Effect of Surface Modification with Electrospun Nanofibers on the Performance of an Ultrafiltration Membrane
Bibliographic record
Abstract
Electrospinning of nanofibrous mats to create new membranes has been widely investigated, however surface modification of commercial membranes by electrospinning of nanofiber layer has received limited attention. In this work, the surface of a commercial polyethersulfone (PES) ultrafiltration membrane was coated with electrospun polyvinylidene fluoride (PVDF) hydrophobic nanofibers (NFs) for different time periods, i.e., 25 min, 125 min, and 250 min., and the effect of coating on the filtration performance was investigated. The membranes were characterized by scanning electron microscopy (SEM), contact angle measurement and further subjected to pure water permeation experiments, as well as the filtration of Ottawa River Water (ORW) and various protein solutes. By a thorough statistical analysis, it was concluded that the coating with the electrospun nanofiber layer enhanced the pure water permeation (PWP) flux. However, the fouling of the composite nanofiber/PES membranes was more severe due to the compaction of the soft nanofiber layer and the entrapment of foulants in the spaces between nanofibers. The nanofiber mat did not impact the separation of protein solutes as the composite and base PES membranes had the same protein removals.
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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".