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Record W2319253592 · doi:10.1021/jp308637z

<sup>17</sup>O Solid-State NMR Study on the Size Dependence of Oxygen Activation over Silver Catalysts

2012· article· en· W2319253592 on OpenAlexaff
Xuefeng Wang, Xiuwen Han, Yining Huang, Junming Sun, Suochang Xu, Xinhe Bao

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsCatalysisOxygenChemistryInorganic chemistryMoleculeMesoporous materialParticle sizeOxidePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The capabilities of silver catalysts with different particle sizes for oxygen activation were systematically studied by 17 O solid-state NMR. The observation of 17 O signal at around 0 ppm for silver catalysts indicates that silver activates 17 O 2 molecules and that subsequent formation of active 17 O species leads to the oxygen exchange with the mesoporous SiO 2 supports. The initial appearances of the 17 O signals at different temperatures for three silver catalysts show that oxygen activation strongly depends on the size of Ag particle. Their ability of activating oxygen molecules is in the order Ag-3/MCM-41 > Ag-5/SBA-15 > Ag-10/SBA-15. Ag-3/MCM-41 is able to activate oxygen molecules and facilitates the oxygen exchange with the support even at room temperature. Although the NMR signal of active 17 O species on silver catalysts is not observed directly, the approach described in this work can be used to indirectly determine the conditions on which the active oxygen species can be produced during catalytic oxidation reactions. The silver catalysts can also be used for 17 O enrichment of the oxide-based materials under mild conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.302
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2012
Admission routes1
Has abstractyes

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