Numerical simulation study of self-driven microdroplet on locally restrictive discontinuous wetting gradient surface using front tracking method
Bibliographic record
Abstract
The motion of droplet on surface with discontinuous wetting gradient is of great importance for understanding lab-on-a-chip systems and other microfluidic devices. Different wetting gradients are known to be the main influencing factor in the droplet self-driven process, but the effect of different wall structures on the droplet migration process also deserves further investigation. In this paper, we analyze the self-driven process of liquid droplets on a local wetting gradient surface under microgravity conditions using front tracking method. The effects of different driving stripe lengths [Formula: see text], different restrictive stripe lengths [Formula: see text], and different surface wetting gradients Δcos θ on the droplet migration process and droplet morphology are analyzed. A theoretical formula that can predict the lateral spreading length of droplets is also proposed. The results show that different driving stripe length [Formula: see text] lengths and the wetting gradient Δcos θ have significant effects on the migration velocity of droplets, while different restrictive stripe length [Formula: see text] lengths have very significant effects on the final morphological characteristics of droplets. When restrictive stripe length [Formula: see text], the hindering effect generated by the restrictive region ΙΙΙ has more and more significant effects on the morphological structure of droplets in the migration process. When the correction factor ε = 0.735 in the prediction equation, the predicted value calculated by the theoretical equation has a good degree of similarity with the numerical simulation results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".