Mechanical interlocking to improve metal–polymer adhesion in polymer-based neural electrodes and its impact on device reliability
Source abstract
Abstract
The polymer–metal interface is one of the most critical parts in polymer-based neural implants in relation to their long-term reliability. In this study, we aim to suggest a feasible fabrication method using mechanical interlocking to improve polymer–metal adhesion in polymer-based neural electrodes and evaluate its impact on device reliability in vitro. Liquid crystal polymer (LCP) is selected as the base material for the polymer-based neural electrode due to its extremely low water absorption rate (<0.04 %). Test samples with mechanical interlocked interface between LCP and a noble metal are designed and fabricated using micro-patterning technologies, in this case photolithography, electroplating, and laser machining. After the metal patterns with undercut profile cross sections were fabricated using a dual photolithography process and electroplating, the LCP and the metal formed a mechanical interlocking pattern during the lamination process. In a 180° peel test, the average maximum adhesion force of the samples with and without mechanical interlocking was 19.24 and 14.27 N, respectively. In vitro accelerated soak tests, which consist of interdigitated electrode patterns and a customized system for measuring the leakage current, are carried out to evaluate the long-term reliability of the LCP-based neural electrodes. Samples with and without interlock failed after 224 and 185 days, respectively, in a 75 °C saline environment. Scanning electron microscopy images revealed that the interlocked LCP–metal interfaces remained intact after water leakage. The results demonstrate the effect of the fabrication method using mechanical interlocking, which can be applicable to other polymer-based neural electrodes for long-term implantation.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Neuroscience and Neural Engineering · EEG and Brain-Computer Interfaces · Muscle activation and electromyography studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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