Welcome to Simone Dimartino's Research Group

In my lab, we focus on developing novel materials for 3D printing that can serve as an excellent packing media or support matrix with perfectly ordered morphology for a range of different bioprocess industries. 


Packed beds are a critical component in most operations in the process industry, with applications including reaction engineering, e.g., catalysis and fermentation, and separation processes, e.g., absorption, adsorption, and distillation. Often, these beds are randomly packed in a column or bioreactor. However, ordered, homogeneous beds have been proposed as a solution to drastically increase process efficiency. Our research group is targeted towards moving away from stereotypical “packed” beds with spherical particles to bespoke monolithic structures to suit a range of specific applications. We combine 3D printing and chemical engineering to develop novel porous adsorptive packing media for chromatography, chemical synthesis, and bioprocess applications.

We focus on developing novel resins that polymerise into solids when exposed to UV light. By careful manipulation of the resin formulation, we can produce solid materials with desired levels of porosity, surface charge, and strength. These materials serve as an excellent packing media or support matrix. Most importantly, using the digital light processing (DLP) based 3D printing technology, these resins can be used to produce support matrices/structures with complex, perfectly ordered morphology. Furthermore, these media support the formation of bacterial biofilms that enable us to create novel 3D printed biofilm bioreactors. We are studying the performance of these media in biotechnology applications such as chromatography (separation of proteins of interest), downstream processing (adsorption-based extraction of desired products from the fermentation broth), and biofilm-based biotransformation reactions by 3D printing them as a perfectly ordered packed bed with a Triply Periodic Minimal Surface (Schoen Gyroid) geometry. We are seeking to expand our applications portfolio, as we undertake machine learning and computational fluid dynamics modelling studies to identify optimal morphologies suitable for these applications.

Please browse through the other pages of this website to know more about us and our research.
Enjoy watching the entertaining science communication video made by our team.