A Novel Electrochemical Strategy for Chloramphenicol Detection in a Water Environment Based on Silver Nanoparticles and Thiophene
Bibliographic record
Abstract
As a synthetic broad-spectrum antibiotic, chloramphenicol (CAP) is widely used in the prevention and treatment of bacterial diseases in aquaculture and animal husbandry, which might lead to severe water contamination and thus threaten our health. Herein, a novel electrochemical strategy for CAP detection is proposed that the sensor was successfully constructed based on the hardly mentioned anodic peak (about −0.56 V) by modifying silver nanoparticles (AgNPs) and thiophene (TP) on a glassy carbon electrode (GCE) as synergistic amplification unit with a simple step-by-step electrodeposition technique. Electrochemical methods, scanning electron microscopy (SEM) and X-ray energy dispersive spectroscopy (EDS) were applied to characterize the as-prepared sensor. The TP/AgNPs/GCE sensor was used for CAP detection by DPV in the concentration range of 100.0 − 1600.0 μ M, the limit of detection (LOD) was 33.0 μ M, and the sensitivity was 0.290 μ A· μ M −1 ·cm −2 . In addition, the sensor has the advantages of simple preparation, low cost, good repeatability, stability and anti-interference. It has been used for the detection of CAP in lake water with a recovery of 101.80–104.85%, and the relative standard deviation (RSD) was lower than 1.22%, which confirms that the sensor has good practicability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".