Aquaporin‐Based Biomimetic Membranes for Low Energy Water Desalination and Separation Applications
Bibliographic record
Abstract
Abstract The emergence of biomimetic materials developed using nature's inspiration and biological domains can drive a paradigm shift in the design and operation of future‐generation materials in separation applications. In recent years, biomimetic membranes have drawn interest of many researchers for water treatment applications. Among the biomimetic membranes, protein‐based membranes, specifically those synthesized by aquaporin, have received much attention in recent years due to their high osmotic water permeability and excellent ability to remove small molecules, thereby overcoming the trade‐off between the water flux and the contaminant's rejection. The separation efficiency and fouling properties are significantly improved by taking advantage of the strategies evolved in nature. This review provides a comprehensive overview of the state‐of‐the‐art aquaporin‐based biomimetic membranes (ABMs), mainly focusing on their synthesis, characterization, and performance as selective layer in composite membranes for reverse osmosis, nanofiltration, and forward osmosis for water desalination. Fabrication methods and characterization techniques of ABMs and their performance in water desalination are also reviewed, while the main obstacles for their successful commercial viability in wastewater treatment are provided. The applications of ABMs in various separation processes other than water desalination and their potential market are presented to inspire future researchers in this versatile area.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".