Detection and quantitative analysis of carbendazim herbicide on Ag nanoparticles via surface‐enhanced Raman scattering
Bibliographic record
Abstract
Carbendazim (MBC) is a fungicide widely used in agriculture, and there are serious concerns regarding the health risks that could be caused by this fungicide. Here, we explore its ultrasensitive detection by surface‐enhanced Raman scattering (SERS). First, to obtain maximum SERS signal, the adsorption of the target molecule onto metallic surface is essential. Therefore, we study the adsorption of the MBC onto the nanoparticle surface by SERS under different experimental conditions, such as different synthesis methods of nanoparticle, variable excitation wavelength, and fungicide concentration with the aim to detect MBC at low concentrations. Experiments are carried out with three kinds of colloidal nanoparticles: Ag and Au reduced by citrate and Ag reduced by hydroxylamine. However, mainly Ag colloids are highly efficient in the SERS detection of MBC. In addition, theoretical calculations of MBC Raman spectrum and that of the surface complex are used to help with the understanding the mechanisms responsible for the interaction between MBC and Ag. Ultraviolet–visible absorption spectroscopy showed displacement to the red of the plasmon resonance of Ag colloid in the presence of MBC. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".