Facile synthesis of metal nanoparticles decorated magnetic hierarchical carbon microtubes with polydopamine-derived carbon layer for catalytic applications
Bibliographic record
Abstract
It is highly desirable but challenging to fabricate a unique hybrid material comprising nanosized copper/cobalt/nickel nanoparticles (NPs) uniformly distributed on magnetic supports. Herein in this work, hierarchical magnetic metal silicate hollow microtubes were prepared using silica coated magnetic N-doped carbon microtubes (NCMTs@Fe3O4@SiO2) as a chemical template; then polydopamine (PDA) was employed to coat onto magnetic metal silicate carbon microtubes (NCMTs@Fe3O4@CuSNTs/CoSNTs/NiSNTs), which can be carbonized to form hierarchical hybrid composites with uniformly-dispersed metallic copper/cobalt/nickel NPs embedded in PDA-derived carbon layers (NCMTs@Fe3O4@SiO2@C/Cu-Co-Ni). Owing to its hierarchical structure, large specific surface area as well as the high density of metal NPs, the resultant NCMTs@Fe3O4@SiO2@C/Ni-Co-Cu could be applied as catalysts towards the reduction of 4-nitrophenol (4-NP). Furthermore, the NCMTs@Fe3O4@SiO2@C/Ni-Co-Cu catalysts could be easily collected and separated by applying an external magnetic field. In particular, it was found that NCMTs@Fe3O4@SiO2@C/Ni exhibited ultra-high catalytic activity on 4-NP reduction in comparison with Cu and Co supported catalysts. In addition, this unique hierarchical structure combined with magnetic recyclability make NCMTs@Fe3O4@SiO2@C/Ni a highly promising candidate for diverse applications.
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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".