Nonequilibrium molecular dynamics simulation of pressure-driven water transport through modified CNT membranes
Bibliographic record
Abstract
Nonequilibrium molecular dynamics (NEMD) simulations are presented to investigate the effect of water-membrane interactions on the transport properties of pressure-driven water flow passing through carbon nanotube (CNT) membranes. The CNT membrane is modified with different physical properties to alter the van der Waals interactions or the electrostatic interactions between water molecules and the CNT membranes. The unmodified and modified CNT membranes are models of simplified nanofiltration (NF) membranes at operating conditions consistent with real NF systems. All NEMD simulations are run with constant pressure difference (8.0 MPa) temperature (300 K), constant pore size (0.643 nm radius for CNT (12, 12)), and membrane thickness (6.0 nm). The water flow rate, density, and velocity (in flow direction) distributions are obtained by analyzing the NEMD simulation results to compare transport through the modified and unmodified CNT membranes. The pressure-driven water flow through CNT membranes is from 11 to 21 times faster than predicted by the Navier-Stokes equations. For water passing through the modified membrane with stronger van der Waals or electrostatic interactions, the fast flow is reduced giving lower flow rates and velocities. These investigations show the effect of water-CNT membrane interactions on water transport under NF operating conditions. This work can help provide and improve the understanding of how these membrane characteristics affect membrane performance for real NF processes.
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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) | 0.001 | 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".