Modeling & Technology
Computational modeling has become an indispensable approach to investigate scientific questions in chemistry and related research areas. By simulation of the system at different time and space scales, It helps to get insights into the reaction processes and provide recipes for optimization or rational design of catalytic materials and reactions. With close collaboration with experimentalists, it is feasible to carry out cross-disciplinary research and realize knowledge exchange and utilization.
In the past year, we continued to apply quantum chemical modeling methodologies to investigate the reaction mechanism of heterogeneous catalytic biomass conversion and numerical simulations to study the mass and heat transfer in porous materials for electrocatalytic conversion of biomass. Meanwhile, new research lines were also opened up in the simulation of encapsulation and separation processes of biological membranes, and mechanisms of CO2 adsorption and capture processes. All research topics have direct collaborations with the ongoing experimental projects of physical chemistry and catalysis/conversion themes.
For the coming period, the objectives are to further develop multiscale and operando modeling approaches to narrow the gap between models and real systems and to strengthen the reaction mechanism investigations of electrocatalytic systems.
Main topics
- Development of kinetic models
Investigation of diffusion in porous materials - Development of kinetic models
Modeling condensation in micro- and mesopores
Reactor/process design and heat integration - Mass transfer in biological membranes
Modeling of encapsulation process of bioactive compounds in lipid droplets
Modeling of protein separations - Electro-conversion of biobased feeds into valuable platform chemicals
Development of model electrocatalyst for biomass conversion - Multiscale modeling of supported solid catalyst for biomass conversion
Investigation of the structure-reactivity relationships of metal carbide catalysts Development of operando modeling approaches