• 文献标题:   Electrochemical Determination of Sunset Yellow Using an Electrochemically Prepared Graphene Oxide Modified - Pencil Graphite Electrode (EGO-PGE)
  • 文献类型:   Article, Early Access
  • 作  者:   TAHTAISLEYEN S, GORDUK O, SAHIN Y
  • 作者关键词:   sunset yellow, food additive, pencil graphite electrode, graphene oxide, electrochemically prepared graphene oxide modified pencil graphite electrode egopge, cyclic voltammetry cv, differential pulse voltammetry dpv, chronoamperometry
  • 出版物名称:   ANALYTICAL LETTERS
  • ISSN:   0003-2719 EI 1532-236X
  • 通讯作者地址:   Yildiz Tech Univ
  • 被引频次:   1
  • DOI:   10.1080/00032719.2020.1767120 EA MAY 2020
  • 出版年:  

▎ 摘  要

Sunset Yellow (E110) is an azo food artificial dye and has a wide range of applications. When ingested in excess, it can lead to many serious health problems, so it is significant to rapidly determine Sunset Yellow. In this study, a modified electrode was developed by making use of electroactive properties of Sunset Yellow. Using a chronoamperometry method, an electrochemically prepared graphene oxide modified pencil graphite electrode was prepared in one-step by applying a + 1.9 V constant potential for 60 seconds in 5.0 M sulfuric acid. Cyclic voltammetry, electrochemical impedance spectroscopy, X-ray powder diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy were used to characterize the electrochemical properties and surface morphology of the modified electrode. Electrochemical measurements were carried out using differential pulse voltammetry. The influence of pH on the electrochemical determination of Sunset Yellow was investigated. The effects of possible substances that may interfere and the repeatability and reproducibility of the developed sensor platform were examined. Under the optimized conditions, the limits of detection and quantitation were 0.057 mu M and 0.19 mu M, respectively. The applicability of the developed protocol to real samples was investigated and good recovery values were achieved. The developed sensor platform stands out with features such as short preparation and analysis time, simplicity, low cost, sensitivity and selectivity.