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Record W4396780088 · doi:10.1021/acsnano.3c12946

Highly Dispersed Ni Atoms and O<sub>3</sub> Promote Room-Temperature Catalytic Oxidation

2024· article· en· W4396780088 on OpenAlexafffund
Ruijie Yang, Wanjian Zhang, Yuefeng Zhang, Yingying Fan, Rongshu Zhu, Jian Jiang, Liang Mei, Zhaoyong Ren, Xiao Li He, Jinguang Hu, Zhangxin Chen, Qingye Lu, Jiang Zhou, Haifeng Xiong, Hao Li, Xiao Cheng Zeng, Zhiyuan Zeng

Bibliographic record

VenueACS Nano · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaState Key Laboratory of Urban Water Resource and EnvironmentCity University of Hong KongScience, Technology and Innovation Commission of Shenzhen MunicipalityAlberta InnovatesResearch Grants Council, University Grants Committee
KeywordsCatalysisTransition metalMaterials scienceOxygenChemical engineeringInorganic chemistryNanotechnologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Transition metal oxides are promising catalysts for catalytic oxidation reactions but are hampered by low room-temperature activities. Such low activities are normally caused by sparse reactive sites and insufficient capacity for molecular oxygen (O 2 ) activation. Here, we present a dual-stimulation strategy to tackle these two issues. Specifically, we import highly dispersed nickel (Ni) atoms onto MnO 2 to enrich its oxygen vacancies (reactive sites). Then, we use molecular ozone (O 3 ) with a lower activation energy as an oxidant instead of molecular O 2 . With such dual stimulations, the constructed O 3 –Ni/MnO 2 catalytic system shows boosted room-temperature activity for toluene oxidation with a toluene conversion of up to 98%, compared with the O 3 –MnO 2 (Ni-free) system with only 50% conversion and the inactive O 2 –Ni/MnO 2 (O 3 -free) system. This leap realizes efficient room-temperature catalytic oxidation of transition metal oxides, which is constantly pursued but has always been difficult to truly achieve.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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; a candidate call from one teacher head, not a consensus.

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

Citations19
Published2024
Admission routes2
Has abstractyes

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