A Novel Molecularly Imprinted Quartz Crystal Microbalance Sensor Based on Erbium Molybdate Incorporating Sulfur-Doped Graphitic Carbon Nitride for Dimethoate Determination in Apple Juice Samples

Abstract

Dimethoate (DIM) as an organophosphorus pesticide is widely utilized especially in the cultivation of vegetables and fruits due to its killing effect on harmful insects. However, unconscious use of DIM in large amounts can also cause serious health problems. For these reasons, rapid and reliable detection of DIM from food samples is significant. In this study, a novel quartz crystal microbalance (QCM) sensor based on erbium molybdate incorporating sulfur-doped graphitic carbon nitride (EM/S-g-C3N4) and a molecularly imprinting polymer (MIP) was designed for DIM detection in apple juice samples. Firstly, an EM/S-g-C3N4 nanocomposite with high purity was prepared under hydrothermal conditions at high temperatures over a long period of time. After the modification of the EM/S-g-C3N4 nanocomposite on a QCM chip, the polymerization solution including N,N′-azobisisobutyronitrile (AIBN) as an initiator, ethylene glycol dimethacrylate (EGDMA) as a cross-linker, methacryloylamidoglutamic acid (MAGA) as a monomer, and DIM as an analyte was prepared. Then, the polymerization solution was dropped on an EM/S-g-C3N4 nanocomposite modified QCM chip and an ultraviolet polymerization process was applied for the formation of the DIM-imprinted polymers on the EM/S-g-C3N4 nanocomposite modified QCM chip. After the polymerization treatment, some characterization studies, including electrochemical, microscopic, and spectroscopic methods, were performed to illuminate the surface properties of the nanocomposite and the prepared QCM sensor. The values of the limit of quantification (LOQ) and the detection limit (LOD) of the prepared QCM sensor were as 1.0 × 10−9 M and 3.3 × 10−10 M, respectively. In addition, high selectivity, stability, reproducibility, and repeatability of the developed sensor was observed, providing highly reliable analysis results. Finally, thanks to the prepared sensor, it may be possible to detect pesticides from different food and environmental samples in the future.