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Reliability of Neural Implants—Effective Method for Cleaning and Surface Preparation of Ceramics

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Neural implants provide effective treatment and diagnosis options for diseases where pharmaceutical therapies are missing or ineffective. These active implantable medical devices (AIMDs) are designed to remain implanted and functional… Click to show full abstract

Neural implants provide effective treatment and diagnosis options for diseases where pharmaceutical therapies are missing or ineffective. These active implantable medical devices (AIMDs) are designed to remain implanted and functional over decades. A key factor for achieving reliability and longevity are cleaning procedures used during manufacturing to prevent failures associated with contaminations. The Implantable Devices Group (IDG) at University College London (UCL) pioneered an approach which involved a cocktail of reagents described as “Leslie’s soup”. This process proved to be successful but no extensive evaluation of this method and the cocktail’s ingredients have been reported so far. Our study addressed this gap by a comprehensive analysis of the efficacy of this cleaning method. Surface analysis techniques complemented adhesion strengths methods to identify residues of contaminants like welding flux, solder residues or grease during typical assembly processes. Quantitative data prove the suitability of “Leslie’s soup” for cleaning of ceramic components during active implant assembly when residual ionic contaminations were removed by further treatment with isopropanol and deionised water. Solder and flux contaminations were removed without further mechanical cleaning. The adhesive strength of screen-printed metalisation layers increased from 12.50 ± 3.83 MPa without initial cleaning to 21.71 ± 1.85 MPa. We conclude that cleaning procedures during manufacturing of AIMDs, especially the understanding of applicability and limitations, is of central importance for their reliable and longevity.

Keywords: implants effective; reliability neural; surface; method; neural implants; reliability

Journal Title: Micromachines
Year Published: 2021

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