Correlation Engine 2.0
Clear Search sequence regions


Sizes of these terms reflect their relevance to your search.

The current research presents the evaluation of supramolecular proficiency of the designed platform for electrocatalytic determination of pernicious food colorants, amaranth and fast green. The approach involving surface modification of glassy carbon electrode with beta cyclodextrin decorated strontium ferrite reduced graphene oxide nanocomposite (SFrGO-βCD) to ensure fast and reversible electro-oxidation of hydroxyl groups of the colorant molecules. The synergy between SF and rGO facilitated the sensor with enhanced surface area and conductivity through faradic redox reaction. Tremendous decrease in the obtained values of peak separation potential and impedance as manifested in CV and EIS analysis, enabled by electrostatic interactions between surface functionalities of rGO and βCD has resulted in the significant augmentation of sensitivity. The value of charge transfer coefficient, number of electrons involved, nature of electron transport process at electrode electrolyte interface during the analysis of electrochemical detection were explored through CV experiments. Food samples analysis (without spiking) utilizing screen printed electrode manifested the sensor as portable device for real time monitoring. Outstanding detection limit (0.022 nM for amaranth and 0.051 nM for fast green), excellent regenerability (Relative standard deviation less than 3%) and apparent recovery rate (above 90%) of the modified electrode presented a colossal potential for the development of sustainable and commercially competitive electrochemical sensor in food sector. Copyright © 2022 Elsevier B.V. All rights reserved.

Citation

Jyoti, Deepeka, Paramdeep Kaur, Vinod Kumar, Kulbhushan Tikoo, Shweta Rana, Sonal Singhal. Appraising the electrocatalytic performance of beta-cyclodextrin embellished supramolecular recognition system for pernicious food colorants. Analytica chimica acta. 2023 Feb 01;1240:340753

Expand section icon Mesh Tags

Expand section icon Substances


PMID: 36641148

View Full Text