Effect of Organic Ions on The Formation and Collapse of Nanometric Bubbles in Ionic Liquid/Water Solutions: A Molecular Dynamics Study
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Molecular dynamics simulation is applied to investigate the effect of two ionic liquids (IL) on the nucleation and growth of (nano)cavities in water under tension and on the cavities’ collapse following the release of tension. Simulations of the same phenomena in two pure water samples of different sizes are carried out for comparison. The first IL, i.e., tetra-ethylammonium mesylate ([Tea][Ms]), is relatively hydrophilic and its addition to water at 25 wt % concentration decreases its tendency to nucleate cavities. Apart from quantitative details, cavity formation and collapse are similar to those taking place in water and qualitatively follow the Rayleigh–Plesset (RP) equation. The second IL, i.e., tetrabutyl phosphonium 2,4-dimethylbenzenesulfonate ([P 4444 ][DMBS]), is amphiphilic and forms nanostructured solutions with water. At 25 wt % concentrations, [P 4444 ][DMBS] favors the nucleation of bubbles that tend to form at the interface between water-rich and IL-rich domains. Cavity collapse in [P 4444 ][DMBS]/water solutions are greatly hindered by a shell of ions decorating the interface between the solution and the vapor phase. A similar effect is observed for the equilibration of a population of bubbles of different sizes. The drastic slowing down of the bubbles’ relaxation processes suggests ways to produce long-lived nanometric cavities in the liquid phase that could be useful for nanotechnology and drug delivery.
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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 | 0.000 |
| 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 | 0.001 |
| 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".