Topology-Informed Design Rules for Deconstructable Thermoset Copolymer Networks

Area of Science:

• Polymer Chemistry
• Materials Science
• Computational Materials Science

Background:

• Existing thermoset deconstruction models use a mean-field reverse gel point paradigm.
• These models focus on stoichiometry but neglect network architecture's role.

Purpose of the Study:

• To investigate how network topology influences thermoset deconstructability.
• To extend existing models by incorporating topological factors.

Main Methods:

• Reactive coarse-grained molecular dynamics simulations.
• Graph-theoretic analysis, including betweenness centrality.
• Analysis of hierarchical network organization (local and mesoscale).

Main Results:

• Deconstructability is governed by curing-imprinted network topology, not solely stoichiometry.
• Cleavable comonomers must intercept high-centrality bridging strands for effective deconstruction.
• Network modularity limits fragment uniformity; chain stiffness offers independent control over thermomechanical properties.

Conclusions:

• Thermoset design should prioritize network topology for predictable deconstruction.
• Findings provide guidelines for engineering thermoset copolymers with tailored performance.
• This work reframes thermoset design around topology and deconstructability.