4.6 Article

Electrochemically Exfoliated Carbon Quantum Dots Modified Electrodes for Detection of Dopamine Neurotransmitter

Journal

JOURNAL OF THE ELECTROCHEMICAL SOCIETY
Volume 165, Issue 12, Pages G3112-G3119

Publisher

ELECTROCHEMICAL SOC INC
DOI: 10.1149/2.0191812jes

Keywords

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Funding

  1. Department of Science and Technology-Science and Engineering Research Based (DST-SERB), Government of India [ECR/2016/001446]
  2. SRM IST
  3. Government of India (MNRE Project) [31/03/2014-15/PVSE-RD]

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A simple electroanalytical method was developed to detect dopamine (DA) neurotransmitter by using carbon quantum dots (CQDs) modified electrode. To synthesis CQDs, a green electrochemical method was adopted and graphite rods were used as anode and cathodes in 0.1 M NaOH/ethanol (EtOH) as the electrolyte solution. As-synthesized CQD showed different particle sizes depending on the applied current with time as characterized by UV-visible spectroscopy. The particle size, lattice structure and functional groups of CQDs were analyzed by the HR-TEM, XRD and FT-IR, respectively. The CQD exhibited a green fluorescence under UV light (365 nm). Moreover, CQD dispersion was used to modify glassy carbon electrode (GCE) and screen-printed carbon electrode (SPCE) to study their electrochemical and electrocatalytic properties. The both GCE/CQD and SPCE/CQD showed higher electrocatalytic activity toward oxidation of dopamine (DA) in phosphate buffered saline (PBS) solution (pH = 7.4). In order to avoid interferences, Nafion (Nf) layer was coated on the CQD film modified electrode. The effect of scan rate on DA oxidation was studied from 10 to 150 mV/s. The calibration curve was recorded for DA from 1 to 7 mu M using a SPCE/CQD and the limit of detection was found to be 0.099 mu M. The observed electro-catalytic activity of the CQD was attributed to their negatively charged functional groups which attracted positively charged DA in 0.1 M PBS. In addition, detection of DA in spiked human urine sample was demonstrated with satisfactory recovery analysis. (C) The Author(s) 2018. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License.

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