A Novel Electrochemical Sensing Platform for the Sensitive Detection and Degradation Monitoring of Methylene Blue

Abstract

Methylene blue is a toxic dye that is extensively used as a colorant in textile industries. Industrial effluent containing methylene blue, when drained into water bodies without proper treat-ment, poses a serious threat to aquatic and human lives. In order to protect the biocycle, various methods have been established to detect and remove hazardous dyes from aqueous systems. Electrochemical methods are preferred, owing to their characteristic features of simplicity, portability, potential selectivity, cost effectiveness, and rapid responsiveness. Based on these considerations, an electrochemical sensor consisting of amino-group-functionalized, multi-walled carbon nanotubes (NH2-f MWCNTs) immobilized on a glassy carbon electrode (GCE) was developed for the sensitive detection of methylene blue in aqueous solutions. The performance of the designed sensor was ana-lyzed by electrochemical impedance spectroscopy, cyclic voltammetry, and square wave voltammetry. The developed sensing tool demonstrated promising features of sensitivity, selectivity, stability, fast responsiveness, and the ability to work with a very small volume of the analyte, i.e., in microliters, for analysis. Amino groups rich in electrons provide a negative charge to multi-walled carbon nanotubes, which significantly enhances the electrocatalytic activity of NH2-f MWCNTs for cationic dyes such as methylene blue. Using the designed sensing platform, a linear calibration plot with a limit of detection of 0.21 nM was obtained for methylene blue under optimized conditions. The designed sensor was also employed to monitor the extent and kinetics of the degradation of methylene blue. Titania nanoparticles were used for photocatalytic degradation, and the kinetics of degradation was monitored by both UV-Visible spectroscopic and electrochemical methods. The results revealed more than 95% removal of methylene blue in a time span of just 30 min.