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Scientific Publication Citing Dragonfly

Quantitative 3D reconstruction of porous polymers using FIB-SEM tomography

Cecilia Fager (1)
Fager's Doctoral Thesis, 2020.


focused ion beam, scanning electron microscopy, tomography, 3D, soft material, insulating material, connectivity, polymer film, controlled drug release


Porous networks are found in a wide range of different advanced and technologically important materials and influence the materials properties. The networks are active components in for example batteries, food and pharmaceuticals. The interconnectivity of a network strongly influences the transport properties. One example is polymer film coatings for controlled drug release where the porous network acts as a transport path for drugs. The correlation between the detailed structure of the network and the transport properties illustrates the importance of quantifying the interconnectivity in 3D. One approach to image material in 3D is sequential imaging (tomography). Examples of tomography techniques are confocal laser scanning microscopy, x-ray and neutron tomography where the spatial resolution is limited to the micrometre length scale. Transmission electron microscopy tomography and focused ion beam (FIB) combined scanning electron microscope (SEM) tomography are examples of techniques with higher spatial resolution ranging from micrometre to sub-nanometre. In this work the focus is on the understanding of the correlation between the structure and materials properties of phase-separated polymer film coatings used for controlled drug release. We acquired high spatial 3D resolution data on microporous ethyl cellulose and hydroxypropyl cellulose film coatings using FIB-SEM tomography. The tomography was performed after the water soluble hydroxypropyl cellulose phase had been removed leaving a porous network providing a transport path for the drug. We optimised the FIB-SEM parameters and established a generic protocol for porous and poorly conducting materials in order to overcome challenges such as redeposition, curtaining, shadowing effects, charging and sub-surface information due to the pores. In addition, a new self-learning segmentation algorithm was introduced to enable an automatic separation between pores and matrix. The quantification of the porous network was carried out by determining the pore size distribution, tortuosity and interconnectivity. As a final step, diffusion simulations were performed on the FIB-SEM data and correlated with experimentally measured permeability.

Author Affiliation

(1) Department of Physics, Chalmers University of Technology.

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