Display options
Cite
Barman S, Bolin D. A three-dimensional statistical model for imaged microstructures of porous polymer films. J Microsc. 2017;269(3):247-258doi: 10.1111/jmi.12623.
Barman, S., & Bolin, D. (2018). A three-dimensional statistical model for imaged microstructures of porous polymer films. Journal of microscopy, 269(3), 247-258. https://doi.org/10.1111/jmi.12623
Barman, S, and Bolin, D. "A three-dimensional statistical model for imaged microstructures of porous polymer films." Journal of microscopy vol. 269,3 (2018): 247-258. doi: https://doi.org/10.1111/jmi.12623
Barman S, Bolin D. A three-dimensional statistical model for imaged microstructures of porous polymer films. J Microsc. 2018 Mar;269(3):247-258. doi: 10.1111/jmi.12623. Epub 2017 Sep 08. PMID: 28884819.
Copy Download .nbib Format: NLM
Share it on

J Microsc. 2018 Mar;269(3):247-258. doi: 10.1111/jmi.12623. Epub 2017 Sep 08.

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

Journal of microscopy

S Barman, D Bolin

Affiliations

  1. Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden.
  2. Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.

PMID: 28884819 DOI: 10.1111/jmi.12623

Abstract

A thresholded Gaussian random field model is developed for the microstructure of porous materials. Defining the random field as a solution to stochastic partial differential equation allows for flexible modelling of nonstationarities in the material and facilitates computationally efficient methods for simulation and model fitting. A Markov Chain Monte Carlo algorithm is developed and used to fit the model to three-dimensional confocal laser scanning microscopy images. The methods are applied to study a porous ethylcellulose/hydroxypropylcellulose polymer blend that is used as a coating to control drug release from pharmaceutical tablets. The aim is to investigate how mass transport through the material depends on the microstructure. We derive a number of goodness-of-fit measures based on numerically calculated diffusion through the material. These are used in combination with measures that characterize the geometry of the pore structure to assess model fit. The model is found to fit stationary parts of the material well.

© 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

Keywords: Gaussian Markov random field; Gaussian field; Markov Chain Monte Carlo; model validation; porous media

Publication Types