Signal enhancement in amperometric peroxide detection by using graphene materials with low number of defects

Autor: Masoumeh Sisakthi, Jonathan Eroms, Thomas Hirsch, Alexander Zöpfl, Christoph Strunk, Frank-Michael Matysik
Rok vydání: 2015
Předmět:
Zdroj: Microchimica Acta. 183:83-90
ISSN: 1436-5073
0026-3672
DOI: 10.1007/s00604-015-1600-y
Popis: Two-dimensional carbon nanomaterials ranging from single-layer graphene to defective structures such as chemically reduced graphene oxide were studied with respect to their use in electrodes and sensors. Their electrochemical properties and utility in terms of fabrication of sensing devices are compared. Specifically, the electrodes have been applied to reductive amperometric determination of hydrogen peroxide. Low-defect graphene (SG) was obtained through mechanical exfoliation of natural graphite, while higher-defect graphenes were produced by chemical vapor deposition (CVDG) and by chemical oxidation of graphite and subsequent reduction (rGO). The carbonaceous materials were mainly characterized by Raman microscopy. They were applied as electrode material and the electrochemical behavior was investigated by chronocoulometry, cyclic voltammetry, electrochemical impedance spectroscopy and amperometry and compared to a carbon disc electrode. It is shown that the quality of the graphene has an enormous impact on the amperometric performance. The use of carbon materials with many defects (like rGO) does not result in a significant improvement in signal compared to a plain carbon disc electrode. The sensitivity is 173 mA center dot M-1 center dot cm(-2) in case of using CVDG which is about 50 times better than that of a plain carbon disc electrode and about 7 times better than that of rGO. The limit of detection for hydrogen peroxide is 15.1 mu M (at a working potential of -0.3 V vs SCE) for CVDG. It is concluded that the application of two-dimensional carbon nanomaterials offers large perspectives in amperometric detection systems due to electrocatalytic effects that result in highly sensitive detection.
Databáze: OpenAIRE
Externí odkaz: