Hybrid Electrospun Carbon Nanofiber/Bi<sub>2</sub>MoO<sub>6</sub> Nanocomposites for Trace-Level Detection of Phenolic Aldehyde Vanillin in Food Samples
Thilak Sabareesh Malayalam Amarnath, Divya Sampath, Subramanian Sakthinathan, Ching‐Lung Chen, N. Venkateswaran, Te‐Wei Chiu
Abstract
High Resolution Image Download MS PowerPoint Slide Vanillin, known for its pleasant aroma, is a common artificial flavoring in the food and beverage industry. Due to the increasing use of synthetically produced vanillin over conventionally made vanillin, reliable detection methods are required to ensure quantity, authenticity, and consumer safety. Several analytical methods can be employed to detect vanillin; however, these methods are often costly and time-consuming. We developed a bismuth molybdate nanoplate-embedded carbon nanofiber-modified electrode (CNF/Bi 2 MoO 6 /GCE) for the detection of vanillin. The CNF/Bi 2 MoO 6 nanocomposite was prepared through an ultrasonic process and characterized using XRD, FE-SEM, HR-TEM coupled with energy-dispersive X-ray spectroscopy (EDX), Raman spectroscopy, and XPS to confirm structural, surface, and morphological properties. DFT studies were employed to understand the reactivity of the vanillin molecule. The modified CNF/Bi 2 MoO 6 /GCE exhibited excellent electrochemical performance and significantly superior active surface area, relative to other fabricated electrodes. The optimal conditions for CNF/Bi 2 MoO 6 /GCE to detect vanillin were determined by using cyclic voltammetry (CV) through various electrochemical analyses. The modified CNF/Bi 2 MoO 6 /GCE enabled precise vanillin detection with wide linearity (0.01–9.19 and 14.19–367 μM) and a low detection limit (0.0014–0.013 μM) in differential pulse voltammetry (DPV). CNF/Bi 2 MoO 6 /GCE demonstrated high selectivity, reproducibility, and repeatability for vanillin detection with good cyclic stability. Our proposed sensor was evaluated using real-world food samples such as ice cream and chocolate, demonstrating very good recovery rates (92.2–103.3%). These results highlight its potential as a cost-effective alternative for food quality monitoring.