Plasma assisted micro poling of glassy surfaces: a new tool to achieve liquid crystal multi-domain alignments [Invited]
Bibliographic record
Abstract
We propose an innovative approach to program the alignment of liquid crystal (LC) assemblies allowing for the formation of multi-domain alignments whose orientation axis and sizes are controlled at the micrometer scale by an electrically patterned glass surface. The glass surface preparation is based on a thermo-electrical imprinting process to induce localized space charge implantations in the glass matrix just below its anode surface. To demonstrate this new approach, a commercial soda-lime glass slide has been polarized using as anode a simple micrometric nickel grid. Characterizing the polarized glass surface by second harmonic generation polarized microscopy; we show an accurate control of both location and spatial components of frozen static fields embedded in the glass as a function of the electrode patterns. The polarized glassy surface is then used in the conception of a LC cell in which homeotropic or planar alignments can be controlled following the electrical pattern induced on the glass surface. This study also points out the importance of plasma discharges spatially controlled along the electrode pattern during the process in order to promote the in-plane electrical polarization effects, which are essential for the programming of the in-plane LC alignment on the polarized glass surface.
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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".