Influence of polyethyleneimine layer and zinc nitrate on morphology and structure of PES‐based membranes with highly selective properties
Bibliographic record
Abstract
Abstract In this paper, nanofiltration (NF) polymer membranes based on polyestersulphone (PES) were prepared by the phase inversion method. Polyethyleneimine (PEI) and zinc nitrate (Zn(NO3)2) as a surface modifier and glutealdehyde (GA) as cross‐linker was used. Fourier transform infrared spectroscopy analysis (FTIR) was used to confirm the chemical composition on the membrane surface. Membranes were also characterized using field emission scanning electron microscopy (FESEM) and 3D surface images. Water contact angle, average pore size and porosity measurements, water flux, salt rejection, and membrane anti‐fouling ability were discussed. Modified membranes showed a smoother surface than the original membrane. The amount of pure water flux decreased with increasing the concentration of modifiers at the surface, but the yield of Na2SO4 salt increased, 53% in virgin membrane and 83% in M3 membrane. Modified membranes had better anti‐fouling and hydrophilicity properties than primary membranes. The lowest contact angle value was 26.2° for M4. Also, the best anti‐clogging comparable properties were for the M3 membrane with FRR = 63.37%, Rr = 10.69%, Rir = 36.6%, and Rt = 47.3%. By increasing the concentration of modifiers, the removal of CuNO3− and CuSO4 improved that the M1 membrane (97.59%) had the highest Cu(NO3)2 separation and the M4 membrane (87.5%) had the most increased CuSO4 separation.
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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 | 0.000 |
| 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) | 0.000 | 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".