Dynamic wetting model for the isotropic-to-nematic transition over a flat substrate
Bibliographic record
Abstract
Phase ordering over solid substrates is a ubiquitous and important soft material transformation process whose description incorporates wetting, anchoring and phase transition kinetics. In this paper the kinetics of the isotropic-to-nematic isothermal phase transition over a flat solid surface in a growing spherical drop is analyzed based on the Landau-de Gennes Q-tensor order parameter equations. The model, based on a previously derived interface force balance and a newly derived contact line force balance, is shown to be consistent with the generic model of conservative interface and contact line motions. The advancing dynamic contact angle equation is extracted from kinematic compatibility between the moving isotropic-nematic interface and contact line. A tractable surface phase transition kinetic model obtained by focusing on the dominant phase transition and wetting driving forces yields: (i) the constant advancing dynamic contact angle θ, and (ii) the contact line speed as a function of undercooling ΔT. It is shown that as undercooling increases, the surface phase transition mode approaches the bulk phase transition mode, such that θ approaches π. The elastic and wetting parameters that control the phase transformation process are identified and experiments for their determination are defined. These dynamic wetting and surface phase transition results significantly expand existing characterization methods of LC-substrate interfaces based on static phase transition droplet methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".