Bismuth telluride decorated on graphitic carbon nitrides based binary nanosheets: Its application in electrochemical determination of salbutamol (feed additive) in meat samples

Highlights: • We proposed a novel binary nanosheets of Bi₂Te₃/GCN nanocomposite via facile approach. • Binary composite of Bi₂Te₃/GCN/SPCE was applied to the detection of food additive chemical. • Bi₂Te₃/GCN/SPCE has exhibits wide linear range and low LOD by DPV method. • Bi₂Te₃/GCN/SPCE was successfully investigated in chicken and red meat samples. The design and fabrication of effective electrochemical sensor for ultrasensitive detection of feed additive and multidrug are highly significant in food analysis. In this work, we explored to develop the possibility for rapid detection of feed additive drug using bismuth telluride (Bi₂Te₃) decorated graphitic carbon nitrides (GCN) nanostructures as a modified electrode for electrochemical sensing. Herein, the modified electrode was focused on the development of electrocatalytic performances for the determination of salbutamol in food products. The electrochemical sensors are developed by bismuth telluride sheets interconnected with graphitic carbon nitrides sheets (Bi₂Te₃/GCN) on to a screen-printed carbon electrode. The binary nanosheets of Bi₂Te₃/GCN exhibited an enhanced electrocatalytic ability towards salbutamol detection owing to their selective adsorption, by the combination of electrostatic interaction of binary nanosheets and the formation of charge assisted interactions between salbutamol and Bi₂Te₃/GCN surfaces. A nanomolar limit of detection (1.36 nM) was calculated in 0.05 M phosphate buffer (PB) supporting electrolyte (pH 7.0) using differential pulse voltammetry. The linear dynamic ranges with respect to salbutamol concentration were 0.01–892.5 μM, and the sensitivity of the sensor was 36.277 μA μM^–1 cm^–2. The sensor stability and reproducibility performances were observed. However, the obtained results are highly satisfactory which suggest the application of binary nanosheets in real-time food analysis.