Enhanced catalytic oxidation via nano <scp> Cu <sub>0.5</sub> Mg <sub>0.5</sub> Fe <sub>2</sub> O <sub>4</sub> </scp> embedded in <scp>CNTs</scp> for adsorption–reduction of hexavalent chromium
Bibliographic record
Abstract
Abstract A catalyst consisting of Cu 0.5 Mg 0.5 Fe 2 O 4 (CMF) supported on carbon nanotubes (CNTs) which exhibits great potential as an adsorbent for treating Cr(VI)‐contaminated wastewater has been successfully prepared. The ferrite possesses excellent magnetic properties, while CNTs have the advantage of a large surface area. This composite material not only prevents the aggregation of magnetic materials and enhances the exposure of active sites but also effectively solves the recycling problem of CNTs. Our results show that the adsorption capacity of Cu 0.5 Mg 0.5 Fe 2 O 4 –carbon nanotubes (CMF‐CNTs) for Cr(VI) wastewater (45.60 mg/g) is 1.49 times higher than that of Cu 0.5 Mg 0.5 Fe 2 O 4 (30.48 mg/g). Compared to a single catalyst, CMF‐CNTs not only improve the dispersibility of magnetic materials but also exhibit synergistic effects between the composite materials, enhancing the chemical adsorption capacity. After five consecutive adsorption and desorption experiments, the adsorption capacity of CMF‐CNTs remains at 88% of its initial value. Furthermore, the study of the catalyst before and after adsorption by XPS reveals that the valence state transition of Fe 3+ /Fe 2+ and Cu 2+ /Cu + plays a crucial role in the adsorption process. The results of this study demonstrate the potential of using waste materials for effective wastewater treatment and provide insights into the development of new adsorbents for pollutant removal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".