An Electrochemical Sensor for Sulfadiazine Determination Based on a Copper Nanoparticles/Molecularly Imprinted Overoxidized Polypyrrole Composite
Abstract
To protect consumers from risks related to overexposure to sulfadiazine, total residues of this antibacterial agent in animal-origin foodstuffs not exceed international regulations. To this end, a new electrochemical sensor based on a molecularly imprinted polymer nanocomposite using overoxidized polypyrrole and copper nanoparticles for the detection of sulfadiazine is elaborated. After optimization of the preparation of the electrochemical sensors, their differential pulse voltammetric signal exhibits an excellent stability and reproducibility at 1.05 V, with a large linear range between 10−9 and 10−5 mol L−1 and a low detection limit of 3.1 × 10−10 mol L−1. The produced sulfadiazine sensor was successfully tested in real milk samples. The combination of the properties of the electrical conduction of copper nanoparticles with the properties of the preconcentration of the molecularly imprinted overoxidized polypyrrole allows for the highly sensitive detection of sulfadiazine, even in real milk samples. This strategy is new and leads to the lowest detection limit yet achieved, compared to those of the previously published sulfadiazine electrochemical sensors.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.