Ultrasensitive quantification of neonicotinoid thiamethoxam in environment using MOF-derived CuCo2O4/3D rGO based electrochemical sensor integrated with optimized neural network

Abstract

Accurate quantification of neonicotinoid insecticides is pivotal to ensure environmental safety by examining and mitigating their potential harmful effects on pollinators and aquatic ecosystems. In this scenario, detection of neonicotinoid insecticide, thiamethoxam (TMX), is significant for safeguarding ecological balance and human health. Hence, we developed a highly sensitive electrochemical sensor for detection of TMX in environmental samples, utilizing a novel nanocomposite with superior electrocatalytic properties and integrating an optimized neural network for accurate data analysis. The nanocomposite was synthesized via sonochemical approach, combining metal-organic framework (MOF)-derived spinel copper cobaltite (M-CuCo₂O₄) with three-dimensional reduced graphene oxide (3DrGO). Important characterizations were performed on prepared M-CuCo₂O₄/3DrGO composite and was immobilized on a screen-printed carbon electrode (SPCE) for electrochemical investigations. The synergistic effects of M-CuCo₂O₄ and 3DrGO enabled M-CuCo₂O₄/3DrGO/SPCE to achieve exceptional performance towards TMX detection. The sensor exhibited low limit of detection (LOD) of 0.6 nM and wide linear range of 0.15-174.52 μM. Furthermore, neural network model demonstrated excellent accuracy in estimating TMX concentrations, achieving a root mean square error (RMSE) of 2.01 and mean absolute error (MAE) of 1.33. The sensor showed remarkable stability and reliability in real samples including agricultural wastewater, red soil, and brown rice, highlighting its practical applicability for TMX monitoring in environmental and agricultural contexts.

Keywords: Electrochemical sensor; Environmental sustainability; Neonicotinoid insecticide; Optimized neural network; Thiamethoxam; Voltammetry.