Smectic Gardening on Curved Landscapes
Bibliographic record
Abstract
Focal conic domains (FCDs) form in smectic-A liquid crystal films with hybrid anchoring conditions with eccentricity and size distribution that depend strongly on interface curvature. Assemblies of FCDs can be exploited in settings ranging from optics to material assembly. Here, using micropost arrays with different shapes and arrangement, we assemble arrays of smectic flower patterns, revealing their internal structure as well as defect size, location, and distribution as a function of interface curvature, by imposing positive, negative, or zero Gaussian curvature at the free surface. We characterize these structures, relating free surface topography, substrate anchoring strength, and FCD distribution. Whereas the largest FCDs are located in the thickest regions of the films, the distribution of sizes is not trivially related to height, due to Apollonian tiling. Finally, we mold FCDs around microposts of complex shape and find that FCD arrangements are perturbed near the posts, but are qualitatively similar far from the posts where the details of the confining walls and associated curvature fields decay. This ability to mold FCD defects into a variety of hierarchical assemblies by manipulating the interface curvature paves the way to create new optical devices, such as compound eyes, via a directed assembly scheme.
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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) | 0.002 | 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".