Effects of sonochemical approach and induced contraction of core-shell bismuth sulfide/graphitic carbon nitride as an efficient electrode materials for electrocatalytic detection of antibiotic drug in foodstuffs

Ultrasonic-enhanced surface-active bismuth trisulfide based core-shell nanomaterials were developed and used as an efficient modified electrode material to construct a highly sensitive antibiotic sensor. The core-shell Bi2S3@GCN electrode material was directly synthesized by in-situ growth of GCN on Bi2S3 to form core-shell like nanostar (Ti-horn, 30 kHz, and 70 W/cm(2)). The electrocatalyst of Bi2S3@GCN nanocomposites was efficaciously broadened towards electrochemical applications. As synthesized Bi2S3@GCN promoted the catalytic ability and electrons of GCN to transfer to Bi2S3. The single-crystalline GCN layers were uniformly grown on the surface of the Bi2S3 nanostars. Under the optimal conditions of electrochemical analysis, the CPL sensor exhibited responses directly proportional to concentrations (toxic chemical) over a range of 0.02-374.4 ktM, with a nanomolar detection limit of 1.2 mu M (signal-to-noise ratio S/N = 3). In addition, the modified sensor has exhibited outstanding selectivity under high concentrations of interfering chemicals and biomolecules. The satisfactory CPL recoveries in milk product illustrated the credible real-time application of the proposed Bi2S3@GCN sensors for real samples, indicating promising potential in food safety department and control. Additionally, the proposed electrochemical antibiotic sensor exhibited outstanding performance of anti-interfering ability, high stability and reproducibility.

Automated summary

The ultrasonic-enhanced surface-active bismuth trisulfide based core-shell nanomaterials showed efficient performance as a modified electrode material for constructing a highly sensitive antibiotic sensor. The electrocatalyst of Bi2S3@GCN nanocomposites was significantly expanded for electrochemical applications, showing outstanding selectivity, stability, and reproducibility in real samples.