Preparation of ZIF-67@PAN Nanofibers for CO2 Capture: Effects of Solvent and Time on Particle Morphology
Bibliographic record
Abstract
Advanced materials including metal–organic frameworks (MOFs) are a critical piece of the puzzle in the search for solutions to various scientific and technological challenges, such as climate change due to the ever-increasing emissions of greenhouse gas. There is intense interest in MOFs due to their potential use for a variety of environmental applications, including catalysis and gas storage. In this work, we specifically focus on the in situ growth of zeolitic imidazolate framework-67 (ZIF-67) on poly(acrylonitrile) (PAN) fibers and its potential application in CO2 adsorption. Nanofibers were spun from a solution containing PAN and cobalt (II) nitrate hexahydrate using electrospinning. Then, the fibers were immersed in solution with 2-methylimidazole for different time durations. Via the diffusion of the cobalt ions through the fibers and interaction with the ligands in the solution, ZIF-67 was formed. From analysis via SEM, FTIR, PXRD, and CO2 adsorption, it is evident that varying different parameters—the type of solvent, immersion time, and ligand concentration—affected the morphology of the formed ZIF-67. It was found that immersion for 4 h in 6.0 mg/mL of ligands in methanol created the ZIF-67@PAN best suited for CO2 adsorption, showing a CO2 uptake of 0.4 mmol/g at 1.2 bar and 273 K.
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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".