Molecular Dynamics Simulations of Liquid Transport through Nanofiltration Membranes
Bibliographic record
Abstract
Nanofiltration (NF) is a pressure-driven membrane separation process, which is a nonequilibrium process because of the pressure difference and concentration difference across the membrane. As one type of molecular dynamics (MD) simulations, nonequilibrium molecular dynamics (NEMD) simulations can provide the dynamics properties of NF transport on a molecular level description, which can serve as a complement to conventional experimental studies. In this thesis, NEMD simulations are proposed to study pressure-driven liquid flows through carbon nanotube (CNT) membranes and polyamide (PA) membranes at realistic NF conditions. Pure water flows passing through the membranes are studied primarily, and organic flows passing through the CNT membranes are also studied. Little research, that we are aware of, has been done to show the NF transport properties. The results of the NEMD simulations are analyzed to investigate the transport properties and the effects of the membrane structures on liquid transport, and the simulation results are compared with traditional models and/or literature data. This work shows that show that the liquid transport through the CNT membrane is extremely fast and cannot be predicted by the continuum equations due to the special properties of the CNT, and the water transport of the PA membrane is strongly related to the free-volume properties of the amorphous polymeric membrane. The MD simulation studies proposed in this thesis are feasible as a tool for describing and investigating pressure-drive liquid transport and can provide some fundamental basis for NF 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) | 0.001 | 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 | 0.001 |
| 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".