A Critical Assessment of Surface-Patterned Membranes and Their Role in Advancing Membrane Technologies
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Surface patterning of membranes has emerged as a nonchemical approach to improving the performance of water separation and ion exchange membranes. These patterns reduce the interactions between foulants and the membrane, which ultimately hinder foulant adsorption and deposition. Therefore, in water separation membranes, such surface patterns can be beneficial in battling membrane fouling. Additionally, surface patterns can increase the effective membrane surface area, leading to enhanced water permeation compared to that of the flat membranes. They can also reduce ionic resistance and improve the current/power density of the ion exchange membranes (IEMs) used in fuel cells and electrodialysis. This critical review offers a thorough evaluation of more than two decades of research regarding membrane surface patterning with a specific focus on how it enhances membrane performance and advances our understanding of surface patterning methods. It also covers the underlying antifouling mechanisms, the impact of surface patterns on water filtration processes, and their influence on the current/power density of IEMs. Understanding the correlation between surface patterning techniques and membrane properties is essential for successful and efficient application in membrane processes. Through this exploration, this review offers valuable perspectives for future research that can help in developing more effective surface-patterned membranes for improved performance.
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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 | 0.003 | 0.003 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".