Computational modeling of the propagation of light through liquid crystals containing twist disclinations based on the finite-difference time-domain method
Bibliographic record
Abstract
The finite-difference time-domain (FDTD) method is used to compute propagation of light through textured uniaxial nematic-liquid crystal (NLC) films containing various types of twist disclination (defect) lines. Computational modeling by the FDTD method provides an accurate prediction of the optical response in multidimensional and multiscale heterogeneities in NLC films in which significant spatial optic axis gradients are present. The computations based on the FDTD method are compared with those of the classic Berreman matrix-type method. As expected, significant deviations between predictions from the two methods are observed near the twist disclination line defects because lateral optic axis gradients are ignored in the matrix Berreman method. It is shown that the failure of Berreman's method to take into account lateral optic axis gradient effects leads to significant deviations in optical output. In addition, it is shown that the FDTD method is able to distinguish clearly different types of twist disclination lines. The FDTD optical simulation method can be used for understanding fundamental relationships between optical response and complex NLC defect textures in new liquid-crystal applications including liquid-crystal-based biosensors and rheo-optical characterization of flowing liquid crystals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".