A scalable and chemical-free strategy for antifouling ultrafiltration PVDF membranes via hydrophilic macromolecular surface modification
Bibliographic record
Abstract
Fouling is a major challenge in oily wastewater treatment, leading to increased operational costs and reduced membrane performance. This study aims to develop a modified PVDF ultrafiltration (UF) membrane with enhanced antifouling properties using hydrophilic surface-modifying macromolecules (LSMMs) through a simple blending and phase inversion process. PVDF membranes were fabricated by incorporating LSMMs into the dope solution. During phase inversion, LSMMs spontaneously migrated to the membrane-air interface, forming a stable hydrophilic and negatively charged surface layer. The membranes were characterized for their permeability, oil rejection, antifouling performance, and long-term stability under continuous operation. The optimized L0.50 T-PVDF membrane exhibited a 58% increase in pure water flux (880 L m−2 h−1) and 99.9% oil rejection. Irreversible fouling was eliminated (Rir = 0%), with a 100% flux recovery ratio (FRR) sustained over five cleaning cycles. Continuous 24 h filtration maintained a stable permeate flux of 775 L m−2 h−1, indicating excellent durability. LSMM-induced surface modification effectively mitigates membrane fouling by preventing pore blockage and foulant adhesion, eliminating the need for chemical cleaning. This approach offers a sustainable, scalable, and cost-effective solution for industrial oily wastewater treatment. Future work will explore pilot-scale validation, LSMM formulation optimization, and performance evaluation under varied operating conditions. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".