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Neural Stimulation and Recording with Bidirectional, Soft Carbon Nanotube Fiber Microelectrodes

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† Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
‡ Department of Electrical Engineering and Computer Sciences and Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720, United States
§ Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States
∥ Department of Chemistry, Department of Material Science & NanoEngineering, and The Smalley Institute for Nanoscale Science & Technology, Rice University, Houston, Texas 77005, United States
*Address correspondence to [email protected]
Cite this: ACS Nano 2015, 9, 4, 4465–4474
Publication Date (Web):March 24, 2015
https://doi.org/10.1021/acsnano.5b01060
Copyright © 2015 American Chemical Society
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    The development of microelectrodes capable of safely stimulating and recording neural activity is a critical step in the design of many prosthetic devices, brain–machine interfaces, and therapies for neurologic or nervous-system-mediated disorders. Metal electrodes are inadequate prospects for the miniaturization needed to attain neuronal-scale stimulation and recording because of their poor electrochemical properties, high stiffness, and propensity to fail due to bending fatigue. Here we demonstrate neural recording and stimulation using carbon nanotube (CNT) fiber electrodes. In vitro characterization shows that the tissue contact impedance of CNT fibers is remarkably lower than that of state-of-the-art metal electrodes, making them suitable for recording single-neuron activity without additional surface treatments. In vivo chronic studies in parkinsonian rodents show that CNT fiber microelectrodes stimulate neurons as effectively as metal electrodes with 10 times larger surface area, while eliciting a significantly reduced inflammatory response. The same CNT fiber microelectrodes can record neural activity for weeks, paving the way for the development of novel multifunctional and dynamic neural interfaces with long-term stability.

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