Texture formation under phase ordering and phase separation in polymer-liquid crystal mixtures.

Area of Science:

• Materials Science
• Soft Matter Physics
• Computational Physics

Background:

• Polymer/liquid crystal mixtures exhibit complex phase behavior driven by coupled phase separation and ordering.
• Understanding texture formation is crucial for tailoring material properties.
• Topological defects play a significant role in self-assembly processes.

Purpose of the Study:

• To computationally model texture formation in polymer/liquid crystal mixtures.
• To investigate the influence of topological defects on phase separation and ordering.
• To analyze the emergence of different biphasic structures: PDLCs, CFNs, and RFNs.

Main Methods:

• A unified computational model using the nematic tensor order parameter and gradient orientation elasticity.
• Simulation of defect nucleation, interactions, and morphological features.
• Analysis of concentration and order parameter spatiotemporal behavior.

Main Results:

• The model successfully captures PDLC, CFN, and RFN structures.
• PDLCs form via concentration fluctuations, with defects leading to bipolar droplets.
• CFNs emerge from ordering fluctuations, with defects pinning polymer crystals.
• RFNs arise from combined fluctuations, with defect networks influencing polymer droplet/fibril morphology.

Conclusions:

• Topological defects are key to understanding phase separation-phase ordering in these mixtures.
• The interplay between defects and fluctuations dictates the final material texture.
• This work provides insights into defect-driven self-assembly in complex fluids.