Precise Control of Lyotropic Chromonic Liquid Crystal Alignment through Surface Topography
Bibliographic record
Abstract
Many emerging applications, such as water-based electronic devices and biological sensors, require local control of anisotropic properties. Lyotropic chromonic liquid crystals (LCLCs) are an exciting class of materials, which are usually biocompatible and provide uniaxial anisotropy through a director field but, to date, remain difficult to control. In this work, we introduce a simple strategy to realize an arbitrary orientation of LCLCs director field in two dimensions (2D). Our alignment strategy relies on surface topographical micro/nanostructures fabricated by two-photon laser writing. We show that the alignment of LCLCs can be: (a) precisely controlled with a remarkable pixel resolution of 2.5 μm and (b) patterned into an arbitrary 2D alignment (e.g., +2 topological defect) by a pixelated design and arrangement of micro/nanostructures. Using a similar strategy, we achieve a patternable homeotropic alignment of LCLCs with nanopillars. Finally, we demonstrate that a self-assembled three-dimensional alignment of LCLCs can be obtained due to the versatility of our alignment strategy. Our demonstration of LCLC director field control, which is not only straightforward to achieve but also compatible with other conventional micro/nanofabrication techniques, will provide new opportunities for the manufacturing of LC-based electronic and biological devices.
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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".