Simulated conduction rates of water through a (6,6) carbon nanotube strongly depend on bulk properties of the model employed
Bibliographic record
Abstract
We investigate pressure driven flow rates of water through a (6,6) carbon nanotube (CNT) for the TIP3P, SPC/E, and TIP4P/2005 water models. The flow rates are shown to be strongly model dependent, differing by factors that range from ∼6 to ∼2 as the temperature varies from 260 to 320 K, with TIP3P showing the fastest flow and TIP4P/2005 the slowest. For the (6,6) CNT, the size constraint allows only single-file conduction for all three water models. Hence, unlike the situation for the larger [(8,8) and (9,9)] CNTs considered in our earlier work [L. Liu and G. N. Patey, J. Chem. Phys. 141, 18C518 (2014)], the different flow rates cannot be attributed to different model-dependent water structures within the nanotubes. By carefully examining activation energies, we trace the origin of the model discrepancies for the (6,6) CNT to differing rates of entry into the nanotube, and these in turn are related to differing bulk mobilities of the water models. Over the temperature range considered, the self-diffusion coefficients of the TIP3P model are much larger than those of TIP4P/2005 and those of real water. Additionally, we show that the entry rates are approximately inversely proportional to the shear viscosity of the bulk liquid, in agreement with the prediction of continuum hydrodynamics. For purposes of comparison, we also consider the larger (9,9) CNT. In the (9,9) case, the flow rates for the TIP3P model still appear to be mainly controlled by the entry rates. However, for the SPC/E and TIP4P/2005 models, entry is no longer the rate determining step for flow. For these models, the activation energies controlling flow are considerably larger than the energetic barriers to entry, due in all likelihood to the ring-like water clusters that form within the larger nanotube.
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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 | 0.000 | 0.000 |
| 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".