journal article·2013-05

Poly(3,4-ethylenedioxythiophene)/multiwall carbon nanotube composite coatings for improving the stability of microelectrodes in neural prostheses applications

Haihan Zhou, Xuan Cheng, Li Rao, Tao Li, Yanwen Y. Duan

Acta Biomaterialia· Volume 9· Issue 5
Publisher page

Abstract

With the purpose of improving the stability of microelectrodes under continuous high charge density stimulation, which is required for neural prostheses applications such as visual prostheses, multiwall carbon nanotube (MWCNT)-doped poly(3,4-ethylenedioxythiophene) (PEDOT) composite films were coated onto a platinum microelectrode by electrochemical polymerization. Galvanostatically polymerized PEDOT/MWCNT films demonstrated superior characteristics compared to polystyrene sulfonate doping and potentiostatic polymerization, including a three-dimensional cone morphology and enhanced electrochemical performance (the safe charge injection limit reached 6.2 mC cm(-2) for cathodic-first pulses). Most important of all, the improved stability of the coatings has been revealed through stimulation for 96 h using 3.0 mCc m(-2) current pulses in bicarbonate- and phosphate-buffered saline solution. Cell assays revealed that PEDOT/MWCNT films could promote the adhesion and neurite outgrowth of rat pheochromocytoma cells. Finally, platinum wires coated with PEDOT/MWCNT films were implanted into rat cortex for 6 weeks for histological evaluation. Glial fibrillary acidic protein and neuronal nuclei staining revealed that the films elicit a lower tissue response compared to platinum implants. These results suggest that the galvanostatically polymerized PEDOT/MWCNT films can improve the stability of stimulation microelectrodes and that PEDOT/MWCNT is an excellent candidate material for electrode coating for neural prostheses applications.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Neuroscience and Neural Engineering · Conducting polymers and applications · Advanced Memory and Neural Computing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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