Nanoporous Polyether Sulfone Membrane, Preparation and Characterization: Effect of Porosity and Mean Pore Size on Performance
Bibliographic record
Abstract
Flat sheet membranes were prepared by phase inversion technique using polyether sulfone (PES) dissolved in dimethylacetamide (DMAc) with and without adding polyvinylpyrrolidone (PVP) or polyethyleneglycol (PEG). The characteristics of the prepared membranes were evaluated using Scanning Electron Microscope (SEM) images, Atomic Force Microscopy (AFM), and Optical Contact Angle (OCA) measurements, and porosity tests. The porosity test and SEM images show that increasing additives to a certain value increases the porosity of the membrane. Also, as the coagulation bath temperature is increased, the porosity of the membrane is increased. The roughness of the membrane is increased by increasing the additive concentration. The analysis of AFM images confirms the nanoporous structure of the prepared membranes, and that the membranes with appropriate pore size distribution can be prepared by the applied method. Permeability tests using single-layer membranes show that the direct relationship between porosity and the flux of pure water or salt solution is dominated by the effect of applied additive while the salt rejection shows an inverse relationship with the mean pore size regardless of the applied additive. The salt permeation flux is a function of total porosity while the salt rejection is a function of surface porosity. Pervaporation tests show that both permeation flux and enrichment factor depend on the total porosity of the support membrane.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".