After graduating from the Bachelor Chemistry at the University of Amsterdam, Tamika van ‘t Hoff changed discipline and started the Masters Computational Science in Amsterdam. After an inspiring thesis project, she was persuaded to start a PhD Computational Polymer Chemistry in September 2020, which’ subject combines both disciplines. Besides doing research, other interests of Tamika van ‘t Hoff are traveling, hiking and swimming.
Computational Polymer Chemistry
The process of polymerisation is large and complex, and thus difficult to model with methods that describe molecules on an atomic level. Rather than focusing on the atomic interactions that cause reactions in every monomer, we strip the chemical problem to the bare necessities and use Random Graph Theory to model the process.
Random Graph Theory is a mathematical theory that describes a network defined by a probability distribution depending on the connectivity of the nodes. When we apply this model to a chemical problem, we need to make sure that the probability distribution contains the chemical restrictions of the modeled monomers. Therefore, we only use information about the formed crosslinks between the monomers, rather than the rest of the molecule. This way, we reduce the size of the problem considerably and are even able to predict global properties of the formed polymer using Random Graph Formalism. For example, the exact moment the system forms a polymer and the size distribution of the connected components in the polymer network.
Although we already have a working model, we are trying to make the connection between the mathematical model and experimental studies. For example, it is impossible to compare a size distribution with an experimental mass spectrum when the monomers do not have a constant mass, which is the case for a lot of polymers. In addition, we are trying to incorporate more chemical properties in the model to make the random graph more realistic. For instance, we need to include the formation of cycles in the polymer network.