A numerical study for thermocapillary induced patterning of thin liquid films
Bibliographic record
Abstract
The underlying mechanism of thermal induced patterning is investigated using a numerical phase-field model. Research on the subject has been mostly restricted to lubrication approximation, which is only valid for the cases that the initial film thickness is smaller than the characteristic wavelength of induced instabilities. Since the long-wave approximation is no longer valid in the later stages of pattern evolution, we employed the full governing equations of fluid flow and the thermally induced Marangoni effect to track the interface between the polymer film and the air bounding layer. Conducting a systematic study on the impact of influential parameters, we found that an increase in the temperature gradient, thermal conductivity ratio, and initial thickness of the thin film resulted in shorter processing time and faster pattern formation. Additionally, the contact angle between the polymer film and the bounding plates showed a significant effect on the shape of created features. Compared to the reported experimental observation by Dietzel and Troian [“Mechanism for spontaneous growth of nanopillar arrays in ultrathin films subject to a thermal gradient,” J. Appl. Phys. 108, 074308 (2010)], our numerical modeling provided a more accurate prediction of the characteristic wavelength against the linearized model currently used in the literature. The numerical findings in this study provide valuable insight into thermal-induced patterning, which can be a useful guide for future experimental works.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".