Control of boundary slip by interfacial nanobubbles: A perspective from molecular dynamics simulations
Bibliographic record
Abstract
Enhancing boundary slip using interfacial nanobubbles (INBs) has gained significant interest in nanofluidic transport. In this study, we conducted a comprehensive investigation on the influence of INBs on boundary conditions for both smooth and rough substrates using molecular dynamics simulations. We analyzed the impact of INB protrusion angle, coverage percentage, quantity, and fluidity on the slip length. Our results showed that INBs always increase the slip length on a smooth substrate, with a linear increase in slip length observed with increasing surface coverage. On a rough substrate, we found that the protrusion angle, quantity, and fluidity of INBs play a crucial role in determining the slip length. Smaller protrusion angles and fewer quantities of INBs were found to be more favorable for enhancing the slip length when the INB coverage is fixed, while the correlation between boundary slip and INB quantity depended on the wetting state of the substrate when the size of the INBs was fixed with a low protrusion angle. Additionally, we revealed that the fluidity of gas molecules inside the INBs dominated the enhancement of slip length by INBs. Overall, our findings are expected to provide valuable insight into drag reduction based on INBs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".