Electrochemical Sensors for Chloramphenicol: Advances in Food Safety and Environmental Monitoring
Bibliographic record
Abstract
Excessive use of antibiotics can lead to antibiotic resistance, posing a significant threat to human health and the environment. Chloramphenicol (CAP), once widely used, has been banned in many regions for over 20 years due to its toxicity. Detecting CAP residues in food products is crucial for regulating safe use and preventing unnecessary antibiotic exposure. Electrochemical sensors are low-cost, sensitive, and easily detect CAP. This paper reviews recent research on electrochemical sensors for CAP detection, with a focus on the materials and fabrication techniques employed. The sensors are evaluated based on key performance parameters, including limit of detection, sensitivity, linear range, selectivity, and the ability to perform simultaneous detection. Specifically, we highlight the use of metal and carbon-based electrode modifications, including gold nanoparticles (AuNPs), nickel–cobalt (Ni-Co) hollow nano boxes, platinum–palladium (Pt-Pd), graphene (Gr), and covalent organic frameworks (COFs), as well as molecularly imprinted polymers (MIPs) such as polyaniline (PANI) and poly(o-phenylenediamine) (P(o-PD)). The mechanisms by which these modifications enhance CAP detection are discussed, including improved conductivity, increased surface-to-volume ratio, and enhanced binding site availability. The reviewed sensors demonstrated promising results, with some exhibiting high selectivity and sensitivity, and the effective detection of CAP in complex sample matrices. This review aims to support the development of next-generation sensors for antibiotic monitoring and contribute to global efforts to combat antibiotic resistance.
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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) | 0.001 | 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".