Molecular simulation of gas entrapment near a nanoscale cavity: The interplay of surface wettability, cavity shape, and gas type
Bibliographic record
Abstract
• Methylated square-shaped cavity enables stable nanobubble entrapment for both CO 2 and N 2 . • N 2 and CO 2 adsorb differently (nanobubble vs. monolayer) on hydroxylated surface. • Gas solubility and configuration near surface are key to the entrapment mode. • V-shaped cavity lacks the ability to trap nanobubbles. Surface cavities play a crucial role in trapping and stabilizing gases, a phenomenon that can be used to facilitate applications in separation and drying processes. Understanding surface characteristics in relation to fluid-surface interactions is essential for designing interfacial properties that enable effective processes. In this work, we adopt a molecular simulation approach to investigate gas adsorption and entrapment in surface defects (cavities), focusing on the influence of surface wettability, cavity shape, and gas type. For this purpose, two types of silica-based surfaces with different levels of wettability (methylated and hydroxylated, with the former having lower water wettability), two cavity shapes with identical opening width and depth (V-shaped and square-shaped), and two types of gas (carbon dioxide and nitrogen) are considered. Our observations indicate that a stable gas nanobubble forms when a methylated surface combines with a square-shaped cavity, regardless of the gas type. In contrast, for hydroxylated surface, gas type becomes important; in a square-shaped cavity, nitrogen forms a stable nanobubble while CO 2 is trapped in a monolayer fashion. The interfacial forces between the gas and surface which affects gas configuration near the surface, along with the gas molecules’ self-interaction manifested by solubility, are key to the different entrapment modes for N 2 and CO 2 . Moreover, the square-shaped cavity exhibits better capability in trapping gas compared to the V-shaped cavity due to its higher surface area and smaller opening-to-interior ratio. The results of our molecular simulations can guide the design of surface features and processing systems to modify gas entrapment modes and stabilize nanoscale gas bubbles without altering thermodynamic conditions or fluid properties.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".