Magnetic Field‐Induced Shape Transitions in Multiphase Polymer‐Liquid Crystal Blends
Bibliographic record
Abstract
Abstract Summary:This paper presents a computational study of phase separation‐phase ordering‐texturing in blends of polymer coils and rod‐like nematic liquid crystals under the presence of magnetic fields, using an extended version of the Matsuyama‐Evans‐Cates model (Phys. Rev. E2000,61, 2977). This work demonstrates that demixing in these blends leads to droplet morphologies with tunable droplet shapes and director textures. In contrast to filled nematics, where solids are suspended in a nematic liquid crystal matrix, demixing in coil‐mesogenic rods blends leads to nematic emulsions, in which the deformable viscoelastic polymer drops are suspended in a nematic matrix. Under strong anchoring conditions, the imposition of a magnetic field leads to a director re‐orientation that due to strong anchoring produces a droplet shape change. Magnetic field‐induced shape transitions in these blends are shown to be second order with a finite critical field threshold that diverges as anchoring strength vanishes. A morphological‐texture diagram summarizes the magnetic field‐anchoring conditions that promote anisotropic shapes. This work presents additional material processing routes to design and control bi‐phasic morphologies in polymer‐liquid crystal blend. Computed morphology phase diagram in terms of magnetic field strength ΛMand anchoring strength. ΛϕQ. magnified image Computed morphology phase diagram in terms of magnetic field strength ΛMand anchoring strength. ΛϕQ.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".