Innovative crosslinking strategies for integrally skinned asymmetric membranes in organic solvent nanofiltration: A review of recent advances
Bibliographic record
Abstract
Organic solvent nanofiltration (OSN) is an important separation process in various industries, including food, semiconductor, pharmaceutical, chemical, and petrochemical, due to its energy efficiency and versatility. Integrally skinned asymmetric (ISA) membranes, typically prepared via the phase inversion of polymer solutions, are widely utilized in OSN applications due to their ease of one-step preparation. However, achieving the required pore size for OSN in ISA membranes often involves cost-intensive processes such as high polymer content, use of volatile solvents, or thermal modification (annealing). Moreover, maintaining purification performance in organic solvent media over extended periods presents another challenge for ISA membranes in this field. Crosslinking can effectively reduce pore size while enhancing membrane resistance to harsh solvents and extreme pH conditions. So far, various crosslinking methods, including UV, ionic, thermal, and chemical crosslinking, have been proposed. Crosslinking, however, can decrease membrane permeance by increasing permeation resistance and limiting free volumes between polymer chains. This challenge limits the broader industrial implementation of OSN membranes. This review explores recent innovative crosslinking strategies that aim to enhance the chemical stability and rejection of ISA polymeric membranes, with a focus on methods that overcome the trade-off between crosslinking density and permeability, and those that significantly improve chemical stability and selectivity with minimal permeability sacrifice. The review also highlights a selection of studies that incorporate creative or unconventional ideas, offering fresh perspectives for advancing membrane design. Finally, the review discusses the challenges and future outlooks associated with OSN membrane preparation. By providing insights into cutting-edge research, this review serves as a valuable resource for the design and development of high-performance OSN membranes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".