Electron-rich CuO<sub>x</sub>@Al<sub>2</sub>O<sub>3</sub> Catalyst for Sustainable O<sub>2</sub> Activation in Fenton-Like Reactions
Bibliographic record
Abstract
Molecular oxygen (O 2 ) activation is pivotal in advancing green chemistry and catalysis, addressing processes such as energy conversion and environmental remediation. However, the inherent inertness of the O 2 necessitates highly efficient catalysts. In this study, an electron-rich CuO x @Al 2 O 3 catalyst with high metal loading and dispersion was synthesized via the ion-exchange inverse-loading method. The novel CuO x @Al 2 O 3 significantly enhanced O 2 activation due to the accelerated Cu 0 → Cu + → Cu 2+ redox cycle, achieving the 85% chlorobenzene removal in Fenton-like reaction. This is substantially higher than the chlorobenzene removal observed with conventional CuO x /Al 2 O 3 (45%). Experiments and density functional theory (DFT) calculations revealed that Cu–Cu sites over CuO x @Al 2 O 3 greatly facilitated charge transfer, weakened O–O bonds, and promoted synergistic O 2 and H 2 O 2 activation to produce • OH and O 2 •–, thereby enhancing oxidants utilization efficiency. This study provides a sustainable pathway for pollutant degradation by achieving O 2 activation and offers valuable insights for designing advanced Cu-based catalysts in green oxidation processes and environmental remediation.
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".