Biological plywood film formation from para-nematic liquid crystalline organization
Bibliographic record
Abstract
In vitro non-equilibrium chiral phase ordering processes of biomacromolecular solutions offer a systematic and reproducible way of generating material architectures found in Nature, such as biological plywoods. Accelerated progress in biomimetic engineering of mesoscopic plywoods and other fibrous structures requires a fundamental understanding of processing and transport principles. In this work we focus on collagen I based materials and structures to find processing conditions that lead to defect-free collagen films displaying the helicoidal plywood architecture. Here we report experimentally-guided theory and simulations of the chiral phase ordering of collagen molecules through water solvent evaporation of pre-aligned dilute collagen solutions. We develop, implement and a posteriori validate an integrated liquid crystal chiral phase ordering-water transport model that captures the essential features of spatio-temporal chiral structure formation in shrinking film domains due to directed water loss. Three microstructural (texture) modes are identified depending on the particular value of the time-scale ratio defined by collagen rotational diffusion to water translational diffusion. The magnitude of the time scale ratio provides the conditions for the synchronization of the helical axis morphogenesis with the increase in the mesogen concentration due to water loss. Slower than critical water removal rates leads to internal multiaxial cellular patterns, reminiscent of the classical columnar-equiaxed metallurgical casting structures. Excessive water removal rates lead to destabilization of the chiral axis and multidomain defected films. The predictions of the integrated model are in qualitative agreement with experimental results and can potentially guide solution processing of other bio-related mesogenic solutions that seek to mimic the architecture of biological fibrous composites.
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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".