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Record W2787834433 · doi:10.1139/cjc-2017-0503

A nanocatalyst-assisted protocol to the synthesis of bis(pyrazolyl) methane derivatives bearing aroyl groups by the use of arylglyoxals in the presence of ZnO nanowires as a highly efficient, recyclable, and green catalyst

2018· article· en· W2787834433 on OpenAlexvenueno aff
Khalil Eskandari, Bahador Karami

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicMulticomponent Synthesis of Heterocycles
Canadian institutionsnot available
FundersYasouj University
KeywordsChemistryCatalysisMethaneEnvironmentally friendlyNanowireZincProton NMRReaction conditionsFourier transform infrared spectroscopyChemical engineeringNuclear chemistryOrganic chemistryNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

An efficient, promoted three-component (2+1) catalytic reaction between 3-methyl-1-phenyl-2-pyrazolin-5-one and arylglyoxal derivatives is described by the use of zinc oxide nanowires (ZnO NWs) as a green catalyst. By this achievement, some newly prepared bis(pyrazolyl)methane derivatives containing an aroyl group have been obtained. Starting materials were successfully condensed to obtain target products via three C–C bond formation in the presence of ZnO NWs as an efficient, cost-effective, environmentally friendly, and recyclable nanocatalyst. All synthesized products were obtained with good to excellent yields, and their structures were deduced by FTIR, 1 H NMR, and 13 C NMR spectroscopies and CHNS elemental analyses. It was shown that ZnO NWs can be separated, recovered after each run, and reused in further cycles (up to six runs) without significant loss of activity.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.031
GPT teacher head0.258
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations5
Published2018
Admission routes1
Has abstractyes

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