Simulations of water transport through carbon nanotubes: How different water models influence the conduction rate
Bibliographic record
Abstract
The conduction rate of water through (8,8) and (9,9) carbon nanotubes at 300 K and a pressure difference of 220 MPa is investigated using molecular dynamics simulations. The TIP3P, SPC/E, and TIP4P/2005 water models are considered. The pressure-driven flow rate is found to be strongly model dependent for both nanotubes. The fastest model (TIP3P) has a flow rate that is approximately five times faster than the slowest (TIP4P/2005). It is shown that the flow rate is significantly influenced by the structure taken on by the water molecules confined in the nanotube channels. The slower models, TIP4P/2005 and SPC/E, tend to favor stacked ring arrangements, with the molecules of a ring moving together through the nanotube, in what we term a "cluster-by-cluster" conduction mode. Confined TIP3P water has a much weaker tendency to form ring structures, and those that do form are fragile and break apart under flow conditions. This creates a much faster "diffusive" conduction mode where the water molecules mainly move through the tube as individual particles, rather than as components of a larger cluster. Our results demonstrate that water models developed to describe the properties of bulk water can behave very differently in confined situations.
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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 | 0.001 |
| 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) | 0.001 | 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".