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Record W4410389180 · doi:10.1021/acs.est.5c02193

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

2025· article· en· W4410389180 on OpenAlexaff
Fei Xiao, Xiaowen Xie, Zhenxu Yang, Tao Dong, Ruijie Xie, Tao Ban, Biyuan Liu, Huanran Zhong, Dennis Y.C. Leung, Michael K.H. Leung

Bibliographic record

VenueEnvironmental Science & Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsMinistry of Transportation of Ontario
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsCatalysisMaterials scienceChemistryInorganic chemistryChemical engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0020.005
Scholarly communication0.0010.004
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.005
GPT teacher head0.233
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2025
Admission routes1
Has abstractyes

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