Recent Advances in Metal-Organic Framework (MOF) Asymmetric Membranes/Composites for Biomedical Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it