Enzyme-based sensor for the real-time detection of atrazine: Evidence from electrochemical and docking studies
Abstract
This study focused on strategically employing the carboxylesterase enzyme Ha006a, derived from the pesticide-resistant microorganism Helicoverpa armigera, to detect atrazine. A comprehensive analysis through biochemical, biophysical and bioinformatics approaches was conducted to determine the interaction between the Ha006a protein and the herbicide atrazine. These experimental findings elucidated the potential of leveraging the inherent pesticide sequestration mechanism of the Ha006a enzyme for sensor fabrication. Numerous optimizations were undertaken to ensure the precision, reproducibility and convenient storage of the resulting electrochemical sensor, Ha006a/MCPE. This biosensor exhibited exceptional performance in detecting atrazine, demonstrating outstanding selectivity with a lower limit of detection of 5.4 µM. The developed biosensor has emerged as a reliable and cost-effective green tool for the detection of atrazine from diverse environmental samples. The Ha006a-based biosensor fabrication has expanded the possibilities for the efficient integration of insect enzymes as analytical tools, paving the way for the design of cost-effective biosensors capable of detecting and quantifying pesticides.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.