Bibliographic record
Abstract
Optical simulations of a liquid crystal biosensor device are performed using an integrated optical/textural model based on the equations of nematodynamics and two optical methods: the Berreman optical matrix method [J. Opt. Soc. Am. 62, 502 (1972)] and the discretization of the Maxwell equations based on the finite difference time domain (FDTD) method. Testing the two optical methods with liquid crystal films of different degrees of orientational heterogeneities demonstrates that only the FDTD method is suitable to model this device. Basic substrate-induced texturing process due to protein adsorption gives rise to an orientation correlation function that is nearly linear with the transmitted light intensity, providing a basis to calibrate the device. The sensitivity of transmitted light to film thickness, protein surface coverage, and wavelength is established. A crossover incident light wavelength close to lambda(co) approximately 500 nm is found, such that when lambda>lambda(co) thinner films are more sensitive to the amount of protein surface coverage, while for lambda<lambda(co) the reverse holds. In addition it is found that for all wavelengths the sensitivity increases with the amount of protein coverage. The integrated device model based on FDTD optical simulations in conjunction with the Landau-de Gennes nematodynamics model provides a rational basis for further progress in liquid crystal biosensor devices.
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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".