-year history.
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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".