Texture formation mechanisms in faceted particles embedded in a nematic liquid crystal matrix
Bibliographic record
Abstract
This paper presents a computational study of filled nematics with the aim of characterizing novel texturing processes that occur when the embedded particles have geometric singularities such as edges. As a generic texturing process due to interacting material and geometric singularities, two dimensional numerical simulations of a single square particle embedded in a calamitic thermotropic nematic liquid crystal were performed using the Landau–de Gennes model and material properties corresponding to 5CB. The results were condensed into texture phase diagrams in terms of temperature and particle size. In addition to the usual bulk defect modes found in filled nematics with smooth geometries, the square corners introduce surface defects that interact with each other or with bulk defects. The net result is that for faceted particles three modes are possible: (i) string defect modes, (ii) mixed surface/bulk defect modes, and (iii) surface defect modes. The modes' stability and transitions are explained in terms of defect energies. Accurate simulations that target transitions between the modes demonstrate critical slowing down such that long lived unstable complex textures are pinned. The present results offer new routes to engineered texturing of nematic liquid crystals by embedding faceted particles.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".