Network Topology and Percolation in Model Covalent Adaptable Networks

Area of Science:

• Materials Science
• Polymer Chemistry
• Chemical Engineering

Background:

• Thermosets are typically unrecyclable due to permanent crosslinks.
• Dynamic covalent chemistry enables the creation of adaptable networks (CANs) with potential for reprocessing.
• Network topology, particularly percolation, significantly influences CAN properties.

Purpose of the Study:

• To evaluate mean-field percolation theory as a predictive tool for CAN topology.
• To assess the accuracy of mean-field theory using experimental and simulation data.
• To provide design principles for enhancing the reprocessability of CANs.

Main Methods:

• Utilized a model glassy disulfide-based CAN.
• Compared mean-field percolation theory predictions with experimental data.
• Employed coarse-grained molecular dynamics simulations for validation.

Main Results:

• Mean-field percolation theory provides a surprisingly accurate description of CAN topology.
• The theory is effective even with simplifying assumptions.
• The approach is particularly well-suited for mixed-composition CANs.

Conclusions:

• Mean-field percolation theory is a valuable tool for understanding and designing CANs.
• Accurate prediction of network topology facilitates the development of recyclable thermosets.
• This work offers practical guidance for designing materials with enhanced reprocessability.