Three-dimensional alignment of liquid crystals in nanostructured porous thin films
Bibliographic record
Abstract
Oblique evaporation of inorganic materials has long been used to induce alignment in liquid crystals, often for the purpose of controlling the pretilt angle in a liquid crystal cell. These alignment layers are relatively dense, keeping the liquid crystals above the surface of the inorganic layer. By evaporating at increasingly oblique angles (> 80°), the alignment layer can be made porous, allowing liquid crystals to infiltrate the film and to align to individual nanostructures. By coupling simultaneous computer controlled substrate motion during evaporation, a process known as glancing angle deposition (GLAD), the nanostructures can be grown in a variety of useful shapes, including helices, polygonal spirals, zigzags and periodically bent S-shaped columns. Alone, these films exhibit properties such as linear and circular polarization selective Bragg reflection, and full three-dimensional photonic bandgaps. By infiltrating liquid crystals into the voids of the film, one can align liquid crystals in three dimensions, as well as tune and switch the film's optical properties. Additionally, the GLAD film can be used to template polymerizable liquid crystals for subsequent monomer infiltration. In this work, using spectroscopic ellipsometry, we examine the effects of liquid crystal infiltration on various film structures made from a variety of metal oxides, for both varying film thickness and deposition angle. Techniques for filling a porous film with a known volume of liquid crystals are also presented. Additionally, we examine the switching behaviour for these films under applied electric fields. Finally, we compare experimental and simulated results used to predict and optimize the optical properties of these hybrid films.
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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) | 0.000 | 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".