A three-dimensional statistical model for imaged microstructures of porous polymer films

Area of Science:

• Materials Science
• Computational Modeling
• Chemical Engineering

Background:

• Porous materials are crucial in drug delivery systems, particularly polymer coatings for controlled release.
• Understanding the relationship between microstructure and mass transport is essential for optimizing drug release profiles.

Purpose of the Study:

• To develop a flexible and computationally efficient model for porous material microstructure.
• To investigate the influence of microstructure on mass transport in polymer blends used for drug release coatings.
• To assess model fit using geometric and diffusion-based goodness-of-fit measures.

Main Methods:

• Development of a thresholded Gaussian random field model based on stochastic partial differential equations.
• Implementation of a Markov Chain Monte Carlo algorithm for model fitting to 3D microscopy images.
• Numerical calculation of diffusion and characterization of pore geometry for model validation.

Main Results:

• The Gaussian random field model successfully captures the microstructure of porous polymer blends.
• The model allows for efficient simulation and fitting, facilitating the study of non-stationary materials.
• Goodness-of-fit measures confirm the model's ability to represent stationary regions of the material.

Conclusions:

• The developed model provides a robust framework for analyzing porous material microstructure.
• This approach enhances the understanding of mass transport phenomena in drug delivery systems.
• The findings support the optimization of polymer coatings for controlled pharmaceutical release.