Fabrication of carboxymethylated cellulose fibers supporting Ag NPs@MOF‐199s nanocatalysts for catalytic reduction of 4‐nitrophenol
Bibliographic record
Abstract
In this work, we prepared high‐performance and recyclable nanocatalysts that consist of small and well‐dispersed silver nanoparticles (Ag NPs) immobilized onto Cu‐ based metal–organic framework (MOF‐199 s) supported by carboxymethylated cellulose fibers (CCFs). The as‐prepared green nanohybrid catalysts, namely Ag NPs@ MOF‐199 s/CCFs, were characterized using SEM, TEM, XRD and FT‐IR techniques. The catalytic performances showed that Ag NPs@ MOF‐199 s/CCFs catalysts exhibited a very high catalytic efficiency towards the reduction of 4‐nitrophenol to 4‐aminophenol. The enhanced catalytic performances are attributed to the improved dispersity, small particles of Ag NPs stabilized by the MOF‐199 s, and the porous catalyst structures. The introduction of cellulose fiber further facilitates the reuse and sustainability of the nanohybrid catalysts, showing a stable and high reusability (more than 91% of catalytic activity) even after five runs.
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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".