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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".