Aquaporin-Based Biomimetic and Bioinspired Membranes for New Frontiers in Sustainable Water Treatment Technology: Approaches and Challenges
Bibliographic record
Abstract
Biomimetic and bioinspired membranes are the efficient membrane technology when it comes to multiple usage scenarios, including next generations of biomaterials within the commercial separation applications, as well as, water and wastewater treatment technologies. In recent years, aquaporin biomimetic membranes for water separation have raised considerable interest. These membranes have displayed distinguished properties and outstanding performances, as diverse interactions, varying selective transport mechanisms, superior stability, maximum resistance to membrane fouling, and distinct adaptability. The biomimetic membranes have made significant contributions when it comes to water stress, environmental threats and energy. It has the potential to produce clean water more efficiently than reverse osmosis membranes (RO), while saving up to 80% of the energy used for desalination processes. More than half of the 15000 desalination plants around the world utilize RO technologies, and the implementation of biomimetic membranes on a large scale could save hundreds of millions of dollars in energy cost annually (potential savings of $1.45 million/year for 100 ML/day desalination plant). This paper discusses the interplay of the main components of aquaporin biomimetic membranes: aquaporin proteins, block copolymers for aquaporin proteins reconstitution, and polymer-based supporting structures. We focus specifically on the challenges and review recent developments on the interplay between aquaporin proteins and block copolymers. The recent efforts in embedding reconstituted aquaporin proteins in membrane designs that are based on conventional thin film interfacial polymerization techniques are evaluated. In addition, emerging challenges and opportunities for biomimetic membranes are studied from the perspective of current and future applications.
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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 | 0.000 |
| 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 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".