Computational Analysis to Optimize the Performance of Thin Film Liquid Crystal Biosensors
Bibliographic record
Abstract
A nonlinear unsteady-state mathematical model employing torque balance and Frank free energy according to the Leslie-Ericksen continuum theory is developed and implemented to simulate the performance of nematic liquid crystal biosensor films with aqueous interfaces. A transient liquid crystal-aqueous interface realignment is modeled using the Euler–Lagrange equation by changing the easy axis when the surfactant molecules at the interface are introduced. In our study, we evaluated the dynamics between bulk and interface by controlling surface properties of the interface, such as homeotropic anchoring energy and surface viscosity. In addition, transient optical interference and response time have been examined in this study. Our parametric study results indicated that both homeotropic anchoring energy and surface viscosity at the interface contribute to bulk reorientation. Furthermore, the obtained numerical results indicate that as homeotropic anchoring strength increases, the effective birefringence decreases more gradual due to the increasing surfactant concentration at the aqueous interface, consistent with available experimental observations. Our results have been validated and compared to experimental results from thin-film liquid crystal biosensors in this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".