Molecular dynamics simulation of the coalescence of nanometer-sized water droplets in <i>n</i>-heptane
Bibliographic record
Abstract
Molecular dynamics simulations using a modified Drieding 2.21 force field were carried out to study the coalescence behavior of nanometer-sized water droplets in vacuum and in n-heptane. The coalescence mechanisms of the water droplets in the above-noted environments are fairly similar in a sense that the water droplets form a bridge linking the droplets before they merge. However, in the latter situation, due to the presence of n-heptane molecules in between the water droplets, the coalescence was observed to be slowed down considerably, especially in the first 10 ps of the process. However, once the bridge is formed, the water droplets, in both situations, spend about the same amount of time to form a single droplet. The maximum distance between the droplets above which coalescence does not occur was found to be 10 A. In terms of the dynamics, the diffusion coefficient of n-heptane in the emulsion system was very close to its value in the pure liquid form. This may be because n-heptane is the continuous phase. Nonetheless, the dynamic behavior of water in n-heptane is different from that of pure water during and after the coalescence. In particular, the self-diffusion coefficient of water molecules in n-heptane is about 20% higher than the experimental value of pure water. Due to the lack of strong attraction forces between water and n-heptane molecules, the n-heptane molecules were observed to orient themselves perpendicularly to the water/n-heptane interfaces so that the contacting area is minimized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".