Backup mandate Research Council: Understanding and accelerating the Diels-Alder kinetics of self- healing polymer networks using a combined density functional theory and molecular dynamics approach

Project Details


Self-healing network materials (SHNs) have the potential to entirely change the way we view and use materials. A self-healing material can extend the lifetime and durability of a product tremendously, leading to a reduction in cost of material and waste treatment. Self- healing through Diels-Alder chemistry will play a large role in the development of these materials, as it already proved its use in self- healing robotics and coatings. However, the major issue is the slow kinetics of the material. Elevating the temperature to ensure proper healing can be, in the less severe case, unpractical but also lead to loss of mechanical strength or unwanted side reactions. Research suggests that the reactions may be accelerated by functionalizing the Diels-Alder components and polymer spacers. Moreover, non- covalent interactions, like hydrogen bonds, could increase the
number of crosslinks and potentially catalyze the reaction. In literature, only the furan-maleimide Diels-Alder system has been thoroughly studied for application in SHNs, leaving a gap in our knowledge regarding alternative Diels-Alder reactions. Research is also lacking regarding the impact of the polymer network (which could lead to a decrease in mobility) on the kinetics of the system. A multi-scale approach that unravels the effects of functionalization on the kinetics through both static quantum-chemical calculations and molecular dynamics simulations is therefore the central objective of this research.
Effective start/end date1/11/2131/10/22


  • Self-healing networks
  • Reversible Diels-Alder reaction kinetics
  • Effect of functionalization and hydrogen bonding
  • Static and molecular dynamics calculations

Flemish discipline codes

  • Theory and design of materials
  • Chemical thermodynamics and energetics
  • Quantum chemistry
  • Statistical mechanics in chemistry
  • Computational materials science