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Investigation of effects of copper, zinc, and strontium doping on electrochemical properties of titania nanotube arrays for neural interface applications

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journal contribution
posted on 2021-12-01, 00:00 authored by D Khudhair, J Gaburro, H A Hamedani, A Barlow, H Garmestani, Asim BhattiAsim Bhatti
Direct interaction with the neuronal cells is a prerequisite to deciphering useful information in understanding the underlying causes of diseases and functional abnormalities in the brain. Precisely fabricated nanoelectrodes provide the capability to interact with the brain in its natural habitat without compromising its functional integrity. Yet, challenges exist in terms of the high cost and complexity of fabrication as well as poor control over the chemical composition and geometries at the nanoscale, all imposed by inherent limitations of current micro/nanofabrication techniques. In this work, we report on electrochemical fabrication and optimization of vertically oriented TiO2 nanotube arrays as nanoelectrodes for neural interface application. The effects of zinc, strontium, and copper doping on the structural, electrochemical, and biocompatibility properties of electrochemically anodized TiO2 nanotube arrays were investigated. It was found that doping can alter the geometric features, i.e., the length, diameter, and wall thickness, of the nanotubes. Among pure and doped samples, the 0.02 M copper-doped TiO2 nanotubes exhibited superior electrochemical properties, with the highest specific storage capacitance of 130 F g−1 and the lowest impedance of 0.295 KΩ. In addition, regeneration of Vero cells and neurons was highly promoted on (0.02 M) Cu-doped TiO2 nanotube arrays, with relatively small tube diameters and more hydrophilicity, compared with the other two types of dopants. Our results suggest that in situ doping is a promising method for the optimization of various structural and compositional properties of electrochemically anodized nanotube arrays and improvement of their functionality as a potential nanoelectrode platform for neural interfacing.








Article number

ARTN 2099


1 - 16


MDPI / MDPI AG (Multidisciplinary Digital Publishing Institute)


Basel, Switzerland





Publication classification

C1 Refereed article in a scholarly journal