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Computational Modeling of Diffusion-Based Delamination for Active Implantable Medical Devices.

Minh-Hai Nguyen^1,2, Adrian Onken^1,2,3, Anika Wulff²

¹Department of Otolaryngology and Cluster of Excellence "Hearing4all", Hannover Medical School MHH, 30625 Hannover, Germany.

Bioengineering (Basel, Switzerland)

|May 27, 2023

Summary

This summary is machine-generated.

Delamination in active implantable medical devices (AIMDs) like cochlear implants (CIs) is a key failure mode. This study presents a mathematical model to understand and predict this failure, validated by experimental data.

Keywords:

COMSOL Multiphysics®active implantable medical devices cochlear implant interface diffusion lifetime prediction moving boundary diffusion volume diffusion

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Area of Science:

Bioengineering
Materials Science
Mechanical Engineering

Background:

Delamination at material interfaces is a critical failure mode in active implantable medical devices (AIMDs).
Body fluid infiltration into polymer substrates and along metal-polymer interfaces complicates modeling of AIMDs, such as cochlear implants (CIs).
Existing digital twin models lack the complexity to address biofluid infiltration in AIMDs.

Purpose of the Study:

To develop and validate a mathematical model for delamination and body fluid infiltration in AIMDs, specifically CIs.
To enhance understanding of failure mechanisms in silicone rubber and metal-based AIMDs.
To provide a framework for digital twin development in bioengineering.

Main Methods:

Implementation of a mathematical model using COMSOL Multiphysics®.
Incorporation of volume diffusion and interface diffusion (delamination) models.
Validation against experimental data from a newly developed test for AIMDs/CIs.

Main Results:

The model successfully derived diffusion coefficients from experimental data.
A good qualitative and functional match was observed between experimental and modeling results.
The interface diffusion model accurately approximated previous experimental findings.

Conclusions:

The developed mathematical model provides a better understanding of AIMD failure mechanisms.
The model's validation against real-life data supports its utility for predicting device performance.
This work advances the development of complex digital twins for bioengineering applications.