Monte Carlo methods are heuristic algorithms that use probabilities to select an outcome
among several possible events in a given process. Monte Carlo methods are useful in …

The primary need for advanced porous materials with well-understood and enhanced
properties is identified for implementations in the fields of life science and engineering. The …

Stochastic modeling techniques have emerged as a powerful tool to study the time evolution
of processes in many research fields including (bio) chemical engineering and biology. One …

Compartmentalization refers to the physical confinement of reactants within confined space
of nanosize dimensions and is a concept exploited by nature for the synthesis of various …

The molar mass distribution (MMD), which is also known as the molecular weight distribution
(MWD), is a key molecular property for a polymerization process, as it determines polymer …

The strength of combining experimental design and advanced kinetic Monte Carlo (kMC)
modeling to understand step-growth network synthesis, commencing with a linear/star …

Bulk and solution radical polymerization is important in daily live. A challenge is still to
maximize polymerization rate and control over molecular characteristics such as the molar …

A comprehensive kinetic Monte Carlo algorithm has been developed to investigate the
formation process of a polyurethane/poly (methyl methacrylate)(PU/PMMA) interpenetrating …

Complete pulsed laser polymerization (PLP) log-molar mass distributions (log-MMDs) are
accurately simulated using kinetic Monte Carlo (k MC) modeling to gain not only knowledge …

A challenge in the field of polymer network synthesis by a step-growth mechanism is the
quantification of the relative importance of inter-vs. intramolecular reactions. Here we use a …