Advertisement
Abstract| Volume 16, ISSUE 1, P117, January 2023

Novel materials and fabrication strategies for multimodal neuroelectronics

      Abstract
      Neuroelectronic technologies are essential tools to treating neurological disorders, restoring and repairing lost functions, and modulating neural circuitry to control mood and behavior. Traditionally, metals and silicon have been the materials of choice for neurolectronics. However, these materials are intrinsically inadequate to address the mechanical, chemical, and electrical properties of neural tissues. Furthermore, they are complex to source and process, which makes the manufacturing of neuroelectronic devices time consuming and expensive. Thus, the development and successful clinical translation of safe, biocompatible, and long-term stable neuroelectronics require significant innovations in both materials and fabrication strategies. In this talk, I will discuss how nanoscale soft conductors can be engineered into high-resolution, minimally invasive neuroelectronic interfaces designed to seamlessly record and stimulate neural circuits at multiple scales. Specifically, I will introduce 2D transition metal carbides (a.k.a. MXenes), discuss their electrochemical properties at the interface with biological tissues, and show how they translate into significant impedance and noise reduction when MXene-based microelectrode arrays are used in vivo. Then, I will present novel scalable, rapid manufacturing processes designed to translate the exceptional material properties at the molecular scale into high-resolution, low impedance neuroelectronic interfaces that are also compatible with clinical neuroimaging modalities, such a magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, I will present examples of applications in both implantable and wearable interfaces for neural recording and stimulation.
      Research Category and Technology and Methods
      Basic Research: 13. Other Brain Stimulation Technology
      Keywords: Neuroelectronics, Neural interfaces