Characterization and Modeling of Conductive and Insulating Coatings for Neural Interfaces

Neural interfaces are engineered with implantable electrodes that are key in forming efficient connection between the brain and a machine. The implantable electrodes are essentially a combination of exposed conductive regions and passivated coatings. The conductive coatings act as sensors or stimulators while the insulation encapsulates the conductive tracks and device to provide protection against the harsh in vivo environment. For efficient design of implantable electrodes it is important to understand the factors affecting the interface of conductive coatings and neural tissue. Assuming the conductive materials are noble and corrosion-free, the reliability of the device would highly depend on the long term stability of the encapsulation. While the efficiency metric is different for conductive and insulating materials, in both cases electrochemical impedance spectroscopy and equivalent circuit model fitting can be used to diagnose their performance. This thesis is a culmination of experimental and modeling work performed on implantable conducting and insulating coatings for neural interfaces. For conductive coatings (iridium oxide and carbon nanotubes) the goal was to characterize the difference between in vitro and in vivo performance. For insulation (Parylene C and Al2O3-Parylene C) the aim was to estimate the mean time to failure and to understand the modes of failure.;The immediate transition between in vitro to an in vivo environment did not affect the electrochemical properties of the carbon nanotube electrodes. In well controlled, low frequency stimulation acute experiments, we show that multi-walled carbon nanotube electrodes maintain their charge carrying capacity and impedance in vivo. We also report on the transcription levels of the pro-inflammatory cytokine IL-1β and TLR2 receptor as an immediate response to low frequency stimulation using RT-PCR. We show here that the IL-1β is part of the inflammatory response to low frequency stimulation, but TLR-2 is not significantly increased in stimulated tissue when compared to controls. Iridium oxide, unlike carbon nanotubes, demonstrated changes in its electrochemical characteristic in vivo and its performance was also dependent on low frequency stimulation. A 30% decrease in in vivo CSCs was observed, and an increase in impedance for all electrodes. For low frequency stimulated electrodes, increase in impedance was observed in the low and high frequency spectrum. While the un-stimulated group impedance increased across the spectrum. Two equivalent circuit models were used to explain this difference. An increase in charge transfer resistance and diffusion impedance was observed. These changes reflect a modification in the double layer composition due to electro-bio-chemical interactions occurring in vivo. Here we discuss relevant considerations when using in vitro models to predict the performance of implantable electrodes for deep brain stimulation and for recording of neurophysiological signals. A critical factor for an implanted insulation's performance is its barrier properties that limit access of biological fluids to the underlying device/metal electrode. Here we report a comprehensive study to examine the mean time to failure of Parylene C and Al2O 3-Parylene C coated devices using accelerated lifetime testing. Samples were tested at 60 °C for up to 3 months while performing electrochemical measurements to characterize the integrity of the insulation. The mean time to failure for Al2O3-Parylene C was 4.6 times longer than Parylene-C coated samples. In addition, based on modeling of the data using electrical circuit equivalents, we show here that there are two main modes of failure. Our results suggest that failure of the insulating layer is either due to pore formation or blistering as well as thinning of the coating over time. The enhanced barrier properties of the bilayer Al2O 3-Parylene C over Parylene C makes it a promising candidate as an encapsulating neural interface.