Control of Membrane Surface Roughness and Pattern Wave Length by Changing the Nonsolvent (Water) Influx Rate
Bibliographic record
Abstract
The control of surface roughness of polyvinylidene fluoride (PVDF), polyethersulfone (PES), polysulfone (PS) and cellulose (CE) membranes was attempted by changing the rate of nonsolvent influx in the phase inversion process. PVDF and CE were chosen to represent membranes of high hydrophobicity and hydrophilicity, respectively, while PES and PS were chosen to represent membranes of intermediate hydrophobicity/-philicity. The concentration of sodium chloride (NaCl) in the aqueous coagulation medium was increased from 0 to 1.9 mol/L to decrease the rate of nonsolvent (water) influx in the solvent/nonsolvent exchange process. As well, the effect of polymer concentration and solvent on the surface roughness was investigated with respect to PVDF and PES. It was observed that the membrane surface roughness increased and decreased, respectively, for the hydrophobic PVDF and hydrophilic CE membrane as the rate of nonsolvent influx was decreased. For the PES and PS membranes of intermediate hydrophilic/-philicity, no significant roughness change was observed. The surface roughness tended to increase as the solution viscosity decreased. It was also observed that the pattern wave length of the hydrophobic membrane did not change significantly while that of the hydrophilic membrane increased significantly as the solvent influx rate was reduced. This trend is predictable by considering the shrinking or swelling of the cast polymer solution during the solvent/nonsolvent exchange process.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".