Pattern formation, structure and functionalities of wrinkled liquid crystal surfaces: A soft matter biomimicry platform
Bibliographic record
Abstract
This review presents an integrated theoretical and computational characterization and analysis of surface pattern formation in chiral and achiral liquid crystal self-assembly and the mechanical/optical/tribological/tissue engineering surface functionalities that emerge from various wrinkling processes. Strategies to target surface patterns include linear, non-linear, multidirectional and multiscale wrinkling phenomena. The focus of the review is to show the unique surface structure-functionalities that emerge from anisotropic liquid crystal soft matter, eliminating or reducing the need of aggressive solvents, extreme pressure/temperature conditions, erosion and other surface morphing approaches. The surface pattern formation theoretical-modelling- computational results are then connected and validated with actual biological surfaces that are considered solid liquid crystal analogues, such as exocuticles of insects, fish scales, and flowers. A unique feature of the in silico surface pattern formation platform used throughout this review is the generalized liquid crystal shape equation that includes surface anchoring elasticity, membrane elasticity, and stress loads from liquid crystals orientation gradients. Clear characterization of surface shapes, curvatures, roughness, that are behind surface functionalities are introduced and applied to strengthen validation of predictions with actual nature’s surfaces. Wrinkling scaling laws, and the dependence of material properties on morphing mechanisms are elucidated. The predictions capture very well the two-scale wrinkling patterns in tulips, wrinkling gradients that display water sensor capabilities, egg carton shapes in rose petals and their potential for cell alignment, and the ability to create surface roughness with targeted kurtosis and skewness to control and optimize friction and tribological functionalities. The results are summarized in terms of surface geometry (open or closed) mechanisms and phenomena (anchoring, membrane elasticity), material properties (anchoring coefficients, membrane bending modulus, Frank elasticity), wrinkling scales and scaling laws (amplitude, wave-lengths, skewness, kurtosis) and functionalities (optical iridescence, friction, wettability, structural color, curvature-driven cell alignment and differentiation). Taken together, the range of surface geometries and surface functionalities captured by the liquid crystal biomimetic in silico platform provides a foundation for future experimental green manufacturing pathways based on anisotropic soft matter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".