Computational modelling of light propagation in textured liquid crystals based on the finite‐difference time‐domain (FDTD) method
Bibliographic record
Abstract
Light propagation through uniaxial rod‐like nematic liquid crystal films containing singular (thin) and non‐singular (thick) line disclinations is computed using the finite‐difference time‐domain method (FDTD), which is based on accurate numerical solutions to the governing Maxwell equations. The results obtained by the FDTD method are compared with classical matrix‐type methods, including the aggregate model and Berreman's method. It is found that the optical signals for singular and non‐singular defects predicted by the matrix methods deviate significantly from the FDTD method because director gradient effects on the plane normal to the incident light are not properly taken into account . It is also found that the FDTD optical signal for singular thin lines has a characteristic length scale associated with the wavelength of the incident light, while for non‐singular thick lines the scale is associated with the defect escaped core dimensions. The FDTD method offers an accurate quantitative tool for use in new applications including liquid crystal‐based biosensors and rheo‐optical characterization of liquid crystalline polymers.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".