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Network Topology and Percolation in Model Covalent Adaptable Networks.

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Dynamic covalent adaptable networks (CANs) offer recyclability for thermosets. Mean-field percolation theory accurately predicts CAN topology, guiding the design of reprocessable materials.

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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.