Structure and Pattern Formation in Biological Liquid Crystals: Insights From Theory and Simulation of Self-Assembly and Self-Organization
Bibliographic record
Abstract
This review presents theory and simulation of liquid crystal phase ordering in biological fibrous materials, solutions, and composites in the presence of elastic fields, second phase inclusions, and transport phenomena, including complex shear-extensional flow and mass transfer. Liquid crystal self-assembly through phase ordering on elastic deformable membranes is first applied to characterize the mechanisms that control the structures in plant cell walls, highlighting how curvophobic and curvophilic effects introduce new structuring fields beyond hard-core repulsion. Then chiral nematic self-assembly is simulated in a mesophase containing fibrillar colloidal inclusions (liquid crystal-fibre composites) to demonstrate how the inclusion positional order generates defects and disclinations as shown in the plant cell wall. Coupling phase ordering to tuned transport phenomena is shown how and why it leads to self-organization such as paranematic states of dilute acidic aqueous collagen solutions. Further directed dehydration of well-organized paranematic collagen leads to defect free cholesteric films only when directed dehydration is synchronized with chirality formation. In addition, the ubiquitous surface nanowrinkling of cholesterics is captured with surface anchoring. In these four representative systems, the new mechanisms that enhance the well-known exclude volume interactions are identified quantified and validated with experimental data. Future directions to create new advanced multifunctional materials based on principles of self-assembly and self-organization are identified by leveraging the new couplings between material structure, geometry, and transport phenomena.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".