High Customization Computed Tomography for in situ Imaging of Fluid-Solid Interaction

The project is focused on designing non-invasive imaging systems based on tomography to capture the dynamics in chemical processes occurring at multiple lengths scales. Developed setup will be employed to study, e.g., oxygen diffusion and pH evolution in hydrogel, or to characterize the evolution of fluid-solid interfaces using X-ray.

Imaging is indispensable for measuring the complex movement of matters in motion.

The first phase of this study is focused on developing an optical tomography setup equipped with an in-house image reconstruction toolbox to produce high fidelity images.

The setup will be used for monitoring of the geometrical evolution of polymers in 3D volumetric printing to improve the quality of printed geometries.

Currently, x-ray computed tomography is being employed to judge the print quality, but only after the printing process has ended.

We aim to develop an in-situ monitoring system which helps find the optimum light exposure and monitors the concentration of oxygen as the inhibitor.

The second phase of this project will aim to improve the temporal resolution of the captured images by application of machine learning and deep learning algorithms such as convolutional neural networks to regenerate the missing frames based on the information learned from the captured images.

The third phase will focus on the application of previously developed optical tomography setup to capture complex phenomena in porous media.

 

Main supervisor:

Yi Yang

 

Co- supervisor:

Wei Yan

Henning Osholm Sørensen

Knud Dideriksen

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Hossein Safari Mozajin
PhD student
DTU Chemistry

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Wei Yan
Associate Professor
DTU Chemistry

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Yi Yang
Assistant Professor
DTU Chemistry