Morphology of elastic nematic liquid crystal membranes
Bibliographic record
Abstract
Liquid crystalline phases found in many biological materials, such as actin, DNA, cellulose, and collagen can be responsible for the deformation of cell membranes. In this paper, cell membrane deformation is investigated through the coupling between liquid crystal anisotropy and membrane bending elasticity. The generalized shape equation for anisotropic interfaces, which resort to the Cahn-Hoffman capillarity vector, the Rapini-Papoular anchoring energy, and the Helfrich elastic energy, is applied to gain insight into the deformation of closed liquid crystal membranes. This study presents a general morphological phase diagram of membrane surface patterns, in which two characteristic regimes of membrane shapes can be classified with respect to the most dominant factor between liquid crystal anisotropy and bending elasticity. To that end, we consider a 2D nematic liquid crystal droplet immersed in a isotropic phase in the presence of an interfacial layer of surfactants, which leads to an additional elastic contribution to the free energy of the system. The presented results indicate that, depending on the bending elasticity of the cell membrane, the liquid crystal might be able to deform the cell, thereby resulting in anisotropic asymmetric shapes. As liquid crystal anisotropy dominates the bending elasticity, spindle-like or tactoid shapes, which are extensively observed in experiments, can be formed. The findings provide a foundational framework to better understand membrane topologies in living soft matters. Furthermore, the coupling between order and curvature of membranes shed new light into the design of novel functional soft materials.
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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".