Chromatography: 3D printed adsorptive media

We aim to create adsorptive porous media using 3D printing technologies. Current adsorptive media present irregular pores and random interconnections, resulting in product variability and sub-optimal performance. Fine control of the packing geometry is achieved using 3D printing. This idea is currently employed to generate porous media for chromatographic applications, but the same approach could be used in a range of industries in the chemical engineering arena. We explore alternative chromatographic stationary phases for the purification of large biomolecules. These chromatographic media are necessary to overcome the cost and performance limitations of actual bead-based resins.

Bioprocessing: 3D printed bioreactors & adsorbents for product recovery

We employ the concept of having perfectly ordered packing to bioreactors and for downstream processing.

Biomedical: 3D printed phantoms

We develop bespoke devices and materials for the manufacture of life-changing drugs. 3D printing enables the fabrication of purification devices with custom and ordered designs that will maximize production efficiency while minimising costs. This will represent a significant step-change from traditional separation methods still relying on technologies developed in 1940. In this project, we implement 3D printing technology to serve healthcare needs by developing 3D printable materials with a complex geometry to address FDBK separation challenges. This project is cofounded by FDBK (in the framework of the FDB Centre of Excellence in Bioprocessing 2.0) and the Scottish Research Partnership in Engineering.

Modelling studies: CFD modelling and Machine learning

We search for structures with optimized separation efficiency. Some ordered structures, which can be manufactured by 3D printing, have been proven to have better column performance compared to the randomly packing. Finding out other optimal structures, without any guidance, can be frustratingly slow and inefficient due to the vast combination of space. In this project, CFD simulations were performed to analyze the general pattern of the optimal ordered structures and a machine learning method was utilized to seek more optimized structures automatically.