Anisotropy Enhanced Phase Separation in Polymer Dispersed Liquid Crystals
Bibliographic record
Abstract
Phase separated blends of polymers and low molecular weight liquid crystals, commonly known as polymer dispersed liquid crystals in short PDLCs, are investigated. These materials offer a realm of applications in modern technologies, including sensors, commutable windows, display devices and telecommunication systems. A particular attention is given to the effects of anisotropy of the liquid crystal on the phase behavior under equilibrium and non equilibrium conditions. The theoretical formalism used is based on the lattice model of isotropic mixing, combined with standards theories of nematic and smectic-A orders. Considering the equilibrium phase behavior, we find that the nematic order enhances the polymer / solvent phase separation, and that the osmotic pressure shows substantial changes for relatively small polymer volume fractions. We find that the anisotropy enhanced phase separation is more pronounced for a smectic-A liquid crystal, and the miscibility gap is widened. The kinetics of swelling by nematic LCs is examined using a linear solvent diffusion process, with a rate of swelling directly related to the derivative of the osmotic pressure. An abrupt swelling / de-swelling transition is found, due to overwhelming effects of the anisotropic interaction beyond the threshold LC concentration. Anisotropy enhanced phase separation is also investigated in the method of synthesis based on the polymerization induced phase separation mechanism. We find that the kinetics of separation during early stages of polymerization is faster, due to the anisotropic interaction of the low molecular weight solvent. The kinetics speed up is favored by the long range viscous flow effects due to hydrodynamic interactions. A limited selection of experimental data in the literature is chosen to validate some theoretical predictions obtained from the present formalisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".