title: Modeling of microcavity electrodes for medical implants.
abstract: A common, limiting factor in neuroprosthesis design is the safe charge-carrying capacity of the metallic electrodes that deliver electrical stimuli to biological tissue. If exceeded, adverse effects can occur, including electrode dissolution and cell necrosis. A straightforward method of addressing charge-carrying capacity limitations is to increase the surface area of the stimulating electrodes. However, for planar electrode arrays, this approach typically requires a corresponding increase in the distance between electrodes which can be detrimental to the efficacy of the device, particularly in sensory applications such as visual or auditory prostheses where densely-packed electrodes may offer advantage. An alternative approach involves fabricating electrodes such that they have a three-dimensional structure and, thus allow electrode spacing to be maintained while increasing the surface area. Here we describe a mathematical model that assists in the exploration of cup-shaped, micro-cavity electrodes within an insulating substrate. This model simulates the electrical fields generated by these electrodes and is used to explore the relationship between the micro-cavity electrode depth and the electrical field generated within the electrolyte. For electrode diameters of 350 µ, spaced at a pitch of 600 εm, the model predicts that the most efficacious microcavity depth is 400 εm.
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2052, Australia.
authors.forename: Umar
authors.initials: U
authors.lastname: Ansari
authors.forename: Socrates
authors.initials: S
authors.lastname: Dokos
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
authors.forename: Gregg J
authors.initials: GJ
authors.lastname: Suaning
mesh_terms: D003198:Computer Simulation; D017076:Computer-Aided Design; D004599:Electric Stimulation Therapy / Q000295:instrumentation*; D004567:Electrodes, Implanted; D004867:Equipment Design; D019544:Equipment Failure Analysis; D008904:Miniaturization; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*