Influence of Electrostatic and Chemical Heterogeneity on the Electric-Field-Induced Destabilization of Thin Liquid Films
Bibliographic record
Abstract
A numerical model for thin liquid film (<100 nm) drainage in the presence of an external electric field is developed. Long-wave theory is applied to approximate and simplify the governing equations. A spatiotemporal film morphology evolution equation thus obtained is then solved using a combination of finite difference to resolve the spatial dimensions and an adaptive time step ODE solver for the temporal propagation. The effect of fluid properties, namely, viscosity and surface tension, on the film drainage time is observed for a homogeneous electric field, which leads to random dewetting spots. Electrically heterogeneous fields, achieved by modeling electrodes with various periodic patterns, are explored to identify their effect on the drainage time and behavior. Finally, the chemical heterogeneity of the substrate is coupled with the periodic electric heterogeneity to understand the implications of combined heterogeneity. It is observed that the introduction of any heterogeneity results in faster drainage of the film when compared to that of the homogeneous field. In all cases, the thin film is drained, leaving submicrometer-scale structures at the interface. Well-controlled surface patterns are found on the application of periodic heterogeneity. This study effectively demonstrates the immense potential of electrically induced thin film drainage as a means for faster de-emulsification and for the creation of ordered submicrometer-scale surface patterns on soft materials.
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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 | 0.000 | 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 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".