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Phase-field modeling of elastic microphase separation

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arxiv 2505.01389 v1 pith:LWP7CGJZ submitted 2025-05-02 cond-mat.mtrl-sci

classification cond-mat.mtrl-sci
keywords elasticlengthpolymercharacteristiccoarseningcouplingenergyterm
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We propose a novel phase-field model to predict elastic microphase separation in polymer gels. To this end, we extend the Cahn-Hilliard free-energy functional to incorporate an elastic strain energy and a coupling term. These contributions are naturally obtained from a derivation that starts from an entropic elastic energy density combined with the assumption of weak compressibility, upon second-order approximation around the swollen state. The resulting terms correspond to those of a poroelastic formulation where the coupling energetic term can be interpreted as the osmotic work of the solvent within the polymer matrix. Additionally, a convolution term is included in the total energy to model non-local forces responsible for coarsening arrest. With analytical derivations in 1D and finite element computations in 2D we show that the mechanical deformation controls the composition of the stable phases, the initial characteristic length and time, the coarsening rates and the arrested characteristic length. Moreover, we demonstrate that the proposed coupling is able to predict the arrest of coarsening at a length scale controlled by the stiffness of the dry polymer. The numerical results show excellent agreement with the experimental evidence in terms of phase-separated morphology and scaling of the characteristic length with the stiffness of the dry polymer.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Thermodynamics of microphase separation in a swollen, strain-stiffening polymer network

    cond-mat.soft 2025-06 conditional novelty 6.0 of 10

    Adding strain-stiffening elasticity to Flory-Huggins theory quantitatively predicts the phase boundaries of elastic microphase separation in swollen polymer networks from independently measured mechanical and solubility data.

  2. Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model

    cs.LG 2025-09 conditional novelty 4.0 of 10

    E-UNO, a U-shaped Fourier neural operator with a D4 equivariance loss, predicts Cahn-Hilliard microstructure evolution more accurately than FNO and UNO baselines in reported experiments.

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