Molecular Dynamics Simulations of Liquid Transport through Nanofiltration Membranes
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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 it