Neural Electrode Array Based on Aluminum: Fabrication and Characterization

A unique neural electrode design is proposed with 3 mm long shafts made from an aluminum-based substrate. The electrode is composed by 100 individualized shafts in a 10 × 10 matrix, in which each aluminum shafts are precisely machined via dicing-saw cutting programs. The result is a bulk structure of aluminum with 65 ° angle sharp tips. Each electrode tip is covered by an iridium oxide thin film layer (ionic transducer) via pulsed sputtering, that provides a stable and a reversible behavior for recording/stimulation purposes, a 40 mC/cm2 charge capacity and a 145 Ω impedance in a wide frequency range of interest (10 Hz-100 kHz). Because of the non-biocompatibility issue that characterizes aluminum, an anodization process is performed that forms an aluminum oxide layer around the aluminum substrate. The result is a passivation layer fully biocompatible that furthermore, enhances the mechanical properties by increasing the robustness of the electrode. For a successful electrode insertion, a 1.1 N load is required. The resultant electrode is a feasible alternative to silicon-based electrode solutions, avoiding the complexity of its fabrication methods and limitations, and increasing the electrode performance.

Predicting the mechanical behavior of amorphous polymeric materials under strain through multi-scale simulation

Polymeric materials have become the reference material for high reliability and performance applications. However, their performance in service conditions is difficult to predict, due in large part to their inherent complex morphology, which leads to non-linear and anisotropic behavior, highly dependent on the thermomechanical environment under which it is processed. In this work, a multiscale approach is proposed to investigate the mechanical properties of polymeric-based material under strain. To achieve a better understanding of phenomena occurring at the smaller scales, the coupling of a finite element method (FEM) and molecular dynamics (MD) modeling, in an iterative procedure, was employed, enabling the prediction of the macroscopic constitutive response. As the mechanical response can be related to the local microstructure, which in turn depends on the nano-scale structure, this multiscale approach computes the stress-strain relationship at every analysis point of the macro-structure by detailed modeling of the underlying micro- and meso-scale deformation phenomena. The proposed multiscale approach can enable prediction of properties at the macroscale while taking into consideration phenomena that occur at the mesoscale, thus offering an increased potential accuracy compared to traditional methods.