Advanced functional membranes based on amphiphilic copolymers
Bibliographic record
Abstract
Membranes with advanced and novel functions play important roles in emerging applications ranging from industrial separations, water purification , energy harvesting and storage, healthcare, biomimetic membranes and more. The performance of membranes in these critical applications is fundamentally determined by their interfacial interactions with surrounding ions, molecules, particles, emulsions, and bioactive agents . Amphiphilic copolymers containing both hydrophobic and hydrophilic segments will spontaneously assemble into multiphase and hierarchical structures, providing a general solution for regulating the surface physicochemical properties of membranes used in the aforementioned urgent applications. Controlled synthesis of amphiphilic copolymers and the methods for fabricating membranes from these copolymers with predetermined performance are fundamentally important for their applications. In this work, we first summarize the polymerization techniques for synthesizing amphiphilic copolymers used for membrane materials. We then review the methods for fabricating membranes from amphiphilic copolymers and highlight the urgent applications of advanced functional membranes derived from them. We also discuss some remaining challenges and provide insights into future directions, especially as the circular polymer economy and artificial intelligence are setting new requirements for polymer science . This work offers a comprehensive overview of recent advances in functional materials based on amphiphilic polymers, including the working principles and relationships between polymer structure , processing strategies, and membrane performance , which provides new insights into the development of high-performance and next-generation polymeric membranes through the precise, functionality-driven synthesis of novel amphiphilic copolymers and the controlled fabrication of 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 | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".