Fabrication and characterization of high flux poly(vinylidene fluoride) electrospun nanofibrous membrane using amphiphilic polyethylene‐block‐poly(ethylene glycol) copolymer
Bibliographic record
Abstract
Abstract Electrospun nanofiber membranes (ENM) made of polymers such as polysulfone and poly(vinylidene fluoride) (PVDF) have a much higher contact angle (CA) and also more hydrophobic when compared to the virgin polymers. For water treatment applications, membranes with hydrophilic nature are highly desirable in order to achieve high flux and less fouling potentials. Hence, in the present study, highly hydrophilic electrospun nanofiber membranes (ENMs) were prepared by blending PVDF polymer with amphiphilic polyethylene‐block‐poly (ethylene glycol) (PE‐b‐PEG) copolymer. Resulting amphiphilic ENMs were highly porous (77%–92%) and the breaking elongation of 140% with a young's modulus of 2.55 MPa was observed. When compared with the control PVDF membrane, PE‐b‐PEG blended ENMs revealed higher water permeation flux owing to the enrichment of the hydrophilic PEG segments at the membrane surface, which was confirmed by using X‐ray photoelectric spectroscopy and Energy‐dispersive spectroscopy measurements. When compared to the phase inversion process (CA of 97.3°) blended ENM had CA of 0°, which indicates that besides hydrophilic block copolymer segments, the nature of membrane formation also contributes its role in influencing the hydrophilicity of the membrane. This improved hydrophilicity in combination with larger pore sizes of the PVDF/ PE‐b‐PEG membranes have contributed to enhancement of pure water flux, protein solution permeability and water flux recovery, which can be applied potentially for water treatment applications.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".