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This research investigates how reaction induced phase separation (RIPS) of thermoplastic, which occurs during glassy polymer network cure, is determined by viscosity. Utilizing high Tg engineering thermoplastics in high viscosity thermoset systems, dissolution of multiple loading levels of thermoplastic and thermoset pre-polymer conversion will be achieved through use of a high shear continuous reactor. Samples will be cured using various isothermal curing profiles and characterized for morphology type and domain size as well as rheologically to determine minimum viscosity, time to gelation, time from phase separation to gelation, and average viscosity. The influence of cure conditions, thermoplastic loading levels, thermoplastic composition, and molecular weight on structural morphology will be resolved, establishing a well-defined rheological well during cure that leads to tunable and controllable phase separated morphologies, from dispersed droplet to co-continuous. By controlling viscosity of thermoplastic dispersed network pre-polymers through phase composition, cure schedule, molecular weight, directed phase separation will be achieved. Rheological profiles will be related to resulting network structure, which will lead to the ability to control and direct complex thermoplastic filled thermoset systems to targeted unique morphologies.
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This content will become publicly available on August 13, 2026
Data-Driven and Physics-Guided Design of Viscosity-Modifying Polymers
Modifying solution viscosity is a key functional application of polymers, yet the interplay of molecular chemistry, polymer architecture, and intermolecular interactions makes tailoring precise rheological responses challenging. We introduce a computational framework coupling topology-aware generative machine learning, Gaussian process modeling, and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles. Targeting thirty rheological profiles of varying difficulty, Bayesian optimization identifies polymers that satisfy all low- and most medium-difficulty targets by modifying topology and solvophobicity, with other variables fixed. In these regimes, we find and explain design degeneracy, where distinct polymers produce near-identical rheological profiles. However, satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space; this is rationally guided by physical scaling theories. This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.
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- Award ID(s):
- 2320649
- PAR ID:
- 10636757
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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