Modeling Textural Processes during Self-Assembly of Plant-Based Chiral-Nematic Liquid Crystals
Bibliographic record
Abstract
Biological liquid crystalline polymers are found in cellulosic, chitin, and DNA based natural materials. Chiral nematic liquid crystalline orientational order is observed frozen-in in the solid state in plant cell walls and is known as a liquid crystal analogue characterized by a helicoidal plywood architecture. The emergence of the plywood architecture by directed chiral nematic liquid crystalline self assembly has been postulated as the mechanism that leads to optimal cellulose fibril organization. In natural systems, tissue growth and development takes place in the presence of inclusions and secondary phases leaving behind characteristic defects and textures, which provide a unique testing ground for the validity of the liquid crystal self-assembly postulate. In this work, a mathematical model, based on the Landau-de Gennes theory of liquid crystals, is used to simulate defect textures arising in the domain of self assembly, due to presence of secondary phases representing plant cells, lumens and pit canals. It is shown that the obtained defect patterns observed in some plant cell walls are those expected from a truly liquid crystalline phase. The analysis reveals the nature and magnitude of the viscoelastic material parameters that lead to observed patterns in plant-based helicoids through directed self-assembly. In addition, the results provide new guidance to develop biomimetic plywoods for structural and functional applications.
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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 | 0.001 | 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".