Waste organic dye removal using MOF-based electrospun nanofibers of high amine density
Bibliographic record
Abstract
The exceptional structural stability of UiO-66 metal–organic framework (MOF) and ligands' functional groups render UiO-66 as a versatile candidate for multiple applications, including wastewater treatment. The possibility of a broad spectrum of post-synthetic modification of UiO-66 further expands its prospective uses. However, commercial applications of UiO-66 have been hindered by the polycrystalline nature of the powder. In this research, modification to obtain UiO-66-NH2 and post-processing with nanofibers comprising chitosan and polyvinyl alcohol (PVA) were applied to faciliate deployment as a suitable option for water decontamination. MOF nanoparticles were post-synthetically modified with 2,4,6-trichloro-1,3,5-triazine (TCT) and 5-phenyl-tetrazole (PT) to produce high-amine containing UiO-66, thus introducing active nitrogen-containing functional groups that enhanced the removal efficiency of targeted molecules from aqueous media. A systematic study was undertaken to optimize the supporting nanofibers and to demonstrate that even a low MOF functionalization, of up to 7- wt%, offered a maximum methyl orange adsorption capacity of 619 mg/g, superior to most adsorbents reported so far. Furthermore, selectivity, regeneration ability, and the effect of ambient conditions were demonstrated.
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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) | 0.000 | 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".