Свето-индуцированная скачкообразная гидродинамическая инверсия направления молекулярной ориентации в жидких кристаллах
Bibliographic record
Abstract
Численно решена задача о лазерно-индуцированной гидродинамической переориентации (ЛИГП) директора гибридно-ориентированного нематического жидкого кристалла (НЖК) с прозвольными граничными условиями. ЛИГП изучена при различных мощностях лазера и для двух противоположных направлений градиента температуры. Градиент скорости гидродинамического потока приводит к небольшому увеличению кривизны гибридной ориентации, когда свет создает градиент температуры вовне кривизны. Кривизна меняет знак, когда свет создает градиент температуры снаружи вовнутрь кривизны гибридной ориентации. Исследована также зависимость переориентации от энергии крепления молекул к границам. The problem of laser-induced hydrodynamic reorientation (LIHR) of the director of a hybrid-oriented nematic liquid crystal (NLC) with arbitrary boundary conditions has been numerically solved. LIHR is studied at different laser powers and for two opposite directions of the temperature gradients. The hydrodynamic flow velocity gradient leads to a small increase in curvature when light creates temperature gradient in the out-of-curvature side of the hybrid orientation. The curvature changes sign when light creates temperature gradient from outside to inside the curvature of the hybrid orientation. The dependence of reorientation on the anchoring energy of molecules to the boundaries is also investigated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".