Quantitative Prediction of Stress Relaxation Kinetics in Dissociative Covalent Adaptable Networks

Area of Science:

• Materials Science
• Polymer Chemistry
• Chemical Engineering

Background:

• Covalent adaptable networks (CANs) exhibit unique stress relaxation properties.
• Reversible chemistries, like Diels-Alder reactions, are key to CAN functionality.
• Understanding stress relaxation is crucial for designing advanced materials.

Purpose of the Study:

• To develop a quantitative kinetic-structural model for stress relaxation in Diels-Alder based CANs.
• To correlate network de-cross-linking with stress relaxation behavior.
• To enable prediction and design of materials with specific dynamic properties.

Main Methods:

• Developed a kinetic-structural model integrating Diels-Alder reaction kinetics and network analysis (Macosko-Miller approach).
• Incorporated stress-induced bond activation and reduced bond exchange efficiency.
• Validated the model using rheological data from two Diels-Alder networks.

Main Results:

• The model accurately predicted stress relaxation behavior, including characteristic times and shape parameters.
• Only two temperature-independent parameters were needed for accurate predictions across various conditions.
• Model successfully predicted initial stresses using cross-linking kinetics and equilibrium conversions.

Conclusions:

• The developed model provides accurate predictions and enables extraction of kinetic/thermodynamic parameters.
• Its low computational cost facilitates rapid exploration for designing application-specific materials.
• This framework bridges dynamic covalent chemistry with functional material development and processing.