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Record W3015079749 · doi:10.1039/c9ra10941b

Palladium supported on a novel ordered mesoporous polypyrrole/carbon nanocomposite as a powerful heterogeneous catalyst for the aerobic oxidation of alcohols to carboxylic acids and ketones on water

2020· article· en· W3015079749 on OpenAlexaff
Nasim Ganji, Babak Karimi, Sepideh Najafvand-Derikvandi, Hojatollah Vali

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsMcGill University
FundersInstitute for Advanced Studies in Basic Sciences
KeywordsPolypyrroleCatalysisNanocompositeMesoporous materialSelectivityPalladiumChemical engineeringAlcohol oxidationMaterials scienceNanoparticleSorptionSolventCarbon fibersChemistryOrganic chemistryComposite numberNanotechnologyPolymerComposite materialPolymerizationAdsorption

Abstract

fetched live from OpenAlex

sorption confirmed the preparation of a material with ordered mesoporous structure, uniform pore size distribution, high surface area and high stability. This nanocomposite was then used for the immobilization of palladium nanoparticles. The nanoparticles were almost uniformly distributed on the support with a narrow particle size of 20-25 nm, confirmed by various analysis methods. Performance of the Pd@PPy/OMC catalyst was evaluated in the aerobic oxidation of various primary and secondary alcohols on water as a green solvent, giving the corresponding carboxylic acids and ketones in high yields and excellent selectivity. The catalyst could also be reused for at least 10 reaction runs without losing its catalytic activity and selectivity. High catalytic efficiency of the catalyst can be attributed to a strong synergism between the PPy/OMC and that of supported Pd nanoparticles.

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 score0.006
Threshold uncertainty score0.937

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 science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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