Long-term stability of neural interfaces is a challenge that has still to be overcome. In this study, we manufactured a highly stable multi-layer thin-film class of carbon-based devices for electrocorticography (ECoG) incorporating silicon carbide (SiC) and amorphous carbon (DLC) as adhesion promoters between glassy carbon (GC) electrodes and polyimide (PI) substrate and between PI and platinum (Pt) traces. We aged the thin-film electrodes in 30 mM H2O2at 39 °C for one week - to mimic the effects of post-surgery inflammatory reaction - and subsequently stressed them with 2500 CV cycles. We additionally performed stability tests stimulating the electrodes with 15 million biphasic pulses. Finally, we implanted the electrodes for 6 weeks into rat models and optically characterized the explanted devices. Results show that the fabricated ECoG devices were able to withstand the in vitro and in vivo tests without significant change in impedance and morphology.

Improved long-term stability of thin-film glassy carbon electrodes through the use of silicon carbide and amorphous carbon

Carli, Stefano;Zucchini, Elena;Maggiolini, Emma;Fadiga, Luciano;
2017

Abstract

Long-term stability of neural interfaces is a challenge that has still to be overcome. In this study, we manufactured a highly stable multi-layer thin-film class of carbon-based devices for electrocorticography (ECoG) incorporating silicon carbide (SiC) and amorphous carbon (DLC) as adhesion promoters between glassy carbon (GC) electrodes and polyimide (PI) substrate and between PI and platinum (Pt) traces. We aged the thin-film electrodes in 30 mM H2O2at 39 °C for one week - to mimic the effects of post-surgery inflammatory reaction - and subsequently stressed them with 2500 CV cycles. We additionally performed stability tests stimulating the electrodes with 15 million biphasic pulses. Finally, we implanted the electrodes for 6 weeks into rat models and optically characterized the explanted devices. Results show that the fabricated ECoG devices were able to withstand the in vitro and in vivo tests without significant change in impedance and morphology.
2017
9781538619162
Artificial Intelligence; Mechanical Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2383177
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