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Record W2893611349 · doi:10.1139/cjc-2017-0733

Cu(I)–PNF, an organic-based nanocatalyst, catalyzed C–O and C–S cross-coupling reactions

2018· article· en· W2893611349 on OpenAlexvenueno aff
Zahra Taherinia, Arash Ghorbani‐Choghamarani

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicSulfur-Based Synthesis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryCatalysisCopperLigand (biochemistry)NanomaterialsCoupling (piping)Combinatorial chemistrySolubilityCoupling reactionChemical engineeringOrganic chemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Peptide nanofiber has been prepared via a self-assembly protocol and decorated with Cu(I) to prepare a nanostructural catalyst. The catalytic activity of this prepared nanomaterial (Cu(I)–PNF) was examined in C–O and C–S cross-coupling reactions. Compared with conventional copper–ligand catalytic systems, CuNP–PNF has unique advantages such as water solubility, high efficiency, and low cost, which makes it a highly efficient and beneficial catalyst to reuse in cross-coupling reactions.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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)0.0000.000

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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
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

Citations21
Published2018
Admission routes1
Has abstractyes

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