Recent Developments in the Applications of GO/rGO-Based Biosensing Platforms for Pesticide Detection

Abstract

Pesticides are often used in different applications, including agriculture, forestry, aquaculture, food industry, etc., for the purpose of controlling insect pests and weeds. The indiscriminate usage of pesticides poses a massive threat to food, environmental, and human health safety. Hence, the fabrication of a sensitive and reliable sensor for the detection of pesticide residues in agro products and environmental samples is a critical subject to be considered. Recently, the graphene family including graphene oxide (GO) and reduced graphene oxide (rGO) have been frequently employed in the construction of sensors owing to their biocompatibility, high surface-area-to-volume ratio, and excellent physiochemical, optical, and electrical properties. The integration of biorecognition molecules with GO/rGO nanomaterials offers a promising detection strategy with outstanding repeatability, signal intensity, and low background noise. This review focuses on the latest developments (2018 to 2022) in the different types of GO/rGO-based biosensors, such as surface plasmon resonance (SPR), fluorescence resonance energy transfer (FRET), and electrochemical-based techniques, among other, for pesticide analysis. The critical discussions on the advantages, limitations, and sensing mechanisms of emerging GO/rGO-based biosensors are also highlighted. Additionally, we explore the existing hurdles in GO/rGO-based biosensors, such as handling difficult biological samples, reducing the total cost, and so on. This review also outlines the research gaps and viewpoints for future innovations in GO/rGO-based biosensors for pesticide determination mainly in areas with insufficient resources.

Glyphosate as a Food Contaminant: Main Sources, Detection Levels, and Implications for Human and Public Health

“…Despite their advantages, SPR biosensors face challenges, such as sample matrix effects, non-specific binding, and assay optimization, which necessitate ongoing research efforts to address. Integration of SPR biosensors with emerging technologies, such as nanomaterials and machine learning algorithms, holds promise for improving analytical performance and addressing complex sample matrices in pesticide detection [ 63 , 74 ].…”