Polymer membranes for organic solvent nanofiltration: Recent progress, challenges and perspectives
Bibliographic record
Abstract
The development of polymer materials and polymer membrane fabrication techniques in recent years greatly elevates the importance and feasibility of organic solvent nanofiltration (OSN) technology, while challenges from different perspectives still hinder the wider applications of polymer based OSN membranes. This article reviews the OSN membrane research specifically from the perspective of polymer membrane materials, starting by recapping the recent progress of polymer based integrally skinned asymmetric (ISA) and thin film composite (TFC) OSN membranes. Comparing to commercially available polyimide ISA membranes and polyamide TFC membranes, multiple categories of emerging polymer materials result in membranes with much improved permselectivity for highly efficient molecular separation. In view of adopting OSN membranes for engineering applications, this review also summarizes some key challenges unique to polymer membranes including material swelling, physical aging and membrane compaction, and recent efforts to overcome them. The future research direction and application prospects of polymer OSN membranes are briefly discussed in the latter part of the article, noting that improved membrane formation control and crosslinking strategies, and the development of emerging polymer membrane materials is at high necessity to break through the application constraints of OSN in terms of permselectivity and performance stability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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".