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Digital Structure Analysis


The analysis of the material structures consists of several distinct parts, all focused on understanding how structural properties of the materials determine functionality, such as transport properties.

Virtual Design

Virtual design of digital material structures is used to investigate structure-property relationships on a large scale, where thousands of 3D structures can easily be produced. Statistical models are used to create material structures which are similar to real materials. Examples include fibre structures, paperboard coatings, phase-separated polymer materials and packed particles.

Segmentation

Segmentation of microscopy data is essential to separate pores and material in the images. In some cases, a simple thresholding is satisfactory, perhaps with some image processing to remove noise and artifacts. However, more advanced methods are required if the materials are complex, consisting of different components, or when imaging techniques such as FIB-SEM are used. We use a range of image processing and segmentation tools, including machine learning methods, to achieve high quality results for further analysis.

Structure Analysis

Once segmented, the pore structure can be analysed in a range of ways to compute its characteristics. We compute descriptors such as porosity, tortuosity, constrictivity and location of bottlenecks using our software Mist, which is freely available at mist.math.chalmers.se.
These descriptors are then used to find correlations with mass transport simulation results that can be used for prediction.

Want to know more?

Please reach out to one of our experts in the field

Sandra Barman

PhD

Sandra Barman

Researcher at RISE Research Institutes of Sweden. Expertise in segmentation, virtual design, and the relationship between material structure and functionality

sandra.barman@ri.se

+467 353 23 45

Aila Särkkä

Professor

Aila Särkkä

Professor in Mathematical Sciences at Chalmers University of Technology. Expertise in spatial and spatio-temporal modeling, with a focus on modeling of material structures.

aila@chalmers.se

+46 31 772 35 42

Related publications

Explore some publications related to the concept.

New descriptors of connectivity-bottleneck effects improve understanding and prediction of diffusive transport in pore geometries

Sandra Barman, Holger Rootzén, David Bolin
2025
Link

Correlating 3D porous structure in polymer films with mass transport properties using FIB-SEM tomography

Cecilia Fager, et al.
2021
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Stochastic modelling of 3D fiber structures imaged with X-ray microtomography

Philip Townsend, et al.
2021
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Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release

Fredrik Skärberg, et al.
2021
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Tessellation-based stochastic modelling of 3D coating structures imaged with FIB-SEM tomography

Philip Townsend, et al.
2021
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Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures

Benedikt Prifling, Magnus Röding, et al
2021
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New Characterization Measures of Pore Shape and Connectivity Applied to Coatings used for Controlled Drug Release

Sandra Barman, et al.
2021
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3D high spatial resolution visualisation and quantification of interconnectivity in polymer films

Cecilia Fager, et al
2020
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Prediction of diffusive transport through polymer films from characteristics of the pore geometry

Sandra Barman, Holger Rootzén, David Bolin
2019
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Computational high-throughput screening of fluid permeability in heterogeneous fiber materials

Magnus Röding, et al.
2016
Link