Recent Advances in Metal-Organic Framework (MOF) Asymmetric Membranes/Composites for Biomedical Applications
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are a new class of porous crystalline materials composed of metal and organic material. MOFs have fascinating properties, such as fine tunability, large specific surface area, and high porosity. MOFs are widely used for environmental protection, biosensors, regenerative medicine, medical engineering, cell therapy, catalysts, and drug delivery. Recent studies have reported various significant properties of MOFs for biomedical applications, such as drug detection and delivery. In contrast, MOFs have limitations such as low stability and low specificity in binding to the target. MOF-based membranes improve the stability and specificity of conventional MOFs by increasing the surface area and developing the possibility of MOF-ligand binding, while conjugated membranes dramatically increase the area of active functional groups. This special property makes them attractive for drug and biosensor fabrication, as both the spreading and solubility components of the porosity can be changed. Asymmetric membranes are a structure with high potential in the biomedical field, due to the different characteristics on its two surfaces, the possibility of adjusting various properties such as the size of porosity, transfer rate and selectivity, and surface properties such as hydrophilicity and hydrophobicity. MOF assisted asymmetric membranes can provide a platform with different properties and characteristics in the biomedical field. The latest version of MOF materials/membranes has several potential applications, especially in medical engineering, cell therapy, drug delivery, and regenerative medicine, which will be discussed in this review, along with their advantages, disadvantages, and challenges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".