Carbon fiber on polyimide ultra-microelectrodes

聚酰亚胺超微电极上的碳纤维

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Abstract

OBJECTIVE: Most preparations for making neural recordings degrade over time and eventually fail due to insertion trauma and reactive tissue response. The magnitudes of these responses are thought to be related to the electrode size (specifically, the cross-sectional area), the relative stiffness of the electrode, and the degree of tissue tolerance for the material. Flexible carbon fiber ultra-microelectrodes have a much smaller cross-section than traditional electrodes and low tissue reactivity, and thus may enable improved longevity of neural recordings in the central and peripheral nervous systems. Only two carbon fiber array designs have been described previously, each with limited channel densities due to limitations of the fabrication processes or interconnect strategies. Here, we describe a method for assembling carbon fiber electrodes on a flexible polyimide substrate that is expected to facilitate the construction of high-density recording and stimulating arrays. APPROACH: Individual carbon fibers were aligned using an alignment tool that was 3D-printed with sub-micron resolution using direct laser writing. Indium deposition on the carbon fibers, followed by low-temperature microsoldering, provided a robust and reliable method of electrical connection to the polyimide interconnect. MAIN RESULTS: Spontaneous multiunit activity and stimulation-evoked compound responses with SNR  >10 and  >120, respectively, were recorded from a small (125 µm) peripheral nerve. We also improved the typically poor charge injection capacity of small diameter carbon fibers by electrodepositing 100 nm-thick iridium oxide films, making the carbon fiber arrays usable for electrical stimulation as well as recording. SIGNIFICANCE: Our innovations in fabrication technique pave the way for further miniaturization of carbon fiber ultra-microelectrode arrays. We believe these advances to be key steps to enable a shift from labor intensive, manual assembly to a more automated manufacturing process.

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