Mechanisms and shape predictions of nematic disclination branching under conical confinement
Bibliographic record
Abstract
Liquid crystals (LCs) are self-organizing anisotropic viscoelastic soft materials that flow like viscous liquids and display anisotropies like crystals. When a nematic liquid crystal is confined to a capillary tube with strong anchoring conditions, disclination defects of higher (+1) and lower (+1/2) topological charges can coexist, connected through a defect branch point. The shape of the +1/2 disclination lines emanating from the branch point are functions of confinement and bulk elasticity. Previous work shows that nematic liquid crystals under cylindrical confinement display a radial (one +1 line)-to-planar polar (two +1/2 lines) defect texture transition through the nucleation and uniform motion of a disclination branch point. Here we present analysis, scaling and modeling based on a non-linear non-local nematic elastic equation that shows that a branch point also can be generated from disclinations in a liquid crystal confined to different conical geometries with homeotropic anchoring conditions. The cone aperture increases the bending stiffness but decreases the curvature of the disclination. These competing effects lead to a decrease in the total disclination curvature, increase in elastic energy and volume of the branching region. The results are summarized into power laws and integrated into a shape/energy diagram that reveals the effects of confinement and its gradient (cone angle) on disclination shape selection. These new findings are useful to assess the Frank elasticity of new nematic liquid crystals and to predict novel defect structures in complex confinement, including biological microfluidics and mesophase fiber spinning.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".