Nano-Cellulose/MOF Derived Carbon Doped CuO/Fe3O4 Nanocomposite as High Efficient Catalyst for Organic Pollutant Remedy
Bibliographic record
Abstract
Metal⁻organic framework (MOF)-based derivatives are attracting increased interest in various research fields. In this study, nano-cellulose MOF-derived carbon-doped CuO/Fe₃O₄ nanocomposites were successfully synthesized via direct calcination of magnetic Cu-BTC MOF (HKUST-1)/Fe₃O₄/cellulose microfibril (CMF) composites in air. The morphology, structure, and porous properties of carbon-doped CuO/Fe₃O₄ nanocomposites were characterized using SEM, TEM, powder X-ray diffraction (PXRD), X-ray photoelectron spectroscopy (XPS), and vibrating sample magnetometry (VSM). The results show that the as-prepared nanocomposite catalyst is composed of Fe₃O₄, CuO, and carbon. Compared to the CuO/Fe₃O₄ catalyst from HKUST-1/Fe₃O₄ composite and CuO from HKUST-1, this carbon-doped CuO/Fe₃O₄ nanocomposite catalyst shows better catalytic efficiency in reduction reactions of 4-nitrophenol (4-NP), methylene blue (MB), and methyl orange (MO) in the presence of NaBH₄. The enhanced catalytic performance of carbon-doped CuO/Fe₃O₄ is attributed to effects of carbon preventing the aggregation of CuO/Fe₃O₄ and providing high surface-to-volume ratio and chemical stability. Moreover, this nanocomposite catalyst is readily recoverable using an external magnet due to its superparamagnetic behavior. The recyclability/reuse of carbon-doped CuO/Fe₃O₄ was also investigated.
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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".