Performance Fabrics Obtained by <i>In Situ</i> Growth of Metal–Organic Frameworks in Electrospun Fibers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Metal–organic frameworks (MOFs) exhibit an exceptional surface area-to-volume ratio, variable pore sizes, and selective binding, and hence, there is an ongoing effort to advance their processability for broadening their utilization in different applications. In this work, we demonstrate a general scheme for fabricating freestanding MOF-embedded polymeric fibers, in which the fibers themselves act as microreactors for the in situ growth of the MOF crystals. The MOF-embedded fibers are obtained via a two-step process, in which, initially, polymer solutions containing the MOF precursors are electrospun to obtain microfibers, and then, the growth of MOF crystals is initiated and performed via antisolvent-induced crystallization. Using this approach, we demonstrate the fabrication of composite microfibers containing two types of MOFs: copper (II) benzene-1,3,5-tricarboxylic acid (HKUST-1) and zinc (II) 2-methylimidazole (ZIF-8). The MOF crystals grow from the fiber’s core toward its outer rims, leading to exposed MOF crystals that are well rooted within the polymer matrix. The MOF fibers obtained using this method can reach lengths of hundreds of meters and exhibit mechanical strength that allows arranging them into dense, flexible, and highly durable nonwoven meshes. We also examined the use of the MOF fiber meshes for the immobilization of the enzymes catalase and horse radish peroxidase (HRP), and the enzyme-MOF fabrics exhibit improved performance. The MOF-embedded fibers, demonstrated in this work, hold promise for different applications including separation of specific chemical species, selective catalysis, and sensing and pave the way to new MOF-containing performance fabrics and active membranes.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".