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Record W2020812016 · doi:10.1139/v11-133

The role of magnetic nanoparticles (MNP) as reducing agents in an MNP-supported Pd–catalyst for the reductive homocoupling of aryl halides

2011· article· en· W2020812016 on OpenAlexvenueno aff
Jie Zheng, Shengyue Lin, Biwang Jiang, Todd B. Marder, Zhen Yang

Bibliographic record

VenueCanadian Journal of Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsChemistryCatalysisReducing agentHalideArylCombinatorial chemistryPalladiumPincer movementNanoparticleAryl halideCoupling reactionReductive eliminationOrganic chemistryNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

A palladium pincer catalyst grafted onto the surface of magnetic nanoparticles (MNPs) has been developed. This material effectively catalyzes the reductive homocoupling of various aryl halide substrates, with the MNP support acting as the reducing agent. The catalyst can be recycled up to five times in the absence of additional reducing agent to give almost quantitative yields of biaryl homocoupling products. After the reducing power of the MNP has been depleted, the supported Pd complex remains an effective catalyst for Suzuki–Miyaura cross-coupling.

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)0.001
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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

Citations11
Published2011
Admission routes1
Has abstractyes

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