Heterogeneous Catalysis

Metal-organic frameworks for photo(catalysis) and photoluminescence applications
Wafaa Ahmed - Wafaa.Ahmed [at] UGent.be
Metal-organic frameworks (MOFs) are tunable for many applications. Bismuth-based MOFs (Bi-MOFs) are underexplored despite their immense application potential. My research focuses on designing new Bi-MOFs, particularly for gas adsorption, catalysis, and photoluminescence. We developed a novel robust Bi-MOF containing a flexible triazine-based tricarboxylate linker [Bi(TATAB)DMF₃], an initially dense, non-porous framework. By adjusting the crystallization rate as a function of the synthesis temperature and time, we introduced linker vacancies, which enhanced CO₂ and water sorption, and catalytic performance. Additionally, we uncovered a unique solvent effect during synthesis, which plays a key role in the defect engineering process. Another important aspect of my study is exploring how variations in defect density impact the material's photophysical behavior, such as light absorption, charge transfer, and emitted light color.

Photoenzymatic platforms for organic synthesis fueled by light
Meng.Li4 [at] UGent.be
While many concentrate on breeding dogs and cats, my attention is directed toward developing my own "pet" photobiocatalysts. This involves combining covalent organic frameworks (COFs) with natural enzymes to create artificial photoenzymatic platforms in a one-pot process. We anticipate that these enzyme-coupled photocatalysts will lead to the development of innovative methodologies for various applications in photobiocatalysis.
 
Defect Engineering in Zr-Based Metal-Organic Frameworks for Enhanced Catalytic Activity
Wafaa Ahmed – Eline.Jansen [at] UGent.be
In this research, defect-engineered Metal-Organic Frameworks (MOFs) are synthesized, with a focus on UiO-66, by introducing carefully controlled defects, such as missing linkers and clusters. These defects are designed to enhance the material’s catalytic properties, enabling fine-tuning of active sites, for targeted chemical reactions. The project integrates experimental techniques, including advanced microscopy and spectroscopy, with computational modeling to study the effects of defects on the material’s behavior across different scales. The computational work, in collaboration with Professor Van Speybroeck's team, uses cutting-edge machine learning potentials (MLPs) to accurately simulate both the structural and reactive properties of these systems. The study applies this combined experimental and theoretical approach to key catalytic reactions, aiming to establish structure-reactivity relationships and optimize reaction performance.