Chronic In Vivo Evaluation of PEDOT/CNT for Stable Neural Recordings

PEDOT/CNT在稳定神经记录中的长期体内评估

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Abstract

OBJECTIVE: Subcellular-sized chronically implanted recording electrodes have demonstrated significant improvement in single unit (SU) yield over larger recording probes. Additional work expands on this initial success by combining the subcellular fiber-like lattice structures with the design space versatility of silicon microfabrication to further improve the signal-to-noise ratio, density of electrodes, and stability of recorded units over months to years. However, ultrasmall microelectrodes present very high impedance, which must be lowered for SU recordings. While poly(3,4-ethylenedioxythiophene) (PEDOT) doped with polystyrene sulfonate (PSS) coating have demonstrated great success in acute to early-chronic studies for lowering the electrode impedance, concern exists over long-term stability. Here, we demonstrate a new blend of PEDOT doped with carboxyl functionalized multiwalled carbon nanotubes (CNTs), which shows dramatic improvement over the traditional PEDOT/PSS formula. METHODS: Lattice style subcellular electrode arrays were fabricated using previously established method. PEDOT was polymerized with carboxylic acid functionalized carbon nanotubes onto high-impedance (8.0 ± 0.1 MΩ: M ± S.E.) 250-μm(2) gold recording sites. RESULTS: PEDOT/CNT-coated subcellular electrodes demonstrated significant improvement in chronic spike recording stability over four months compared to PEDOT/PSS recording sites. CONCLUSION: These results demonstrate great promise for subcellular-sized recording and stimulation electrodes and long-term stability. SIGNIFICANCE: This project uses leading-edge biomaterials to develop chronic neural probes that are small (subcellular) with excellent electrical properties for stable long-term recordings. High-density ultrasmall electrodes combined with advanced electrode surface modification are likely to make significant contributions to the development of long-term (permanent), high quality, and selective neural interfaces.

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