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Record W2488075854 · doi:10.1002/aoc.3398

Immobilized copper(II) on nitrogen‐rich polymer‐entrapped Fe<sub>3</sub>O<sub>4</sub> nanoparticles: a highly loaded and magnetically recoverable catalyst for aqueous click chemistry

2015· article· en· W2488075854 on OpenAlexaff
Nasrin Zohreh, Seyed Hossein Hosseini, Ali Pourjavadi, Craig Bennett

Bibliographic record

VenueApplied Organometallic Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsAcadia University
Fundersnot available
KeywordsChemistryCatalysisCopperClick chemistryAlkyneAlkylAzideAqueous solutionPolymer chemistryBromideSodium azideArylNanoparticleMagnetic nanoparticlesHalideThermal stabilityInorganic chemistryNuclear chemistryOrganic chemistryChemical engineering

Abstract

fetched live from OpenAlex

A heterogeneous magnetic copper catalyst was prepared via anchoring of copper sulfate onto multi‐layered poly(2‐dimethylaminoethyl acrylamide)‐coated magnetic nanoparticles and was characterized using various techniques. The catalyst was found to be active, effective and selective for one‐pot three‐component reaction of alkyl halide, sodium azide and alkyne, known as copper‐catalyzed click synthesis of 1,2,3‐triazoles. As little as 0.3 mol% of catalyst was found to be effective under the optimum conditions. The catalyst could also be recycled and reused up to seven times without significant loss of activity. Thermal stability, high loading level of copper on catalyst, broad diversity of alkyl/benzyl/allyl bromide/chloride and alkyl/aryl terminal alkynes without isolation of azide intermediate, and good to excellent yields of products make this procedure highly economical. Copyright © 2015 John Wiley & Sons, Ltd.

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 score0.001
Threshold uncertainty score0.003

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations32
Published2015
Admission routes1
Has abstractyes

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