A molecular dynamics investigation of the influence of water structure on ion conduction through a carbon nanotube
Bibliographic record
Abstract
Molecular dynamics simulations are employed to investigate pressure-driven water and ion transport through a (9,9) carbon nanotube (CNT). We consider NaCl solutions modeled with both the TIP3P and TIP4P/2005 water models. Concentrations range from 0.25 to 2.8 mol l−1 and temperatures from 260 to 320 K are considered. We discuss the influences on flow rates of continuum hydrodynamic considerations and molecular structural effects. We show that the flow rate of water, sodium, and chloride ions through the CNT is strongly model dependent, consistent with earlier simulations of pure water conduction. To remove the effects of different water flow rates, and clearly expose the influence of other factors on ion flow, we calculate ion transport efficiencies. Ion transport efficiencies are much smaller for TIP4P/2005 solutions than for those using the TIP3P model. Particularly at lower temperatures, the ion transport efficiencies for the TIP4P/2005 model are small, despite the fact that the nanotube conducts water at a significant rate. We trace the origin of small ion transport efficiencies to the presence of ring-like water structures within the CNT. Such structures occur commonly for the TIP4P/2005 model, but less frequently for TIP3P. The water structure acts to reduce ion “solvation” within the CNT, posing an additional barrier to ion entry and transport. Our results demonstrate that increasing the water structure within the CNT by decreasing the temperature strongly inhibits ion conduction, while still permitting significant water transport.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".