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Issue 15, 2018

An interdigitated electrode with dense carbon nanotube forests on conductive supports for electrochemical biosensors

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Abstract

A highly sensitive interdigitated electrode (IDE) with vertically aligned dense carbon nanotube forests directly grown on conductive supports was demonstrated by combining UV lithography and a low temperature chemical vapor deposition process (470 °C). The cyclic voltammetry (CV) measurements of K4[Fe(CN)6] showed that the redox current of the IDE with CNT forests (CNTF-IDE) reached the steady state much more quickly compared to that of conventional gold IDE (Au-IDE). The performance of the CNTF-IDE largely depended on the geometry of the electrodes (e.g. width and gap). With the optimum three-dimensional electrode structure, the anodic current was amplified by a factor of ∼18 and ∼67 in the CV and the chronoamperometry measurements, respectively. The collection efficiency, defined as the ratio of the cathodic current to the anodic current at steady state, was improved up to 97.3%. The selective detection of dopamine (DA) under the coexistence of L-ascorbic acid with high concentration (100 μM) was achieved with a linear range of 100 nM–100 μM, a sensitivity of 14.3 mA mol−1 L, and a limit of detection (LOD, S/N = 3) of 42 nM. Compared to the conventional carbon electrodes, the CNTF-IDE showed superior anti-fouling property, which is of significant importance for practical applications, with a negligible shift of the half-wave potential (ΔE1/2 < 1.4 mV) for repeated CV measurements of DA at high concentration (100 μM).

Graphical abstract: An interdigitated electrode with dense carbon nanotube forests on conductive supports for electrochemical biosensors

Supplementary files

Article information


Submitted
22 Mar 2018
Accepted
05 Jun 2018
First published
12 Jun 2018

This article is Open Access

Analyst, 2018,143, 3635-3642
Article type
Paper

An interdigitated electrode with dense carbon nanotube forests on conductive supports for electrochemical biosensors

H. Sugime, T. Ushiyama, K. Nishimura, Y. Ohno and S. Noda, Analyst, 2018, 143, 3635 DOI: 10.1039/C8AN00528A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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