Modulating the Geometry of the Carbon Nanofiber Electrodes Provides Control over Dopamine Sensor Performance

Ayesha Kousar, Ishan Pande, Laura Ferrer Pascual, Emilia Peltola, Jani Sainio, Tomi Laurila*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

One of the major challenges for in vivo electrochemical measurements of dopamine (DA) is to achieve selectivity in the presence of interferents, such as ascorbic acid (AA) and uric acid (UA). Complicated multimaterial structures and ill-defined pretreatments have been frequently utilized to enhance selectivity. The lack of control over the realized structures has prevented establishing associations between the achieved selectivity and the electrode structure. Owing to their easily tailorable structure, carbon nanofiber (CNF) electrodes have become promising materials for neurobiological applications. Here, a novel yet simple strategy to control the sensitivity and selectivity of CNF electrodes toward DA is reported. It consists of adjusting the lengths of CNF by modulating the growth phase during the fabrication process while keeping the surface chemistries similar. It was observed that the sensitivity of the CNF electrodes toward DA was enhanced with the increase in the fiber lengths. More importantly, the increase in the fiber length induced (i) an anodic shift in the DA oxidation peak and (ii) a cathodic shift in the AA oxidation peak. As the UA oxidation peak remained unaffected at high anodic potentials, the electrodes with long CNFs showed excellent selectivity. Electrodes without proper fibers showed only a single broad peak in the solution of AA, DA, and UA, completely lacking the ability to discriminate DA. Hence, the simple strategy of controlling CNF length without the need to carry out any complex chemical treatments provides us a feasible and robust route to fabricate electrode materials for neurotransmitter detection with excellent sensitivity and selectivity.

Original languageEnglish
Pages (from-to)2983-2991
Number of pages9
JournalAnalytical Chemistry
Volume95
Issue number5
Early online date2022
DOIs
Publication statusPublished - 7 Feb 2023
MoE publication typeA1 Journal article-refereed

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