Unveiling the fundamental mechanisms of graphene oxide selectivity on the ascorbic acid, dopamine, and uric acid by density functional theory calculations and charge population analysis

Journal article

Authors/Editors

THANA SUTTHIBUTPONG

Strategic Research Themes

Simulation (Computational Science and Engineering)

Publication Details

Author list: Prasert K., Sutthibutpong T.

Publisher: MDPI

Publication year: 2021

Volume number: 21

Issue number: 8

ISSN: 1424-8220

eISSN: 1424-8220

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104041323&doi=10.3390%2fs21082773&partnerID=40&md5=6fb1d7836e8412c149ef82189de768ab

Languages: English-Great Britain (EN-GB)

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Abstract

The selectivity of electrochemical sensors to ascorbic acid (AA), dopamine (DA), and uric acid (UA) remains an open challenge in the field of biosensing. In this study, the selective mechanisms for detecting AA, DA, and UA molecules on the graphene and graphene oxide substrates were illustrated through the charge population analysis from the density functional theory (DFT) calculation results. Our substrate models contained the 1:10 oxygen per carbon ratio of reduced graphene oxide, and the functionalized configurations were selected according to the formation en-ergy. Geometry optimizations were performed for the AA, DA, and UA on the pristine graphene, epoxy‐functionalized graphene, and hydroxyl‐functionalized graphene at the DFT level with vdW‐ DF2 corrections. From the calculations, AA was bound to both epoxy and hydroxyl‐functionalized GO with relatively low adsorption energy, while DA was adsorbed stronger to the electronegative epoxy groups. The strongest adsorption of UA to both functional groups corresponded to the largest amount of electron transfer through the pi orbitals. Local electron loss created local electric fields that opposed the electron transfer during an oxidation reaction. Our analysis agreed with the results from previous experimental studies and provided insight into other electrode modifications for electrochemical sensing. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

Biosensors, Density functional theory (DFT)