Thermodynamic and Flow Modeling of Meso- and Macrotextures in Polymer−Liquid Crystal Material Systems
Bibliographic record
Abstract
We present a multiscale theory and simulation of thermodynamic and hydrodynamic meso- and macrotexture formation to provide fundamental principles for control and optimization of structures in polymer−liquid crystal material systems. In thermotropic flow-aligning nematic polymers, the process of texture formation is driven by the hydrodynamic instabilities. It is found that, as the shear rate increases, the pathway between an oriented nonplanar state and an oriented planar state is through texture formation and coarsening. It is found that the texture transition cascade (unaligned monodomain ⇒ defect lattice ⇒ defect gas ⇒ aligned monodomain) is remarkably consistent with the experimentally observed textural transitions of sheared lyotropic nematic polymers. On the other hand, properties of multiphase polymer−liquid crystal blends are greatly influenced by the presence of textures, or spatial distribution of topological defects, and the process of texture formation is mainly driven by the thermodynamic instabilities. The phase separation mechanism is completely different from isotropic−isotropic mixing because the continuous phase exhibits liquid crystalline ordering. The homogeneous and gradient energy of the system establishes the dynamics and correlation of the morphological structures. A pair of topological defects form inside each droplet and separate because of the presence of repulsive Peach−Koehler forces. Defect structures form cellular polygonal networks that are mostly four-sided, and the side of each polygon ends either at the droplet or at another defect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".