A powerful electrochemical sensor based on Fe(3)O(4)nanoparticles-multiwalled carbon nanotubes hybrid for the effective monitoring of sunset yellow in soft drinks

摘要

The investigation of synthetic dyes in beverages is significant, owing to their pathogenic and low toxicity a Fe3O4-MWCNTs electrochemical sensor was developed for the determination of sunset yellow (SY) in beverages. A simple one-step hydrothermal method was implemented for the synthesis of Fe3O4-MWCNTs nanocomposite. The structural and surface morphologies of Fe3O4-MWCNTs were studied using XRD, TEM, EDS and FE-SEM techniques. The synthesized nanocomposite showed cubic face centered Fe(3)O(4)nanostructure with a probable crystallite size. The anchoring of Fe(3)O(4)nanoparticles in MWCNTs were further conformed with TEM analysis. The FE-SEM analysis showed that the cubical shaped Fe(3)O(4)nanoparticles were strongly anchored with an estimated granules size. The synthesized composite was utilized for the fabrication of sensitive and selective electrochemical sensor based on the drop casting of Fe3O4-MWCNTs composite on glassy carbon electrode (GCE) (Fe3O4-MWCNTs/GCE). The fabricated sensor was used for the quantification of SY. The sensor exhibited enhanced oxidation activity towards SY determination, by providing operational access to its determination in the field of food safety control. CV, DPV, and EIS techniques were utilized for the investigation of the electrochemical performance of SY. The influence of pH value of phosphate buffer solution (PBS) and effect of scan rate on the peak currents of SY were deliberated. The oxidation peak currents of SY were proportional to its concentrations in the range from 0.2 to 3.5 mu M with a low limit of detection of 1.4 nM. The newly fabricated sensor exhibited good selectivity, stability, repeatability and reproducibility. Moreover, it was successfully applied for the detection of SY in soft drinks with acceptable recoveries. The developed sensor was viable with a good sensitivity and simple option for SY detection in the field of food safety.